IF 3.2 4区计算机科学

Journal of Mechanisms and Robotics-Transactions of the Asme Pub Date : 2025-11-01 Epub Date: 2025-08-04 DOI: 10.1115/1.4069154

Huan Liu, Qiaode Jeffrey Ge, Mark P Langer

{"title":"KINEMATIC CONVEX COMBINATIONS OF MULTIPLE POSES OF A BOUNDED PLANAR OBJECT BASED ON AN AVERAGE-DISTANCE MINIMIZING MOTION SWEEP.","authors":"Huan Liu, Qiaode Jeffrey Ge, Mark P Langer","doi":"10.1115/1.4069154","DOIUrl":"10.1115/1.4069154","url":null,"abstract":"<p><p>Convex combination of points is a fundamental operation in computational geometry. By considering rigid-body displacements as points in the image spaces of planar quaternions, quaternions and dual quaternions, respectively, the notion of convexity in Euclidean three-space has been extended to kinematic convexity in <math><mi>S</mi> <mi>E</mi> <mo>(</mo> <mn>2</mn> <mo>)</mo> <mo>,</mo> <mi>S</mi> <mi>O</mi> <mo>(</mo> <mn>3</mn> <mo>)</mo></math> , and <math><mi>S</mi> <mi>E</mi> <mo>(</mo> <mn>3</mn> <mo>)</mo></math> in the context of computational kinematic geometry. This paper deals with computational kinematic geometry of bounded planar objects rather than that of infinitely large moving spaces. In this paper, we present a new formulation for kinematic convexity based on an average-distance minimizing motion sweep of a bounded planar object. The resulting 1-DOF motion sweep between two planar poses is represented as a convex combination in the configuration space defined by <math><mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>,</mo> <mi>z</mi> <mo>)</mo></math> where <math><mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo></math> is associated with the location of the centroid of the planar object and <math><mi>z</mi> <mo>=</mo> <mtext>sin</mtext> <mspace></mspace> <mi>θ</mi></math> with <math><mi>θ</mi></math> being the angle of rotation. For three poses, a 2-DOF motion sweep is developed that not only minimizes the combined average squared distances but also attains a convex-combination representation so that existing algorithms for convex hull of points can be readily applied to the construction and analysis of kinematic convex hulls. This results in a new type of convex hull for planar kinematics such that its boundaries are defined by the average-distance minimizing sweeps of the bounded planar object.</p>","PeriodicalId":49155,"journal":{"name":"Journal of Mechanisms and Robotics-Transactions of the Asme","volume":"17 11","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12349904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PolarFusion: A multi-modal fusion algorithm for 3D object detection based on polar coordinates. PolarFusion：一种基于极坐标的三维目标检测多模态融合算法。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2025-10-01 Epub Date: 2025-06-18 DOI: 10.1016/j.neunet.2025.107704

Peicheng Shi, Runshuai Ge, Xinlong Dong, Chadia Chakir, Taonian Liang, Aixi Yang

{"title":"PolarFusion: A multi-modal fusion algorithm for 3D object detection based on polar coordinates.","authors":"Peicheng Shi, Runshuai Ge, Xinlong Dong, Chadia Chakir, Taonian Liang, Aixi Yang","doi":"10.1016/j.neunet.2025.107704","DOIUrl":"10.1016/j.neunet.2025.107704","url":null,"abstract":"<p><p>Existing 3D object detection algorithms that fuse multi-modal sensor information typically operate in Cartesian coordinates, which can lead to asymmetrical feature information and uneven attention across multiple views. To address this, we propose PolarFusion, the first multi-modal fusion BEV object detection algorithm based on polar coordinates. We designed three specialized modules for this approach: the Polar Region Candidates Generation Module, the Polar Region Query Generation Module, and the Polar Region Information Fusion Module. In the Polar Region Candidates Generation Module, we use a region proposal-based segmentation method to remove irrelevant areas from images, enhancing PolarFusion's information processing efficiency. These segmented image regions are then integrated into the point cloud segmentation task, addressing feature misalignment during fusion. The Polar Region Query Generation Module leverages prior information to generate high-quality target queries, reducing the time spent learning from initialization. For the Polar Region Information Fusion Module, PolarFusion employs a simple yet efficient self-attention to merge internal information from images and point clouds. This captures long-range dependencies in image texture information while preserving the precise positional data from point clouds, enabling more accurate BEV object detection. We conducted extensive experiments on challenging BEV object detection datasets. Both qualitative and quantitative results demonstrate that PolarFusion achieves an NDS of 76.1% and mAP of 74.5% on the nuScenes test set, significantly outperforming Cartesian-based methods. This advancement enhances the environmental perception capabilities of autonomous vehicles and contributes to the development of future intelligent transportation systems. The code will be released at https://github.com/RunshuaiGe/PolarFusion.git.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"190 ","pages":"107704"},"PeriodicalIF":6.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Towards a robust android malware detection model using explainable deep learning 使用可解释的深度学习实现强大的android恶意软件检测模型

