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Machine learning speeds up search for new sustainable materials

TSUKUBA, Japan, May 25, 2022 - (ACN Newswire via SEAPRWire.com) - Researchers from Konica Minolta and the Nara Institute of Science and Technology in Japan have developed a machine learning method to identify sustainable alternatives for composite materials. Their findings were published in the journal Science and Technology of Advanced Materials: Methods.Researchers are looking for sustainable options, such as recyclable materials or biomass, to substitute the constituent materials in composites which are used in various applications including electrical and information technologies.Composite materials are compounds made of two or more constituent materials. Due to the complex nature of the interactions between the different components, their performance can greatly exceed that of single materials. Composite materials, such as fibre-reinforced plastics, are very important for a wide range of industries and applications, including electrical and information technologies.In recent years, there has been increasing demand for more environmentally sustainable materials that help reduce industrial waste and plastic use. One way to achieve this is to substitute the constituent materials in composites with recyclable materials or biomass. However, this can reduce performance compared to the original material, not only due to the features of the individual constituent materials, such as their physicochemical properties, but also due to the interactions between the constituents."Finding a new composite material that achieves the same performance as the original using human experience and intuition alone takes a very long time because you have to evaluate countless materials while also taking into account the interactions between them," explains Michihiro Okuyama, assistant manager at Konica Minolta, Inc.Machine learning offers a potential solution to this problem. Scientists have proposed several machine learning methods to conduct rapid searches among a large number of materials, based on the relationship between the materials' features and performance. However, in many cases the properties of the constituent materials are unknown, making these types of predictive searches difficult.To overcome this limitation, the researchers developed a new type of machine learning method for finding alternative materials. A key advantage of the new method is that it can quantitatively evaluate the interactions among the component materials to reveal how much they contribute to the overall performance of the composite. The method then searches for replacement constituents with similar performance to the original material. The researchers tested their method by searching for alternative constituent materials for a composite consisting of three materials - resin, a filler and an additive. They experimentally evaluated the performance of the substitute materials identified by machine learning and found that they were similar to the original material, proving that the model works."In developing alternatives, that make up composite materials, our new machine learning method removes the need to test large numbers of candidates by trial and error, saving both time and money." says Okuyama.The method could be used to quickly and efficiently identify sustainable substitutes for composite materials, reducing plastic use and encouraging the use of biomass or renewable materials.Further informationMichihiro OkuyamaKONICA MINOLTA, INC.Email: michihiro.okuyama@konicaminolta.comAbout Science and Technology of Advanced Materials: Methods (STAM Methods)STAM Methods is an open access sister journal of Science and Technology of Advanced Materials (STAM), and focuses on emergent methods and tools for improving and/or accelerating materials developments, such as methodology, apparatus, instrumentation, modeling, high-through put data collection, materials/process informatics, databases, and programming. https://www.tandfonline.com/STAM-MDr. Masanobu NaitoSTAM Methods Publishing DirectorEmail: NAITO.Masanobu@nims.go.jpPress release distributed by Asia Research News for Science and Technology of Advanced Materials. Copyright 2022 ACN Newswire. All rights reserved. (via SEAPRWire)

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Showa Denko Introduces Machine Learning Operations into AI-based Prediction Systems for Accelerating Materials Development

TOKYO, Apr 21, 2022 - (ACN Newswire via SEAPRWire.com) - Showa Denko K.K. (SDK; TSE:4004) has introduced MLOps* (Machine Learning Operations) for efficient management of machine learning models deployed into Artificial Intelligence (AI) systems for materials design ahead of its competitors. Machine learning models can predict material properties based on formulations and manufacturing-process conditions of materials. This time, we automated input of the latest data into computers that develop machine learning models and data processing in those computers. This automation has reduced the time required to build and operate machine learning models from five days to one day per month. In addition, the introduction of MLOps enabled us to accelerate materials development by predicting material properties based on the latest data.Machine learning process from model development to operationSDK utilizes AI systems for efficient materials development, such as exploring the optimal material formulation. Machine learning models deployed into the AI systems predict material properties from formulations or suggest formulations that improve material properties. The machine learning process for managing the AI systems includes inputting the latest data, data processing, and continuous training of machine learning models. Previously, data scientists had to input and process the latest data for themselves. These steps accounted for about 80% of the time required for the entire machine learning process. In addition, machine learning models deployed into the AI systems are built specifically for each material. Therefore, before introducing MLOps, the development of machine learning models required a lot of time and effort due to the necessary work specialized for each material.Aiming to address these issues caused by applying AI systems to the development of numerous materials in the Company and operating machine learning models efficiently, we have installed programs to automate the input of the latest data and data processing into our AI systems. Moreover, we have introduced technologies that enable data scientists responsible for building machine learning models and software engineers responsible for building AI systems to develop systems collaboratively even if there are differences in operating systems and programming languages they use. By introducing MLOps ahead of our competitors to manage machine learning models efficiently, we could reduce the time required to develop machine learning models and their operation, improve prediction accuracy, and stably operate dozens of AI systems. As a result, now we can propose ideal materials to our customers promptly.The Showa Denko Group will apply the fruits of basic research in AI and computational science to materials development and quickly provide solutions that solve our customers' problems, thereby contributing to the development of a sustainable society.*MLOps: The method and philosophy for integrating the development and operation of machine learning models. MLOps include continuous training of machine learning models, automating the machine learning process, and establishing tools and operational rules for collaborative development between data scientists and software engineers.About Showa Denko K.K.Showa Denko K.K. (SDK; TSE:4004, ADR:SHWDY) is a major manufacturer of chemical products serving from heavy industry to computers and electronics. The Petrochemicals Sector provides cracker products such as ethylene and propylene, the Chemicals Sector provides industrial, high-performance and high-purity gases and chemicals for semicon and other industries, the Inorganics Sector provides ceramic products, such as alumina, abrasives, refractory/graphite electrodes and fine carbon products. The Aluminum Sector provides aluminum materials and high-value-added fabricated aluminum, the Electronics Sector provides HD media, compound semiconductors such as ultra high bright LEDs, and rare earth magnetic alloys, and the Advanced Battery Materials Department (ABM) provides lithium-ion battery components. For more information, please visit www.sdk.co.jp/english/.Media contact:Showa Denko K.K., Public Relations Group, Brand Communication Department, Tel: 81-3-5470-3235 Copyright 2022 ACN Newswire. All rights reserved. (via SEAPRWire)

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MHI-AC&R Develops Large-Capacity Brine Refrigeration System for Cryo-Temperatures, Featuring Nitrogen (N2) Refrigerant

