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28th International Electrical Power Distribution Conference, EPDC 2024

作者： Es'haghi, Amir-Saeed Abbaspour, Ali Marini, Abbas Sharif University of Technology Department of Electrical Engineering Tehran Iran Niroo Research Institute Power System Operation and Planning Research Group Tehran Iran

ISBN: (纸本)9798350385502

In recent years, cryptocurrency mining activities have been raised due to its high profitability and secure environment, especially in countries with low energy costs. However, this trend has brought various challenges, including excessive energy consumption and volatile energy prices. These issues have led to several problems, such as increased strain on the power grid, electricity theft, harmonic distortion in the grid, degradation of power quality, and inaccurate load behaviour estimation. To tackle these challenges, this article represents an innovative approach that involves the strategic placement of power quality meters in the distribution network to identify unauthorized mining loads. Since miners utilize electronic power switching devices, they introduce harmonics into the system, making them identifiable as specific harmonic loads. Hence, it has been proposed to utilize harmonic state estimation for identification of miners. The proposed approach is implemented and tested using DigSILENT software on 18-bus IEEE test network. The results demonstrate the effectiveness of proposed method in accurately pinpointing the locations of harmonic mining loads. © 2024 IEEE.

关键词： Power quality

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An ECRAM-Based Analog Compute-in-Memory Neuromorphic system with High-Precision Current Readout

An ECRAM-Based Analog Compute-in-Memory Neuromorphic System ...

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2023 IEEE Biomedical Circuits and systems Conference, BioCAS 2023

作者： Um, Minseong Kang, Minil Kwak, Hyunjeong Noh, Kyungmi Kim, Seyoung Lee, Hyung-Min Korea University School of Electrical Engineering Seoul Korea Republic of Korea University Department of Semiconductor System Engineering Seoul Korea Republic of Pohang University of Science and Technology Department of Materials Science & Engineering Pohang Korea Republic of

ISBN: (纸本)9798350300260

This paper proposes a high-precision analog compute-in-memory (CIM) neuromorphic system that adopts a nonvolatile electro-chemical random-access memory (ECRAM) to improve linearity, symmetry, and endurance of the synapse array. For on-chip synapse training and inference, activation modules and matrix processing units adaptively form a neural network to perform analog-based update and read operations, respectively. The proposed neuromorphic system also utilizes current scaling and offset bias control to optimize the output sensing and matrix processing with ECRAM synapses. The 250-nm CMOS neuromorphic chip was fully verified with the 32 x 32 ECRAM synapse array, enabling linear update and accurate read operations. The proposed system can update and read the ECRAM synapse with 1000 weight levels, leading to high data throughput. The output error rates over 32 synapse read columns were measured within 2.59% when sweeping the weight level. The 32 x 32 ECRAM-based neuromorphic system consumes 5.9 mW when performing the inference. © 2023 IEEE.

关键词： CMOS compute-in-memory current scaling ECRAM matrix processing neural networks neuromorphic

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Bat Algorithm: A Review on Theory, Modifications and Applications 3

Bat Algorithm: A Review on Theory, Modifications and Applica...

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3rd International Conference on Intelligent Cybernetics technology and Applications, ICICyTA 2023

作者： Singh, Hakam Witarsyah, Deden Misra, Alok Tiwari, Raj Gaurang Chitkara University School of Engineering and Technology Department of Computer Science and Engineering Himachal Paradesh Baddi India Telkom University School of Industrial and System Engineering Information System Departement Bandung Indonesia Lovely Professional University School of Computer Application Punjab Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350394559

Due to its many applications in a variety of domains, including clustering, cloud computing, and power systems, metaheuristic algorithms have attracted a lot of interest from the scholarly community. By using their self-explorative and adaptive processes, these algorithms provide certain benefits over conventional methods. One well-known metaheuristic approach is effective in resolving optimization problems with constraints and without constraints. This technique was inspired by nature, notably the echolocation ability of bat species. The Bat Algorithm (BA) makes use of a powerful search mechanism to provide the best results when compared to other algorithms of the same class. The theoretical underpinnings of the BA are explored in depth in this research, along with several changes and advancements that have improved its functionality. These enhancements include adjustments to search tactics as well as other developments. The report also thoroughly examines how the BA may be used across a variety of fields, demonstrating its competence and adaptability. The BA has been effectively used in several real-world applications, from cloud computing to clustering, and each one is carefully investigated. The study also offers a thoughtful examination of the BA's possible advancements and future possibilities. It also entails investigating novel modifications and tactics that might improve its effectiveness in handling challenging optimization issues. This study seeks to direct practitioners and academics toward more effective and efficient metaheuristic algorithms, thereby stimulating improvements in optimization approaches. It does this by summarising the present state of the BA and imagining its future development. © 2023 IEEE.

