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An end-to-end network for co-saliency detection in one single image

science China(Information sciences) 2023年第11期66卷 5-22页

作者： Yuanhao YUE Qin ZOU Hongkai YU Qian WANG Zhongyuan WANG Song WANG School of Cyber Science and Engineering Wuhan University School of Computer Science Wuhan University Department of Electrical Engineering and Computer Science Cleveland State University Department of Computer Science and Engineering University of South Carolina

Co-saliency detection within a single image is a common vision problem that has not yet been well addressed. Existing methods often used a bottom-up strategy to infer co-saliency in an image in which salient regions are firstly detected using visual primitives such as color and shape and then grouped and merged into a co-saliency map. However, co-saliency is intrinsically perceived complexly with bottom-up and top-down strategies combined in human vision. To address this problem, this study proposes a novel end-toend trainable network comprising a backbone net and two branch nets. The backbone net uses ground-truth masks as top-down guidance for saliency prediction, whereas the two branch nets construct triplet proposals for regional feature mapping and clustering, which drives the network to be bottom-up sensitive to co-salient regions. We construct a new dataset of 2019 natural images with co-saliency in each image to evaluate the proposed method. Experimental results show that the proposed method achieves state-of-the-art accuracy with a running speed of 28 fps.

关键词： saliency detection convolutional neural network regional feature mapping co-saliency detection deep learning

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A Hybrid P&O-Fuzzy-Based Maximum Power Point Tracking (MPPT) Algorithm for Photovoltaic Systems under Partial Shading Conditions

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IEEE Access 2025年 13卷 86046-86056页

作者： Karimi, Hamed Siadatan, Alireza Rezaei-Zare, Afshin Khatam University Department of Electrical Engineering Tehran Iran York University Department of Electrical Engineering and Computer Science Toronto Canada

Photovoltaic arrays receive varying levels of solar radiation due to factors such as shadows created by clouds, surrounding buildings, and other obstructions. Therefore, an effective Maximum Power Point Tracking (MPPT) algorithm should be used to maximize power generation from non-uniform arrays. Most MPPT algorithms do not perform well during partial shadow conditions, as they can become trapped in local maxima and fail to identify the absolute global maximum point (GP). This paper proposes a new MPPT approach that combines Perturb & Observe (P&O) and Fuzzy Logic Controller (FLC) with the Particle Swarm Optimization (PSO) algorithm. The FLC algorithm is then used to maximize search accuracy. The configurations of this system and the study results demonstrate the dynamic and promising performance of the proposed method under various environmental and shading conditions. In addition to its simplicity and ease of implementation, the proposed algorithm exhibits high accuracy in tracking the maximum power point. This has been evidenced through both simulation and laboratory results of the hardware. © 2025 Institute of electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Deep Learning to Localize Photoacoustic Sources in Three Dimensions: Theory and Implementation

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IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 2025年第6期72卷 786-805页

作者： Gubbi, Mardava R. Lediju Bell, Muyinatu A. Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD United States Johns Hopkins University Department of Electrical and Computer Engineering Department of Computer Science Department of Biomedical Engineering BaltimoreMD United States

Surgical tool tip localization and tracking are essential components of surgical and interventional procedures. The cross sections of tool tips can be considered as acoustic point sources to achieve these tasks with deep learning applied to photoacoustic channel data. However, source localization was previously limited to the lateral and axial dimensions of an ultrasound transducer. In this paper, we developed a novel deep learning-based three-dimensional (3D) photoacoustic point source localization system using an object detection-based approach extended from our previous work. In addition, we derived theoretical relationships among point source locations, sound speeds, and waveform shapes in raw photoacoustic channel data frames. We then used this theory to develop a novel deep learning instance segmentation-based 3D point source localization system. When tested with 4,000 simulated, 993 phantom, and 1,983 ex vivo channel data frames, the two systems achieved F1 scores as high as 99.82%, 93.05%, and 98.20%, respectively, and Euclidean localization errors (mean ± one standard deviation) as low as 1.46±1.11 mm, 1.58±1.30 mm, and 1.55±0.86 mm, respectively. In addition, the instance segmentation-based system simultaneously estimated sound speeds with absolute errors (mean ± one standard deviation) of 19.22±26.26 m/s in simulated data and standard deviations ranging 14.6-32.3 m/s in experimental data. These results demonstrate the potential of the proposed photoacoustic imaging-based methods to localize and track tool tips in three dimensions during surgical and interventional procedures. © 1986-2012 IEEE.