TOKYO, Mar 11, 2022 - (JCN Newswire via SEAPRWire.com) - Mitsubishi Heavy Industries Air-Conditioning and Refrigeration Corporation (MHI-AC&R), a Group company of Mitsubishi Heavy Industries, Ltd. (MHI), has newly developed a large-capacity brine refrigeration system adopting a nitrogen (N2) refrigerant having both zero ozone depletion potential (ODP)(1) and zero global warming potential (GWP)(2). The new system can accommodate even cryotemperature needs, placing it at the pinnacle of industry offerings(3). The Company has just delivered a unit of the new system to The Honjo Chemical Corporation (Neyagawa, Osaka), a manufacturer of organic chemicals and other products, and further sales in the domestic market will be pursued going forward.Large-capacity air-refrigerant (N2) brine refrigeration system flowchartThe new system can refrigerate at ultra-low and cryo-temperatures (brine temperature: -45℃ to -100℃) through application of MHI-AC&R's proprietary (patented) air refrigeration cycle technology. The unit also features one of the industry's most compact sizes, enabling easy handling and transport. Its compression expansion machine incorporates MHI Group's accumulated high technologies in gas turbines. In addition to capturing the energy generated during air expansion cooling and using it as drive power, stable operation is achieved through the integration of high technologies such as energy-saving inverter control.The new refrigeration system contributes significantly to curbing global warming through the adoption of N2, a natural refrigerant having zero environmental impact. In recent years, initiatives to prevent climate change have accelerated in momentum. In Japan, since April 2015, when the Act on Rational Use and Appropriate Management of Fluorocarbons(4) went into effect, the low-temperature refrigeration machine market has been called on to adopt low-GWP refrigerants. Internationally, in tandem with implementation of the Kigali Amendment to the Montreal Protocol on Substances that Deplete the Ozone Layer in January 2019, along with revisions to the Vienna Convention for the Protection of the Ozone Layer, production of chlorofluorocarbon (CFC) substitutes and phased reduction in energy consumption have become mandatory.Today, because there are few refrigerant options in the ultra-low-temperature refrigeration field, many refrigeration systems continue to use CFC refrigerants. However, demand for CFC-free refrigeration systems is steadily rising in order to mitigate impact on the environment. The refrigerant used in MHI-AC&R's new refrigeration system uses nitrogen, which accounts for approximately 78% of air content, so it is safe both to the environment and to humans. Use of a CFC-free refrigerant also eliminates the inspection procedures mandated under the Act on Rational Use and Appropriate Management of Fluorocarbons, and the new refrigerant is also exempt from the High Pressure Gas Safety Act which regulates the production of high-pressure gases and their consumption, etc. Another benefit is the adoption of magnetic bearings in the system's compression expansion machine, which eliminates the need for lubricating oil and enables a virtually unlimited service life, thereby helping to ease the user's maintenance and operating burdens.(1) ODP is a coefficient expressing a refrigerant's ozone depletion potential compared to the previously widely used CFC-11 (trichlorofluoromethane), which is assigned a value of 1.0. The lower the ODP value, the smaller is the deleterious impact on the ozone layer.(2) GWP is a coefficient expressing a refrigerant's global warming potential compared to CO2, which is assigned a value of 1.0. The lower the GWP, the higher is the refrigerant's environmental performance.(3) Based on MHI-AC&R's in-house survey.(4) The Act on Rational Use and Proper Management of Fluorocarbons is a totally revised update, carried out in April 2015, to the original Fluorocarbons Recovery and Destruction Law enacted in 2001. Under the revised legislation, commercial refrigeration condensing units and stationary refrigeration units with a refrigeration capacity exceeding 1.5 kilowatt (kW) (approx. 2HP) are required by 2025 to adopt refrigerants with a GWP below 1500 (CO2 =1).Delivery OverviewClient and destination: The Honjo Chemical Corporation, Naoshima Organic Chemical Plant(Naoshima-cho, Kagawa-gun, Kagawa Pref.)Intended usage: Removal of heat of reaction produced during production of organic chemicalsSpecification: Brine temperature -80degC (down to -100degC)About MHI GroupMitsubishi Heavy Industries (MHI) Group is one of the world's leading industrial groups, spanning energy, logistics & infrastructure, industrial machinery, aerospace and defense. MHI Group combines cutting-edge technology with deep experience to deliver innovative, integrated solutions that help to realize a carbon neutral world, improve the quality of life and ensure a safer world. For more information, please visit www.mhi.com or follow our insights and stories on spectra.mhi.com. Copyright 2022 JCN Newswire. All rights reserved. (via SEAPRWire)

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Fujitsu and RIKEN Claim 1st Place for MLPerf HPC Benchmark with Supercomputer Fugaku

TOKYO, Nov 18, 2021 - (JCN Newswire via SEAPRWire.com) - Fujitsu and RIKEN today announced that the supercomputer Fugaku took the first place for the CosmoFlow training application benchmark (1), one of the key MLPerf HPC benchmarks for large-scale machine learning processing tasks requiring capabilities of a supercomputer. Fujitsu and RIKEN leveraged approximately half of Fugaku's resources (2) to achieve this result, demonstrating the world's fastest performance in this key benchmark.MLPerf HPC measures how many deep learning models can be trained per time unit (throughput performance, 3). Software technology that further refines Fugaku's parallel processing performance has achieved a processing speed approximately 1.77 times faster than that of other systems, demonstrating the world's highest level of performance in the field of large-scale scientific and technological calculations using machine learning.These results were announced as MLPerf HPC version 1.0 on November 17th (November 18th Japan time) at the SC21 High-Performance Computing Conference, which is currently being held as a hybrid event.Fugaku Claims World's Highest Level of Performance in the Field of Large-scale Scientific and Technological Calculations Using Machine LearningMLPerf HPC is a performance competition composed of three separate benchmark programs: CosmoFlow, which predicts cosmological parameters, one of the indicators used in the study of the evolution and structure of the universe, DeepCAM (4), which identifies abnormal weather phenomena, and Open Catalyst (5), which estimates how molecules react on the catalyst surface.For CosmoFlow, Fujitsu and RIKEN used approximately half of the Fugaku system's entire computing resources to train multiple deep learning models to a certain degree of prediction accuracy and measured from the start time of the model that started the training to the end time of the model that completed the training last to evaluate throughput performance. To further enhance the parallel processing performance of Fugaku, Fujitsu and RIKEN applied technology to programs used on the system that reduce the mutual interference of communication between CPUs, which occurs when multiple learning models are processed in parallel, and also optimize the amount of data communication between CPU and storage. As a result, the system trained 637 deep learning models in 8 hours and 16 minutes, a rate of about 1.29 deep learning models per minute.The measured value of Fugaku claimed first place amongst all the systems for the CosmoFlow training application benchmark category, demonstrating performance at rates approximately 1.77 times faster than other systems. This result revealed that Fugaku has the world's highest level of performance in the field of large-scale scientific and technological calculations using machine learning.Going forward, Fujitsu and RIKEN will make software stacks such as libraries and AI frameworks available to the public that accelerate large-scale machine learning processing developed for this measurement. Widely sharing the knowledge of large-scale machine learning processing using supercomputers gained through this exercise will allow users to leverage world-leading systems for the analysis of simulation results, leading to potential new discoveries in astrophysics and other scientific and technological fields. These resources will also be applied to other large-scale machine learning calculations, such as natural language processing models used in machine translation services, to accelerate technological innovation and contribute to solving societal and scientific problems.About MLPerf HPCMLPerf HPC is a machine learning benchmark created in 2020 by MLCommons, a community that conducts machine learning benchmarks, to evaluate the system performance of a supercomputer for large-scale machine learning calculations, which take an enormous amount of time, to create a performance list of systems that execute machine learning applications. It is used for supercomputers around the world and is anticipated to become a new industry standard.MLPerf HPC was designed to assess the performance of large-scale machine learning models requiring the use of supercomputers. Performance evaluation was carried out for 3 applications: CosmoFlow, DeepCAM, and Open Catalyst. In addition, a benchmark that measures the number of deep learning models trained per time unit has also been newly established.All measurement data are available on the following website: Related links: https://mlcommons.org/(1) CosmoFLow:A deep learning model for predicting cosmological parameters from three-dimensional simulation results of dark matter distributed in outer space.(2) Approximately half of the whole Fugaku system:Since this measurement was conducted during the operation of Fugaku, the measurement scale was halved in consideration of the impact on other researches using Fugaku.(3) Measure how many deep learning models can be learned per unit time (throughput performance):A new measurement method for MLPerf. By learning multiple models simultaneously, the total performance of a supercomputer can be extracted, and by measuring the number of models that can be learned per unit time, it is possible to compare the performance of the entire system of a supercomputer.(4) DeepCAM:A Deep Learning Model for Identifying Abnormal Meteorological Phenomena from Global Climate Prediction Simulation Data.(5) Open Catalyst:A deep learning model that estimates the relaxation energy of molecules on the catalyst surface from simulation data of atomic and intermolecular reactions.About FujitsuFujitsu is the leading Japanese information and communication technology (ICT) company offering a full range of technology products, solutions and services. Approximately 126,000 Fujitsu people support customers in more than 100 countries. We use our experience and the power of ICT to shape the future of society with our customers. Fujitsu Limited (TSE:6702) reported consolidated revenues of 3.6 trillion yen (US$34 billion) for the fiscal year ended March 31, 2021. For more information, please see www.fujitsu.com.About RIKEN Center for Computational ScienceRIKEN is Japan's largest comprehensive research institution renowned for high-quality research in a diverse range of scientific disciplines. Founded in 1917 as a private research foundation in Tokyo, RIKEN has grown rapidly in size and scope, today encompassing a network of world-class research centers and institutes across Japan including the RIKEN Center for Computational Science (R-CCS), the home of the supercomputer Fugaku. As the leadership center of high-performance computing, the R-CCS explores the "Science of computing, by computing, and for computing." The outcomes of the exploration - the technologies such as open source software - are its core competence. The R-CCS strives to enhance the core competence and to promote the technologies throughout the world. Copyright 2021 JCN Newswire. All rights reserved. (via SEAPRWire)

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Daiwa Securities Upgrades Rating of Parent Company of Precision Tsugami (1651.HK) to “Outperform”