关键词： Cloud computing

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Predicting Car Accidents with YOLOv7 Object Detection and Object Relationships

Predicting Car Accidents with YOLOv7 Object Detection and Ob...

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2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Wu, Ming-Xuan Chang, Chi-Sheng Miao, Jr-Ming Lee, Chia-Yen National Pingtung University of Science and Technology Department of Information Management System Pingtung Taiwan National United University Department of Electrical Engineering Miaoli Taiwan National Pingtung University of Science and Technology Department of Biomechatronics Engineering Pingtung Taiwan

ISBN: (纸本)9798350313154

In this paper, we propose a method for predicting potential accidents using object detection and object relationship analysis. We use YOLOv7 object detector to identify various objects on roads and highways, and analyze the relationships between them to predict potential accidents. Our model is trained on a large dataset from Kaggle competitions, which includes driving record videos of different types of vehicles such as buses, cars, trailers, trucks, and lorries. We analyze the patterns of detected accident vehicle objects to determine whether an accident occurred in consecutive frames. Experimental results show that our method can reasonably predict whether an accident will occur in the next 20 frames. The use of object detection allows for the identification of multiple objects, thus improving the accuracy of accident prediction. Although we did not achieve better performance or accuracy, this approach has the potential to improve the safety performance of autonomous vehicles and reduce the occurrence of traffic accidents. © 2023 IEEE.

关键词： Object recognition

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Parametric Effects of a Heat Transfer system using Supercritical Carbon Dioxide as Working Fluid

Parametric Effects of a Heat Transfer System using Supercrit...

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6th IEEE Global Power, Energy and Communication Conference, GPECOM 2024

作者： Wang, Lei Pan, Yu-Cheng Lee, Jin-Der Chen, Shao-Wen Fu, Ben-Ran Pan, Chin Jiangxi University of Science And Technology School of Architectural And Surveying And Mapping Engineering Jiangxi China National Tsing Hua University Department of Engineering And System Science Hsinchu Taiwan National Tsing Hua University Nuclear Science And Technology Development Center Hsinchu Taiwan National Tsing Hua University Institute of Nuclear Engineering And Science Department of Engineering And System Science Hsinchu Taiwan National Yang Ming Chiao Tung University Department of Mechanical Engineering Hsinchu Taiwan City University of Hong Kong Department of Mechanical Engineering Kowloon Hong Kong

ISBN: (纸本)9798350351088

This study experimentally explores the parametric effects on the heat transfer efficiency and pressure drop of supercritical CO2 tested in a horizontal circular heating tube having an inner diameter of 1.0 mm and a length of 170 mm. The experimental results illustrate that the working condition with a longer region after the pseudocritical point will generally deteriorate the heat transfer in the tube. The parametric effect of mass flux can improve the heat transfer efficiency, while the parametric effects of system pressure and inlet fluid temperature can reduce the heat transfer efficiency. The effect of heat flux on the heat transfer efficiency would depend on the outlet flow condition. The optimal heat flux with a highest heat transfer efficiency of about 91.38% occurs in the flow state at tube outlet around the pseudocritical point. Moreover, the measured pressure drops are in the order of several kPa, which could neglect their effects on the local flow properties. The increase of heat flux, inlet fluid temperature and mass flux all tend to enlarge the pressure drop of the heating tube, while the increase of system pressure will lower the tube pressure drop. © 2024 IEEE.

