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Optimized Distribution Systems Reconfiguration Considering Protection Devices Constraints

IEEE Transactions on Power Delivery 2025年第3期40卷 1267-1278页

作者： Kahnamouei, Ali Shakeri Lotfifard, Saeed Rostami, Mohammadali Washington State University School of Electrical Engineering and Computer Science PullmanWA99164 United States

This paper presents a method for the optimized reconfiguration of radial distribution systems that explicitly considers the protection systems constraints. A fully automated method based on graph analysis is proposed to automatically identify the primary-backup relation between each pair of protection devices in different network topologies. Accordingly, protection systems constraints are derived automatically and incorporated into the optimization problem. The linearized and convexified nonlinear power flow equations, along with protection system coordination constraints, are solved using the second-order cone programming (SOCP) that assures the feasibility of the results while accelerating the convergence rate. The effectiveness of the proposed methodology is demonstrated through the simulation of the 33-node IEEE test system. © 1986-2012 IEEE.

关键词： Second-order cone programming

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Highly-efficient(>70%)and Wide-spectral(400-1700 nm)sub-micron-thick InGaAs photodiodes for future high-resolution image sensors

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Light(science & Applications) 2024年第12期13卷 3224-3235页

作者： Dae-Myeong Geum Jinha Lim Junho Jang Seungyeop Ahn SeongKwang Kim Joonsup Shim Bong Ho Kim Juhyuk Park Woo Jin Baek Jaeyong Jeong SangHyeon Kim School of Electrical Engineering Korea Advanced Institute of Science and Technology(KAIST)Daejeon 34141Republic of Korea Department of Electrical and Computer Engineering Inha UniversityIncheon 22212Republic of Korea

This paper demonstrates the novel approach of sub-micron-thick InGaAs broadband photodetectors(PDs)designed for high-resolution imaging from the visible to short-wavelength infrared(SWIR)*** approaches encounter challenges such as low resolution and crosstalk issues caused by a thick absorption layer(AL).Therefore,we propose a guided-mode resonance(GMR)structure to enhance the quantum efficiency(QE)of the InGaAs PDs in the SWIR region with only sub-micron-thick *** TiOx/Au-based GMR structure compensates for the reduced AL thickness,achieving a remarkably high QE(>70%)from 400 to 1700 nm with only a 0.98μm AL InGaAs PD(defined as 1μm AL PD).This represents a reduction in thickness by at least 2.5 times compared to previous results while maintaining a high ***,the rapid transit time is highly expected to result in decreased electrical *** effectiveness of the GMR structure is evident in its ability to sustain QE even with a reduced AL thickness,simultaneously enhancing the transit *** breakthrough offers a viable solution for high-resolution and low-noise broadband image sensors.

关键词： thick resolution image

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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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Performance Analysis of ZF and RZF in Low-Resolution ADC/DAC Massive MIMO Systems

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China Communications 2024年第8期21卷 115-126页

作者： Talha Younas Shen Jin Muluneh Mekonnen Gao Mingliang Saqib Saleem Sohaib Tahir Mahrukh Liaqat School of Electrical and Electronic Engineering Shandong University of TechnologyZibo 255000China Department of Electrical and Computer Engineering COMSATS University IslamabadSahiwal Campus Department of Electrical and Computer Engineering College of EngineeringDhofar UniversitySalalah 211Oman Department of Electrical and Computer Engineering Addis Ababa Science and Technology UniversityAddis AbabaEthiopia Department of Electrical Engineering College of Electrical and Mechanical EngineeringNUSTIslamabadPakistan

Large number of antennas and higher bandwidth usage in massive multiple-input-multipleoutput(MIMO)systems create immense burden on receiver in terms of higher power *** power consumption at the receiver radio frequency(RF)circuits can be significantly reduced by the application of analog-to-digital converter(ADC)of low *** this paper we investigate bandwidth efficiency(BE)of massive MIMO with perfect channel state information(CSI)by applying low resolution ADCs with Rician *** start our analysis by deriving the additive quantization noise model,which helps to understand the effects of ADC resolution on BE by keeping the power constraint at the receiver in *** also investigate deeply the effects of using higher bit rates and the number of BS antennas on bandwidth efficiency(BE)of the *** emphasize that good bandwidth efficiency can be achieved by even using low resolution ADC by using regularized zero-forcing(RZF)combining *** also provide a generic analysis of energy efficiency(EE)with different options of bits by calculating the energy efficiencies(EE)using the achievable *** emphasize that satisfactory BE can be achieved by even using low-resolution ADC/DAC in massive MIMO.