HONG KONG, Sep 6, 2021 - (ACN Newswire via SEAPRWire.com) - Daiwa Securities, in its recently published research report, has upgraded the rating for the parent company of Precision Tsugami (China) Corporation Limited ("Precision Tsugami" or the "Company", stock code: 1651), which is the largest foreign-owned CNC high-precision machine tool manufacturer in the PRC . The "Outperform" rating was given to the firm as Daiwa Securities was particularly optimistic about Precision Tsugami's strong business foundation in the PRC, and the higher-than-expected number of orders received in July. In the report, Daiwa Securities noted that Precision Tsugami brought a new plant on stream in Anhui Province, the PRC, in March 2021, thus increasing total production capacity by just above 15%. The ability to supply the needed volume of products by the deadlines demanded by customers is particularly important in the PRC. The Group commands a lofty share of the local market for small- and mid-size sliding headstock automatic lathes (60-70% by its estimate). Leveraging its strong profile, the Company has established close relations with a number of local area suppliers and appears to be well positioned when it comes to securing the parts, materials and workforce needed for business activities. At this juncture, the procurement difficulties have not had a major impact on the Company's production. In view of the higher-than-expected number of orders in July, Daiwa Securities raised its revenue forecast for the parent company of Precision Tsugami. The forecasted revenue and operating profit for FY21 are 100.5 billion yen (HK$7.084 billion) and 20.5 billion yen (HK$1.445 billion), respectively, and 107 billion yen (HK$7.542 billion) and 21.8 billion yen (HK$1.537 billion) in FY22, respectively. Daiwa Securities believes that orders will remain at a high level. In the first quarter of FY22, Precision Tsugami accounted for 70% of the parent company's revenue.Since its establishment, Precision Tsugami has been committed to developing, manufacturing and selling modern CNC machine tools. Currently, it manufactures various high-end precision CNC machine tools such as automatic lathes, turret machines, machining centres and grinding machines that are used in diverse industries, spanning automotive parts, smart phones and 5G communications to automation, medical equipment and construction machinery. The Company is the largest overseas production base of Tsugami Japan. In addition to selling its products in the PRC, the Company also exports to Japan, South Korea, Taiwan, Europe, the United States and other markets.About Precision Tsugami (China) Corporation LimitedThe Group is an established foreign-owned CNC high-precision machine tool manufacturer in the PRC which primarily engages in the manufacture and sales of a wide range of CNC high precision machine tools under the TSUGAMI brands. The Group has been listed on the Main Board of The Stock Exchange of Hong Kong Limited since 25 September 2017. The Group's CNC high-precision machine tools can be broadly classified into four major product categories, namely, precision lathes, precision machining centres, precision grinding machines and precision thread and form rolling machines. The Group offers CNC high-precision machine tools that are of standardised design and specifications to its customers and is able to provide machine tool solutions to them and make various specifications and/or customisations to CNC high-precision machine tools. According to Frost & Sullivan, in terms of revenue in 2017, the Group ranked third in the CNC high-precision machine tool industry in the PRC and was the largest foreign-owned CNC high-precision machine tool manufacturer in the PRC. The Group also ranked first in the PRC precision automatic lathe market in terms of revenue in 2017, with a market share of approximately 49.8%. Copyright 2021 ACN Newswire. All rights reserved. (via SEAPRWire)

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MHI Machine Tool to Expand Metal 3D Printing Services

TOKYO, Jul 8, 2021 - (JCN Newswire via SEAPRWire.com) - Mitsubishi Heavy Industries Machine Tool Co., Ltd., a group company of Mitsubishi Heavy Industries, Ltd. (MHI) based in Ritto, Shiga Prefecture, will offer expanded services in metal 3D　printing services commencing on July 15, including prototype production and contract production by metal 3D (three-dimensional) printers applying laser-based Metal Additive Manufacturing (AM) technologies. In addition to its previously offered Directed Energy Deposition (DED)(1) type metal 3D printers for large-scale parts, the Company will newly add services using binder jetting (BJT)(2) metal 3D printers for small-scale parts. The expanded service lineup will enable manufacture of a full range of metal parts, from small components of 1mm size to ultra-large-scale parts exceeding 1 meter."DMP2500" developed by Digital MetalThe addition to MHI Machine Tool's lineup is the DMP2500 metal 3D printer developed by Digital Metal, a group company of Hoganas of Sweden. The DMP2500's BJT technology not only enables extremely precise manufacturing but also is engineered especially for high-volume production. Currently, MHI Machine Tool has provided metal printing services applying unique DED technology of "LAMDA" AM systems; now, with the introduction of a different type of printer, the Company can respond to a broad range of metal 3D print needs and propose and provide the optimal manufacturing method and equipment for each parts.MHI Machine Tool concluded a contract with Digital Metal in July 2020 on marketing its DMP2500 and other metal 3D printers in Japan. By adding these BJT type systems to its own sales lineup, MHI Machine Tool will be in a position to offer a broad portfolio of not only sales but also after-sales services.While metal 3D printers are receiving attention for their innovative advances to production processes, they also present challenges relating to the difficulty of their production and quality assurance, etc. With the new expansion of services, MHI Machine Tool will focus on providing solutions relating to additive manufacturing, including provision of related expertise, to accelerate early adaptation to production parts.Going forward, MHI Machine Tool will develop its metal printing services structure spanning from small-scale high-precision to ultra-large-scale items, enabling the Company to respond to a broad wide-range of prototype production needs and contract production. In these ways, MHI Machine Tool will encourage manufacturers to expand into manufacturing parts using metal 3D printers and contribute to the industrial supply chain as a whole.(1) Directed Energy Deposition (DED) is an additive manufacturing (AM) process in which focused thermal energy is utilized to continuously feed metal powder materials by nozzle to laser fusing points, with pinpoint precision.(2) Binder jetting is an additive manufacturing process in which a binder is selectively sprayed by nozzle onto a thin layer of metal powder, causing solidification. With metals, heat treatment (binder removal and sintering) is then carried out to achieve the final product. Copyright 2021 JCN Newswire. All rights reserved. (via SEAPRWire)

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MHI Machine Tool Launches Two New Hobbing Machines With Higher Speed, Precision and Efficiency

TOKYO, May 31, 2021 - (JCN Newswire via SEAPRWire.com) - Mitsubishi Heavy Industries Machine Tool Co., Ltd., a part of Mitsubishi Heavy Industries (MHI) Group, announces June 2021 launch of the new GE15HS and GE25HS models of hobbing machines. Emphasizing high precision and efficiency, the new machines focus on electric and hybrid cars amid the global trend toward decarbonization. The new machines produce high-precision gears required in these new vehicles. These new machine models are a significantly improved addition to the GE Series of hobbing machines, which has shipped more than 2,800 units since its launch in 2004.The GE15HS model is for gears with a maximum diameter of 150mm, widely used in automobiles and motorcycles. The high-speed, high-torque direct-drive motor(1) for the main cutting spindleprovides a maximum spindle speed of 6,000 min-1(2), three times faster than previous models. The high efficiency work table spindle holding the work piece uses a special table that provides high rigidity and high-speed rotation to handle the necessary thrust load(3) for high efficiency machining. Further, processing with the use of Mitsubishi super-hard cutting tools yields a surface roughness of less than Ra0.4(4), on a par with gear grinding. The GE15HS provides process efficiency, eliminating the finishing process of shaving prior to heat treatment, improving productivity and reducing processing cost.The GE25HS model for large-diameter gears up to 250mm in diameter, such as automobile differential gears. With its high-efficiency processing, this model utilizes a high-speed, high-power spindle eliminating the effects of temperatures variation during production. The high rigidity table has the backlash eliminator incorporated as standard equipment. In addition, the motor torque and maximum spindle rotation speed of the main spindle have been increased 1.5 times from previous models, providing a 42% reduction in processing time(5). Used in combination with MHI Machine Tool's new materials and coatings for cutting tools, the GE25HS model provide stable mass production with a cutting speed of more than 400m/min.Demand for mass production of high-precision gears is continuing to rise with the shift to electrification of vehicles. With the need for improvements in NVH and fuel efficiency, and the move toward low-cost manufacturing. MHI Machine Tool, with expertise in both gear machine tools and cutting tools, offers a full lineup of gear production machines, including these two new models. By delivering precision cutting tools and processing solutions to achieve high-precision, high-efficiency processing, MHI Machine Tool provides comprehensive support for manufacturing in a wide variety of industries.(1) Direct-drive mechanism motors utilize the torque coming from a motor without passing through a gear box or other mechanism in order to control driving loss due to friction and reduce wear on parts.(2) The "min-1" notation is a unit expressing the number of turns in one minute, synonymous with "revolutions/rotations per minute" (rpm).(3) Thrust load is the force applied to the shaft in a horizontal (parallel) direction (the axial direction of the rotor).(4) In-house machining result with GE15S (Workpiece data: Module 1.6mm; No. of teeth 19; Torsion angle: 24degrees; Tooth width: 24mm)(5) In-house machining result with GE25A-S (Workpiece data: Module 3mm; No. of teeth 54; Torsion angle: 30degrees; Tooth width: 40mm) Copyright 2021 JCN Newswire. All rights reserved. (via SEAPRWire)

