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Formulations and Approximations of Branch Flow Model for General Power Networks

Journal of Modern Power Systems and Clean Energy 2022年第5期10卷 1110-1126页

作者： Zhao Yuan Electrical Power Systems Laboratory(EPS-Lab) Department of Electrical and Computer EngineeringUniversity of IcelandReykjavikIceland IEEE

The formulations and approximations of the branch flow model for general(radial and mesh) power networks(General-BranchFlow) are given in this paper. Using different sets of the power flow equations, six formats of the exact General-BranchFlow model are listed. The six formats are mathematically equivalent with each other. Linear approximation and second-order cone programming(SOCP) are then used to derive the six formats of the convex General-BranchFlow model. The branch ampacity constraints considering the shunt conductance and capacitance of the transmission line Π-model are derived. The key foundation of deriving the ampacity constraints is the correct interpretation of the physical meaning of the transmission line Π-model. An exact linear expression of the ampacity constraints of the power loss variable is derived. The applications of the General-BranchFlow model in deriving twelve formats of the exact optimal power flow(OPF) model and twelve formats of the approximate OPF model are formulated and analyzed. Using the Julia programming language, the extensive numerical investigations of all formats of the OPF models show the accuracy and computational efficiency of the General-BranchFlow model. A penalty function based approximation gap reduction method is finally proposed and numerically validated to improve the AC-feasibility of the approximate General-BranchFlow model.

关键词： Branch flow linear approximation second-order cone programming ampacity constraint optimal power flow radial network mesh network

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OLLIE: Imitation Learning from Offline Pretraining to Online Finetuning 41

OLLIE: Imitation Learning from Offline Pretraining to Online...

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41st International Conference on Machine Learning, ICML 2024

作者： Yue, Sheng Hua, Xingyuan Ren, Ju Lin, Sen Zhang, Junshan Zhang, Yaoxue Department of Computer Science and Technology Tsinghua University Beijing China Zhongguancun Laboratory Beijing China Department of Computer Science University of Houston Texas United States Department of Electrical and Computer Engineering University of California Davis United States

In this paper, we study offline-to-online Imitation Learning (IL) that pretrains an imitation policy from static demonstration data, followed by fast finetuning with minimal environmental interaction. We find the naïve combination of existing offline IL and online IL methods tends to behave poorly in this context, because the initial discriminator (often used in online IL) operates randomly and discordantly against the policy initialization, leading to misguided policy optimization and unlearning of pretraining knowledge. To overcome this challenge, we propose a principled offline-to-online IL method, named OLLIE, that simultaneously learns a near-expert policy initialization along with an aligned discriminator initialization, which can be seamlessly integrated into online IL, achieving smooth and fast finetuning. Empirically, OLLIE consistently and significantly outperforms the baseline methods in 20 challenging tasks, from continuous control to vision-based domains, in terms of performance, demonstration efficiency, and convergence speed. This work may serve as a foundation for further exploration of pretraining and finetuning in the context of IL. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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A Methodology for Estimating Allocation of Electric Vehicle Grid Interfaces in a Distribution System

A Methodology for Estimating Allocation of Electric Vehicle ...

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2024 IEEE PES/IAS PowerAfrica, PowerAfrica 2024

作者： Subasinghe, Imanthi Kalanika Venayagamoorthy, Ganesh K. Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University Clemson United States Electronic and Computer Engineering University of Pretoria Department of Electrical South Africa

ISBN: (纸本)9798350389388

The integration of electric vehicles (EVs) into the smart grid has introduced new challenges and opportunities for optimizing power and energy management. This paper presents a simple method using a decision-tree to estimate allocation of electric vehicle grid interfaces (EVGIs) for EV charging/discharging, leveraging the nodal capabilities of a distribution system. The future distribution system will cater for vehicle-to-grid (V2G) transactions in addition to grid-to-vehicle (G2V) transactions, thus EVGIs allowing for bidirectional (G2V/V2G) power flows need to be deployed. The proposed decision-tree method is based on nodal power flow ratings and peak demand at the nodes assuming the nodal voltages are maintained within their stability envelopes by volt-var optimization and other voltage control methods. The allocation of EVGIs in the IEEE 34 test node feeder distribution system using the decision-tree method is illustrated. The EVGI allocation using decision-trees is a simple method that can provide insights of a typical distribution system's capability to support EV integration with emerging smart grid technologies. © 2024 IEEE.

关键词： DC distribution systems

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DDT-Net:Deep Detail Tracking Network for Image Tampering Detection

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computers, Materials & Continua 2025年第5期83卷 3451-3469页

作者： Jim Wong Zhaoxiang Zang Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China College of Computer and Information Technology ChinaThree Gorges UniversityYichang443002China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650504China

In the field of image forensics,image tampering detection is a critical and challenging *** methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of *** deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational *** address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection *** contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise *** design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level ***,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region *** with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k *** addition,it has been extensively validated on other datasets,including CASIA and *** results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.