关键词： Ultrasonic transducers

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DenseRSE-ASPPNet: An Enhanced DenseNet169 with Residual Dense Blocks and CE-HSOA-Based Optimization for IoT Botnet Detection

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International Journal of Advanced computer science and Applications 2025年第4期16卷 688-699页

作者： Khan, Mohd Abdul Rahim Department of Electrical Engineering and Computer Science A’sharqiyah University IBRA-400 Oman

The growing prevalence of Internet of Things (IoT) devices has heightened vulnerabilities to botnet-based cyberattacks, necessitating robust detection mechanisms. This paper proposes DenseRSE-ASPPNet, an advanced deep learning framework for botnet detection, incorporating comprehensive preprocessing, feature extraction, and optimization. The preprocessing pipeline includes data cleaning and Min-Max normalization to ensure high-quality input data. The DenseNet169 backbone is enhanced with Residual Squeeze-and-Excitation (RSE) blocks for channel-wise attention recalibration and Atrous Spatial Pyramid Pooling (ASPP) for capturing multi-scale spatial patterns, enabling effective feature extraction. Hyperparameter optimization is performed using the Cyclone-Enhanced Humboldt Squid Optimization Algorithm (CE-HSOA), which balances global exploration and local exploitation, ensuring faster convergence and enhanced robustness. Experimental results demonstrate the superior performance of the proposed framework, achieving 99.00 per cent accuracy, 96.40 per cent sensitivity, and 99.95 per cent specificity, significantly minimizing false positives and false negatives. The proposed DenseRSE-ASPPNet provides an efficient, scalable, and effective solution for mitigating botnet threats in IoT environments. © (2025), (science and Information Organization). All rights reserved.

关键词： Feature Selection

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Maximizing aged photovoltaic array power:a computer modelling study

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Clean Energy 2024年第1期8卷 171-187页

作者： Fadi N.Sibai Electrical&Computer Engineering Department Gulf University for Science and TechnologyMishrefKuwait

Photovoltaic(PV)modules age with time for various reasons such as corroded joints and terminals and glass coating defects,and their ageing degrades the PV array *** the help of the PV array numerical model,this paper explores the effects of PV module ageing on the PV array power,and the power gains and costs of rearranging and recabling aged PV modules in a PV *** numerical PV array model is first revised to account for module ageing,rearrangement and recabling,with the relevant equations presented *** updated numerical model is then used to obtain the array powers for seven different PV *** power results are then analysed in view of the attributes of the seven PV array examples.A guiding method to recommend recabling after rearranging aged modules is then proposed,leading to further significant power gains,while eliminating intra-row *** certain conditions are met,it was shown that recabling PV modules after rearranging them may lead to further significant power gains,reaching 57%and 98%in two considered PV array *** gains are possible in other arrays.A cost-benefit analysis weighing annual power gains versus estimated recabling costs is also given for the seven considered PV array examples to guide recabling decisions based on technical and economic *** the considered examples,recabling costs can be recovered in<4 *** with the powers of the aged arrays,power gains due to our proposed rearranging and recabling the PV arrays ranged between 73%and 131%in the considered examples—well over the gains reported in the ***,the cost of our static module rearrangement and recabling method outshines the costs of dynamic reconfiguration methods recently published in the literature.

关键词： solar energy optimizing array power photovoltaic array modelling photovoltaic modules ageing photovoltaic module rearrangement module recabling and reconfiguration PV array mismatch

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ESG Investing: A Statistically Valid Approach to Data-Driven Decision Making and the Impact of ESG Factors on Stock Returns and Risk

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IEEE Access 2024年 12卷 69434-69444页

作者： Teja, Kamurthi Ravi Liu, Chuan-Ming National Taipei University of Technology International Program of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

This study examines the impact of environmental, social, and governance (ESG) factors on economic investment from a statistical perspective, aiming to develop a tested investment strategy that capitalizes on the connection between ESG factors and financial performance. ESG investing: A statistically valid approach to data-driven decision-making (ESGI-SVADDM) investment strategy is based on a rigorous, statistically valid approach that utilizes data, math, statistics, and data science libraries to drive investment decisions, eliminating the need for personal opinions and subjectivity. The process includes establishing an investment thesis, formulating testable hypotheses (HPS), retrieving and refining relevant data, calculating relevant measures, and testing and validating all hypotheses. The study uses S&P 500 stock data and ESG data from Sustainalytics to test the hypotheses. The empirical tests conducted revealed a negative correlation between ESG risk and expected returns, as well as a positive trend in the relationship between ESG risk and the overall risk of stocks. Moreover, the study found that higher ESG risk scores are associated with lower returns for investors, and that adopting a strategy of investing in stocks with low ESG risk and shorting stocks with high ESG risk yields superior returns compared to the market portfolio. © 2013 IEEE.

