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Thermal Hall effect in insulating quantum materials

The Innovation 2022年第5期3卷 3-4页

作者： Shucheng Guo Youming Xu Ran Cheng Jianshi Zhou Xi Chen Department of Electrical and Computer Engineering University of CaliforniaRiversideRiversideCA 92521USA Department of Physics and Astronomy University of CaliforniaRiversideRiversideCA 92521USA Materials Science and Engineering Program Department of Mechanical EngineeringUniversity of Texas at AustinAustinTX 78712USA

The emerging field of quantum materials involves an exciting new class of materials in which charge,spin,orbital,and lattice degrees of freedom are intertwined,exhibiting a plethora of exotic physical *** materials include,but are not limited to,superconductors,topological quantum matter,and systems with frustrated spins,which enable a wide range of potential applications in biomedicine,energy transport and conversion,quantum sensing,and quantum information processing。

关键词： properties. quantum topological

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Development of A Wireless Sensor Node for Storage Bin Volume Measurement 15

Development of A Wireless Sensor Node for Storage Bin Volume...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Omar, Jaafar J. Aleluya, Earl Ryan M. Clar, Steve E. Pao, Jeanette C. Salaan, Carl John O. Bahinting, Maria Fe P. Miano, Joel I. College of Computer Studies Mindanao State University-Iligan Institute of Technology Department of Computer Applications Iligan City9200 Philippines College of Engineering Mindanao State University-Iligan Institute of Technology Department of Computer Engineering and Mechatronics Iligan City9200 Philippines College of Engineering Mindanao State University-Iligan Institute of Technology Department of Electrical Engineering Iligan City9200 Philippines Graduate School of Engineering Mindanao State University-Iligan Institute of Technology Iligan City9200 Philippines

ISBN: (纸本)9798350381177

One of the crucial processes in the food manufacturing industry is monitoring and measuring the raw products stored in large storage structures, typically called Silos. These procedures are pivotal in dictating the industry's production, quality control, and efficiency. In this study, a wireless sensor node was developed to automatically measure the volume of the product stored in the created mock-up storage bin and a 3D surface reconstruction for visualization. The sensor node utilized a single-broad computer for its computing and wireless functionality. The sensing device comprised LiDAR sensor and a servo motor for data acquisition. The system used an open-source ROS framework to simplify the software along with its data processing pipline and hardware interface integration. The single- board and the end user devices were connected to the same network and used Remote Desktop Protocol (RDP) for remote functionality. Results from three different testing setups revealed an average error of 1.309% between the actual and predicted volume of the flour materials within the mock-up storage bin. This deviation from the actual content volume implies that the developed system offered an alternative method for efficient volume measuring of the storage bin through remote data collection and 3D visualization. © 2023 IEEE.

关键词： Volume measurement

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Long short-Term memory (LSTM) model-based reinforcement learning for nonlinear mass spring damper system control 7

Long short-Term memory (LSTM) model-based reinforcement lear...

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7th International Conference on computer Science and Computational Intelligence, CSCI 2022

作者： Wijaya, Santo Heryadi, Yaya Arifin, Yulyani Suparta, Wayan Lukas Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta55281 Indonesia Universitas Katolik Indonesia Atma Jaya Jakarta12930 Indonesia

The Neural Networks (NN) model which is incorporated in the control system design has been studied, and the results show better performance than the mathematical model approach. However, some studies consider that only offline NN model learning and does not use the online NN model learning directly on the control system. As a result, the controller's performance decreases due to changes in the system environment from time to time. The Reinforcement Learning (RL) method has been investigated intensively, especially Model-based RL (Mb-RL) to predict system dynamics. It has been investigated and performs well in making the system more robust to environmental changes by enabling online learning. This paper proposes online learning of local dynamics using the Mb-RL method by utilizing Long Short-Term Memory (LSTM) model. We consider Model Predictive Control (MPC) scheme as an agent of the Mb-RL method to control the regulatory trajectory objectives with a random shooting policy to search for the minimum objective function. A nonlinear Mass Spring Damper (NMSD) system with parameter-varying linear inertia is used to demonstrate the effectiveness of the proposed method. The simulation results show that the system can effectively control high-oscillating nonlinear systems with good performance. © 2022 Elsevier B.V.. All rights reserved.

关键词： Model predictive control

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On-Board Integrated Charger Based on Open-End Winding AC Machine

On-Board Integrated Charger Based on Open-End Winding AC Mac...

