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IEEE Transactions on Technology and Society 2024年第2期5卷 217-230页

作者： Pangelinan, Gabriella Bhatta, Aman Wu, Haiyu King, Michael C. Bowyer, Kevin W. Florida Institute of Technology Department of Electrical Engineering and Computer Science MelbourneFL32901 United States University of Notre Dame Department of Computer Science and Engineering Notre DameIN46556 United States

Concerns related to the proliferation of automated face recognition technology with the intent of solving or preventing crimes continue to mount. The technology being implicated in wrongful arrest following 1-to-many face identification has heightened scrutiny in the media and spawned lawsuits. We analyze the accuracy of 1-to-many face identification across African-American and Caucasian demographic groups and in the presence of blur and reduced resolution in the probe image, as might occur in "surveillance camera quality"images. Because of the high accuracy of modern face recognition algorithms with "government ID quality"images and the size of available datasets, we use the following indirect metrics to assess the relative propensity for false positive identifications: (1) the traditional d-prime statistic between mated and non-mated score distributions, (2) absolute score difference between thresholds in the high-similarity tail of the non-mated distribution and the low-similarity tail of the mated distribution, and (3) distribution of (mated - non-mated rank-one scores) across the set of probe images. We find that demographic variation patterns in 1-to-many accuracy largely, but not perfectly, follow that observed in 1-to-1 accuracy. We show that, different from the case with 1-to-1 matching accuracy, demographic comparison of 1-to-many accuracy can be affected by different numbers of identities and images across demographics. We also show that increased blur or reduced resolution of the face in the probe image can significantly increase the false positive identification rate. This point is important because the reputation of modern face recognition algorithms for high accuracy in 1-to-many face identification comes from statistics based on analyzing "government ID quality"rather than "surveillance camera quality"images. © 2024 IEEE.

关键词： Face recognition

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Transfer Learning Enhanced Deep Reinforcement Learning for Volt-Var Control in Smart Grids

Transfer Learning Enhanced Deep Reinforcement Learning for V...

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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Kumar, Kundan Ravikumar, Gelli Iowa State University Department of Computer Science and Electrical and Computer Engineering IA50010 United States

ISBN: (纸本)9798350352528

The integration of renewable energy resources has made power system management increasingly complex. DRL is a potential solution to optimize power system operations, but it requires significant time and resources during training. The control policies developed using DRL are specific to a single grid and require retraining from scratch for other grids. Training the DRL model from scratch is computationally expensive. This paper proposes a novel TL with a DRL framework to optimize VV C across different grids. This framework significantly reduces training time and improves VVC control performance by fine-tuning pre-trained DRL models for various grids. We developed a policy reuse classifier that transfers the knowledge from the IEEE-123 Bus system to the IEEE-13 Bus system. We performed an impact analysis to determine the effectiveness of TL. Our results show that TL improves the VVC control policy by 69.51 %, achieves faster convergence, and reduces the training time by 98.14%. © 2025 IEEE.

关键词： Deep reinforcement learning

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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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DrugEL: Ensemble Learning Model for Identification of Druggable Proteins

DrugEL: Ensemble Learning Model for Identification of Drugga...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ali, Md. Mamun Ahmed, Kawsar Bui, Francis M. Wu, Fang-Xiang University of Saskatchewan Division of Biomedical Engineering Saskatoon Canada University of Saskatchewan Department of Electrical and Computer Engineering Saskatoon Canada University of Saskatchewan Division of Biomedical Engineering Department of Mechanical Engineering Department of Computer Science Saskatoon Canada

ISBN: (纸本)9798350386226

Druggable proteins are defined as proteins that can interact with drugs to modulate certain biological activity. The identification of druggable proteins holds significant clinical importance, directly impacting the development of targeted therapies for diseases like cancer and metabolic disorders. Identifying druggable proteins involves various methods, including computational prediction models, mass spectrometry (MS), and biochemical assays, but achieving high accuracy remains a challenge. This study proposes DrugEL, an ensemble learning model that uses Bayesian inference to integrate predictions from multiple algorithms: Random Forest (RF), K-Nearest Neighbor (KNN), LightGBM (LGBM), and Decision Tree (DT) with seven feature extraction methods (LSA, AAC, PAAC, GAAC, NMBroto, AAIndex, and KNN). The results show that DrugEL outperforms existing models in terms of accuracy (0.9758), MCC (0.9515), AUC (0.9758), sensitivity (0.9742) and specificity (0.9774), particularly excelling with the LSA method. © 2024 IEEE.