关键词： Investments

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An active learning framework for adversarial training of deep neural networks

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Neural Computing and Applications 2025年第9期37卷 6849-6876页

作者： Ghosh, Susmita Chatterjee, Abhiroop Fiondella, Lance Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Electrical and Computer Engineering University of Massachusetts Dartmouth United States

This article introduces a novel approach to bolster the robustness of Deep Neural Network (DNN) models against adversarial attacks named "Targeted Adversarial Resilience Learning (TARL)". The initial evaluation of a baseline DNN model reveals a significant accuracy decline when subjected to adversarial examples generated through techniques like FGSM, PGD, Carlini Wagner, and DeepFool attacks. To address this vulnerability, the article proposes an active learning framework, wherein the model iteratively identifies and learns from the most uncertain and misclassified instances. The key components of this approach include uncertainty estimation score in predicting the class of the input sample, selecting challenging samples based on this uncertainty score, labeling these challenging examples and augmenting them into the training set, and thereafter retraining the model with the expanded training set. The iterative active learning process, governed by parameters such as the number of iterations and batch size, demonstrates the potential to systematically enhance the resilience of DNN against adversarial threats. The proposed methodology has been investigated on several popular datasets such as the SARS-CoV-2 CT scan, MNIST, CIFAR-10, and Caltech-101, and demonstrated to be effective. Experiments illustrate that the learning framework improves the adversarial accuracies from 17.4% to 98.71% for the SARS-CoV-2 dataset, from 8.4% to 99.89% for the MNIST dataset, 1.6% to 78.84% for the CIFAR-10, and 12% to 92.92% for Caltech-101. Further, comparative analysis with several state-of-the-art methods suggests that the proposed framework offers superior defense against various attack methods and offers promising defensive mechanisms to deep neural networks. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Active learning

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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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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

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Optimal Design of a Planning and Contracting Framework to Enable Vehicle to Building and Grid Services via Demand Response

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7542-7556页

作者： Elsayed, Ahmed Abd Elaziz Saxena, Shivam Farag, Hany E. Z. Electrical Engineering and Computer Science Department Lassonde School of Engineering York University Keele Campus Toronto ON Canada Department of Electrical and Computer Engineering University of New Brunswick Fredericton NB Canada

Bidirectional electric vehicle (EV) charging enables stored energy to reduce peak loads for buildings (V2B) and the grid (V2G). However, building owners investing in V2B infrastructure while generating revenue from V2G services face challenges in planning and coordinating with EV owners due to uncertainties in their schedules and profit-sharing expectations. Additionally, misaligned building and grid peak times can create conflicts between V2B and V2G goals, which may negatively impact the building electricity bill. Unlike previous studies that used a contract-free approach for aggregating V2B and V2G, resulting in inconsistent participation, this paper proposes a novel planning and contracting framework that enables building owner to determine the optimal contract parameters with EV owners. These parameters include minimum participation time in DR events, minimum arrival State of Charge (SoC), and permitted emergency departure hours. The framework supports V2B aggregation with on-site distributed energy resources for DR and V2G services, ensuring transparency and fairness through shared profits and performance-based penalties, while compensating building electricity bills due to V2G activities. The framework is composed of a tri-stage optimization process that uses Monte Carlo Simulations to generate EV owner profit assessments, select optimal EV candidates based on charger availability, estimate contract parameters with profit/penalty sharing, and assign contracts between power system operators, building owners, and EV owners under multiple virtual scenarios. Simulation results validate the contract parameter assessment and its performance via a three day case study with real-world data sets that demonstrates net revenue generation of $209 for each EV owner and $950 for building owners during DR events with a Return on Investment (ROI) of 134.5% 130.7% for the EVs and Building owners, respectively. © 2015 IEEE.

关键词： Vehicle-to-grid

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