关键词： Image forensics image tampering detection image manipulation detection noise flow Bayar

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Comments on 'VERSA: Verifiable Secure Aggregation for Cross-Device Federated Learning'

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IEEE Transactions on Dependable and Secure Computing 2024年第1期21卷 499-500页

作者： Luo, Fucai Wang, Haiyan Yan, Xingfu Zhejiang Gongshang University College of Computer and Information Engineering Hangzhou310000 China Peng Cheng Laboratory Department of New Networks Shenzhen518066 China South China Normal University School of Computer Science Guangzhou510631 China

Federated learning (FL) allows a large number of users to collaboratively train machine learning (ML) models by sending only their local gradients to a central server for aggregation in each training iteration, without sending their raw training data. The main security issues of FL, that is, the privacy of the gradient vector and the correctness verification of the aggregated gradient, are gaining increasing attention from industry and academia. To protect the privacy of the gradient, a secure aggregation was proposed;to verify the correctness of the aggregated gradient, a verifiable secure aggregation that requires the server to provide a verifiable aggregated gradient was proposed. In 2021, Hahn et al. proposed VERSA, a verifiable secure aggregation. However, in this article, we will point out a flaw in VERSA, which indicates that VERSA does not work. To address the flaw, we present several approaches with different advantages and disadvantages. We hope that by identifying the flaw, similar errors can be avoided in future designs of verifiable secure aggregation. © 2004-2012 IEEE.

关键词： Learning systems

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Push-Based Content Dissemination and Machine Learning-Oriented Illusion Attack Detection in Vehicular Named Data Networking

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computers, Materials & Continua 2023年第9期76卷 3131-3150页

作者： Arif Hussain Magsi Ghulam Muhammad Sajida Karim Saifullah Memon Zulfiqar Ali State Key Laboratory ofNetworking and Switching Technology Beijing University of Post and TelecommunicationBeijing100876China Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China School of Computer Science and Electronic Engineering University of EssexColchesterCO43SQUK

Recent advancements in the Vehicular Ad-hoc Network(VANET)have tremendously addressed road-related ***,Named Data Networking(NDN)in VANET has emerged as a vital technology due to its outstanding ***,the NDN communication framework fails to address two important *** current NDN employs a pull-based content retrieval network,which is inefficient in disseminating crucial content in Vehicular Named Data Networking(VNDN).Additionally,VNDN is vulnerable to illusion attackers due to the administrative-less network of autonomous *** various solutions have been proposed for detecting vehicles’behavior,they inadequately addressed the challenges specific to *** deal with these two issues,we propose a novel push-based crucial content dissemination scheme that extends the scope of VNDN from pullbased content retrieval to a push-based content forwarding *** addition,we exploitMachine Learning(ML)techniques within VNDN to detect the behavior of vehicles and classify them as attackers or *** trained and tested our system on the publicly accessible dataset Vehicular Reference Misbehavior(VeReMi).We employed fiveML classification algorithms and constructed the bestmodel for illusion attack *** results indicate that RandomForest(RF)achieved excellent accuracy in detecting all illusion attack types in VeReMi,with an accuracy rate of 100%for type 1 and type 2,96%for type 4 and type 16,and 95%for type ***,RF can effectively evaluate the behavior of vehicles and identify attacker vehicles with high *** ultimate goal of our research is to improve content exchange and ***,ourML-based attack detection and preventionmechanismensures trustworthy content dissemination and prevents attacker vehicles from sharing misleading information in VNDN.

关键词： Named data networking vehicular networks pull-push illusion attack machine learning

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A review on simulation models of cascading failures in power systems

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iEnergy 2023年第4期2卷 284-296页

作者： Zhenping Guo Kai Sun Xiaowen Su Srdjan Simunovic Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTNUSA Department of Electrical and Computer Engineering University of Texas at San AntonioSan AntonioTXUSA Computational Sciences&Engineering Division Oak Ridge National LaboratoryOak RidgeTNUSA

Among various power system disturbances,cascading failures are considered the most serious and extreme threats to grid operations,potentially leading to significant stability issues or even widespread power *** power systems’behaviors during cascading failures is of great importance to comprehend how failures originate and propagate,as well as to develop effective preventive and mitigative control *** intricate mechanism of cascading failures,characterized by multi-timescale dynamics,presents exceptional challenges for their *** paper provides a comprehensive review of simulation models for cascading failures,providing a systematic categorization and a comparison of these *** challenges and potential research directions for the future are also discussed.