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WitLink Crashes the Soft Cap Target during the On-Going Pre-Sale

London, UK - With increased adoption of artificial intelligence (AI) and machine learning (ML) to solve different industrial processes, the field of AI and ML is certainly the future of human existence. In a bid to close the existing gap and revolutionize the present-day AI space, the team behind WitLink has introduced its decentralized marketplace to the benefit of AI enthusiasts. WitLink is a project geared towards giving AI enthusiasts, including students, trainers, professionals, enterprises, and AI experts the opportunity to profit from the artificial intelligence and machine learning space. WAI Token WAI is an ERC-compliant token and the native token of the WitLink project. The WAI token can be used for different purposes on the WitLink network, including to offer projects, purchase computing resources, and reward payouts for computing resources. Additionally, POS token holders will be rewarded with the WAI token for their activities on the WitLink platform. Plus, the token can be used to pay platform fees and other goods and services. On-going presale! WitLink continues to present opportunities and that through the on-going Presale. With the Soft-Cap achieved, now it eyes the hard-cap! WAI token is available to be purchased through BTC, ETH, BCH, and USDT. The price of 1 WAI token is 0.15 USD. And the listing price is confirmed at $0.25! https://witlink.ai/dashboard/user/contribute  In the first phase tokens will be sold with a bonus of 1% in the purchase and referral bonus of 3%. The second phase a bonus of 0.5% in the purchase and referral bonus of 2%. The third and last phase with a bonus of 0.25% in the purchase and referral bonus of 1%. Minimum token purchase of $1000 applies to receive bonus in the purchase. About WitLink WitLink is a blockchain-powered marketplace for everything Artificial Intelligence and Machine Learning. The intention of the project is to bridge the existing which technology firms face while accessing artificial intelligence and machine learning experts. WitLink accommodates a wide range of AI enthusiasts, including AI experts, students, AI service providers, enterprises, as well as, AI trainers. WitLink parades a team of experienced AI and ML experts who understands the blockchain ecosystem and how to use data to achieve automation. The team is also proficient in using artificial intelligence and machine learning to solve the challenges of mankind.   Social links Twitter: https://twitter.com/WitLink Telegram: https://t.me/WitLinkcom  LinkedIn: www.linkedin.com/company/witlinkai Instagram: www.instagram.com/witlinkai/ Medium: https://medium.com/@WitLink    Media contact Company: WitLink Contact: Aidan Graham Website: https://www.witlink.ai     SOURCE: WitLink The article is provided by a third-party content provider. SEAPRWire ( www.seaprwire.com ) makes no warranties or representations in connection therewith. Any questions, please contact cs/at/SEAPRWire.com Sectors: Top Story, Daily News SEA PRWire: PR distribution in Southeast Asia (Indonesia, Thailand, Vietnam, Singapore, Malaysia, Philippines & Hong Kong )

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MHI Machine Tool Develops Gear Shaping Machine for High-Precision Small-Module Gears Used in Robots

TOKYO, Mar 1, 2021 - (JCN Newswire) - Mitsubishi Heavy Industries Machine Tool Co., Ltd., a Shiga-based part of Mitsubishi Heavy Industries, Ltd. (MHI) Group, has newly developed the "SE25FR Plus," a gear shaping machine dedicated to making high-precision small-module(1) gears used in robots. The company has simultaneously developed a small-module cutting tool specifically for the new gear shaping machine. Full-fledged marketing of both new items will commence in March. By providing this dual support in high-precision gear cutting machines and cutting tools from a single source, MHI Machine Tool looks to respond to the need for reduction gears of increasingly higher precision in the expanding global robot market.MHI Machine Tool launched its "FR Series"(2) of high-precision gear cutting machines in August 2020. The new SE25FR Plus is a high-end model developed especially for shaping strain wave gears(3), which require high precision. Outstanding rotation precision has been achieved through the adoption of ultra-high-precision bearings and direct-drive motors(4) in the two core components: the work table and the cutter head. This provides gear cutting precision of ISO class 3, enabling cutting precision higher than the model SE25FR, which is of ISO class 6.The small-module cutting tool to be launched together with the SE25FR Plus features a newly developed dedicated tool material and a special coating, "MightyShield micro," for micromachining. The tool material incorporates carbide particles offering improved toughness and wear resistance, while the new coating produces a uniform thin film below 2 micrometers (μm) thick that has no impact on tool shape error. The result is outstanding shaping even with difficult-to-cut materials, and the ability to achieve gear shapes down to the submicron level. Furthermore, MHI Machine Tool provides one-stop support in gear cutting machines and cutting tools, from the prototype development stage through mass production.MHI Machine Tool is Japan's only manufacturer producing both gear cutting machines and cutting tools. Moreover, the company possesses comprehensive proposal capability - encompassing not only its high-precision gear cutting machines but also all aspects relating to gear cutting, including cutting knowhow and automated systems. Going forward, as a leading producer of gear cutting machines to support not only the manufacturing industry but also the market for robots, which are increasingly adopted in the healthcare and service industries, MHI Machine Tool will continue to lead the way in "monozukuri": the traditional Japanese concept of craftsmanship(5).(1) Module (m) is a unit representing the size of a gear tooth. It is derived by dividing the diameter (mm) of the pitch circle by the number of teeth.(2) The name "FR Series" derives from "Fine Pitch used Reducer for Robot." Its development was undertaken in response to market expansion for industrial and life-support robots in recent years, which led to a sharp rise in demand for the high-precision small-module gears inside the precision reduction gears used in robot joints.(3) Strain wave gears are mechanical devices that utilize the variance between elliptical and circular movements to reduce and output dynamic rotation speed.(4) Direct-drive motors drive their target utilizing torque from the motor directly, without passing through a gear box or other intermediary mechanism; this enables suppression of wear on parts and driving loss due to friction, etc.(5) Business in machine tools currently performed by MHI Group, including MHI Machine Tool, is scheduled to be transferred to Nidec Corporation and Nidec Group in May of this year. Copyright 2021 JCN Newswire. All rights reserved. www.jcnnewswire.com

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Fujitsu and Hokkaido University Develop “Explainable AI” Technology Providing Users with Concrete Steps to Achieve Desired Outcomes

