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17th International Conference on Development and Application Systems, DAS 2024

作者： Rata, Mihai Fechet, Radu Rata, Gabriela Graur, Adrian Faculty of Electrical Engineering and Computer Science Ștefan cel Mare University of Suceava Suceava Romania

ISBN: (纸本)9798350349290

Nowadays, power quality issues are more and more frequent due to disturbances such as harmonics, flicker, swells/sags, interruptions, unbalanced voltage, etc. This paper presents a solution developed by the authors for the monitoring of electrical energy consumption and power quality in one USV (University "Stefan cel Mare", Suceava) laboratory using two Janitza measurement devices (UMG507E and UMG96S). The UMG 507E device has the gateway integrated and can be used as a Modbus RTU master and UMG 96S is Modbus RTU (slave) in Modbus network. The data from both measuring devices is collected through Modbus master using ScadaBR software. ©2024 IEEE.

关键词： SCADA systems

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Adopting Graph Neural Networks to Understand and Reason About Dynamic Driving Scenarios

IEEE Open Journal of Intelligent Transportation Systems

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IEEE Open Journal of Intelligent Transportation Systems 2025年 6卷 579-589页

作者： Su, Peng Xiang, Conglei Chen, Dejiu KTH Royal Institute of Technology Faculty of Engineering Design Department Stockholm100 44 Sweden KTH Royal Institute of Technology Electrical Engineering and Computer Science School Stockholm100 44 Sweden

With advances in Deep Neural Networks (DNN), Automated Driving Systems (ADS) enable the vehicle to perceive their surroundings in dynamic driving scenarios and perform behaviors by collecting operational data from sensors such as LiDAR and cameras. Current DNN typically detect objects by analyzing and classifying unstructured data (e.g., image data), providing critical information for ADS planning and decision-making. However, advanced ADS, particularly those required to perform the Dynamic Driving Task (DDT) autonomously, are expected to understand driving scenarios across various Operational Design Domains (ODD). This capability requires the support for a continuous comprehension of driving scenarios according to operational data collected by sensors. This paper presents a framework that adopts Graph Neural Networks (GNN) to describe and reason about dynamic driving scenarios via analyzing graph-based data based on collected sensor inputs. We first construct the graph-based data using a meta-path, which defines various interactions among different traffic participants. Next, we propose a design of GNN to support both the classification of the node types of objects and predicting relationships between objects. As results, the performance of the proposed method shows significant improvements compared to the baseline method. Specifically, the accuracy of node classification increases from 0.77 to 0.85, while that of relationships prediction rises from 0.74 to 0.82. To further utilize graph-based data constructed from dynamic driving scenarios, the proposed framework supports reasoning about operational risks by analyzing the observed nodes and relationships in the graph-based data. As a result, the model achieves a MRR of 0.78 in operational risks reasoning. To evaluate the practicality of the proposed framework in real-world systems, we also conduct a real-time performance evaluation by measuring the average process time and the Worst Case Execution Time (WCET). Com

关键词： Graph neural networks

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Large circuit models: opportunities and challenges

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science China(Information sciences) 2024年第10期67卷 25-66页

作者： Lei CHEN Yiqi CHEN Zhufei CHU Wenji FANG Tsung-Yi HO Ru HUANG Yu HUANG Sadaf KHAN Min LI Xingquan LI Yu LI Yun LIANG Jinwei LIU Yi LIU Yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi YAN Jun YANG Bei YU Mingxuan YUAN Evangeline F.Y.YOUNG Xuan ZENG Haoyi ZHANG Zuodong ZHANG Yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah’s Ark Lab School of Integrated Circuits Peking University Faculty of Electrical Engineering and Computer Science Ningbo University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering The Chinese University of Hong Kong School of Integrated Circuits Southeast University Huawei HiSilicon Peng Cheng Laboratory School of Computer Science Peking University School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University School of Artificial Intelligence Shanghai Jiao Tong University

Within the electronic design automation(EDA) domain, artificial intelligence(AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding,overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level(RTL) designs, circuit netlists,and physical layouts. We champion the creation of large circuit models(LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area(PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

