Computers & Electrical Engineering最新文献_第7页

AMOUE: Adaptive modified optimized unary encoding method for local differential privacy data preservation AMOUE：用于局部差分隐私数据保护的自适应修正优化单值编码方法

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-10-25 DOI: 10.1016/j.compeleceng.2024.109791

Tianchong Gao , Hailong Fu , Shunwei Wang , Niu Zhang

{"title":"AMOUE: Adaptive modified optimized unary encoding method for local differential privacy data preservation","authors":"Tianchong Gao , Hailong Fu , Shunwei Wang , Niu Zhang","doi":"10.1016/j.compeleceng.2024.109791","DOIUrl":"10.1016/j.compeleceng.2024.109791","url":null,"abstract":"<div><div>Deep learning has gained popularity recently, and privacy concerns have increased simultaneously. Adversaries gain unauthorized access to the private training data and model parameters through model inversion attacks and membership inference attacks. To address these problems, researchers proposed several defense mechanisms based on a decisive privacy criterion - Local Differential Privacy (LDP). Although the LDP-based deep learning model preserves data privacy well, its strict privacy criterion sometimes affects accuracy. It is a non-trivial task to intelligently add noise that satisfies LDP and minimizes its impact on learning results. This paper proposes a novel LDP-based deep learning method named AMOUE with a novel encoding technique. Because input data has different proportions of 1s and 0s, adding fixed noise to 1s and 0s may result in unnecessary data utility loss. The proposed encoding method dynamically adjusts the noise added on 1s and 0s according to the input data distribution. Theoretical analysis demonstrates that AMOUE has a lower error expectation and variance. Experiments on real-world datasets show that AMOUE outperforms other LDP-based mechanisms in deep learning classification accuracy.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109791"},"PeriodicalIF":4.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust load frequency control in interval power systems via reduced-order generalized active disturbance rejection control 通过降阶广义有源干扰抑制控制实现区间电力系统的鲁棒负载频率控制

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-10-23 DOI: 10.1016/j.compeleceng.2024.109788

Safiullah, Yogesh V. Hote

{"title":"Robust load frequency control in interval power systems via reduced-order generalized active disturbance rejection control","authors":"Safiullah, Yogesh V. Hote","doi":"10.1016/j.compeleceng.2024.109788","DOIUrl":"10.1016/j.compeleceng.2024.109788","url":null,"abstract":"<div><div>Abrupt load changes, structural discrepancies, and parametric uncertainties cause degraded performance of the high-order power systems. This situation creates a problematic endeavor while analyzing the performance of such high-order systems. Hence, a simple and efficient lower-order control methodology can be deployed to sort out the issues related to load frequency control (LFC) in such systems. This study resolves the LFC problem in parametric bounded power systems by developing a worst-case reduced-order generalized active disturbance rejection control (WRGADRC) method. The core concept of the proposed technique entails that a controller will perform well in nominal scenarios if it performs satisfactorily in worst-case conditions. Therefore, an interval system’s worst-case reduced-order model is first obtained from its different uncertain models; the reduced order controller is then designed using the GADRC technique. The proposed scheme is rigorously validated on various parametric bounded minimum and non-minimum phase single-area and multi-area power systems, instilling confidence in its ability to achieve minimum frequency deviation in multiple scenarios. The supremacy of the proposed scheme is highlighted over some well-established control techniques in the literature related to the LFC problem.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109788"},"PeriodicalIF":4.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A load frequency control strategy based on double deep Q-network and upper confidence bound algorithm of multi-area interconnected power systems 基于双深 Q 网络和多区域互联电力系统置信上限算法的负载频率控制策略

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-10-23 DOI: 10.1016/j.compeleceng.2024.109778