关键词： low-bit analog-digital converter massive(multiple-input-multiple-output)MIMO minimum mean square error(MMSE) regularized zero forcing zero forcing

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An End-to-End Two-Branch Network Towards Robust Video Fingerprinting

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2371-2384页

作者： Xu, Yingying Zhou, Yuanding Li, Xinran Zhao, Gejian Qin, Chuan University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

With the increasing number of edited videos, many robust video fingerprinting schemes have been proposed to solve the problem of video content authentication. However, most of them either deal with the temporal and spatial features symmetrically or insufficiently consider the temporal information. In this work, an end-to-end two-branch network toward robust video fingerprinting (RVFNet) is proposed, where the two branches focus on the temporal and spatial information, respectively. The temporal branch aims to comprehensively capture complex motion patterns by combining subtle motion changes with the overall motion trend. The spatial branch exploits the pixel-level information obtained by multiple receptive fields while preserving significant structural features. Deep metric learning is employed in the training process, and we adopt hard triplet loss to constrain the generation of fingerprints. Furthermore, we construct a large-scale and complex dataset for the robust video fingerprinting task based on multiple video content-preserving manipulations in actual scenarios. The size of our dataset exceeds most datasets adopted in the current robust video fingerprinting schemes. Based on the proposed dataset, experimental results demonstrate that our scheme achieves outstanding performance improvements compared with the state of the art. © 2020 IEEE.

关键词： Convolution

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Unsupervised anomaly detection and localization via bidirectional knowledge distillation

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Neural Computing and Applications 2024年第29期36卷 18499-18514页

作者： Wang, Xiaoming Wang, Yongxiong Pan, Zhiqun Wang, Guangpeng School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Knowledge distillation has demonstrated significant potential in addressing the challenge of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in the teacher–student (T-S) model provides evidence for anomaly detection and localization. However, the teacher model is pretrained for classification, while the anomaly scores in the distillation-based anomaly detection method are indirectly derived from the classification scores. The mismatch between the two tasks can hinder the optimization of the model. To tackle this issue, we propose an innovative bidirectional knowledge distillation model. In this approach, forward knowledge distillation is pivotal in bolstering the model’s capacity for generalization. Simultaneously, backward knowledge distillation promotes diversity in representing anomalies. This reciprocal knowledge exchange effectively wards off potential performance declines due to target inconsistency. Through bidirectional knowledge distillation, we establish a more encompassing and resilient framework for knowledge transfer. Additionally, we introduce a novel data augmentation strategy to simulate anomalies and effectively eliminate unnecessary noise. In experiments on the MVTec AD, the proposed model achieves competitive results compared to state-of-the-art methods, 97.47% on image-level AUC, 98.23% on pixel-level AUC, and 94.77% on instance-level PRO. These results demonstrate the practicality of our approach in anomaly detection and localization. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Anomaly detection

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Semi-supervised Domain Adaptation for Semantic Segmentation via Active Learning with Feature- and Semantic-Level Alignments

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Wen, Lu Xu, Yuanyuan Feng, Zhenghao Zhou, Jiliu Zhou, Luping Wang, Yan School of Computer Science Sichuan University China School of Electrical and Information Engineering The University of Sydney Sydney NSW Australia

Unsupervised domain adaptation (UDA) is a popular technique to reduce the manual annotation cost in semantic segmentation. However, due to the absence of strong supervision in the target domain, UDA is prone to biasing the decision boundary towards the source domain. To alleviate this issue, this paper proposes a more effective semi-supervised domain adaptation (SSDA) method for semantic segmentation via active learning with feature- and semantic-level alignments. Specifically, active learning is utilized to select those samples with high diversity and uncertainty from the target domain for labeling. These selected data could provide reliable clues for domain transfer since they reveal the intrinsic distribution of the target domain as well as including hard samples at boundaries. Moreover, to better adapt the segmentation model from the source data to the labeled target data selected above, we propose a scheme based on both feature- and semantic-level domain alignments. The feature-level domain alignment imposes the distribution consistency between the Transformer features of the two domains by adversarial learning, which is a global alignment. In contrast, the semantic-level domain alignment optimizes the affinity and divergence of the semantic representations across domains via contrastive learning, which is a local alignment. These two alignments jointly bridge the domain gap from both the global and the local views, respectively. In addition, the pseudo labels of the unlabeled data are generated to expand the labeled data and further strengthen the cross-domain segmentation in a self-training manner. Extensive experiments on segmentation benchmarks demonstrate the effectiveness of our proposed method. IEEE

