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Distribution Locational Marginal Pricing Based Equilibrium Optimization Strategy for Data Center Park with Spatial-temporal Demand-side Resources

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Journal of Modern Power Systems and Clean Energy 2023年第6期11卷 1959-1970页

作者： Zhihao Yang Anupam Trivedi Ming Ni Haoming Liu Dipti Srinivasan IEEE College of Energy and Electrical Engineering Hohai UniversityNanjing 211100China Department of Electrical&Computer Engineering National University of SingaporeSingapore

This paper proposes a distribution locational marginal pricing(DLMP) based bi-level Stackelberg game framework between the internet service company(ISC) and distribution system operator(DSO) in the data center park. To minimize electricity costs, the ISC at the upper level dispatches the interactive workloads(IWs) across different data center buildings spatially and schedules the battery energy storage system temporally in response to DLMP. Photovoltaic generation and static var generation provide extra active and reactive power. At the lower level, DSO calculates the DLMP by minimizing the total electricity cost under the two-part tariff policy and ensures that the distribution network is uncongested and bus voltage is within the limit. The equilibrium solution is obtained by converting the bi-level optimization into a single-level mixed-integer second-order cone programming optimization using the strong duality theorem and the binary expansion method. Case studies verify that the proposed method benefits both the DSO and ISC while preserving the privacy of the ISC. By taking into account the uncertainties in IWs and photovoltaic generation, the flexibility of distribution networks is enhanced, which further facilitates the accommodation of more demand-side resources.

关键词： Bi-level optimization congestion management data center demand response distribution locational marginal pricing(DLMP) robust optimization

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Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 66-75页

作者： Lingkang Zeng Wei Yao Ze Hu Hang Shuai Zhouping Li Jinyu Wen Shijie Cheng State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronics EngineeringHuazhong University of Science and TechnologyWuhan 430074China Dispatching and Control Center Central China Branch of State Grid Corporation of ChinaWuhan 430077China. Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA

Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and ***,GTS is composed of offline predetermination and real-time scenario ***,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power *** improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of ***,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability ***,linear action space is developed to reduce dimensionality of action space for multiple controllable ***,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making *** can enhance the efficiency and learning ***,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning *** simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.

关键词： Deep reinforcement learning generator tripping scheme graph convolutional network invalid action masking knowledgefusion

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PEP: An Accurate and Efficient Pupil Detection Framework Based on Pupil Edge Points

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IEEE Sensors Journal 2025年第12期25卷 22385-22396页

作者： Zhang, Wei Liu, Yu Lv, Linfeng Wang, Xiang Li, Ziqiang Liu, Lei Li, Bin Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230026 China Carnegie Mellon University Department of Electrical and Computer Engineering Pittsburgh United States Nanjing University of Information Science and Technology School of Computer Science Nanjing China

With the rapid development of headmounted devices, eye tracking as an emerging human-computer interaction technology, has gained increasing importance. However, pupil detection, the core algorithm in eye tracking, suffered low accuracy in complex scenarios and long runtime, which limits the frame rate. This paper proposes PEP (neural network based on Pupil Edge Points), an efficient pupil detection framework that not only achieves high detection accuracy but also significantly reduces computational costs, thereby addressing the performance bottlenecks of existing methods in challenging environments. The core innovation of the PEP framework lies in its focus on detecting edge-representative points (the points at the edge of the pupil) along the pupil edge, a method more precise than traditional full-region segmentation and better suited for accurate pupil ellipse fitting. Additionally, PEP enhances the accuracy of pupil edge point predictions through two key features: (1) a loss function that includes natural ellipse prior regularization term and (2) a data augmentation strategy that randomly masks edges. We evaluated PEP against state-of-the-art (SOTA) model-based and learning-based methods across multiple datasets. The evaluation results show that PEP achieved superior overall performance regarding pupil detection rate and accuracy. Moreover, PEP’s parameter number and computational cost are 80% lower than other learning-based methods, making it highly efficient. The proposed framework is practical, scalable, and demonstrates exceptional potential for pupil detection and gaze-tracking applications. © 2001-2012 IEEE.

关键词： Virtual reality

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Printing Stretchable Electronics With Fused Filament Fabrication

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年第2期4卷 98-107页

作者： Jahan, Sharmin Lazarus, Nathan University of Delaware Electrical and Computer Engineering Department NewarkDE19716 United States

Additive manufacturing is a powerful approach forintegrating flexible and stretchable conductors into complex3-D structures, but many current printing technologies, suchas direct ink writing (DIW), are expensive and challengingto access for many potential users, such as hobbyists andsmall companies. In this work, a low-cost commercial fusedfilament fabrication (FFF) 3-D printer is used to manufacturelow-resistivity stretchable serpentine conductors. By using dualextruder heads, serpentines are printed in a highly conductivemetal-particulate-based composite on a thermoplastic elastomer(TPE) part and demonstrated to survive mechanical strains of upto 25%, sufficient for use in close contact with the human *** electroless plating is also demonstrated to metalize theprinted traces, depositing a thin layer of copper for improvedelectrical conductivity. Using this approach, we demonstrateconductivity improvements by about three orders of magnitude(∼ 103) over past FFF printed serpentines, a major milestonein making stretchable electronics cheaper and more *** resulting stretchable conductors are then demonstrated toprint devices, including stretchable inductors and stretchableinterconnect on a soft robotic actuator. © 2022 IEEE.