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IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)

作者： Thidarat Thanakam Neerakorn Jarutus Yuttana Kumsuwan Department of Electrical Engineering Faculty of Engineering Graduate Program in Electrical Engineering Chiang Mai University Chiang Mai Thailand Department of Electrical Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand

This paper presents an on-board integrated charger for electric vehicles (EVs) based on an open-end winding AC machine. To reduce weight, volume, and costs, including additional space area in EVs, the main component of the traction system is utilized as a part of the charger system, i.e., the wound rotor open-end winding AC machine fed by a dual converter with a single battery source. In charging operation, the rotor winding side of the AC machine is at standstill and reconfigured through magnetic contactors to interface with the three-phase utility grid, whereas the stator winding side is still connected to the converters. It performs as a transformer isolated by itself. On the other hand, the rotor windings are shorted together for the driving operation. Besides, the charger system assists the stability of the utility grid by consuming constant power, absorbing maximum power rated at 22 kW. To enhance excellent control, the modulation strategy of the dual converter is determined by means of different phase angles of 120° As a result, the differential-mode voltage problem is suppressed. The simulation results are validated to confirm the performance of the proposed system.

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IoT-Enabled Deep Learning Algorithm for Estimation of State-of-Charge of Lithium-ion Batteries∗

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Journal of Circuits, Systems and computers 2024年第7期33卷 2450134-2450134页

作者： Pushpavanam, B. Kalyani, S. Prasanna, M. Arul Sangaiah, Arun Kumar Department of Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul India Kamaraj College of Engineering and Technology Madurai India Department of Electrical and Electronics Engineering Kamaraj College of Engineering and Technology Madurai India International Graduate Institute of AI National Yunlin University of Science and Technology Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Battery Management System (BMS) functions to monitor individual cell in a battery pack and its crucial task is to maintain stability throughout the battery pack. The BMS is responsible for maintaining the safety of the battery as well as not to harm the user or environment. The parameters that are to be monitored in a battery are Voltage, Current and Temperature. With the collected data, BMS carefully monitors the charging-discharging behavior of the battery particularly in the Lithium-ion (Li-ion) batteries in which charging and discharging behavior are completely different. This paper proposes a real-time IOT connected deep learning algorithm for estimation of State-of-Charge (SoC) of Li-ion batteries. This paper provides unique objectives and congruence between model-based conventional methods and state-of-the-art deep learning algorithm, specifically Feed Forward Neural Network (FNN) which is nonRecurrent. This paper also highlights the advantages of Internet-of-Things (IoT) connected deep learning algorithm for estimation of State-of-Charge of Li-ion batteries in Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs). The major advantage of the proposed method is that the Artificial Intelligence (AI)-based techniques aim to bring the estimation error less than 2% at a low cost and time without the model of the battery, at par with conventional method of Extended Kalman Filter (EKF) which is the best ever practical estimation theory. Another advantage of the proposed method is that in an abnormal condition (i.e., Unsafe Temperature) the IF This Then That (IFTTT) IoT mobile application interfaced with BMS through ThingSpeak cloud, sends a notification alert to the battery expert or to the user prior to an emergency. Finally, the real-time data of the battery parameters are collected through ThingSpeak cloud platform for future research and analysis. © 2024 World Scientific. All rights reserved.

关键词： Battery management systems

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A Review on Active Customers'Participation in Smart Grids

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Journal of Modern Power Systems and Clean Energy 2023年第1期11卷 3-16页

作者： Konrad Schmitt Rabindra Bhatta Manohar Chamana Mahtab Murshed Ilham Osman Stephen Bayne Luciane Canha Electrical and Computer Engineering Department Texas Tech UniversityLubbockTX79409USA Wind Energy Program Texas Tech UniversityLubbockTX79409USA Electromechanical and Power Systems Department Federal University of Santa MariaSanta MariaRS97105-900Brazil Design Release Engineer at Electrification and Power conversion General MotorsGM Tech Center RoadWarrenMI48092USAl IEEE

Industrial,commercial,and residential facilities are progressively adopting automation and generation *** having flexible demand and renewable energy generation,traditional passive customers are becoming active participants in electric power system *** profound coordination among grid operators and active customers,the facilities’capability for demand response(DR)and distributed energy resource(DER)management will be valuable asset for ancillary services(ASs).To comply with the increasing demand and flexible energy,utilities urgently require standards,regulations,and programs to efficiently handle load-side resources without trading off stability and *** study reviews different types of customers’flexibilities for DR,highlighting their capabilities and limitations in performing local ancillary services(LASs),which should benefit the power grid by profiting from it through incentive *** financial incentives and techniques employed around the world are presented and *** potential barriers in technical and regulatory aspects are successfully identified and potential solutions along with future guidance are discussed.