关键词： Pressure drop

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Wet tissue adhesive polymeric powder hydrogels for skeletal muscle regeneration

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Bioactive Materials 2024年第10期40卷 334-344页

作者： Mingyu Lee Daun Seo Junggeon Park Sun Hong Lee Jin Jeon Woochan Kim Jangho Kim Hee Seok Yang Jae Young Lee School of Materials Science and Engineering Gwangju Institute of Science and TechnologyGwangju61005Republic of Korea Department of Nanobiomedical Science&BK21 FOUR NBM Global Research Center for Regenerative Medicine Dankook UniversityCheonan31116Republic of Korea Department of Convergence Biosystems Engineering Chonnam National UniversityGwangju61186Republic of Korea Department of Rural and Biosystems Engineering Chonnam National UniversityGwangjuRepublic of Korea Interdisciplinary Program in IT-Bio Convergence System Chonnam National UniversityGwangju61186Republic of Korea

Volumetric muscle loss(VML)frequently results from traumatic incidents and can lead to severe functional *** have been widely employed for VML tissue regeneration,which are unfortunately ineffective because of the lack of intimate contact with injured tissue for structural and mechanical *** hydrogels allow for strong tissue connections for wound ***,conventional adhesive hydrogels exhibit poor tissue adhesion in moist,bleeding wounds due to the hydration layer at the tissue–hydrogel interfaces,resulting in insufficient *** this study,we developed a novel,biocompatible,wet tissue adhesive powder hydrogel consisting of dextran-aldehyde(dex-ald)and gelatin for the regeneration of *** powder absorbs the interfacial tissue fluid and buffer solution on the tissue,spontaneously forms a hydrogel,and strongly adheres to the tissue via various molecular interactions,including the Schiff base *** particular,the powder composition with a 1:4 ratio of dex-ald to gelatin exhibited optimal characteristics with an appropriate gelation time(258 s),strong tissue adhesion(14.5 kPa),and ***-ald/gelatin powder hydrogels presented strong adhesion to various organs and excellent hemostasis compared to other wet hydrogels and fibrin glue.A mouse VML injury model revealed that the dex-ald/gelatin powder hydrogel significantly improved muscle regeneration,reduced fibrosis,enhanced vascularization,and decreased ***,our wet-adhesive powder hydrogel can serve as an effective platform for repairing various tissues,including the heart,muscle,and nerve tissues.

关键词： Powder Hydrogel Wet-adhesive Skeletal muscle regeneration

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HNN-Transformer Integrated Network for Estimating Robot Position 23

HNN-Transformer Integrated Network for Estimating Robot Posi...

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23rd International Conference on Control, Automation and systems, ICCAS 2023

作者： Kang, Chang Ho Kim, Sun Young Kumoh National Institute of Technology Department of Mechanical System Engineering Department of Aeronautics Mechanical and Electronic Convergence Engineering Gumi39177 Korea Republic of School of Mechanical Engineering Kunsan National University Gunsan54150 Korea Republic of

ISBN: (纸本)9788993215274

This paper proposes a novel model architecture, the Hamiltonian neural network (HNN)-Transformer, which capitalizes on the strengths of both Hamiltonian neural networks and Transformers to effectively model and predict the behavior of physical systems. The proposed structure is designed to solve the problem that arises over time when predicting the state of a 2D robotic system. The HNN component of our model makes it possible to incorporate known physical laws into the learning process, while the Transformer component enables effective processing of sequentially input data. The performance of the proposed method was confirmed through simulation, and it was confirmed that this novel model is possible to leverage the strengths of both HNNs and Transformers to achieve improved performance than the existing independent HNN and Transformer on the challenging task, respectively. This suggests that the integration of physical understanding and sequence processing is a promising direction for modeling complex dynamic systems. © 2023 ICROS.

关键词： Hamiltonians

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PCE: Multi-Agent Path Finding via Priority-Aware Communication & Experience Learning