关键词： Reinforced plastics

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Exploring Hyperdimensional Computing Robustness Against Hardware Errors

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IEEE Transactions on computers 2025年第6期74卷 1963-1977页

作者： Zhang, Sizhe Juretus, Kyle Jiao, Xun Villanova University Department of Electrical and Computer Engineering VillanovaPA19085 United States

Brain-inspired hyperdimensional computing (HDC) is an emerging machine learning paradigm leveraging high-dimensional spaces for efficient tasks like pattern recognition and medical diagnostics. As a lightweight alternative to deep neural networks, HDC offers smaller model sizes, reduced computation, and memory-centric processing. However, deploying HDC in safety-critical applications, such as healthcare and robotics, is challenged by hardware-induced errors. This paper investigates HDC's robustness to memory errors via extensive bit-flip injection experiments on item and associative memories. Results reveal that certain bit-flips severely degrade accuracy. To address this, we introduce the Hyperdimensional Bit-Flip Search (HD-BFS), a similarity-guided method for identifying vulnerabilities and crafting efficient attacks, where flipping just 6 critical bits—3.9% of random bit-flips—reduces accuracy to chance levels. We further propose Hyperdimensional Accelerated Bit-Flip Search (HD-ABFS), which narrows the search space by targeting critical dimensions and most significant bits (MSBs), achieving up to 282x speedup over HD-BFS. Finally, we develop an effective protection mechanism to enhance model safety. These insights highlight HDC's resilience to random errors, offer robust defenses against targeted attacks, and advance the security and reliability of HDC systems. © 1968-2012 IEEE.

关键词： Deep neural networks

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Privacy Preserving Event Detection

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IEEE Transactions on Information Theory 2025年第7期71卷 5633-5651页

作者： Wang, Xiaoshan Wong, Tan F. University of Florida Department of Electrical and Computer Engineering GainesvilleFL32611 United States

This paper presents a privacy-preserving event detection scheme based on measurements made by a network of sensors. A diameter-like decision statistic made up of the marginal types of the measurements observed by the sensors is employed. The proposed detection scheme can achieve the best type-I error exponent as the type-II error rate is required to be negligible. Detection performance with finite-length observations is also demonstrated through a simulation example of spectrum sensing. Privacy protection is achieved by obfuscating the sensors marginal types with random zero-modulo-sum numbers that are generated and distributed via the exchange of encrypted messages among the sensors. The privacy-preserving performance against "honest but curious" adversaries, including colluding sensors, the fusion center, and external eavesdroppers, is analyzed through a series of cryptographic games. It is shown that the probability that any probabilistic polynomial time adversary successfully estimates the sensors measured types cannot be much better than independent guessing, when there are at least two noncolluding sensors. © 1963-2012 IEEE.

关键词： Error statistics

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Enhanced Protein Complex Detection Using Square Clustering Coefficient

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Soft Computing 2025年第7期29卷 3627-3640页

作者： Mirzaee, Parimah Moghaddam Charkari, Nasrollah Roayaei, Mehdy Department of Electrical and Computer Engineering Tarbiat Modares University Tehran14115-111 Iran

Identifying protein complexes from protein-protein interaction networks is one of the crucial tasks in computational biology. Traditional methods, along with their shortcomings in fully understanding protein complex composition, also have inherent limitations and are expensive to implement. In this paper, we introduce a novel method that not only acknowledges but actively tackles these challenges. Our approach, centered around a core-attachment framework, employs a blend of topological metrics, such as square clustering coefficients, in conjunction with traditional clustering coefficients. After establishing the core, we incorporate attachment proteins based on specific conditions employing a depth-first search (DFS) methodology, to form a protein complex. By harnessing multiple metrics, our goal is to elevate the accuracy of protein complex identification beyond what single-metric approaches can achieve. To validate the effectiveness of our approach, we conducted extensive experiments using multiple datasets, including Gavin06, Krogan core, Krogan extend, and DIP datasets, and assessed metrics such as precision, recall, F-measure, and coverage. Our results not only demonstrate the superiority of our method over traditional approaches but also align with findings from related studies. Overall, our study contributes to the ongoing efforts in computational biology by presenting a comprehensive approach to protein complex identification that addresses the shortcomings of previous methods. Through a combination of innovative techniques and insights from recent research, we aim to push the boundaries of accuracy and comprehensiveness in protein complex detection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Clustering algorithms

