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Tightening QC Relaxations of AC Optimal Power Flow Through Improved Linear Convex Envelopes

IEEE Transactions on Power Systems 2025年第2期40卷 1465-1480页

作者： Narimani, Mohammad Rasoul Molzahn, Daniel K. Davis, Katherine R. Crow, Mariesa L. Department of Electrical and Computer Engineering NorthridgeCA91330 United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States Texas A&M University Electrical and Computer Engineering Department College StationTX77840 United States Missouri University of Science and Technology Electrical and Computer Engineering Department RollaMO65409 United States

AC optimal power flow (AC OPF) is a fundamental problem in power system operations. Accurately modeling the network physics via the AC power flow equations makes AC OPF a challenging nonconvex problem. To search for global optima, recent research has developed various convex relaxations that bound the optimal objective values of AC OPF problems. The QC relaxation convexifies the AC OPF problem by enclosing the non-convex terms within convex envelopes. The QC relaxation's accuracy strongly depends on the tightness of these envelopes. This paper proposes two improvements for tightening QC relaxations of OPF problems. We first consider a particular nonlinear function whose projections are the nonlinear expressions appearing in the polar representation of the power flow equations. We construct a polytope-shaped convex envelope around this nonlinear function and derive convex expressions for the nonlinear terms using its projections. Second, we use sine and cosine expression properties, along with changes in their curvature, to tighten this convex envelope. We also propose a coordinate transformation to tighten the envelope by rotating power flow equations based on individual bus-specific angles. We compare these enhancements to a state-of-the-art QC relaxation method using PGLib-OPF test cases, revealing improved optimality gaps in 68% of the cases. © 1969-2012 IEEE.

关键词： Load flow optimization

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News Modeling and Retrieving Information: Data-Driven Approach

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Intelligent Automation & Soft Computing 2023年第11期38卷 109-123页

作者： Elias Hossain Abdullah Alshahrani Wahidur Rahman Electrical&Computer Engineering North South UniversityDhaka1229Bangladesh Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science and Engineering Uttara UniversityDhaka1230Bangladesh

This paper aims to develop Machine Learning algorithms to classify electronic articles related to this phenomenon by retrieving information and topic *** Methodology of this study is categorized into three phases:the Text Classiﬁcation Approach(TCA),the Proposed Algorithms Interpretation(PAI),andﬁnally,Information Retrieval Approach(IRA).The TCA reﬂects the text preprocessing pipeline called a clean *** Global Vec-tors for Word Representation(Glove)pre-trained model,FastText,Term Frequency-Inverse Document Fre-quency(TF-IDF),and Bag-of-Words(BOW)for extracting the features have been interpreted in this *** PAI manifests the Bidirectional Long Short-Term Memory(Bi-LSTM)and Convolutional Neural Network(CNN)to classify the COVID-19 ***,the IRA explains the mathematical interpretation of Latent Dirich-let Allocation(LDA),obtained for modelling the topic of Information Retrieval(IR).In this study,99%accuracy was obtained by performing K-fold cross-validation on Bi-LSTM with Glove.A comparative analysis between Deep Learning and Machine Learning based on feature extraction and computational complexity exploration has been performed in this ***,some text analyses and the most inﬂuential aspects of each document have been explored in this *** have utilized Bidirectional Encoder Representations from Trans-formers(BERT)as a Deep Learning mechanism in our model training,but the result has not been uncovered ***,the proposed system can be adjustable in the real-time news classiﬁcation of COVID-19.

关键词： COVID-19 news retrieving data-driven machine learning BERT topic modelling

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Orbit-Orbit Interaction in Spatiotemporal Optical Vortex

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engineering 2025年第2期45卷 44-51页

作者： Jian Chen Jie Zhao Xi Shen Dewei Mo Cheng-Wei Qiu Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

While spin-orbit interaction has been extensively studied,few investigations have reported on the interaction between orbital angular momenta(OAMs).In this work,we study a new type of orbit-orbit coupling between the longitudinal OAM and the transverse OAM carried by a three-dimensional(3D)spatiotemporal optical vortex(STOV)in the process of tight *** 3D STOV possesses orthogonal OAMs in the x-y,t-x,and y-t planes,and is preconditioned to overcome the spatiotemporal astigmatism effect.x,y,and t are the axes in the spatiotemporal *** corresponding focused wavepacket is calculated by employing the Debye diffraction theory,showing that a phase singularity ring is generated by the interactions among the transverse and longitudinal vortices in the highly confined *** Fourier-transform decomposition of the Debye integral is employed to analyze the mechanism of the orbit-orbit *** is the first revelation of coupling between the longitudinal OAM and the transverse OAM,paving the way for potential applications in optical trapping,laser machining,nonlinear light-matter interactions,and more.