IF 3.7 2区计算机科学

Journal of Information Security and Applications Pub Date : 2025-08-17 DOI: 10.1016/j.jisa.2025.104191

Masumeh Najibi, Amir Jalaly Bidgoly

{"title":"Towards a robust android malware detection model using explainable deep learning","authors":"Masumeh Najibi, Amir Jalaly Bidgoly","doi":"10.1016/j.jisa.2025.104191","DOIUrl":"10.1016/j.jisa.2025.104191","url":null,"abstract":"<div><div>The growing threat of Android malware demands effective and trustworthy detection mechanisms. This paper investigates the robustness of explainable deep learning models for Android malware detection and classification using network flow features. Three deep learning architectures — DNN, 1D-CNN, and BiLSTM — were evaluated on the CICAndMal2017 dataset, with BiLSTM achieving the best performance on unseen samples. Model decisions were analyzed using LIME and SHAP to identify influential and potentially manipulable features. Using domain knowledge, features were categorized based on their resistance to evasion, with emphasis on robust indicators such as TCP flags and initial window sizes. Retraining models using only these robust features resulted in minimal performance degradation while significantly improving explainability and resilience to evasion. On the unseen dataset, the BiLSTM model achieved a 70.90% F1-score for malware detection and 62.84% for classification, with AUC scores of 73.39% and 79.96%, respectively. After removing weak features, the retrained detection model maintained a 71% F1-score, and the classification model achieved 57%, demonstrating that robustness can be improved without major loss in performance. These results highlight the potential for transparent and dependable AI-driven cybersecurity solutions, particularly in adversarial settings where evasion is common. By emphasizing explainability and robustness, this work contributes towards models that balance performance with trust in evolving threat landscapes.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"93 ","pages":"Article 104191"},"PeriodicalIF":3.7,"publicationDate":"2025-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Capacity-aware resource provisioning by prioritizing the highest capacity band in next-generation multi-band elastic optical networks 在下一代多波段弹性光网络中，优先考虑最高容量频带的资源分配