Jing Zhang , Feifei Peng , Lulu Wang , Yang Yang , Yingna Li

{"title":"A load frequency control strategy based on double deep Q-network and upper confidence bound algorithm of multi-area interconnected power systems","authors":"Jing Zhang , Feifei Peng , Lulu Wang , Yang Yang , Yingna Li","doi":"10.1016/j.compeleceng.2024.109778","DOIUrl":"10.1016/j.compeleceng.2024.109778","url":null,"abstract":"<div><div>The reinforcement learning (RL)-based generation control strategies have been widely studied to address the limited adaptability of traditional automatic generation control (AGC) strategies to the load disturbance problem resulting from heterogeneous energy sources. To improve the control accuracy of the RL-based strategy in load frequency control (LFC), a double deep Q-network combined with an upper confidence bound (DDQN-UCB)-based strategy is designed to solve the problem of agent decision-making in a nonlinear environment. Firstly, the area control error (ACE) and control performance standard 1 (CPS1) of the LFC power system are considered in the design of the RL reward function. Secondly, the actual and estimated Q-values are calculated using the Q-network and the target Q-network combined with the reward value. Thirdly, the deviation loss of the two Q-values is calculated, and the network is updated based on the loss value using gradient descent. Finally, the UCB algorithm is introduced to equalize the frequency of being selected for each action during the random exploration of the actions, and the agent uses the greedy algorithm in combination with the UCB algorithm to select a power-compensated control action to send to the environment. In this paper, the IEEE multi-area LFC power system is used as an experimental validation model. A comparison of the proposed RL control algorithm with five other algorithms revealed that the pre-learning convergence accuracy was improved by 57.5%. Furthermore, the LFC effectiveness test demonstrated that the DDQN-UCB control strategy enhances LFC accuracy while simultaneously stabilizing the power exchange of the inter-area tie-line to within 1.8972 MW, thereby maintaining the stability of the power system.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109778"},"PeriodicalIF":4.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Diabetic Retinopathy grading using Feature Fusion for limited data samples 利用特征融合改进有限数据样本的糖尿病视网膜病变分级

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-10-22 DOI: 10.1016/j.compeleceng.2024.109782

K Ashwini, Ratnakar Dash

{"title":"Improving Diabetic Retinopathy grading using Feature Fusion for limited data samples","authors":"K Ashwini, Ratnakar Dash","doi":"10.1016/j.compeleceng.2024.109782","DOIUrl":"10.1016/j.compeleceng.2024.109782","url":null,"abstract":"<div><div>Early detection of Diabetic Retinopathy (DR) and its grading has been a growing demand among researchers in this community. Computer-aided diagnostic (CAD) systems have the potential to enhance the sensitivity and effectiveness of early diagnoses, benefiting ophthalmic specialists by offering additional insights for more efficient treatment options. The proposed study addresses the challenges of improved detection of mild stage and the limited number of samples with fewer parameters. Fundus images are initially pre-processed for this task using resizing, augmentation and oversampling. Oversampling is employed to guarantee the balanced inclusion of images from every grade category throughout the training stage. The proposed approach utilizes a Convolutional Neural Network (CNN) to extract texture and vessel features separately from the fundus images. This methodology exploited Local Binary Pattern (LBP) for improved texture features before applying CNN. Similarly, we utilized Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the blood vessels of the fundus images, enabling the extraction of relevant features using CNN. The extracted features are combined and classified using fully connected layers. The proposed approach is validated using standard datasets such as IDRiD, APTOS, DDR, and EyePACS with limited samples. The experimental results demonstrate that the proposed model in this research outperforms state-of-the-art models across all standard performance metrics, with classification accuracies of 92.46%, 98.08%, 95.66% and 88.84%.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109782"},"PeriodicalIF":4.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fractional order PI-PD controller design for time delayed processes 时延过程的分数阶 PI-PD 控制器设计

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-10-22 DOI: 10.1016/j.compeleceng.2024.109776

Erdal Cokmez, Ibrahim Kaya

引用次数: 0

A reinforcement learning-based optimization method for task allocation of agricultural multi-robots clusters 基于强化学习的农业多机器人集群任务分配优化方法

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-10-22 DOI: 10.1016/j.compeleceng.2024.109752

Zaiwang Lu , Yancong Wang , Feng Dai , Yike Ma , Long Long , Zixu Zhao , Yucheng Zhang , Jintao Li

