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2021 IEEE International Conference on Artificial Intelligence and computer Applications, ICAICA 2021

作者： Xu, Zhenpeng Liu, Hongwei Liu, Yu Jiangsu Automation Research Institute Department of Computer Lianyungang China School of Computer Engineering Weifang University Weifang China

ISBN: (纸本)9781665418676

PCIe's point-to-point transmissions provides a low data latency in a computer host. To eliminate the TCP/IP stack bottleneck and improve the service performance in data center, a PCIe P2P transmission for the high-performance heterogeneous computing system is addressed. Based on basic elements including the specific PCIe adapter and PCIe switch, a PCIe P2P data transfers mechanism among the heterogeneous service nodes had been proposed. To implement the point-to-point data transfers mechanism, the related functions and interfaces in the portable software stack had been designed based on the fast-remote memory access network. It does not introduce extra processing overheads to the data transmission process among multiple service hosts, and simplifies the function calling of the related P2P data transfer mechanism. The performance analysis shows that the proposed data transfers mechanism is able to achieve a low latency, high-throughput distributed computing solutions, and can provide a kind of peer-to-peer transmission solution with low-latency and high-bandwidth to improve the processing for the computing cluster and high-performance data center. © 2021 IEEE.

关键词： Data transfer

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Startup Strategy for Mmc-Upqc with Photovoltaic

SSRN

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SSRN 2024年

作者： Jiang, Chang Sahani, Mrutyunjaya Zhang, Shaohua Panda, Sanjib Kumar School of Mechatronic Engineering and Automation Shanghai University Baoshan District Shanghaiand 200444 China Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore

The smooth startup of the converter is essential to maintain the equipment's normal operation. This paper presents a startup strategy aimed at achieving rapid and seamless startup for the unified power quality conditioner (UPQC) based on modular multilevel converter (MMC) integrated with photovoltaic (PV). First, the shunt-side sub-modules (SMs) capacitors are charged as the system operates, thereby establishing the direct current (DC) side voltage. Subsequently, the voltage of series side is gradually increased through the DC side. However, the two sides arm voltages are not identical because of the distinct charging techniques. Thus, to equalize the voltage on both sides, a method controlling the number of conducting SMs on the series side is employed. In addition, a feedback linearization control method is introduced to further increase the voltage during the controlled rectification stage. Then, the PV-side switch is closed and the power control strategy can increase the power output. Finally, the method for controlling voltage on the series-side transformer is utilized to minimize voltage drop upon its connection. The effectiveness of the proposed startup strategy is demonstrated through MATLAB/Simulink simulations and real-time laboratory (RT-LAB) based hardware-in-the-loop (HIL) experiments. © 2024, The Authors. All rights reserved.

关键词： Quality control

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An Adaptive-Gain Recurrent Neural Network for Solving the Joint-Angle Drift Issues of Redundant Manipulators

An Adaptive-Gain Recurrent Neural Network for Solving the Jo...

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2023 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2023

作者： Zheng, Boyu Li, Chunquan Zhang, Zhijun Yu, Junzhi Liu, P.X. School of Information Engineering Nanchang University Nanchang330031 China School of Automation Science and Engineering South China University of Technology Guangzhou510640 China College of Engineering Peking University Beijing100871 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

ISBN: (纸本)9798350309003

This paper proposes a new adaptive-gain recurrent neural network (AG-RNN) to effectively cope with the joint-angle drift issues in redundant manipulators. Specifically, a joint-angle drift-free with the feedback control of the velocity layer motion equation (JADF-FC) is proposed via an optimization criterion for synchronously optimizing linear terms and quadratic. Then, the JADF-FC is reasonably formulated into a standard quadratic programming (QP) issue. Different from the previous recurrent neural networks (RNNs), the AG-RNN proposed in this paper constructs an error-based differential equation with a new adaptive-gain. It should be noted that the proposed adaptive-gain does not gradually approach infinity as time increases, which is more in line with actual hardware implementation requirements than the existing time-variant-gain. The adaptive-gain can reduce the joint-angle drift errors of the redundant manipulator. Therefore, the proposed AG-RNN can solve the QP problem of the manipulator more effectively and quickly. To validate the performance of the proposed AG-RNN, it is compared with representative RNNs. The experimental results indicate that smaller joint-angle drift errors can be get by the proposed AG-RNN solving JADF-FC scheme than the other solutions when solving the joint-angle drift issues. © 2023 IEEE.

关键词： Quadratic programming

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Fault Tolerance Technique Based on State Real-time Synchronization

Fault Tolerance Technique Based on State Real-time Synchroni...