KAWASAKI, Japan, Feb 4, 2021 - (JCN Newswire) - Fujitsu Laboratories Ltd. and Hokkaido University today announced the development of a new technology based on the principle of "explainable AI" that automatically presents users with steps needed to achieve a desired outcome based on AI results about data, for example, from medical checkups."Explainable AI" represents an area of increasing interest in the field of artificial intelligence and machine learning. While AI technologies can automatically make decisions from data, "explainable AI" also provides individual reasons for these decisions - this helps avoid the so-called "black box" phenomenon, in which AI reaches conclusions through unclear and potentially problematic means.While certain techniques can also provide hypothetical improvements one could take when an undesirable outcome occurs for individual items, these do not provide any concrete steps to improve.For example, if an AI that makes judgments about the subject's health status determines that a person is unhealthy, the new technology can be applied to first explain the reason for the outcome from health examination data like height, weight, and blood pressure. Then, the new technology can additionally offer the user targeted suggestions about the best way to become healthy, identifying the interaction among a large number of complicated medical checkups items from past data and showing specific steps to improvement that take into account feasibility and difficulty of implementation.Ultimately, this new technology offers the potential to improve the transparency and reliability of decisions made by AI, allowing more people in the future to interact with technologies that utilize AI with a sense of trust and peace of mind. Further details will be presented at the AAAI-21, Thirty-Fifth AAAI Conference on Artificial Intelligence opening from Tuesday, February 2.Developmental BackgroundCurrently, deep learning technologies widely used in AI systems requiring advanced tasks such as face recognition and automatic driving automatically make various decisions based on a large amount of data using a kind of black box predictive model. In the future, however, ensuring the transparency and reliability of AI systems will become an important issue for AI to make important decisions and proposals for society. This need has led to increased interest and research into "explainable AI" technologies.For example, in medical checkups, AI can successfully determine the level of risk of illness based on data like weight and muscle mass (Figure 1 (A)). In addition to the results of the judgment on the level of risk, attention has been increasingly focused on "explainable AI" that presents the attributes (Figure 1 (B)) that served as the basis for the judgment.Because AI determines that health risks are high based on the attributes of the input data, it's possible to change the values of these attributes to get the desired results of low health risks.IssuesIn order to achieve the desired results in AI automated decisions, it is necessary not only to present the attributes that need to be changed, but also to present the attributes that can be changed with as little effort as is practical.In the case of medical checkups, if one wants to change the outcome of the AI's decision from high risk status to low risk status, achieving it with less effort may seem to increase muscle mass (Figure 2 Change 1) - but it is unrealistic to increase one's muscle mass alone without changing one's weight, so actually increasing weight and muscle mass simultaneously is a more realistic solution (Figure 2 Change 2). In addition, there are many interactions between attributes such as weight and muscle mass, such as causal relationships in which weight increases with muscle growth, and the total effort required to make changes depends on the order in which the attributes are changed. Therefore, it is necessary to present the appropriate order in which the attributes are changed. In Figure 2, it is not obvious whether weight or muscle mass should be changed first in order to reach Change 2 from the current state, so it remains challenging to find an appropriate method of change taking into the account the possibility and order of changes from among a large number potential candidates.About the Newly Developed TechnologyThrough joint research on machine learning and data mining, Fujitsu Laboratories and Arimura Laboratory at the Graduate School of Information Science and Technology, Hokkaido University have developed new AI technologies that can explain the reasons for AI decisions to users, leading to the discovery of useful, actionable knowledge.AI technologies such as LIME (1) and SHAP (2), which have been developed as AI technologies to support decision-making of human users, are technologies that make the decision convincing by explaining why AI made such a decision. The jointly developed new technology is based on the concept of counterfactual explanation (3) and presents the action in attribute change and the order of execution as a procedure. While avoiding unrealistic changes through the analysis of past cases, the AI estimates the effects of attribute value changes on other attribute values, such as causality, and calculates the amount that the user actually has to change based on this, enabling the presentation of actions that will achieve optimal results in the proper order and with the least effort.For example, if one has to add 1 kg of muscle mass and 7 kg to their body weight in order to reduce the risk in the input attribute and its order (Figure 1 (C)) that they change to obtain the desired result in a medical checkup, it's possible to estimate the relationship by analyzing the interaction between the muscle mass and the body weight in advance. That means that if one adds 1 kg of muscle mass, the body weight will increase by 6 kg. In this case, out of the additional 7 kg required for weight change, the amount of change required after the muscle mass change is just 1 kg. In other words, the amount of change one actually has to make is to add 1 kg of muscle mass and 1 kg of weight, so one can get the desired result with less effort than the order changing their weight first.EffectsUsing the jointly developed counterfactual explanation AI technology, Fujitsu and Hokkaido University verified three types of data sets (4) that are used in the following use cases: diabetes, loan credit screening, and wine evaluation. By combining three key algorithms for machine learning - Logistic Regression (5), Random Forest (6), and Multi-Layer Perceptron (7) - with the newly developed techniques, we have verified that it becomes possible to identify the appropriate actions and sequence to change the prediction to a desired result with less effort than the effort of actions derived by existing technologies in all datasets and machine learning algorithm combinations. This proved especially effective for the loan credit screening use case, making it possible to change the prediction to the preferred result with less than half the effort.Using this technology, when an undesirable result is expected in the automatic judgment by AI, the actions required to change the result to a more desirable one can be presented. This will allow for the application of AI to be expanded not only to judgment but also to support improvements in human behavior.Future PlansGoing forward, Fujitsu Laboratories will continue to combine this technology with individual cause-and-effect discovery technologies (8) to enable more appropriate actions to be presented. Fujitsu will also use this technology to expand its action extraction technology (9) based on its proprietary "FUJITSU AI Technology Wide Learning", with the aim of commercializing it in fiscal 2021.Hokkaido University aims to establish AI technology to extract knowledge and information useful for human decision-making from various field data, not limited to the presentation of actions.(1) LIME: An explainable AI technology. Explains in a simple, interpretable model.(2) SHAP: An explainable AI technology. Explains by showing the contribution of the explanatory variable in the model.(3) counterfactual explanation:A method of indicating and explaining a state that is different from the truth, such as "If I had done this, the result would have been".(4) data sets:The UC Irvine Machine Learning Repository is a world-famous repository that provides a number of data sets for comparative evaluation of machine learning, and FICO, a credit scoring company, published data for machine learning in three types of data sets: diabetes, loan credit screening, and wine evaluation.(5) Logistic Regression:A type of machine learning algorithm. A probability model that combines a logistic function with a hyperplane.(6) Random Forest:A type of machine learning algorithm. A prediction model that makes stable decisions by using a large number of decision tree classifiers.(7) Multi-Layer Perceptron:A type of machine learning algorithm. A model for training multiple neural networks.(8) Individual cause-and-effect discovery technologies:Fujitsu Develops Technology to Discover Characteristic Causal Relationships of Individual Data in Medicine, Marketing, and More (2020/12/17)(9) "FUJITSU AI Technology Wide Learning" action extraction technology:Fujitsu Bolsters its AI "Wide Learning" Technology with New Technique to Deliver Optimized Action Plans in Various Fields (2019/9/13)About FujitsuFujitsu is the leading Japanese information and communication technology (ICT) company offering a full range of technology products, solutions and services. Approximately 130,000 Fujitsu people support customers in more than 100 countries. We use our experience and the power of ICT to shape the future of society with our customers. Fujitsu Limited (TSE:6702) reported consolidated revenues of 3.9 trillion yen (US$35 billion) for the fiscal year ended March 31, 2020. For more information, please see www.fujitsu.com.About Fujitsu LaboratoriesFounded in 1968 as a wholly owned subsidiary of Fujitsu Limited, Fujitsu Laboratories Ltd. is one of the premier research centers in the world. With a global network of laboratories in Japan, China, the United States and Europe, the organization conducts a wide range of basic and applied research in the areas of Next-generation Services, Computer Servers, Networks, Electronic Devices and Advanced Materials. For more information, please see: http://www.fujitsu.com/jp/group/labs/en/. Copyright 2021 JCN Newswire. All rights reserved. www.jcnnewswire.com

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EBON: becoming Qualcomm to the Mining Hardware Industry