IF 2.7 3区计算机科学

Optical Fiber Technology Pub Date : 2025-08-17 DOI: 10.1016/j.yofte.2025.104362

Ruchi Srivastava , Yatindra Nath Singh

{"title":"Capacity-aware resource provisioning by prioritizing the highest capacity band in next-generation multi-band elastic optical networks","authors":"Ruchi Srivastava , Yatindra Nath Singh","doi":"10.1016/j.yofte.2025.104362","DOIUrl":"10.1016/j.yofte.2025.104362","url":null,"abstract":"<div><div>The emergence of next-generation multi-band elastic optical networks (MB-EONs) marks a paradigm shift in addressing the escalating bandwidth demands of future communication systems. While conventional C-band provisioning is nearing its spectral saturation point, expanding into additional spectral bands such as L, S, and E bands provides an avenue for scalable capacity enhancement. However, utilization of these heterogeneous spectral resources necessitates efficient provisioning strategies. This paper proposes a capacity aware-resource provisioning technique that prioritizes spectral bands based on their spectral capacity, favoring the highest-capacity band during allocation unlike traditional provisioning methods in the literature which prioritize the C-band for resource provisioning which has least resource availability. Through comprehensive simulation-based evaluation, we show that band sequencing strategies can substantially affect the network performance. Simulations have been conducted on NSF and USNET topologies by considering the following performance metrics: request blocking probability (RBP), bandwidth blocking probability (BBP), spectral efficiency (SE), total bits transmitted, band utilization distribution (BUD) and band resource allocation time. Simulation results demonstrate that our strategy significantly outperforms baseline models in terms of blocking probabilities with a least reduction of RBP and BBP by 0.6% and 2.4% respectively.</div></div>","PeriodicalId":19663,"journal":{"name":"Optical Fiber Technology","volume":"94 ","pages":"Article 104362"},"PeriodicalIF":2.7,"publicationDate":"2025-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858145","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

Nav2Scene: Navigation-driven fine-tuning for robot-friendly scene generation Nav2Scene：用于机器人友好场景生成的导航驱动微调

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-08-17 DOI: 10.1016/j.gmod.2025.101287

Bowei Jiang , Tongyuan Bai , Peng Zheng , Tieru Wu , Rui Ma

{"title":"Nav2Scene: Navigation-driven fine-tuning for robot-friendly scene generation","authors":"Bowei Jiang , Tongyuan Bai , Peng Zheng , Tieru Wu , Rui Ma","doi":"10.1016/j.gmod.2025.101287","DOIUrl":"10.1016/j.gmod.2025.101287","url":null,"abstract":"<div><div>The integration of embodied intelligence in indoor scene synthesis holds significant potential for future interior design applications. Nevertheless, prevailing methodologies for indoor scene synthesis predominantly adhere to data-driven learning paradigms. Despite achieving photorealistic 3D renderings through such approaches, current frameworks systematically neglect to incorporate agent-centric functional metrics essential for optimizing navigational topology and task-oriented interactivity in embodied AI systems like service robotics platforms or autonomous domestic assistants. For example, poorly arranged furniture may prevent robots from effectively interacting with the environment, and this issue cannot be fully resolved by merely introducing prior constraints. To fill this gap, we propose Nav2Scene, a novel plug-and-play fine-tuning mechanism that can be deployed on existing scene generators to enhance the suitability of generated scenes for efficient robot navigation. Specifically, we first introduce path planning score (PPS), which is defined based on the results of the path planning algorithm and can be used to evaluate the robot navigation suitability of a given scene. Then, we pre-compute the PPS of 3D scenes from existing datasets and train a ScoreNet to efficiently predict the PPS of the generated scenes. Finally, the predicted PPS is used to guide the fine-tuning of existing scene generators and produce indoor scenes with higher PPS, indicating improved suitability for robot navigation. We conduct experiments on the 3D-FRONT dataset for different tasks including scene generation, completion and re-arrangement. The results demonstrate that by incorporating our Nav2Scene mechanism, the fine-tuned scene generators can produce scenes with improved navigation compatibility for home robots, while maintaining superior or comparable performance in terms of scene quality and diversity.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101287"},"PeriodicalIF":2.2,"publicationDate":"2025-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Machine Learning Approach of Text Classification for High- and Low-Resource Languages 高资源语言和低资源语言文本分类的机器学习方法

IF 1.7 4区计算机科学

Computational Intelligence Pub Date : 2025-08-16 DOI: 10.1111/coin.70114

Muhammad Owais Raza, Naeem Ahmed Mahoto, Asadullah Shaikh, Nazia Pathan, Hani Alshahrani, M. A. Elmagzoub