{"title":"A reinforcement learning-based optimization method for task allocation of agricultural multi-robots clusters","authors":"Zaiwang Lu , Yancong Wang , Feng Dai , Yike Ma , Long Long , Zixu Zhao , Yucheng Zhang , Jintao Li","doi":"10.1016/j.compeleceng.2024.109752","DOIUrl":"10.1016/j.compeleceng.2024.109752","url":null,"abstract":"<div><div>The Agricultural multi-robot task allocation (AMRTA) can allocate the optimal operation sequence for the cluster of agricultural robots and improve overall operational efficiency, which is an important research direction for the development of intelligent agriculture. In this paper, we first analyzed the practical requirements of multi-robot task allocation in agriculture and reformulate it as Node Workload-Constrained Multi Traveling Salesman Problem (NWC-MTSP), aiming to minimize the maximum operating time of sub-robots while ensuring a balanced distribution of workload as much as possible. Then, we implemented path planning algorithm required for task allocation and constructed an objective function based on it; we also constructed a graph structure containing workloads of nodes, used graph neural networks to obtain node feature information, and propose a Reinforcement Learning-based Attention Mechanism Policy Optimization Network (NWC-APONet) method to find the optimal allocation scheme. Finally, our model evaluated using real agricultural datasets, i.e., the TSPLIB public dataset and random datasets. Experiments results demonstrate that NWC-APONet achieves superior task allocation, which prove our model’s practical applicability and effectiveness in AMRTA.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109752"},"PeriodicalIF":4.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Anonymous quantum-safe secure and authorized communication protocol under dynamic identities for Internet of Drones 无人机互联网动态身份下的匿名量子安全授权通信协议

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-10-22 DOI: 10.1016/j.compeleceng.2024.109774

Dharminder Chaudhary , Cheng-Chi Lee

{"title":"Anonymous quantum-safe secure and authorized communication protocol under dynamic identities for Internet of Drones","authors":"Dharminder Chaudhary , Cheng-Chi Lee","doi":"10.1016/j.compeleceng.2024.109774","DOIUrl":"10.1016/j.compeleceng.2024.109774","url":null,"abstract":"<div><div>The Internet of Drones (IoD) refers to the integration of unmanned aerial vehicles (UAVs) into the broader Internet of Things (IoT) ecosystem. This connection enables a wide range of applications and uses. An authenticated key agreement for the Internet of Drones (IoD) allows mobile device owners to establish a connection with a group of drones remotely for safe and effective services including supply delivery, sending photos and data from surveillance, and other communication services. The exchange of sensitive information through open channel, such as the internet, comes with challenges in terms of data protection and authentication. The invention of Shor’s technique, though, creates complications for authorized and secure communication in the era of highly advanced quantum computers. Therefore, we have proposed a secure authenticated key agreement the Internet of Drones (IoD) based on lattice assumption called ”Ring Learning With Error” on lattices. The RLWE operates on algebraic structures known as polynomial rings, which permits faster and space-efficient cryptographic operations. This efficiency is especially beneficial for limited storage devices (IoD/IoT). This framework is able to withstand quantum attacks, and it is suitable for low computation devices. This protocol provides anonymous communication for both user and drone. Additionally, this protocol ensures user privacy, from breaking session key security, or from withstanding impersonation attacks, and enables mutual authentication, and it utilizes dynamic identities and ensures freshness of messages. The performance analysis indicates that the proposed system performs better than existing state-of-the-art solutions when tested on benchmark datasets using various evaluation metrics.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109774"},"PeriodicalIF":4.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BTTAS: Blockchain-based Two-Level Transferable Authentication Scheme for V2I communication in VANET BTTAS：基于区块链的两级可转移验证方案，用于 VANET 中的 V2I 通信

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-10-21 DOI: 10.1016/j.compeleceng.2024.109767