SHENZHEN, CHINA, Dec 23, 2020 - (ACN Newswire) - Ebon's exclusive license of the AsicBoost patent puts it in a very strong competitive position, and it is likely to develop into the 'Qualcomm' of the mining industry, even absorbing miners heavily dependent on Samsung, creating a new mining company that is capable of challenging the industry leader Bitmex. If this vision holds, Ebon will become the most suitable miner in the current market for investing.On December 1st, Huangshou-based Ebang International Holdings Ltd (Nasdaq: EBON) announced it had signed a technology licensing agreement with AsicBoost patent-holder Circle Line International Limited, granting full exclusivity to the patent. After optimizing a bitcoin mining machine algorithm, AsicBoost can greatly reduce the amount of work a mining ASIC must do in order to compute a hashing attempt, thus reducing the energy consumption of the mining machine. The performance of the mining machine can be improved by about 20%, and the corresponding savings in mining costs are also about 20%.AsicBoost, a SHA256 computing optimization algorithm developed by cryptologist Timo Hanke, PhD, was granted global priority by the inventor in late 2013 and published in May 2015, according to a statement from the patent attorney representing the patent. You can also find Dr. Timo Hanke's revised AsicBoost white paper, published in March 2016. A 2017 letter from Getech, a law firm commissioned by the patent firm, noted that no company or individual had been authorized to file an AsicBoost patent, yet some mining companies had applied the design methods covered in the patent to mining hardware or software. At the time, the Chinese legal community held that the patentee had unsuccessfully applied for the patent, and one could therefore dismiss the patent's claim.Today, things are very different. The patent went to Circle Line and was eventually granted in South Korea and Europe, while the patent request has yet to be approved in the United States and China. Circle Line International, meanwhile, has recently sued Samsung in South Korea for patent infringement, demanding that it should stop making unauthorized AsicBoost equipment and destroy its inventory of finished and semi-finished products, according to We-media Wushuo Blockchain. Mining companies that rely on Samsung will be in an awkward position since the patentees do indeed have South Korean patents and their claims are highly likely to be upheld by South Korean courts. Currently, among the mining machine manufacturers still in the office, Bitmain mainly relies on TSMC (NYSE: TSM), Jianan Yunzhi relies mainly on SMIC (HKG: 981) and Samsung (KS: 005930), with some production at TSMC, Ebon mainly relies on Samsung and TSMC, and Shenzhen Bit Microelectronics (Shenma mining machines) mainly relies on Samsung.The patentees are suing Samsung in South Korea, and if they win, that will put them in an awkward position as well. There have been rumours in the market that Samsung has failed to acquire much capacity of 8 nanometers this year, so it lacks shipping capacity. In the future, if Samsung loses its capacity support again due to the lack of patent authorization, it will completely fall into the dilemma of no rice in the pot. Some would say that Shenma could do anything without AsicBoost, but then the miner's energy consumption would go up significantly and it would no longer be competitive, so this option is unnecessary to follow or consider again.Many investors outside the circle do not understand the current status of the mining machine industry, their understanding is still at the stage of Bitmain, Jianan Yunzhi and Ebon International, but in fact, the industry has already entered the duopoly of Bitmain and Shenma. Shenma was founded by Yang Zuoxing, who once worked in Bitmain and single-handedly created the once-dominant Jihuang S9. The industry delights to talking about whether Yang Zuoxing ever used 16 nm chips to make mining machines, completed more or less to 7 nm mining machine standards from Bitmain in performance. Because of this, Shenma's flagship M20 product series sold as many as 600,000 units in 2019 and may have contributed more than 30EH/s computing power in 2019, meaning nearly half the growth of bitcoin's online computing power in May 2019 came from Shenma mining machines. While the exact average price for this batch of miners is uncertain (prices are subject to adjustments due to bitcoin prices), if the prices of various models from the company's M20 series are anything to go by, what miner's 2019 revenue could be in the hundreds of millions of dollars. Thus, Shenma has pushed Bitmain into a corner, which has become a thorn in the side for the industrial leader.European patents also mean that unauthorized miners can no longer be exported to Europe, especially northern Europe, such as Sweden and Iceland, which are suitable for mining. While TSMC, the world's largest contract chipmaker, will not be bound by South Korean or, in theory, European patents, it is likely to consider partnerships with unauthorised miners carefully, has historically focused on compliance issues in the US and Europe. Firstly, the miners that TSMC supplies must not be exported to Europe, and secondly, it may consider scaling back its co-operation with unauthorised manufacturers. Especially for the chip OEM, mining machine manufacturers are still not mainstream customers, so it's unnecessary to take the risk for mining machine manufacturers.If Samsung can no longer provide capacity and TSMC has concerns, SMIC is left with the option. However, some analysts pointed out that during the CNY bull market 3Q 2020, Jia Nan's profit margin was lower than expected, probably because SMIC's N+1 process yield rate was low, leading to a high cost per machine. However, as a transition process, there is a big question mark over whether N+1 can improve yield rate in the future, whether SMIC can have batch production capacity, and whether SMIC can finally make real 7nm under the background of Liang Mengsong's resignation and the US sanctions against SMIC advanced manufacturing process. SMIC's announcement in late December confirmed the speculation, in saying that "according to our preliminary assessment, our company operations and financial situation in the short term will show no significant impact, but an adverse impact will follow later for our advanced technology research and development and the productivity construction, the company will continue to communicate with relevant departments of the US government, and take all feasible measures as appropriate to actively seek solutions and strive to minimize the adverse impact."The AsicBoost patent spoiler is likely to reshuffle the industry, which this year has limited chip capacity for mining hardware manufacturers because of strong demand in other industries. Shenma is the most affected, if you can't reach a patent licensing agreement with Ebon, you will definitely go hungry. Considering the relationship between TSMC and Samsung, the success probability of turning to TSMC for production capacity is extremely low, while finding SMIC OEM also has to go the same way as Jinan Zhiyun again. Therefore, the best option for Shenma is still to reach a partnership with Ebon, the exclusive licensee of the patent, so as to continue to cooperate with Samsung. However, Samsung is expected to mass-produce 5 nm (equivalent to 7nm+ of TSMC) in 2021, and its production capacity will be greatly expanded. At that time, Ebon will hold a large number of Samsung's 5 nm production capacity, so that its partners can get rid of the current shortage for OEM capacity, and thus occupy a favorable competitive position in the current bitcoin market.According to international practice, the general royalty of patent rights shall be negotiated by both parties, but we can refer to Qualcomm's 5% fee standard. Based on this, it can be inferred that if the revenue in 2019 can be maintained, Ebon shall pay tens of millions of US dollars in license fees, which will be converted into the net profit of Ebon in the vast majority. Ebon, on the other hand, lost $42.4 million in 2019 revenues of just $110 million. In other words, it could become a 'Qualcomm' in the mining industry and build a business model with long-term licensing fees. Which can clearly enjoy higher valuations than mining.But it won't simply end here. For EBON, simply charging patent licensing fees is not maximally beneficial. If it can form a strategic relationship with Shenma through cooperation, to eventually absorb or merge with Shenma, it can return to the first echelon of mining machine, and again challenge the Bitmain, while Shenma can also obtain the listing status, and do not have to struggle for a separate listing. As a matter of fact, it is very difficult for any company to be listed on the stock market without any rice at present, and it is bound to face the problem of capital exhaustion, so it is not a bad choice to borrow chicken eggs. And with Ebon walking on two legs with the exchange, it is entirely possible that the miner business will depend on Shenma, so that Ebon itself can concentrate on developing the exchange business. Therefore, any cooperation with Ebon will lead to a win-win situation, and reshape the new mining machine manufacturer in line for Bitmain's leading position. Meanwhile, Chinese investors will finally have a more interesting mining hardware investment target than Jia-Nan.Media contact:Heidi He, PeanutmediaE: meiyu.he@hstong.comW: www.Peanutmedia.com Copyright 2020 ACN Newswire. All rights reserved. www.acnnewswire.com

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Valarhash Announces First Anniversary with Annual Highlights

CHENGDU, CHINA, Dec 9, 2020 - (ACN Newswire) - Valarhash, a leading provider of mining services, is celebrating its first anniversary. Since its product launch conference in December 2019, attended by more than 400 industry professionals and covered by more than 100 media, the organization has hit new milestones almost monthly. This anniversary is commemorated by community activities as an expression of gratitude to its consumers.Valarhash announced at the launch conference that it would be officially celebrating its first anniversary. The provider of mining services has prepared community activities for its users, as the company takes the opportunity to look back on its achievements over the past year. From December 9th to 15th, Valarhash has organized a week-long promotional campaign in their English Telegram group. Each day during the event, members who sign in with the words "Happy birthday to #Valarhash" will be able to share a value of $50 BTC. This is the first event the company has held for its anniversary.1TMine, a computing power trading platform incubated by Valarhash, successfully attracted the attention of new and old miners during the first 6 months of its establishment. The number of updates in subsequent months was even more numerous. In January 2020, the Valarhash mining pool service was launched, with 1T officially serving as an external service. During the following month, 1TMine launched block transactions to provide miners with customized mining services. According to the platform, the daily income in Bitcoin for users can exceed 60 BTC in the case of the currency market downturn.This past summer marked a transitional period for Valarhash, as it witnessed the launch of its independently developed mine management software "Hashrate Manager" in June, which facilitated real-time supervision of managed mining machine computing power by hosting customers. This was followed by the addition of an English version to the computing power and computing power trading platform of the business during the following month, as an effort towards global expansion. Valarhash also obtained premium media coverage when the co-founder/CTO of Valarhash was interviewed by 36kr, and the CEO of Valarhash, Fiona Lv, was invited to the Binance Block 101 live broadcast to discuss the new mining ecology in the era of 2020. In September, Valarhash collaborated with Canaan Technology and other mining machine suppliers to integrate mining machine resources. More recently, the mining machine hosting service was officially opened to global users, providing customized services for global mining investors. According to BTC.com, the 1THash pool, Valarhash's own mining pool, has placed 8th on the chart, with the daily income generated for users exceeding 80 BTC, while the total number of managed mining machines reached 100,000.Valarhash aims to provide clients with transparent and beneficial mining plans using advanced technology and a lower barrier of entry. Over the last few months, Valarhash focused on exploring new product and demographic markets. 1TMine launched Ethereum mining products and added a primary market to make it more convenient for customers to place orders in batches. At the same time, Valarhash's miner hosting business added Ethereum mining machines.Fiona Lv, CEO of Valarhash, reflected on its first anniversary, saying that the business would "continue to strengthen and we will continue to look for high-quality power resources." The mining service provider is committed to its goal of achieving greater international reach. To cater to a wider audience, Valarhash has begun providing services in Japanese and Korean, with additional mainstream languages such as Spanish, Russian, and French in the making. Valarhash is open to further collaboration with foreign counterparts to further bring mining machines and its mining pool services to the international community.For more information:Website: https://www.valarhash.comTwitter: https://twitter.com/VaIarhashFacebook: https://www.facebook.com/ValarhashLinkedin: https://www.linkedin.com/company/vhash/Medium: https://medium.com/valarhashTelegram: https://t.me/valarhashx1tmine_bitcoin Media Contact: BD@vhash.ioAbout ValarhashChengdu-based Valarhash integrates mining machine sales, miner hosting, mining pool and mine construction services. Led by CEO Fiona Lv, Valarhash aims to provide users with transparent and beneficial mining plans using advanced technology, with a lower barrier of entry. Business operations cover hardware research and development, digital asset transactions and 1TMine hash power contract sharing. With a leading position in the hash power market, Valarhash integrates frontier resources with global vision, providing crypto compute service (CCS) and linking physical and digital worlds with blockchain technology. See www.valarhash.com. Copyright 2020 ACN Newswire. All rights reserved. www.acnnewswire.com