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Ensemble of deep learning techniques to human activity recognition using smart phone signals

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Multimedia Tools and Applications 2024年第42期83卷 89635-89664页

作者： Imanzadeh, Soodabeh Tanha, Jafar Jalili, Mahdi Electrical and Computer Engineering Department University of Tabriz Tabriz Iran School of Engineering RMIT University Melbourne Australia

Human Activity Recognition (HAR) has become a significant area of study in the fields of health, human behavior analysis, the Internet of Things, and human–machine interaction in recent years. Smartphones are a popular choice for HAR as they are common devices used in daily life. However, most available HAR datasets are gathered in laboratory settings, which do not reflect real-world scenarios. To address this issue, a real-world dataset using smartphone inertial sensors, involving 62 individuals, is collected. The collected dataset is noisy, small, and has variable frequency. On the other hand, in the context of HAR, algorithms face additional challenges due to intra-class diversity (which refers to differences in the characteristics of performing an activity by different people or by the same individual under different conditions) and inter-class similarity (which refers to different activities that are highly similar). Consequently, it is essential to extract features accurately from the dataset. Ensemble learning, which combines multiple models, is an effective approach to improve generalization performance. In this paper, a weighted ensemble of hybrid deep models for HAR using smartphone sensors is proposed. The proposed ensemble approach demonstrates superior performance compared to current methods, achieving impressive results across multiple evaluation metrics. Specifically, the experimental analysis demonstrates an accuracy of 97.15%, precision of 96.41%, recall of 95.62%, and an F1-score of 96.01%. These results demonstrate the effectiveness of our ensemble approach in addressing the challenges of HAR in real-world scenarios. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Smartphones

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Switching Dynamic State Estimation and Event Detection for Inverter-Based Resources with Multiple Control Modes

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IEEE Transactions on Power Systems 2024年第4期40卷 3439-3451页

作者： Huang, Heqing Lin, Yuzhang Department of Electrical and Computer Engineering New York University Brooklyn NY USA

The Dynamic State Estimation (DSE) for Inverter-Based Resources (IBRs) is an emerging topic as IBRs gradually replace synchronous generators (SGs) in power systems. Unlike SGs, the dynamic models of IBRs heavily depend on their control algorithms, and conventional DSE methods for SGs, which assume a unchanged state space and dynamic model, cannot handle IBRs with control mode changes in real time, particularly when the power grid operators are unaware of the current control mode of the IBRs. In response to these challenges, an Expectation-Maximization Sliding-Window Iterated Extended Kalman Filter (EM-SW-IEKF) method is proposed in this paper. It theoretically achieves maximum likelihood estimation under different modes through the EM algorithm, providing the most probable control mode of the system as well as the corresponding state estimate. This method is validated in various IBR systems (battery energy storage systems and solar photovoltaic systems) and under different control mode transitions (switching between grid-following and grid-forming controls and between low voltage ride through and maximum power point tracking controls). © 1969-2012 IEEE.

关键词： Expectation maximization algorithm

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Meta-ETI: Meta-Reinforcement Learning with Explicit Task Inference for UAV-IoT Coverage

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IEEE Internet of Things Journal 2025年第13期12卷 23852-23865页

作者： Huang, Songjun Sun, Chuanneng Pompili, Dario Rutgers University-New Brunswick Department of Electrical and Computer Engineering NJ United States

To better enhance the network service for different user devices in various scenarios, unmanned aerial vehicles (UAVs) are increasingly used as aerial base stations (ABSs). However, optimizing coverage for user devices via UAV team control is an NP-hard problem and escalates exponentially in complexity with the growing number of user devices. To address this challenge, researchers have turned to reinforcement learning (RL) for a more practical solution. With the growing prevalence of the Internet of Things (IoT), the diversity of user devices increases, posing challenges for traditional RL, as i) the spatial distribution of devices becomes more complex;ii) variations in device types and device mobility increase the training latency;iii) the high-speed movement of IoT devices can lead to performance deterioration in widely used RL algorithms with discrete action space;and iv) traditional RL struggles to adapt to new environments. To solve these problems, we propose a new meta-RL framework, Meta-RL with Explicit Task Inference (Meta-ETI). Then, we apply this framework to efficiently train an energy-efficient UAV control policy for fair and effective coverage in 3D dynamic environments. Meta-ETI is evaluated in both theoretical and application-related aspects and demonstrates superior performance compared to the baseline frameworks. The result shows that Meta-ETI demonstrates 2 to 3 times faster adaptation speed and a decent performance in sample efficiency. Furthermore, in the UAV-IoT coverage application, Meta-ETI shows 30% to 50% better in energy efficiency and 40% to 60% more served devices because of the fair coverage. © 2014 IEEE.

关键词： Reinforcement learning

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