关键词： Spatiotemporal optical vortex Orbital angular momentum Momentum interaction Highly confined wavepacket Diffraction

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Deep reinforcement learning for online scheduling of photovoltaic systems with battery energy storage systems

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Intelligent and Converged Networks 2024年第1期5卷 28-41页

作者： Yaze Li Jingxian Wu Yanjun Pan the Department of Electrical Engineering University of ArkansasFayettevilleAR 72701USA the Department of Computer Science and Computer Engineering University of ArkansasFayettevilleAR 72701USA

A new online scheduling algorithm is proposed for photovoltaic(PV)systems with battery-assisted energy storage systems(BESS).The stochastic nature of renewable energy sources necessitates the employment of BESS to balance energy supplies and demands under uncertain weather *** proposed online scheduling algorithm aims at minimizing the overall energy cost by performing actions such as load shifting and peak shaving through carefully scheduled BESS charging/discharging *** scheduling algorithm is developed by using deep deterministic policy gradient(DDPG),a deep reinforcement learning(DRL)algorithm that can deal with continuous state and action *** of the main contributions of this work is a new DDPG reward function,which is designed based on the unique behaviors of energy *** new reward function can guide the scheduler to learn the appropriate behaviors of load shifting and peak shaving through a balanced process of exploration and *** new scheduling algorithm is tested through case studies using real world data,and the results indicate that it outperforms existing algorithms such as Deep *** online algorithm can efficiently learn the behaviors of optimum non-casual off-line algorithms.

关键词： photovoltaic(PV) battery energy storage system(BESS) Markov decision process(MDP) deep deterministic policy gradient(DDPG)

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Artificial visual perception neural system using a solution-processable MoS_(2)-based in-memory light sensor

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Light(science & Applications) 2023年第8期12卷 1583-1594页

作者： Dayanand Kumar Lana Joharji Hanrui Li Ayman Rezk Ammar Nayfeh Nazek El-Atab Smart Advanced Memory Devices and Applications(SAMA)LaboratoryElectrical and Computer Engineering ProgramComputer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Kingdom of Saudi Arabia Department of Electrical Engineering and Computer Science Khalifa UniversityAbu Dhabi 127788United Arab Emirates

Optoelectronic devices are advantageous in in-memory light sensing for visual information processing,recognition,and storage in an energy-efficient ***,in-memory light sensors have been proposed to improve the energy,area,and time efficiencies of neuromorphic computing *** study is primarily focused on the development of a single sensing-storage-processing node based on a two-terminal solution-processable MoS2 metal-oxide-semiconductor(MOS)charge-trapping memory structure—the basic structure for charge-coupled devices(CCD)—and showing its suitability for in-memory light sensing and artificial visual *** memory window of the device increased from 2.8 V to more than 6V when the device was irradiated with optical lights of different wavelengths during the program ***,the charge retention capability of the device at a high temperature(100 ℃)was enhanced from 36 to 64%when exposed to a light wavelength of 400 *** larger shift in the threshold voltage with an increasing operating voltage confirmed that more charges were trapped at the Al_(2)O_(3)/MoS_(2) interface and in the MoS_(2) layer.A small convolutional neural network was proposed to measure the optical sensing and electrical programming abilities of the *** array simulation received optical images transmitted using a blue light wavelength and performed inference computation to process and recognize the images with 91%*** study is a significant step toward the development of optoelectronic MOS memory devices for neuromorphic visual perception,adaptive parallel processing networks for in-memory light sensing,and smart CCD cameras with artificial visual perception capabilities.

关键词： visual neural process

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Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

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computer Modeling in engineering & sciences 2024年第9期140卷 2397-2424页

作者： Hongchi Liu Xing Deng Haijian Shao School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China Department of Electrical and Computer Engineering University of NevadaLas Vegas89119USA

The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle,profoundly impeding their effective utilization across various *** methodologies have emerged as pivotal components of image preprocessing,fostering an improvement in the quality of remote sensing *** enhancement renders remote sensing data more indispensable,thereby enhancing the accuracy of target *** defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed *** response to this challenge,a novel UNet Residual Attention Network(URA-Net)is *** paradigmatic approach materializes as an end-to-end convolutional neural network distinguished by its utilization of multi-scale dense feature fusion clusters and gated jump *** essence of our methodology lies in local feature fusion within dense residual clusters,enabling the extraction of pertinent features from both preceding and current local data,depending on contextual *** intelligently orchestrated gated structures facilitate the propagation of these features to the decoder,resulting in superior outcomes in haze *** validation through a plethora of experiments substantiates the efficacy of URA-Net,demonstrating its superior performance compared to existing methods when applied to established datasets for remote sensing image *** the RICE-1 dataset,URA-Net achieves a Peak Signal-to-Noise Ratio(PSNR)of 29.07 dB,surpassing the Dark Channel Prior(DCP)by 11.17 dB,the All-in-One Network for Dehazing(AOD)by 7.82 dB,the Optimal Transmission Map and Adaptive Atmospheric Light For Dehazing(OTM-AAL)by 5.37 dB,the Unsupervised Single Image Dehazing(USID)by 8.0 dB,and the Superpixel-based Remote Sensing Image Dehazing(SRD)by 8.5 *** noteworthy,on the SateHaze1k dataset,URA-Net attains preeminence in overall performance,yieldi