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Fujitsu, AIST, and RIKEN Achieve Unparalleled Speed on the MLPerf HPC Machine Learning Processing Benchmark Leveraging Leading Japanese Supercomputer Systems

TOKYO, Nov 19, 2020 - (JCN Newswire) - Fujitsu, the National Institute of Advanced Industrial Science and Technology (AIST), and RIKEN today announced a performance milestone in supercomputing, achieving the highest performance and claiming the ranking positions on the MLPerf HPC benchmark(1). The MLPerf HPC benchmark measures large-scale machine learning processing on a level requiring supercomputers and the parties achieved these outcomes leveraging approximately half of the "AI-Bridging Cloud Infrastructure" ("ABCI") supercomputer system, operated by AIST, and about 1/10 of the resources of the supercomputer Fugaku, which is currently under joint development by RIKEN and Fujitsu. Utilizing about half the computing resources of its system, ABCI achieved processing speeds 20 times faster than other GPU-type systems. That is the highest performance among supercomputers based on GPUs, computing devices specialized in deep learning. Similarly, about 1/10 of Fugaku was utilized to set a record for CPU-type supercomputers consisting of general-purpose computing devices only, achieving a processing speed 14 times faster than that of other CPU-type systems. The results were presented as MLPerf HPC v0.7 on November 18th (November 19th Japan Time) at the 2020 International Conference for High Performance Computing, Networking, Storage, and Analysis (SC20) event, which is currently being held online.BackgroundMLPerf HPC is a performance competition in two benchmark programs: "CosmoFlow"(2), which predicts cosmological parameters, and "DeepCAM"(3), which identifies abnormal weather phenomena. The ABCI ranked first in metrics of all registered systems in the "CosmoFlow" benchmark program, with about half of the whole ABCI system(4), and Fugaku ranked second with measurement of about 1/10 of the whole system(5). The ABCI system delivered 20 times the performance of the other GPU types, while Fugaku delivered 14 times the performance of the other CPU types. ABCI achieved first place amongst all registered systems in the "DeepCAM" benchmark program as well, also with about half of the system. In this way, ABCI and Fugaku overwhelmingly dominated the top positions, demonstrating the superior technological capabilities of Japanese supercomputers in the field of machine learning. Fujitsu, AIST, RIKEN and Fujitsu Laboratories Limited will release the software stacks including the library and the AI framework which accelerate the large-scale machine learning process developed for this measurement to the public. This move will make it easier to use large-scale machine learning with supercomputers, while its use in analyzing simulation results is anticipated to contribute to the detection of abnormal weather phenomena and to new discoveries in astrophysics. As a core platform for building Society 5.0, it will also contribute to solve social and scientific issues, as it is expected to expand to applications such as the creation of general-purpose language models that require enormous computational performance.About MLPerf HPCMLPerf is a machine learning benchmark community established in May 2018 for the purpose of creating a performance list of systems running machine learning applications. MLPerf developed MLPerf HPC as a new machine learning benchmark to evaluate the performance of machine learning calculations using supercomputers. It is used for supercomputers around the world and is expected to become a new industry standard. MLPerf HPC v0.7 evaluated performance on two real applications, "CosmoFlow" and "DeepCAM," to measure large-scale machine learning performance requiring the use of a supercomputer. All measurement data are available on the following website: Related links: https://mlperf.org/Comments from the PartnersFujitsu, Executive Director, Naoki Shinjo "The successful construction and optimization of the software stack for large-scale deep learning processing, executed in close collaboration with AIST, RIKEN, and many other stakeholders made this achievement a reality, helping us to successfully claim the top position in the MLPerf HPC benchmark in an important milestone for the HPC community. I would like to express my heartfelt gratitude to all concerned for their great cooperation and support. We are confident that these results will pave the way for the use of supercomputers for increasingly large-scale machine learning processing tasks and contribute to many research and development projects in the future, and we are proud that Japan's research and development capabilities will help lead global efforts in this field."Hirotaka Ogawa, Principal Research Manager, Artificial Intelligence Research Center, AIST "ABCI" was launched on August 1, 2018 as an open, advanced, and high-performance computing infrastructure for the development of artificial intelligence technologies in Japan. Since then, it has been used in industry-academia-government collaboration and by a diverse range of businesses, to accelerate R&D and verification of AI technologies that utilize high computing power, and to advance social utilization of AI technologies. The overwhelming results of MLPerf HPC, the benchmark for large-scale machine learning processing, showed the world the high level of technological capabilities of Japan's industry-academia-government collaboration. AIST's Artificial Intelligence Research Center is promoting the construction of large-scale machine learning models with high versatility and the development of its application technologies, with the aim of realizing "easily-constructable AI". We expect that the results of this time will be utilized in such technological development. Satoshi Matsuoka, Director General, RIKEN Center for Computational Science "In this memorable first MLPerf HPC, Fugaku, Japan's top CPU supercomputer, along with AIST's ABCI, Japan's top GPU supercomputer, exhibited extraordinary performance and results, serving as a testament to Japan's ability to compete at an exceptional level on the global stage in the area of AI research and development. I only regret that we couldn't achieve the overwhelming performance as we did for HPL-AI to be compliant with inaugural regulations for MLPerf HPC benchmark. In the future, as we continue to further improve the performance on Fugaku, we will make ongoing efforts to take advantage of Fugaku's super large-scale environment in the area of high-performance deep learning in cooperation with various stakeholders."(1) MLPerf HPC MLPerf HPC evaluates the processing performance of the system as a whole, including the software required for machine learning processing, based on real applications that utilize machine learning. HPL-AI evaluates the basic performance of the hardware used in machine learning processing, such as single-precision and half-precision arithmetic units.(2) CosmoFLow Deep learning models were trained to predict cosmological parameters from the results of three-dimensional simulations of dark matter distributed in space.(3) DeepCAM A deep learning model was trained that identifies abnormal weather phenomena with global climate prediction simulation data.(4) half of the whole ABCI system In accordance with the rules of this time's MLPerf HPC v0.7, the measurement was executing using not the whole system, but just half of ABCI's resources.(5) 1/10 of the whole Fugaku system In accordance with the rules of this time's MLPerf HPC v0.7, the measurement was executing using not the whole system, but just 1/10 of Fugaku's resources.About FujitsuFujitsu is the leading Japanese information and communication technology (ICT) company offering a full range of technology products, solutions and services. Approximately 130,000 Fujitsu people support customers in more than 100 countries. We use our experience and the power of ICT to shape the future of society with our customers. Fujitsu Limited (TSE:6702) reported consolidated revenues of 3.9 trillion yen (US$35 billion) for the fiscal year ended March 31, 2020. For more information, please see www.fujitsu.com.About National Institute of Advanced Industrial Science & Technology (AIST)AIST is the largest public research institute established in 1882 in Japan. The research fields of AIST covers all industrial sciences, e.g., electronics, material science, life science, metrology, etc. Our missions are bridging the gap between basic science and industrialization and solving social problems facing the world. we prepare several open innovation platforms to contribute to these missions, where researchers in companies, university professors, graduated students, as well as AIST researchers, get together to achieve our missions. The open innovation platform established recently is The Global Zero Emission Research Center which contributes to achieving a zero-emission society collaborating with foreign researches. https://www.aist.go.jp/index_en.htmlAbout RIKEN Center for Computational ScienceRIKEN is Japan's largest comprehensive research institution renowned for high-quality research in a diverse range of scientific disciplines. Founded in 1917 as a private research foundation in Tokyo, RIKEN has grown rapidly in size and scope, today encompassing a network of world-class research centers and institutes across Japan including the RIKEN Center for Computational Science (R-CCS), the home of the supercomputer Fugaku. As the leadership center of high-performance computing, the R-CCS explores the "Science of computing, by computing, and for computing." The outcomes of the exploration - the technologies such as open source software - are its core competence. The R-CCS strives to enhance the core competence and to promote the technologies throughout the world. Copyright 2020 JCN Newswire. All rights reserved. www.jcnnewswire.com