关键词： AI-rooted EDA large circuit models (LCMs) multimodal circuit representation learning circuit optimization

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Cooperative Spectrum Sensing: A Multi-Channel Deep Learning Method 18

Cooperative Spectrum Sensing: A Multi-Channel Deep Learning ...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Zha, Ziwei Jin, Ming He, Ming Jiang, Tao Yuan, Tianxi Qiu, Keyan Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798350312201

In this paper, we studied the cooperative spectrum sensing (CSS) problem based on deep learning. The current CSS methods based on deep learning only extract general features of signal samples, without considering the specific details of real and imaginary parts of the signal samples. In order to fully extract features of the signal samples, we propose a multi-channel deep neural network (MCDNN) for CSS, the proposed method firstly utilizes three neural network branches to separately extract the features from real part, imaginary part, combination of real and imaginary parts of signal samples. Simulation results indicate that the detection performance of this method is better than other detection methods, especially in low signal-to-noise ratio (SNR) situations. Additionally, this method is robust to time-varying non-uniform received signals and noise power. © 2023 IEEE.

关键词： Deep neural networks

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Insertion of Additional Image Charges in Method of Images for Small Ground Planes to Improve Prediction in Two-Dimensional Electrostatic Problems

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Journal of The Institution of Engineers (India): Series B 2023年第1期104卷 193-200页

作者： Vetharatnam, Gobi Ghanbarisabagh, Mohammad Senjuti, Sifat Ara Department of Electrical and Electronic Engineering Lee Kong Chian Faculty of Engineering and Science University Tunku Abdul Rahman Cheras Selangor Kajang Malaysia Department of Electrical Engineering Faculty of Electrical Engineering and Computer Sciences Islamic Azad University North Tehran Branch Hakimieh Tehran Iran

Traditional method of images is routinely applied in electrostatic problems with infinite ground planes. However, practical situations almost always deal with finite ground planes. This causes the prediction to be inexact. For the first time, the differences between them are analyzed and a new model for the image is proposed, for small ground planes in two-dimension. In this model, two additional charges are introduced at either side of the ground plane to improve the prediction. This model predicts the potential with less error compared with the traditional method of images. © 2022, The Institution of Engineers (India).

关键词： Forecasting

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DBPR: Dynamic Bidirectional Propagation Relationship Graph Convolution Network for Fake News Detection on Social Media

DBPR: Dynamic Bidirectional Propagation Relationship Graph C...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Zihang Pan, Shanliang Yang, Ze Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798350337020

With the emergence of social networks and digital media, there has been an increasing proliferation of channels through which people receive information, potentially resulting in the widespread dissemination of fake news. Previous approaches to detecting fake news have primarily focused on mining textual features, while neglecting the dynamic spatiotemporal characteristics of news propagation. In this paper, we propose a novel Dynamic Bidirectional Propagation Relationship (DBPR) model that integrates sequential propagation graphs and backtracking trees in the temporal dimension, incorporating positional encoding based on the temporal order of news propagation to more effectively capture its dynamic nature. Extensive experiments conducted on two publicly available datasets demonstrate that our proposed approach achieves state-of-the-art performance, particularly by providing valuable insights for early detection of fake news. © 2023 IEEE.

关键词： fake news detection graph neural network news propagation social media

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Butler Matrix Based on Dual-Mode Transmission Lines for Millimeter Wave Dual-Polarized Applications

Butler Matrix Based on Dual-Mode Transmission Lines for Mill...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Dong, Chunli Shen, Shuhao Hu, Yang Sun, Liying Ye, Min You, Yang Lu, Yunlong Huang, Jifu Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9781733509657

This paper proposes a dual-mode Butler matrix based on the air suspended line (ASL) for millimeter-wave (mm-wave) dual-polarized antenna arrays. The ASL, which can simultaneously support TEM-mode and TE10-mode, is used to design 90° hybrid coupler, 0 dB crossover, phase shift, and construct the dual-mode Butler matrix with high integration and low loss. A 4×4 dual-mode Butler matrix which is operating in mm-wave band is designed. Simulation results show that the reflection coefficients of this design are better than -15 dB in the range of 26.5 to 29.5 GHz, and have stable amplitude and phase response. The proposed butler matrix has wide application potential in 5G mm-wave dual-polarized multi-beam antenna arrays. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Butler matrix