关键词： Cascading failures power systems security and reliability simulation models

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An Effective Lung Cancer Diagnosis Model Using Pre-Trained CNNs

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computer Modeling in engineering & Sciences 2025年第4期143卷 1129-1155页

作者： Majdi Rawashdeh Muath A.Obaidat Meryem Abouali Dhia Eddine Salhi Kutub Thakur Department of Business Information Technology Princess Sumaya University for TechnologyAmman11941Jordan Department of Computer Engineering Al Yamamah UniversityRiyadh11512Saudi Arabia Department of Computer Science John Jay College and the Graduate CenterThe City University of New YorkNew YorkNY 10019USA Department of Computer Science John Jay College at the City University of NewYorkNewYorkNY 10019USA LIMOSE Laboratory Department of Computer ScienceM’hamed Bougara UniversityBoumerdes35000Algeria Department of Professional Security Studies New Jersey City UniversityJersey CityNJ 07305USA

Cancer is a formidable andmultifaceted disease driven by genetic aberrations and metabolic *** 19% of cancer-related deaths worldwide are attributable to lung and colon cancer,which is also the top cause of death *** malignancy has a terrible 5-year survival rate of 19%.Early diagnosis is critical for improving treatment outcomes and survival *** study aims to create a computer-aided diagnosis(CAD)that accurately diagnoses lung disease by classifying histopathological *** uses a publicly accessible dataset that includes 15,000 images of benign,malignant,and squamous cell carcinomas in the *** addition,this research employs multiscale processing to extract relevant image features and conducts a comprehensive comparative analysis using four Convolutional Neural Network(CNN)based on pre-trained models such as AlexNet,VGG(Visual Geometry Group)16,ResNet-50,and VGG19,after hyper-tuning these models by optimizing factors such as batch size,learning rate,and *** proposed(CNN+VGG19)model achieves the highest accuracy of 99.04%.This outstanding performance demonstrates the potential of the CAD system in accurately classifying lung cancer histopathological *** study contributes significantly to the creation of a more precise CNN-based model for lung cancer identification,giving researchers and medical professionals in this vital sector a useful tool using advanced deep learning techniques and publicly available datasets.

关键词： Lung cancer machine learning computer aided diagnosis CNN medical imaging transfer learning

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Three-dimensional Target Detection Algorithm for Dangerous Goods in CT Security Inspection

Three-dimensional Target Detection Algorithm for Dangerous G...

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2023 International Conference on Algorithm, Imaging Processing, and Machine vision, AIPMV 2023

作者： He, Jingze Guo, Yao Song, Qing Department of Computer Science and Technology Tsinghua University Beijing China Pattern Recognition and Intelligent Vision Laboratory Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781510672444

In this paper, a 3D dangerous goods detection method based on RetinaNet is proposed. This method uses the bidirectional feature pyramid network structure of RetinaNet to extract multi-scale features from point cloud data and trains the system using Focal Loss function to achieve fast and accurate detection of dangerous goods. In addition, in order to improve the detection accuracy, this paper also introduces the 3D region proposal network (3D RPN) and non-maximum suppression (NMS) algorithm. The experimental results show that the proposed method performs well on our self-built CT dataset, with high accuracy and low false positive rate, and is suitable for dangerous goods detection tasks in practical scenarios. © 2024 SPIE.

关键词： computerized tomography

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Camera Calibration Without Camera Access - A Robust Validation Technique for Extended PnP Methods 23rd

Camera Calibration Without Camera Access - A Robust Validati...

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22nd Scandinavian Conference on Image Analysis, SCIA 2023

作者： Brissman, Emil Forssén, Per-Erik Edstedt, Johan Computer Vision Laboratory Department EE Linköping University Linköping Sweden Saab Linköping Sweden

ISBN: (纸本)9783031314346

A challenge in image based metrology and forensics is intrinsic camera calibration when the used camera is unavailable. The unavailability raises two questions. The first question is how to find the projection model that describes the camera, and the second is to detect incorrect models. In this work, we use off-the-shelf extended PnP-methods to find the model from 2D-3D correspondences, and propose a method for model validation. The most common strategy for evaluating a projection model is comparing different models’ residual variances—however, this naive strategy cannot distinguish whether the projection model is potentially underfitted or overfitted. To this end, we model the residual errors for each correspondence, individually scale all residuals using a predicted variance and test if the new residuals are drawn from a standard normal distribution. We demonstrate the effectiveness of our proposed validation in experiments on synthetic data, simulating 2D detection and Lidar measurements. Additionally, we provide experiments using data from an actual scene and compare non-camera access and camera access calibrations. Last, we use our method to validate annotations in MegaDepth. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cameras

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