关键词： Active distribution network(ADN) ancillary services energy management flexible demand resiliency

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Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learning

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Mollah, Muhammad Baqer Wang, Honggang Karim, Mohammad Ataul Fang, Hua University of Massachusetts Dartmouth Department of Electrical and Computer Engineering MA02747 United States Yeshiva University Katz School of Science and Health Department of Graduate Computer Science and Engineering NY10016 United States University of Massachusetts Dartmouth Department of Computer and Information Science MA02747 United States

Beamforming techniques are considered as essential parts to compensate severe path losses in millimeter-wave (mmWave) communications. In particular, these techniques adopt large antenna arrays and formulate narrow beams to obtain satisfactory received powers. However, performing accurate beam alignment over narrow beams for efficient link configuration by traditional standard defined beam selection approaches, which mainly rely on channel state information and beam sweeping through exhaustive searching, imposes computational and communications overheads. And, such resulting overheads limit their potential use in vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications involving highly dynamic scenarios. In comparison, utilizing out-of-band contextual information, such as sensing data obtained from sensor devices, provides a better alternative to reduce overheads. This paper presents a deep learning-based solution for utilizing the multi-modality sensing data for predicting the optimal beams having sufficient mmWave received powers so that the best V2I and V2V line-of-sight links can be ensured proactively. The proposed solution has been tested on real-world measured mmWave sensing and communication data, and the results show that it can achieve up to 98.19% accuracies while predicting top-13 beams. Correspondingly, when compared to existing been sweeping approach, the beam sweeping searching space and time overheads are greatly shortened roughly by 79.67% and 91.89%, respectively which confirm a promising solution for beamforming in mmWave enabled communications. © 2015 IEEE.

关键词： Beamforming

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SLMLET: A RISC-V Processor SoC with Tightly-Coupled Area-Efficient eFPGA Blocks

SLMLET: A RISC-V Processor SoC with Tightly-Coupled Area-Eff...

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IEEE Cool Chips

作者： Takuya Kojima Yosuke Yanai Hayate Okuhara Hideharu Amano Morihiro Kuga Masahiro Iida Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan Department of Information and Computer Science Keio University Yokohama Japan Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Computer Science and Electrical Engineering Kumamoto University Kumamoto Japan

ISBN: (数字)9798350384147

ISBN: (纸本)9798350384154

SLMLET is a low-power System-on-a-Chip (SoC), which is a promising device for edge computing. It consists of a RISC-V core and area-saving embedded Field-programmable gate arrays (eFPGA) blocks called Scalable Logic Module (SLM). This paper presents a fabricated SLMLET chip and a developed HW/SW co-design flow. The experimental results demonstrate that the RISC-V core can operate at up to 300MHz, and a hardware design on the SLM blocks can operate at up to 100MHz. By offloading compute-intensive processes to dedicated circuits implemented in the SLM blocks, it is possible to achieve up to 77 % energy reduction compared to software processing on the RISC-V core and other commercial microcontrollers.

关键词： Microcontrollers Voltage Logic gates Software Energy efficiency Timing System-on-chip

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Comparison of 2L and 3L-NPC Power Converter-Based a Level-2 Isolated Off-Board Charger

Comparison of 2L and 3L-NPC Power Converter-Based a Level-2 ...

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electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Thidarat Thanakam Yuttana Kumsuwan Department of Electrical Engineering Faculty of Engineering Graduate Program in Electrical Engineering Chiang Mai University Chiang Mai Thailand Department of Electrical Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand

This paper presents an off-board charger system for any electric vehicle based on a single-stage power converter operation that performs as a three-phase pulse width modulation rectifier. To enhance the safety issue by itself, a step-down transformer with galvanic isolation is added. Additionally, the off-board charger system allows bidirectional power flow in both grid-to-vehicle (G2V) and vehicle-to-grid operations. To analyze this system, only G2V operation is shown by comparing a two-level power converter charger with a three-level neutral point clamped power converter charger. In light of its controller, the off-board charger is designed to charge a battery in constant current and constant voltage mode in compliance with Level 2 of charging power. Based on this controller, it can support grid power quality under a unity power factor. Simulation-based verification is conducted on a maximum power rating of 22 kW to claim the performance of steady-state operation and dynamic operation.

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Vehicle Wheel Hub Recognition Method Based on HOG Feature Extraction and SVM Classifier 14

Vehicle Wheel Hub Recognition Method Based on HOG Feature Ex...

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14th International Conference on Information Technology and electrical engineering, ICITEE 2022

作者： Wang, Bin Juanatas, Ronaldo Niguidula, Jasmin Anhui Technical College of Mechanical and Electrical Engineering Internet and Communications Department Wuhu China Technological University of the Philippines Graduate Program and External Studies Manila Philippines

ISBN: (数字)9781665460774

ISBN: (纸本)9781665460774

In order to avoid the problems of low accuracy of wheel hub recognition and classification and excessive dependence on template image quality in the process of automatic production of the automobile wheel hub, a vehicle wheel hub recognition method based on Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) is proposed. Firstly, the wheel hub images under three different lighting conditions are collected, and the wheel hub images are processed in grayscale;Secondly, the positive and negative samples are made, and hog features are extracted, respectively;Finally, the extracted hog features are trained by SVM classifier, and the trained target classifier is used to recognize the wheel hub photos under three different lighting conditions. The experimental results show that this method has higher recognition accuracy than the traditional template matching method under different lighting conditions. © 2022 IEEE.

关键词： Support vector machines

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