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Gao, Jianqi Li, Yanjie Ye, Zhaohui Wu, Xinyu Department of Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics and School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Multi-agent path finding (MAPF) is an essential issue for warehouse automation, where multiple agents plan collision-free paths from the start to goal positions. Reinforcement learning (RL) has been employed to develop partially observable distributed MAPF methods that can be scaled to any number of agents. However, existing RL-based MAPF methods still have some limitations in handling redundant information and avoiding deadlock, resulting in a low success rate or longer makespan. This paper proposes a Priority-aware Communication & Experience learning method (PCE), which combines RL with a novel priority-aware multi-agent communication and a new priority-aware deadlock experience replay to tackle this challenge. To be specific, our innovation encompasses two-fold. Our proposed communication mechanism aims to handle redundant information, which establishes a dynamic communication topology based on agents' priorities and proposes a two-head priority-aware graph attention network to aggregate information. In order to help the agent avoid deadlock, we prioritize the expert experience that solves the deadlock when performing experience replay. We conduct multiple simulation experiments on warehouse-like structured grid maps. Compared with the state-of-the-art RL-based MAPF methods, PCE performs significantly better with a higher success rate and lower makespan in small and large MAPF and higher average throughput in the lifelong MAPF, which can further improve the efficiency of warehouse automation. Finally, we validate PCE using three Turtlebot3-Burger robots, which shows that PCE can be applied in real warehouse automation scenarios. IEEE

关键词： Aggregates

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Autonomous Power Management and Control Among Interconnected Standalone Hybrid Microgrids 10

Autonomous Power Management and Control Among Interconnected...

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10th IEEE International Conference on Power systems, ICPS 2023

作者： Tasnim, Moshammed Nishat Ahmed, Tofael Ahmad, Shameem Mekhilef, Saad Advanced Power System Laboratory Department of Electrical & Electronic Engineering Chittagong4349 Bangladesh Faculty of Engineering Department of Electrical & Electronic Engineering Dhaka1229 Bangladesh Swinburne University of Technology Hawthorn MelbourneVIC3122 Australia

ISBN: (纸本)9798350318739

Multiple microgrids (MGs) being interconnected permits a high penetration of renewable energy sources while also improving the reliability and generation efficiency of the interconnected MG system. However, an effective power management strategy (PMS) and reliable control structure are required to ensure suitable operation and proper power sharing. Therefore, an effective clustering method of three hybrid MGs with a decentralized control structure is proposed in this paper to ensure sufficient power supply in three MGs depending on their available power generation in islanded mode. The interconnection takes place among the DC buses of each hybrid MG using a bidirectional DC/DC converter, which eliminates the complexity of reactive power management and synchronization and reduces power losses and costs. Moreover, the proposed decentralized PMS is based on the system voltage deviation to achieve proper power sharing without any communication link. Different scenarios for power sharing among interconnected hybrid MGs, including load variations, source failure, and energy storage systems charging or discharging, are analyzed. The performance of the proposed interconnection method and PMS is analyzed and validated using MATLAB/Simulink and a real-time-based OPAL-RT digital simulator. The simulation outcomes show that the proposed method and PMS ensure reliable operation and provide proper power sharing among the three hybrid MGs. © 2023 IEEE.

关键词： DC-DC converters

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Learning Agile Quadrotor Flight in Restricted Environments with Safety Guarantees

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 1-8页

作者： Chen, Shiyu Li, Yanjie Lou, Yunjiang Lin, Ke Wu, Xinyu Department of Control Science and Engineering Harbin Institute of Technology Shenzhen and Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

With the increasing requirement for agile and efficient controllers in safety-critical scenarios, controllers that exhibit both agility and safety are attracting attention, especially in the aerial robotics domain. This paper focuses on the safety issue of Reinforcement Learning (RL)-based control for agile quadrotor flight in restricted environments. To this end, we propose a unified Adaptive Safety Predictive Corrector (ASPC) to certify each output action of the RL-based controller in real-time, ensuring its safety while maintaining agility. Specifically, we develop the ASPC as a finite-horizon optimal control problem, formulated by a variant of Model Predictive Control (MPC). Given the safety constraints determined by the restricted environment, the objective of minimizing loss of agility can be optimized by reducing the difference between the actions of RL and ASPC. As the safety constraints are decoupled from the RL-based control policy, the ASPC is plug-and-play and can be incorporated into any potentially unsafe controllers. Furthermore, an online adaptive regulator is presented to adjust the safety bounds of the state constraints with respect to the environment changes, extending the proposed ASPC to different restricted environments. Finally, simulations and real-world experiments are demonstrated in various restricted environments to validate the effectiveness of the proposed ASPC. IEEE

关键词： Reinforcement learning

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