关键词： Remote sensing image image dehazing deep learning feature fusion

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A Review of Anonymization Algorithms and Methods in Big Data

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Annals of Data science 2025年第1期12卷 253-279页

作者： Shamsinejad, Elham Banirostam, Touraj Pedram, Mir Mohsen Rahmani, Amir Masoud Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran

In the era of big data, with the increase in volume and complexity of data, the main challenge is how to use big data while preserving the privacy of users. This study was conducted with the aim of finding a solution to this challenge. In this study, we examined various data anonymization methods, including differential privacy, advanced encryption, and strong access controls. In addition, the operation, advantages, disadvantages, and use of these methods, the challenges of adapting these methods to big data, and possible solutions for them were also examined. Our results show that traditional data anonymization methods lack scalability, leading to privacy breaches and data loss. When faced with large volumes of data, these methods may not be able to fully process the data. Also, these methods may be ineffective against re-identification attacks, linkage attacks, and inference attacks. We introduced emerging methods that are capable of providing improved privacy with minimal data loss. These methods have scalability for big data. Finally, we examined future research works and raised important questions that can help improve existing algorithms or develop new methods, better manage the complexity and scale of unstructured data. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Big data

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Representing a Model for the Anonymization of Big Data Stream Using In-Memory Processing

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Annals of Data science 2025年第1期12卷 223-252页

In light of the escalating privacy risks in the big data era, this paper introduces an innovative model for the anonymization of big data streams, leveraging in-memory processing within the Spark framework. The approach is founded on the principle of K-anonymity and propels the field forward by critically evaluating various anonymization methods and algorithms, benchmarking their performance with respect to time and space complexities. A distinctive formula for optimized cluster determination in the K-means algorithm is presented, along with a novel tuple expiration time strategy for the efficient purging of clusters. The integration of these components into Spark’s RDD and MLlib modules results in a significant decrease in execution time and data loss rates, even with increasing data volumes. The paper’s notable contributions are its methodological advancements that offer a robust, scalable solution for data anonymization, safeguarding user privacy without sacrificing data utility or processing efficiency. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Big data

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A Novel Deep Learning Approach Toward Efficient and Accurate Recommendation Using Improved Alternating Least Squares in Social Media

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Journal of The Institution of Engineers (India): Series B 2024年第3期105卷 657-675页

作者： Dhawan, Sanjeev Singh, Kulvinder Batra, Amit Choi, Anthony Choi, Ethan Department of Computer Science and Engineering University Institute of Engineering and Technology Kurukshetra University Haryana India Department of Electrical and Computer Engineering Mercer University MaconGA United States

The increasing number of electronic transactions on the Internet has given rise to the design of recommendation systems. The main objective of these systems is to give recommendations to the users about the items (i.e., movies, music, books, etc.) they are likely to purchase in near future. These recommendations are on the foundation of the response provided by the other individuals about these products in the terms of ratings, which are in the form of a five-star rating system. Plenty of algorithms have been proposed in the past for building recommender systems. The most popular of these algorithms comprise content-centered and collaborative filtering recommendation methodologies. Recently, matrix factorization has appeared as a technique in recommender systems. Several algorithms centered on matrix factorization have been suggested in the recent past. In this paper, an improved alternating least squares algorithm has been proposed for building more efficient and accurate recommender systems which improves the performance of matrix factorization. The performance of the model has been evaluated using metrics like root-mean-square-error and execution time. However, in general, the time complexity of both algorithms is O(n^3), where n is the number of users or items. This is because both IALS and ALS involve iteratively solving a system of linear equations, and the time complexity of solving a system of linear equations is O(n^3). However, IALS can achieve significant performance improvements over ALS, particularly for large datasets, due to its incorporation of stochastic gradient descent and parallelization techniques. © The Institution of Engineers (India) 2024.

关键词： Recommender systems

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Coordinated Frequency Control for Isolated Power Systems with High Penetration of DFIG-Based Wind Power

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1399-1414页

作者： Xin Ding Wei Lin Jian Xu Yuanzhang Sun Liangzhong Yao Beilin Mao School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China Department of Electrical Engineering and Computer Science Case Western Reserve UniversityClevelandOH 44106 USA

This paper proposes a coordinated frequency control scheme for emergency frequency regulation of isolated power systems with a high penetration of wind *** proposed frequency control strategy is based on the novel nonlinear regulator theory,which takes advantage of nonlinearity of doubly fed induction generators(DFIGs)and generators to regulate the frequency of the power *** deviations and power imbalances are used to design nonlinear feedback controllers that achieve the reserve power distribution between generators and DFIGs,in various wind speed *** effectiveness and dynamic performance of the proposed nonlinear coordinated frequency control method are validated through simulations in an actual isolated power grid.

关键词： Frequency control isolated power systems nonlinear feedback nonlinear regulator theory wind power

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