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2021 IEEE International Conference on Data Science and computer Application, ICDSCA 2021

作者： Xu, Zhenpeng Liu, Hongwei Liu, Yu Jiangsu Automation Research Institute Department of Computer Lianyungang China Weifang University School of Computer Engineering Weifang China

ISBN: (纸本)9781665440530

Key service hosts in information systems have higher and higher requirements on high reliability and survivability. Combined with the development trend of fault-tolerant technology, the mechanisms of data synchronization, memory synchronization, IO synchronization, fault detection, and transparent switching between active and standby are introduced, and a dual-machine redundancy high availability model based on state real-time synchronization is constructed. In order to reduce the overhead of memory state synchronization, an event-driven memory synchronization trigger mechanism is designed, that is, the key IO event-driven event trigger mechanism is adopted for the synchronization timing of memory checkpoints, so as to minimize unnecessary memory data update operations between the main and standby computers and reduce the overhead of computer state synchronization. At the same time, combined with BMC monitoring interface based on physical host, a fine-grained fault detection method is proposed. Related work can greatly reduce the overhead caused by high availability mechanism, and reduce the impact of fault-tolerant mechanism on system computing performance to a certain extent. At the same time, it has the ability to switch between active and standby quickly, which is helpful to better guarantee the uninterrupted and continuous running ability of task applications in key systems. © 2021 IEEE.

关键词： Fault detection

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Black Soldier Fly Bioconversion System: A Digital Twin Approach 32

Black Soldier Fly Bioconversion System: A Digital Twin Appro...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Yu, Guangtao Nishi, Hiroaki Pang, Cheng Gu, Qingqing Lin, Yonghao Liang, Jinxian Dai, Wenbin Vyatkin, Valeriy INSPRO Science Ltd. Shenzhen China Keio University Graduate School of Science and Technology Japan Changsha Robox Institue Co. Ltd. Changsha China Shenzhen Cynovan Technologies Co. Ltd. Shenzhen China Shanghai Jiao Tong University Department of Automation Shanghai China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Finland

ISBN: (纸本)9798350399714

Insect-based bioconversion is an emerging and economically viable approach that could simultaneously solve the global issues on food waste treatment and protein shortage. An essence of insect-based bioconversion is to feed insects on food wastes and then harvest and refine the developed insects into value-added commodities. This is a circular bioeconomic approach that can efficiently upcycle the nutrients and energy in food waste streams back into agricultural supply chains, but the difficulty is how to realize mass production of insects. This paper reports a commercial-scale insect-based bioconversion system using black soldier flies, whose design and development follow a digital twin paradigm. In particular, an industrialized rearing environment has been established, where the larvae growth can be systematically monitored, controlled, and managed to evaluate the overall bioconversion process efficacy. The system has been deployed in a pilot plant, whose Phase I is capable of handling 20 tons of food waste feedstocks per day and has been consecutively operating to date. © 2023 IEEE.

关键词： Supply chains

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Long Short-Term Memory Network Assisted Evolutionary Algorithm for Computationally Expensive Multiobjective Optimization

Long Short-Term Memory Network Assisted Evolutionary Algorit...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： He, Cheng Li, Hongbin Lin, Jianqing Lu, Zhichao School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China City University Department of Computer Science Hong Kong Hong Kong

ISBN: (纸本)9781665430654

Computationally expensive multiobjective optimization problems (EMOPs) that require significant computational resources are commonly encountered in realworld applications. To address the challenges associated with such problems, using computationally inexpensive surrogate models to approximate objectives has emerged as an effective approach to handle EMOPs. However, the current collaboration between evolutionary algorithms (EAs) and surrogate models is limited, relying on static regression or classification methods that do not fully capture the dynamic evolution process of EAs. This study aims to advance the integration of surrogateassisted multiobjective optimization by incorporating time-series prediction models. The target is to track the evolutionary trajectory of an EA and enhance its search capability. Specifically, long short-term memory (LSTM) networks are embedded into an EA for surrogateassisted optimization (SAO). The role of LSTM networks in SAO is thoroughly investigated through ablation studies. Experimental results on six EMOPs demonstrate the potential of using LSTM networks in SAO. The results are compared with those obtained from four representative surrogateassisted EAs, providing insights into the effectiveness of LSTM-based approaches in addressing EMOPs. © 2023 IEEE.

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Switching Classes: Characterization and Computation

arXiv

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arXiv 2024年

作者： Antony, Dhanyamol Cao, Yixin Pal, Sagartanu Sandeep, R.B. Department of Computer Science and Automation Indian Institute of Science Bengaluru India Department of Computing Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering Indian Institute of Technology Dharwad India

In a graph, the switching operation reverses adjacencies between a subset of vertices and the others. For a hereditary graph class G, we are concerned with the maximum subclass and the minimum superclass of G that are closed under switching. We characterize the maximum subclass for many important classes G, and prove that it is finite when G is minor-closed and omits at least one graph. For several graph classes, we develop polynomial-time algorithms to recognize the minimum superclass. We also show that the recognition of the superclass is NP-complete for H-free graphs when H is a sufficiently long path or cycle, and it cannot be solved in subexponential time assuming the Exponential Time Hypothesis. © 2024, CC BY.