关键词： Contrastive Learning

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Transaction transmission model for blockchain channels based on non-cooperative games

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science China(Information sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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Emergency Vehicle Direction Detection Using Mel-Frequency Cepstral Coefficients and Deep Learning 18

Emergency Vehicle Direction Detection Using Mel-Frequency Ce...

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18th IEEE International Conference on Vehicular Electronics and Safety, ICVES 2024

作者： Bhushan, Shourya Chaturvedi, Manish Iitram Department of Electrical and Computer Science Engineering Ahmedabad India

ISBN: (纸本)9798350387278

Emergency vehicles face difficulty in navigating through traffic and locating the shortest route to the incident site. In the developing countries or under disaster conditions when limited communication and Intelligent Transportation System (ITS) infrastructure is available, the situation becomes more challenging. This paper addresses these challenges by leveraging the unique sound generated by emergency vehicles to detect the direction of the emergency vehicles. We use the Mel-frequency cepstral coefficients (MFCC) and a Convolutional Neural Network (CNN) stacked with Long Short-Term Memory (LSTM) layers to accurately detect the movement direction of outgoing emergency vehicles at a junction. MFCC facilitates effective feature extraction from audio signals, while the CNN-LSTM architecture enables robust temporal and spatial pattern recognition. The information can be used to adapt the traffic lights at the downstream junction to enable the uninterrupted movement of the emergency vehicle through the junction. The experiment results demonstrate 98.56% accuracy in detecting movement direction of an emergency vehicle. The solution can improve emergency response under resource constrained environment and in turn improve public safety and well-being. © 2024 IEEE.

关键词： Convolutional neural networks

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Fast sampling from constrained spaces using the Metropolis-adjusted Mirror Langevin algorithm 37

Fast sampling from constrained spaces using the Metropolis-a...

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37th Annual Conference on Learning Theory, COLT 2024

作者： Srinivasan, Vishwak Wibisono, Andre Wilson, Ashia Department of Electrical Engineering and Computer Science MIT United States Department of Computer Science Yale University United States

We propose a new method called the Metropolis-adjusted Mirror Langevin algorithm for approximate sampling from distributions whose support is a compact and convex set. This algorithm adds an accept-reject filter to the Markov chain induced by a single step of the Mirror Langevin algorithm (Zhang et al., 2020), which is a basic discretisation of the Mirror Langevin dynamics. Due to the inclusion of this filter, our method is unbiased relative to the target, while known discretisations of the Mirror Langevin dynamics including the Mirror Langevin algorithm have an asymptotic bias. For this algorithm, we also give upper bounds for the number of iterations taken to mix to a constrained distribution whose potential is relatively smooth, convex, and Lipschitz continuous with respect to a self-concordant mirror function. As a consequence of the reversibility of the Markov chain induced by the inclusion of the Metropolis-Hastings filter, we obtain an exponentially better dependence on the error tolerance for approximate constrained sampling. © 2024 V. Srinivasan, A. Wibisono & A. Wilson.

关键词： Markov chains

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A High-Performance 220-290 GHz Micromachined Waveguide Switch Based on Interference Between MEMS Reconfigurable Surfaces

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IEEE Transactions on Terahertz science and Technology 2024年第2期14卷 188-198页

作者： Karimi, Armin Shah, Umer Yu, Suxian Oberhammer, Joachim Kth Royal Institute of Technology Micro and Nanosystems Technology Division School of Electrical Engineering and Computer Science StockholmSE-100 44 Sweden

