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Investigation of manufacturing conditions of single roll rapidly solidified ribbon for anode materials of magnesium rechargeable batteries

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Keikinzoku/Journal of Japan Institute of Light Metals 2023年第11期73卷 537-544页

作者： Kirimoto, Yuichi Fuke, Kousuke Tsukeda, Tadayoshi Aida, Tetsuo Nemoto, Yasunori Ikeda, Minoru Tabata, Hironobu Kurihara, Hideki Suzuki, Mayumi Department of Nano and Functional Material Science Graduate School of Science and Engineering for Education University of Toyama 3190 Gofuku Toyama-shi-shiToyama Toyama930-8555 Japan Program of Materials Science and Engineering Studies in Science and Engineering Graduate School of Science and Engineering University of Toyama Toyama-shi-shiToyama Toyama Japan Faculty of Sustainable Design Academic Assembly University of Toyama Toyama-shi Toyama-shi-shiToyama Toyama Japan FLOW Science JAPAN Shizuoka Hamamatsu-shi Japan CHUETSU METAL WORKS CO. LTD Nakaniikawa-gun Toyama Japan Saitama Industrial Technology Center Saitama Kawaguchi-shi Japan Department of Mechanical Systems Engineering Faculty of Engineering Toyama Prefectural University Imizu-shi Toyama Japan

Magnesium rechargeable batteries are attracting attention as next-generation rechargeable batteries because of their resource abundance, cost, safety, and high electrical capacity per volume compared to conventional lithium-ion rechargeable batteries. Our joint research team focused not only on the electrochemically active magnesium composition of ribbon for the negative electrode materials of magnesium rechargeable batteries but also on the production process, focused on the single roll type liquid quenching and rapid solidification method, which can be produced at a lower cost. In this study, the optimal processing conditions were examined by comparing CAE analysis with ribbon actually manufactured, with the aim of further improving manufacturing conditions using the same method, such as length discontinuities, thickness, and surface properties of ribbon. We specifically various process parameters such as injection pressure and roll peripheral speed for evaluation. As a result, several linear cracks were observed on the surface of the ribbon as the peripheral speed increased and as the Gap became wider, and the surface properties tended to deteriorate. Also, It was found that the dominant factor in the length of the ribbon was the peripheral speed. As the peripheral speed increased, the length of the ribbon tended to increase. © 2023 Japan Institute of Light Metals. All rights reserved.

关键词： Surface properties

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Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy systems

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Computers, Materials & Continua 2022年第8期72卷 3999-4017页

作者： Md.Mottahir Alam Ahteshamul Haque Mohammed Ali Khan Nebras M.Sobahi Ibrahim Mustafa Mehedi Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew Delhi 110025India Department of Electrical Power Engineering Faculty of Electrical Engineering and CommunicationBrno University of TechnologyBrno 61600Czech Republic Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah 21589Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of *** with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow,monitoring and maintenance are a few of the prime *** problems were addressed widely in the literature,but most of the research has drawbacks due to long detection time,and high misclassification *** to overcome these drawbacks,and to develop an accurate monitoring approach,this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic(PV)system and highlighted along with a brief overview on existing fault detection *** on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance ***,the system was tested with a 4 kWp grid-connected PV system,and a decision tree-based algorithm was developed for the identification of a *** results identified 94.7%training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.

关键词： Fault detection machine learning fault ride through

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783-MHz fundamental repetition rate CNT mode-locked all Er-doped fiber ring laser

783-MHz fundamental repetition rate CNT mode-locked all Er-d...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Dai, Maolin Liu, Bowen Ma, Yifan Shirahata, Takuma Yang, Ruoao Zhang, Zhigang Set, Sze Yun Yamashita, Shinji Department of Electrical Engineering and Information Systems The University of Tokyo Bunkyo-ku Tokyo113-8656 Japan Research Center for Advanced Science and Technology The University of Tokyo Meguro-ku Tokyo153-8904 Japan State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Center for Quantum Information Technology Peking University Beijing100871 China

We realize 783-MHz fundamental repetition rate in a carbon-nanotubes (CNT) modelocked Er-doped fiber ring laser with all active fibers. The proposed laser self-starts mode-locking at 108-mW pump power and delivers 623... 详细信息

ISBN: (纸本)9781957171395

关键词： Laser excitation

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Recurrent Graph Neural Network-Based Identification of Replay Attacks in Power Networks 4

Recurrent Graph Neural Network-Based Identification of Repla...