关键词： Graph algorithms

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An improved KPLS-KELM method for breast cancer detection 9

An improved KPLS-KELM method for breast cancer detection

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9th IEEE International Conference on Sciences of Electronics, Technologies of Information and Telecommunications, SETIT 2022

作者： Bacha, Sawssen Taouali, Okba Liouane, Noureddine National Engineering School of Monastir Research Laboratory of Automation Signal Processing and Image Monastir5019 Tunisia University of Tabuk Faculty of Computers and Information Technology Department of Computer Engineering Tabuk Saudi Arabia

ISBN: (数字)9781728184425

ISBN: (纸本)9781728184425

The conception of a computer-Aided Diagnosis system (CAD) using Artificial Intelligence (AI) is a hot topic in the domain of medical diagnosis. Recently, many approaches have been developed. In the proposed work, a novel classification technique from mammograms based on Kernel Extreme Learning Machine (KELM) and Kernel Partial Least Square (KPLS) method is introduced. The suggested algorithm first used the KPLS algorithm to extract features from the images. The extracted characteristics were then sent to the KELM classifier. In order to improve the generalization of the proposed approach, the cross-validation strategy was used. The simulation results were tested on the Mammographic Image Analysis Society (MIAS) dataset and measured using accuracy, F score, sensitivity, and specificity analysis. These results were compared to existing approaches tested on the same dataset and it was observed that the proposed work is the most efficient. © 2022 IEEE.

关键词： Mammography

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Short-term power load forecasting of a city in Henan Province using Attention based LTSM

Short-term power load forecasting of a city in Henan Provinc...

引用

IEEE Asia Power and Energy engineering Conference (APEEC)

作者： Jie Yang Zijie Qin Hyotaek Lim Byung Gook Lee Department of Computer Engineering Dongseo University Busan South Korea School of Electrical Engineering and Automation Henan Institute of Technology Xinxiang P.R. China

ISBN: (数字)9798350373479

ISBN: (纸本)9798350373486

In order to ensure the security and stability of power grids and the efficient operation of power markets, grid dispatchers and power market participants have high requirements on the accuracy of power load forecasting. Power load forecasting based on engineering computation and deep learning is an important foundation for power optimization and smart grid dispatching. Addressing the challenge that traditional load forecasting methods cannot simultaneously model the changing patterns of the load itself and its influencing factors, a load forecasting method based on the self-attention mechanism of Long Short-Term Memory units (LSTM-Self Attention, abbreviated as LSTM-SA) is proposed. This method utilizes historical electricity load data and relevant influencing factor data to predict the load for the upcoming day. The critical influencing factors of short-term load forecasting are analyzed first. Using Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) neural networks as examples, the principles of neural network models and their alignment with load forecasting tasks are explained. Subsequently, the self-attention mechanism is introduced to automatically allocate appropriate weights to LSTM hidden layer states, distinguishing the importance of different time load sequences. This effectively reduces the loss of historical information and highlights the information of crucial historical time points. Finally, the ultimate load prediction results are output through a fully connected layer. Experimental analysis is conducted using actual power load data from a city in Henan Province as an example. Based on real load data, the performance of load prediction for different historical time windows and network architectures is validated. The results, compared with other load prediction models, indicate that the proposed model can improve prediction accuracy and is more effective in short-term load forecasting. This model can serve as a reliable basis for power sy

关键词： Recurrent neural networks Load forecasting Urban areas Predictive models Power markets Stability analysis Smart grids

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Phase Shift Effects Analysis on Radiation Pattern of a Ground Plane Antenna Array 16

Phase Shift Effects Analysis on Radiation Pattern of a Groun...

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16th International Conference on Development and Application Systems, DAS 2022

作者： Adomnitei, Cezar-Ion Lesanu, Cezar-Eduard Done, Adrian Dimian, Mihai Chiuchisan, Iulian Yu, Ang Stefan cel Mare University of Suceava Department of Computers Electronics and Automation Suceava Romania Carolina University Winston-Salem Electrical Engineering and Computer Science United States

ISBN: (纸本)9781665481625

This paper presents an analysis on the effect of the controlled phase shift introduced between feeding signals of an antenna array elements on modifying its radiation pattern shape. Following the idea of increasing measurements accuracy on phased antenna arrays, it was designed and built an antenna array containing two radiating telescopic elements located on a contiguous metal ground plane. All the hardware related to the functionality of the phased array was placed on the bottom of the ground plane. Even though the array was designed for the 915 MHz band, allocated to various useful satellite services, it is easily tunable in a wide range of other frequencies and also easily modifiable in a multi-element array, due to the design of the ground plane. This paper also shows a good agreement between the experimental results and those obtained from numerical simulations. © 2022 IEEE.

关键词： Beamforming

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