This article presents a highly integrated novel silicon micromachined single-pole-single-throw waveguide switch based on two microelectromechanically reconfigurable switching surfaces (MEMS-RSs), which allows optimizing the switching performance by tuning the interference between the two such MEMS-RSs utilizing integrated electrostatic comb-drive actuators. The switch prototype is implemented with axially aligned standard WR-3.4 waveguide ports with a total footprint of 3 mm×3.5 mm×1.2 mm. The measured blocking (off) state insertion loss (isolation) and return loss, measured between two standard WR-3.4 waveguide flanges, are 28.5-32.5 dB and better than 0.7 dB, and the propagating (on) state insertion and return losses are 0.7-1.2 dB and better than 17 dB in the 220-290 GHz frequency band, respectively. The measured results were in excellent agreement with the simulation data, implying 27.5% fractional bandwidth, which is very close to a full waveguide band performance. For further investigations, two variants of the switching circuit with only a single MEMS-RS and without any MEMS-RSs have also been fabricated. The single MEMS-RS switch achieved the off-state isolation, on-state insertion loss, and return loss of only 11.5-12.5 dB, 0.8-1.3 dB, and better than 12 dB from 220 to 274 GHz, respectively, which clearly indicates the drastic performance improvement of the interference-based double MEMS-RS switch design. Moreover, measurement of the waveguide-only reference structure showed that the waveguide section alone attributed to 0.2-0.5 dB of the measured on-state insertion loss of the double MEMS-RS switch, and the rest is due to the introduction of the MEMS-RSs inside the waveguides. © 2011-2012 IEEE.

关键词： Millimeter waves

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Developing a Secure Framework Using Feature Selection and Attack Detection Technique

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computers, Materials & Continua 2023年第2期74卷 4183-4201页

作者： Mahima Dahiya Nitin Nitin Department of Electrical Engineering and Computer Science College of Engineering and Applied ScienceUniversity of Cincinnati2600 Clifton AveCincinnatiOH 45221USA

Intrusion detection is critical to guaranteeing the safety of the data in the *** though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior characteristics are becoming increasingly complicated,posing significant hurdles to intrusion *** challenges in terms of false positives,false negatives,low detection accuracy,high running time,adversarial attacks,uncertain attacks,*** to insecure Intrusion Detection System(IDS).To offset the existing challenge,the work has developed a secure Data Mining Intrusion detection system(DataMIDS)framework using Functional Perturbation(FP)feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network(BNM-tGAN)attack detection *** data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as ***,the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique(MLFIMT)that identifies the relationship among the missing values and attack *** on the analysis,the missing values are classified as Missing Completely at Random(MCAR),Missing at random(MAR),Missing Not at Random(MNAR),and handled according to the ***,categorical features are handled followed by feature scaling using Absolute Median division based Robust Scalar(AMDRS)and the Handling of the imbalanced *** selection of relevant features is initiated using FP that uses‘3’Feature Selection(FS)techniques i.e.,Inverse Chi Square based Flamingo Search(ICS-FSO)wrapper method,Hyperparameter Tuned Threshold based Decision Tree(HpTT-DT)embedded method,and Xavier Normal Distribution based Relief(XavND-Relief)filter ***,the selected features are trained and tested for detecting attacks using *** Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the

关键词： Cyber security data mining intrusion detection system(DataMIDS) marginal likelihood fisher information matrix(MLFIM) absolute median deviation based robust scalar(AMD-RS) functional perturbation(FP) inverse chi square based flamingo search optimization(ICS-FSO) hyperparameter tuned threshold based decision tree(HpTT-DT) Xavier normal distribution based relief(XavND-relief) and Bengio Nesterov momentum-based tuned generative adversarial network(BNM-tGAN)

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Modified Frequency Scheduling Droop Control for Decentralized State of Charge Balancing of Battery in Islanded Microgrid 25

Modified Frequency Scheduling Droop Control for Decentralize...

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Rahman, Irham Izzatur Miyauchi, Hajime Takano, Hirotaka Gifu University Department of Electrical Electronic and Computer Engineering Gifu Japan Kumamoto University Department of Computer Science and Electrical Engineering Kumamoto Japan

ISBN: (纸本)9798350378573

This study proposes a decentralized frequency scheduling method to achieve SoC balancing with no communication protocol. Since the frequency scheduling method is based on modifying droop control of inverter, to make the power output of inverter to be proportional to the SoC, the frequency output of inverter would be affected. This study considers to minimize the frequency deviation within European Norm EN50160 (±1% of the nominal value of the frequency), while also able to satisfy SoC balancing. To achieve it, we propose fuzzy logic controller to weight the value of droop coefficient ( ) instead of linear scaling in conventional frequency scheduling. The proposed method is studied in several test to show the performance and compare to conventional method. From the result, the proposed method performs better in SoC balancing, and results in better system reliability. We also study the proposed battery balancing system in various condition of PV and load profile, as well "plug and play"condition of microgrid. The proposed method can perform well in both conditions. © 2024 IEEE.

关键词： State of charge

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