{"title":"A Machine Learning Approach of Text Classification for High- and Low-Resource Languages","authors":"Muhammad Owais Raza, Naeem Ahmed Mahoto, Asadullah Shaikh, Nazia Pathan, Hani Alshahrani, M. A. Elmagzoub","doi":"10.1111/coin.70114","DOIUrl":"https://doi.org/10.1111/coin.70114","url":null,"abstract":"<div>\u0000 \u0000 <p>A large amount of data have been published online in textual format for the last decade because of the advancement of information and communication technologies. This is an open challenge to organize and classify large amounts of textual data automatically, especially for a language that has limited resources available online. In this study, two types of approaches are adopted for experiments. First one is a traditional strategy that uses six (06) classical state-of-the-art classification models (1. decision tree (DT), 2. logistic regression (LR), 3. support vector machine (SVM), 4. k-nearest neighbour (k-NN), 5. Naive Bayes (NB), and 6. random forest (RF)) along with two (02) ensemble methods (1. Adaboost and 2. gradient boosting (GB)) and second modeling technique is our proposed voting based ensembling scheme. Models are trained on a 75-25 split where 75% of data is used for training and 25% for testing. The evaluation of the classification models is carried out based on accuracy, precision, recall, and F1-score indexes. The experimental outcomes witnessed that for the traditional approach, gradient boosting outperformed for the limited resource language with 98.08% F1-score, while SVM performed better (97.34% F1-score) for the resource-rich language.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144853811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Research on Robot Target Classification and Localization Based on Improved Mask R-CNN 基于改进掩模R-CNN的机器人目标分类与定位研究

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-08-16 DOI: 10.1002/cpe.70247

Xinghua Wang, Yuting Tang, Xiaolong Liu, Jie Wang, Jiawen Cao, Ruijin Sun

{"title":"Research on Robot Target Classification and Localization Based on Improved Mask R-CNN","authors":"Xinghua Wang, Yuting Tang, Xiaolong Liu, Jie Wang, Jiawen Cao, Ruijin Sun","doi":"10.1002/cpe.70247","DOIUrl":"https://doi.org/10.1002/cpe.70247","url":null,"abstract":"<div>\u0000 \u0000 <p>The small workpieces are easily missed during detection, and the irregular workpieces are difficult to recognize and segment effectively by traditional detection algorithms in the industrial field. The traditional target detection algorithms have problems such as low accuracy and poor generalization performance. This paper proposes a robot target recognition and positioning method based on the improved Mask R-CNN. First, the network structure is designed to add a Convolutional Block Attention Module (CBAM) in the backbone, replace the Feature Pyramid Network (FPN) structure used in the original model of Mask R-CNN with a Path Aggregation Network (PAN) structure, and increase the receptive field to enhance the recognition of small target objects and the segmentation of multi-objects. Second, after classification is completed, according to the segmentation information, the output is augmented with center coordinates and rotation angle information. Finally, comparative experiments are conducted in the COCO dataset and the industrial part dataset to verify the effectiveness and practicality of the proposed algorithm. The experimental results show that the improved model achieves an AP<sub>50</sub> of 60.6 in the COCO dataset and 99.4 in the industrial parts dataset. Additionally, in single-object and multi-object grasping experiments, the grasping accuracy is 91.5% and 85.3%, respectively.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 21-22","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144853813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Cloudhive: A Cloud-Based Framework for Smart Grid Co-Simulation, Data, and Communication Cloudhive：基于云的智能电网协同仿真、数据和通信框架

IF 1.5 4区计算机科学

Concurrency and Computation-Practice & Experience Pub Date : 2025-08-16 DOI: 10.1002/cpe.70238