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4th International Conference on electrical, Computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Joad, Faaiz Atat, Rachad Mbayed, Hayat Takiddin, Abdulrahman Florida State University Dept. of Electrical & Computer Engineering TallahasseeFL United States Lebanese American University Computer Science Department Beirut Lebanon Florida State University Center for Advanced Power Systems TallahasseeFL United States

ISBN: (纸本)9798350391183

As cyber-physical systems, power networks exchange data to ensure smooth operation. Through communication networks, the exchanged data becomes vulnerable to cyberattacks where malicious entities establish malevolent connections to falsify the data, which exploit the data integrity, leading to network instability. Machine learning-based solutions have been proposed to identify such malevolent actions, including false data injection attacks. This paper aims to (a) enhance the attack detection performance and (b) quantify the performance of non-recurrent and recurrent machine learning-based attack detection systems against multiple replay attacks that involve falsifying data via presenting normal operation patterns from previous timestamps. It turns out that capturing temporal aspects from the data using recurrent models outperforms non-recurrent models by 5-32% in accuracy (ACC). Specifically, we propose a recurrent graph neural network (RGNN) model that captures the temporal and spatial aspects of the network data. The RGNN model outperforms non-recurrent models by 14-32% and other recurrent models by 6-13% in ACC. The experiments are conducted on IEEE 14, 39, and 118-bus power networks where the model performance is enhanced (by up to 8% in ACC) in larger systems due to capturing more spatial and temporal features. © 2024 IEEE.

关键词： Cyber attacks

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ARIM-mdx Data System: Towards a Nationwide Data Platform for Materials science

ARIM-mdx Data System: Towards a Nationwide Data Platform for...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Hanai, Masatoshi Ishikawa, Ryo Kawamura, Mitsuaki Ohnishi, Masato Takenaka, Norio Nakamura, Kou Matsumura, Daiju Fujikawa, Seiji Sakamoto, Hiroki Ochiai, Yukinori Okane, Tetsuo Kuroki, Shin-Ichiro Yamada, Atsuo Suzumura, Toyotaro Shiomi, Junichiro Taura, Kenjiro Mita, Yoshio Shibata, Naoya Ikuhara, Yuichi Information Technology Center The University of Tokyo Tokyo Japan Institute of Engineering Innovation The University of Tokyo Tokyo Japan Department of Electrical Engineering and Information Systems The University of Tokyo Tokyo Japan Department of Chemical System Engineering The University of Tokyo Tokyo Japan The Institute of Statistical Mathematics Research Organization of Information and Systems Tokyo Japan Materials Sciences Research Center Japan Atomic Energy Agency Hyogo Japan Research Institute for Semiconductor Engineering Hiroshima University Hiroshima Japan Center for Emergent Matter Science Riken Saitama Japan

ISBN: (纸本)9798350362480

In modern materials science, effective and high-volume data management across leading-edge experimental facilities and world-class supercomputers is indispensable for cutting-edge research. However, existing integrated systems that handle data from these resources have primarily focused just on smaller-scale cross-institutional or single-domain operations. As a result, they often lack the scalability, efficiency, agility, and interdisciplinarity, needed for handling substantial volumes of data from various researchersIn this paper, we introduce ARIM-mdx data system 1, aiming at a nationwide data platform for materials science in Japan. Currently in its trial phase, the platform has been involving 11 universities and institutes all over Japan, and it is utilized by over 800 researchers from around 140 organizations in academia and industry, being intended to gradually expand its reach. The ARIM-mdx data system, as a pioneering nationwide data platform, has the potential to contribute to the creation of new research communities and accelerate innovations. © 2024 IEEE.

关键词： Big Data Cloud Computing Materials science Shared-use Research Facility Supercomputer

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In situ Observation of Lithium Stripping and Plating Process in an Open-cell All-solid-state Lithium Metal Battery

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1287-1288页

作者： Wu, C. Zhao, L. Ai, Q. Risal, S. Lou, J. Yao, Y. Fan, Zheng Materials Science and Engineering Program and Texas Center for Superconductivity at the University of Houston University of Houston Houston TX United States Department of Electrical and Computer Engineering University of Houston Houston TX United States Department of Materials Science and Nano Engineering Rice University Houston TX United States Department of Engineering Technology University of Houston Houston TX United States

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Deep learning framework for wind speed prediction in Saudi Arabia

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Neural Computing and Applications 2025年第5期37卷 3685-3701页

作者： Alabdulhadi, Arwa Ahmed Rehman, Shafiqur Ali, Amjad Shafiullah, Md Physics Department College of Science and Humanities Imam Abdulrahman Bin Faisal University Jubail3065 Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Control & Instrumentation Engineering Department King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Increased utilization of conventional energy sources severely impacts the environment by increasing the global temperature and contributing to global warming. Sustainable energy sources could contribute to handling the increment while also providing cheap, clean, and abundant energy. Wind energy is considered one of the most reliable sources of sustainable energy due to its abundance and availability during the day and night compared to other renewable resources. However, effective forecasting of such intermittent resources is considered a key challenge for power system operators. This paper develops a novel deep learning framework for forecasting the wind speed in Dhahran City, Saudi Arabia, using two years of data acquired at different heights from the light detection and ranging device, an active remote sensing wind monitoring system. It also segments the data and removes wrong measurements through data pre-processing. The article identifies the best configuration for deep learning models, such as long short-term memory, through a systematic approach. The presented results confirm the efficacy of the developed models against the selected statistical performance measures. The developed model performs better with large data volumes than with lower volumes. Finally, the comparative analysis with the literature-reported results provides confidence in the competency of the proposed model in predicting wind speed for various periods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Clean energy