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Valarhash Officially Launches New Service Series for its Mining Hosting Operations

CHENGDU, CHINA, Oct 31, 2020 - (ACN Newswire) - Valarhash, a leader in digital asset services, officially launches a new series of cryptocurrency miner hosting services, now enabling customers with remote online access to the operations of their mining machines. Registered users will have many features such as selecting their mining pool, withdrawing options and even re-selling their mining machine on the platform. Customers can purchase machines from Valarhash or other providers and have them shipped to the Valarhash mining farms.Valarhash is launching the new service series following an earlier announcement launching miner machine hosting plans. With versatility being of the utmost importance, Valarhash adds the new series of features in order to distinguish itself from its counterparts. The Valarhash mining pool charges a handling fee of 2%, and users are free to assign a designated mining pool for their mining machine based on their needs, offering a customizable experience. This ensures a reliable supply of computing power to consumers which can be viewed in real-time via an app or web management system built by the platform.Kevin Huang, co-founder of Valarhash, said "given that higher BTC prices would result in a rise in the price of a mining machine, we have decided to allow users to opt out of the hosting services and to re-sell their machines and computing power at any time." Valarhash hosts a large mining community and manages 100,000 mining machines in its facilities, with maintenance available 24/7. In order to respond promptly to glitches and other concerns, on-site operations and repairs will be carried out by Valarhash staff as they occur, without the need for customers to pay fees in advance.Valarhash, with its large scale operations, is able to standardize procedures and regulate mining costs because of its longstanding partnerships with local power stations. Customers can take advantage of the Valarhash hosting service for an annual fee of $0.046 (KW/H), in comparison to the rates provided on other sites where the annual fee is $0.055 (KW/H) and above. This hosting fee is inclusive of both the operating and maintenance costs of the mining machines.For information about the new services, please visit our official website to get in touch with a representative.Website: https://www.valarhash.comTwitter: https://twitter.com/VaIarhashFacebook: https://www.facebook.com/ValarhashLinkedin: https://www.linkedin.com/company/vhash/Medium: https://medium.com/@ValarhashTelegram: https://t.me/valarhashx1tmineAbout ValarhashChengdu-based Valarhash integrates mining machine sales, miner hosting, mining pool and mine construction services. Led by CEO Fiona Lv, Valarhash aims to provide users with transparent and beneficial mining plans using advanced technology, with a lower barrier of entry. Business operations cover hardware research and development, digital asset transactions and 1TMine hash power contract sharing. With a leading position in the hash power market, Valarhash integrates frontier resources with global vision, providing crypto compute service (CCS) and linking physical and digital worlds with blockchain technology. Email: BD@vhash.io.

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Valarhash Launches New Service Series for its Mining Hosting Operations

CHENGDU, CHINA, Oct 31, 2020 - (ACN Newswire) - Valarhash, a leader in digital asset services, officially launches a new series of cryptocurrency miner hosting services, now enabling customers with remote online access to the operations of their mining machines. Registered users will have many features such as selecting their mining pool, withdrawing options and even re-selling their mining machine on the platform. Customers can purchase machines from Valarhash or other providers and have them shipped to the Valarhash mining farms.Valarhash is launching the new service series following an earlier announcement launching miner machine hosting plans. With versatility being of the utmost importance, Valarhash adds the new series of features in order to distinguish itself from its counterparts. The Valarhash mining pool charges a handling fee of 2%, and users are free to assign a designated mining pool for their mining machine based on their needs, offering a customizable experience. This ensures a reliable supply of computing power to consumers which can be viewed in real-time via an app or web management system built by the platform.Kevin Huang, co-founder of Valarhash, said "given that higher BTC prices would result in a rise in the price of a mining machine, we have decided to allow users to opt out of the hosting services and to re-sell their machines and computing power at any time." Valarhash hosts a large mining community and manages 100,000 mining machines in its facilities, with maintenance available 24/7. In order to respond promptly to glitches and other concerns, on-site operations and repairs will be carried out by Valarhash staff as they occur, without the need for customers to pay fees in advance. Valarhash, with its large scale operations, is able to standardize procedures and regulate mining costs because of its longstanding partnerships with local power stations. Customers can take advantage of the Valarhash hosting service for an annual fee of $0.046 (KW/H), in comparison to the rates provided on other sites where the annual fee is $0.055 (KW/H) and above. This hosting fee is inclusive of both the operating and maintenance costs of the mining machines.For information about the new services, please visit our official website to get in touch with a representative. Website: https://www.valarhash.comTwitter: https://twitter.com/VaIarhashFacebook: https://www.facebook.com/ValarhashLinkedin: https://www.linkedin.com/company/vhash/Medium: https://medium.com/@ValarhashTelegram: https://t.me/valarhashx1tmineAbout ValarhashChengdu-based Valarhash integrates mining machine sales, miner hosting, mining pool and mine construction services. Led by CEO Fiona Lv, Valarhash aims to provide users with transparent and beneficial mining plans using advanced technology, with a lower barrier of entry. Business operations cover hardware research and development, digital asset transactions and 1TMine hash power contract sharing. With a leading position in the hash power market, Valarhash integrates frontier resources with global vision, providing crypto compute service (CCS) and linking physical and digital worlds with blockchain technology. Email: BD@vhash.io. Copyright 2020 ACN Newswire. All rights reserved. www.acnnewswire.com

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Valarhash Introduces Mining Machine Hosting Plans and Services

CHENGDU, CHINA, Oct 14, 2020 - (ACN Newswire) - Valarhash, a one-stop digital asset service platform, has announced new mining machine hosting plans and services, internationally available since rolling out from the end of September. Valarhash aims to provide customers with one-stop mining services, competitive electricity plans, and professional mining support and maintenance operations, lowering the barriers of entry to mining. "At the moment, most of the available mining pool hosting sites are located in China," said Valarhash CEO Fiona Lv. "While based in Sichuan Province, our mining machine hosting services are available to international users who may choose to host their miners or join pools through any of Valarhash's 9 global data centers."The mining power capacities of the hosting service are 100MW in hydropower and 100MW in thermal power, while the mining machines supported include the Antminer S17 series, Whatsminer M20 series, Whatsminer M30 series, Antminer T19 series, and the highly sought after Antminer S19 series. A minimum of 100 hosting machines are available for this plan while the stock lasts. The GPU graphics machine is also available for mining Ethereum.The service provides customers with a steady source of electricity for mining, calculated seasonally with peak flood season electricity around 0.04 USD (0.25 RMB) per kWh and off-season prices around 0.05-0.06 USD (0.35-0.4 RMB) per kWh, or annually at around 0.05 USD (0.31 RMB) per kWh. Users may register at the Valarhash website and a representative will be in touch. Stability is at the forefront of Valarhash services. In a recent interview with Binance, Fiona Lv said that with greater stability, mining could yield long-term investment. Since mining operations began in July 2019, Valarhash has launched 1THash mining pool and products linked to 9% hash rate, 1TMine cloud mining platform, and mining farm management software Nelson, which enables users to track data clearly, and optimize investor returns. Please visit our official website or join us on social media:Website: https://www.valarhash.com/valarhash/indexTwitter: https://twitter.com/VaIarhashFacebook: https://www.facebook.com/ValarhashLinkedin: https://www.linkedin.com/company/vhash/Medium: https://medium.com/@ValarhashTelegram: https://t.me/valarhashx1tmineAbout ValarhashChengdu-based Valarhash integrates mining machine sales, miner hosting, and mining pool and mine construction services. Led by CEO Fiona Lv, Valarhash aims to provide users with transparent and beneficial mining plans using advanced technology, with a lower barrier of entry. Business operations cover hardware research and development, digital asset transactions and 1TMine hash power contract sharing. With a leading position in the hash power market, Valarhash integrates frontier resources with global vision, linking physical and digital worlds with blockchain technology. Copyright 2020 ACN Newswire. All rights reserved. www.acnnewswire.com

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