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A Hybrid Three-Port Topology for Urban Charging Stations 14

A Hybrid Three-Port Topology for Urban Charging Stations

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Klidbari, Mohammadreza Khodaparast Souri, Naser Habibolahi, Zahra Sadat Montazeri Hedeshi, Hamid K. N. Toosi University of Technology Faculty of Electrical Engineering Tehran Iran Virginia Tech The Bradley Department of Electrical and Computer Engineering United States Iran University of Science and Technology Department of Electrical Engineering Tehran Iran University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9798331516116

Electric vehicles are rapidly gaining popularity as a sustainable alternative to conventional gasoline. In urban areas, chargers with different ratings can accommodate the diverse needs of electric vehicles. However, the available multiport topologies have variable switching frequencies. This paper introduces a hybrid multiport isolated DC-DC converter for urban charging stations, incorporating fast and slow charging ports with a fixed switching frequency. It provides isolation and enables soft switching on the primary side of the converter without circulating current on its secondary side. The primary side does not need feedback, which reduces complexity. The second stage generates a wide output voltage range to charge the electric vehicle battery by employing a switch. In addition, the proposed topology offers reduced component count and simple control with fixed-frequency operation. This paper provides the concept and the operation modes. Experimental results are provided to validate its features. The prototype converter achieves 96% peak efficiency. © 2025 IEEE.

关键词： Topology

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Revisiting Data Poisoning Attacks on Deep Learning Based Recommender Systems 28

Revisiting Data Poisoning Attacks on Deep Learning Based Rec...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Wang, Zhiye Liu, Baisong Lin, Chennan Zhang, Xueyuan Hu, Ce Qin, Jiangcheng Luo, Linze Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798350300482

Deep learning based recommender systems(DLRS) as one of the up-And-coming recommender systems, and their robustness is crucial for building trustworthy recommender systems. However, recent studies have demonstrated that DLRS are vulnerable to data poisoning attacks. Specifically, an unpopular item can be promoted to regular users by injecting well-crafted fake user profiles into the victim recommender systems. In this paper, we revisit the data poisoning attacks on DLRS and find that state-of-The-Art attacks suffer from two issues: user-Agnostic and fake-user-unitary or target-item-Agnostic, reducing the effectiveness of promotion attacks. To gap these two limitations, we proposed our improved method Generate Targeted Attacks(GTA), to implement targeted attacks on vulnerable users defined by user intent and sensitivity. We initialize the fake users by adding seed items to address the cold start problems of fake users so that we can implement targeted attacks. Our extensive experiments on two real-world datasets demonstrate the effectiveness of GTA. © 2023 IEEE.

关键词： Recommender systems

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A Constrained Multi-Objective Evolutionary Algorithm Based on an Infeasible Archive

A Constrained Multi-Objective Evolutionary Algorithm Based o...

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2023 China Automation Congress, CAC 2023

作者： Ma, Yongjian Shi, Xuhua Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798350303759

In the field of engineering design, there is a class of constrained multi-objective optimization problems where the optimal solutions are often found at the constraint boundaries. However, effectively utilizing the infeasible solutions at these boundaries poses a challenge in solving these problems. To address this issue, this paper proposes a constrained multi-objective evolutionary algorithm based on an infeasible archive (CIA). CIA utilizes two populations: one considers both objectives and constraints, while the other ignores all constraints. Additionally, an archive is implemented to store non-dominated infeasible solutions. To improve the search coverage in the initial stage and accelerate convergence in the later stage, a strategy is implemented that reduces infeasible solutions using the infeasible archive. Furthermore, a neighborhood-based offspring generation mechanism is developed, which combines feasible and infeasible solutions to enhance the ability of the algorithm to explore constraint boundaries. Experimental results from three test suites demonstrate that the proposed algorithm performs competitively when compared to four representative algorithms. © 2023 IEEE.

关键词： Evolutionary algorithms

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