Divya Rani, Sachin Tripathi

{"title":"BTTAS: Blockchain-based Two-Level Transferable Authentication Scheme for V2I communication in VANET","authors":"Divya Rani, Sachin Tripathi","doi":"10.1016/j.compeleceng.2024.109767","DOIUrl":"10.1016/j.compeleceng.2024.109767","url":null,"abstract":"<div><div>The progress of the Intelligent Transport System has significantly enhanced vehicle communication with both other vehicles and Road Side Units. This has become crucial due to the necessity for highly accurate information transmission while vehicles operate at high speeds. Additionally, the escalating vehicle count demands heightened processing speed, minimized superfluous computation, reduced data transmission delays, and decentralized storage solutions. Therefore, the proposed work involves a Blockchain-based Two-level Transferable Authentication Scheme (BTTAS) for secure V2I communication in Vehicular ad hoc networks. Unlike existing approaches that rely on centralized frameworks, the suggested model establishes a distributed environment utilizing a Consortium Blockchain furnished with a dedicated communication channel, ensuring the utmost confidentiality. Furthermore, a two-tier transferable authentication mechanism effectively curtails extraneous computations on the vehicles’ end. The Consortium Blockchain is implemented using the Hyperledger Fabric and its performance evaluation is conducted via Hyperledger Caliper. There is an ECC-based protocol for secure communication. The proposed work includes a ROR model-based Formal Analysis, simulation using the Scyther tool, and Informal Analysis. Additionally, by analyzing blockchain performance with different transaction volumes and rates, along with comparative analysis, the proposed work demonstrates enhanced effectiveness and security.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109767"},"PeriodicalIF":4.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wavelet kernel large margin distribution machine-based regression for modelling the river suspended sediment load 基于小波核大边缘分布机的河流悬浮泥沙负荷建模回归方法

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-10-21 DOI: 10.1016/j.compeleceng.2024.109783

Deepak Gupta , Barenya Bikash Hazarika , Mohanadhas Berlin

{"title":"Wavelet kernel large margin distribution machine-based regression for modelling the river suspended sediment load","authors":"Deepak Gupta , Barenya Bikash Hazarika , Mohanadhas Berlin","doi":"10.1016/j.compeleceng.2024.109783","DOIUrl":"10.1016/j.compeleceng.2024.109783","url":null,"abstract":"<div><div>Estimating the suspended sediment load (SSL) in rivers is among the key challenges in rivers. The major reason is that the daily river SSL data may contain non-linear components. Therefore, the traditional models face difficulty in handling the nonlinearity in the datasets. Very recently, a large margin distribution machine-based regression (LDMR) was proposed in the spirit of the large margin distribution machine (LDM). LDMR uses the Gaussian kernel for the selection of nonlinear kernels and tries to reduce the quadratic loss function and insensitive loss function concurrently. Wavelet kernels are very effective in approximating any arbitrary non-linear functions. To realize the benefit of wavelet kernel in LDMR, this paper suggests two novel wavelet kernel-based LDMR models as Morlet kernelized LDMR (MKLDMR) and Mexican hat kernelized LDMR (MHKLDMR) for river SSL estimation. The experiments were performed on a few SSL datasets which were gathered from the Tawang Chu River, India. Further, these models were also applied to a few artificially generated datasets and some real-world datasets. To validate the efficacy of MKLDMR and MHKLDMR, their generalization performance was collated with support vector regression (SVR), twin SVR (TSVR), random vector functional link without direct link (RVFLwoDL), iterative-based Lagrangian twin parametric insensitive SVR (ILTPISVR), robust support vector quantile regression (RSVQR), neuro fuzzy RVFL (NF-RVFL), ensemble deep RVFL (edRVFL) and LDMR. The experimental outcomes on the artificial datasets, real-world datasets and SSL datasets of the MKLDMR and MHKLDMR models imply the usability and effectiveness of the proposed models for SSL prediction.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109783"},"PeriodicalIF":4.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of overhead line glass insulator condition using dual function device and deep learning approach 利用双功能装置和深度学习方法检测架空线路玻璃绝缘子状况

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-10-21 DOI: 10.1016/j.compeleceng.2024.109764

Ali Ahmed Ali Salem , Kwan Yiew Lau , Ahmed Abu-Saida

引用次数: 0