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Full Paper: Comprehensive Analysis and Assessment of An Introduction to engineering and Computing Course 15

Full Paper: Comprehensive Analysis and Assessment of An Intr...

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15th Annual First-Year engineering Experience Conference, FYEE 2024

作者： Zhou, Longfei Kasaraneni, Varun K. Chen, Longyan Abuhussein, Ahmed Department of Biomedical Industrial and Systems Engineering Gannon University EriePA United States Department of Environmental Science and Engineering Gannon University EriePA United States Department of Electrical and Cyber Engineering Gannon University EriePA United States Center for Manufacturing and Technology Gannon University EriePA United States

In fall 2023, for freshman undergraduate students majoring in engineering and Computing studies, Gannon University's School of engineering and Computing (SEC) implemented the Introduction to engineering and Computing course. The new Introduction to engineering and Computing course is a 1-credit course that provides students with an overview of engineering and computing as professions and disciplines. The course is designed to serve as a foundational exploration into the interconnected worlds of engineering principles and computational problem-solving. The course covers professional and ethical considerations, career development, and communication skills vital for success in these fields. Students are also introduced to the resources available in MakerSpace, including but not limited to 3D printing, laser cutting, and microcontroller applications. Through hands-on projects and instructional activities, students explore fundamental concepts in engineering while fostering critical thinking and problem-solving skills. This paper outlines the course, and the project structure details, and provides a summary of student feedback gathered from course evaluations and surveys conducted at least one semester after completion. Future work on how to modify the course structure is also discussed according to the student feedback. © 2024 American Society for engineering Education. All rights reserved.

关键词： Students

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Scaling the "memory Wall" for Multi-Dimensional Seismic Processing with Algebraic Compression on Cerebras CS-2 systems 23

Scaling the "memory Wall" for Multi-Dimensional Seismic Proc...

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2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023

作者： Ltaief, Hatem Hong, Yuxi Wilson, Leighton Jacquelin, Mathias Ravasi, Matteo Keyes, David Elliot Extreme Computing Research Center Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Cerebras Systems Inc. SunnyvaleCA United States

ISBN: (纸本)9798400701092

We exploit the high memory bandwidth of AI-customized Cerebras CS-2 systems for seismic processing. By leveraging low-rank matrix approximation, we fit memory-hungry seismic applications onto memory-austere SRAM wafer-scale hardware, thus addressing a challenge arising in many wave-equation-based algorithms that rely on Multi-Dimensional Convolution (MDC) operators. Exploiting sparsity inherent in seismic data in the frequency domain, we implement embarrassingly parallel tile low-rank matrix-vector multiplications (TLR-MVM), which account for most of the elapsed time in MDC operations, to successfully solve the Multi-Dimensional Deconvolution (MDD) inverse problem. By reducing memory footprint along with arithmetic complexity, we fit a standard seismic benchmark dataset into the small local memories of Cerebras processing elements. Deploying TLR-MVM execution onto 48 CS-2 systems in support of MDD gives a sustained memory bandwidth of 92.58PB/s on 35, 784, 000 processing elements, a significant milestone that highlights the capabilities of AI-customized architectures to enable a new generation of seismic algorithms that will empower multiple technologies of our low-carbon future. © 2023 Owner/Author(s).

关键词： Energy efficiency

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Predefined-Time Attitude Tracking Control for Quadrotor UAV with Input Delay

Predefined-Time Attitude Tracking Control for Quadrotor UAV ...

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2023 China Automation Congress, CAC 2023

作者： Li, Shuo Pei, Hailong Duan, Na College of Automation Science and Enginneering South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle Systems Guangzhou510640 China School of Automation Science and Enginneering South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle Systems Guangzhou510640 China School of Electrical Engineering & Automation Jiangsu Normal University Xuzhou221116 China

ISBN: (纸本)9798350303759

In this paper, the predefined time attitude tracking flight problem for a quadrotor unmanned aerial vehicle (UAV) system with input delays is investigated. By means of a novel auxiliary system, the effects of input delay are solved. Then, on the basis of the backstepping method and predefined performance control technology, the predefined-time attitude tracking controller is constructed to make UAV attitude tracking. Simulation results verify the validity of the proposed scheme. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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