关键词： Semantics

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A Deepfake Detection Algorithm Based on Fourier Transform of Biological Signal

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computers, Materials & Continua 2024年第6期79卷 5295-5312页

作者： Yin Ni Wu Zeng Peng Xia Guang Stanley Yang Ruochen Tan School of Electrical and Electronic Engineering Wuhan Polytechnic UniversityWuhan430023China School of Mathematics and Computer Science Wuhan Polytechnic UniversityWuhan430048China Paul G.Allen School of Computer Science and Engineering University ofWashingtonSeattleWA98195USA School of Computer Science and Engineering University of CaliforniaSanDiegoCA92093USA

Deepfake-generated fake faces,commonly utilized in identity-related activities such as political propaganda,celebrity impersonations,evidence forgery,and familiar fraud,pose new societal *** current deepfake generators strive for high realism in visual effects,they do not replicate biometric signals indicative of cardiac *** this gap,many researchers have developed detection methods focusing on biometric *** methods utilize classification networks to analyze both temporal and spectral domain features of the remote photoplethysmography(rPPG)signal,resulting in high detection ***,in the spectral analysis,existing approaches often only consider the power spectral density and neglect the amplitude spectrum—both crucial for assessing cardiac *** introduce a novel method that extracts rPPG signals from multiple regions of interest through remote photoplethysmography and processes them using Fast Fourier Transform(FFT).The resultant time-frequency domain signal samples are organized into matrices to create Matrix Visualization Heatmaps(MVHM),which are then utilized to train an image classification ***,we explored various combinations of time-frequency domain representations of rPPG signals and the impact of attention *** experimental results show that our algorithm achieves a remarkable detection accuracy of 99.22%in identifying fake videos,significantly outperforming mainstream algorithms and demonstrating the effectiveness of Fourier Transform and attention mechanisms in detecting fake faces.

关键词： Deepfake detector remote photoplethysmography fast fourier transform spatial attention mechanism

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Circular super-Gaussian Airy beams

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Optics Letters 2025年第10期50卷 3401-3404页

作者： Geng, Tao Wang, Lihui School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

By replacing the exponential decay function in the circular Airy beam (CAB) with a super-Gaussian function, we propose a novel abruptly autofocusing beam, the circular super-Gaussian Airy beam (CSGAB). Similar to CAB, CSGAB has a simple and clear mathematical expression. By selecting appropriate parameters, the peak focal intensity of CSGAB can reach 5.3 times that of CAB. The ratio of the maximum intensity at the focal plane to that at the initial plane reaches 1825, which is 38.7 times that of CAB. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Focusing

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Sawtooth-enhanced bend sensor for gesture recognition

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science China(Technological sciences) 2024年第6期67卷 1727-1736页

作者： BAI YanRu ZHANG ZiHang WANG HaoYu GUO Rui LI XiSheng School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China School of Advanced Engineering University of Science and Technology BeijingBeijing100083China School of Precision Instrument and Opto-Electronics Engineering Tianjin UniversityTianjin300072China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China

Gesture recognition has diverse application prospects in the field of human-computer ***,gesture recognition devices based on strain sensors have achieved remarkable results,among which liquid metal materials have considerable advantages due to their high tensile strength and *** improve the detection sensitivity of liquid metal strain sensors,a sawtooth-enhanced bending sensor is proposed in this *** with the results from previous studies,the bending sensor shows enhanced resistance *** addition,combined with machine learning algorithms,a gesture recognition glove based on the sawtooth-enhanced bending sensor is also fabricated in this study,and various gestures are accurately *** the fields of human-computer interaction,wearable sensing,and medical health,the sawtooth-enhanced bending sensor shows great potential and can have wide application prospects.

关键词： liquid metal bending sensor gesture recognition machine learning

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