Kenneth B. Kent, Mengbing Zhou, Gabriel Adeyemo, Yang Wang

{"title":"Cloudhive: A Cloud-Based Framework for Smart Grid Co-Simulation, Data, and Communication","authors":"Kenneth B. Kent, Mengbing Zhou, Gabriel Adeyemo, Yang Wang","doi":"10.1002/cpe.70238","DOIUrl":"https://doi.org/10.1002/cpe.70238","url":null,"abstract":"<div>\u0000 \u0000 <p>The integration of renewable energy has driven the need for smart grid frameworks that enable efficient co-simulation, data management, and secure communication. This paper introduces CloudHive, a cloud-native framework designed to address these challenges by unifying large-scale power-network co-simulation, real-time data communication, and big data analytics in a single modular architecture. Unlike existing co-simulation tools or data platforms that operate in isolation, CloudHive uniquely enables bidirectional interaction between simulation environments (e.g., OpenDSS for power systems, OMNeT++ for communication networks) and real-world smart grids, supported by message-oriented middleware (RabbitMQ, Apache Kafka) for low-latency data exchange and Kubernetes for dynamic scalability. We evaluate CloudHive's accuracy, scalability, and usability through three representative case studies. The results show that CloudHive achieves high accuracy, performs well in real-world scenarios, and scales efficiently with growing workloads in cloud environments.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 21-22","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144853823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Improved biometric data protection: Bounded brute-force strategy for maximum likelihood decoding 改进的生物特征数据保护：最大似然解码的有限蛮力策略

IF 3.7 2区计算机科学

Journal of Information Security and Applications Pub Date : 2025-08-16 DOI: 10.1016/j.jisa.2025.104182

Wen Khai Lai, Ming Jie Lee, Kai Lin Chia, Yen-Lung Lai

{"title":"Improved biometric data protection: Bounded brute-force strategy for maximum likelihood decoding","authors":"Wen Khai Lai, Ming Jie Lee, Kai Lin Chia, Yen-Lung Lai","doi":"10.1016/j.jisa.2025.104182","DOIUrl":"10.1016/j.jisa.2025.104182","url":null,"abstract":"<div><div>Conventional biometric data protection schemes often struggle to provide strong and reliable security guarantees after transformation, largely due to the noise amplification introduced during quantization. This amplified noise can distort the relationship between the protected and original biometric data, creating a gap between the claimed security of the protected representation and the actual security of the raw input. Such a mismatch risks overestimating system robustness and may expose the scheme to vulnerabilities such as pre-image attacks. To address this challenge, we propose a novel secure sketch construction that integrates Locality-Sensitive Hashing (LSH) with a bounded brute-force strategy for maximum likelihood decoding. Our method achieves asymptotically optimal error tolerance while preserving the statistical alignment of inter- and intra-class variability across both unprotected and protected domains. This alignment enables accurate key recovery and enhances resistance to pre-image and decoding attacks. Comprehensive experiments demonstrate that our method consistently outperforms existing approaches in both security and robustness to biometric variability, offering a practical and theoretically grounded solution for biometric authentication.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"93 ","pages":"Article 104182"},"PeriodicalIF":3.7,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Fully distributed constrained optimization algorithm over unbalanced network and its application to smart grids 不平衡网络的全分布约束优化算法及其在智能电网中的应用

IF 2.5 3区计算机科学

Systems & Control Letters Pub Date : 2025-08-16 DOI: 10.1016/j.sysconle.2025.106211

Xiasheng Shi , Lei Xu

{"title":"Fully distributed constrained optimization algorithm over unbalanced network and its application to smart grids","authors":"Xiasheng Shi , Lei Xu","doi":"10.1016/j.sysconle.2025.106211","DOIUrl":"10.1016/j.sysconle.2025.106211","url":null,"abstract":"<div><div>In practical engineering, due to the limited bandwidth and other physical constraints, directed network communication topology among agents is more common. This paper studies the distributed constrained optimization problem over the weight-unbalanced directed network, considering local inequality and coupled equality constraints. First, a distributed consensus scheme with a time-based generator is provided to estimate the left eigenvector of the Laplacian matrix. Building on this estimator, an adaptive proportional–integral-based gradient flow scheme is designed to solve the coupled constraint, introducing a time-varying control parameter to remove the requirement of the Laplacian matrix’s eigenvalues. Subsequently, a fully distributed primal–dual optimization method is proposed based on KKT conditions for the constrained optimization problem with a nonsmooth objective function. The optimality and convergence analysis are conducted through Lyapunov theory under the strongly connected and weight-unbalanced directed network. Finally, the established method is applied to the economic dispatch problem in smart grids.</div></div>","PeriodicalId":49450,"journal":{"name":"Systems & Control Letters","volume":"204 ","pages":"Article 106211"},"PeriodicalIF":2.5,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858208","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

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