Connection Science最新文献_第8页

High-performance computing for static security assessment of large power systems 大型电力系统静态安全评估的高性能计算

4区计算机科学

Connection Science Pub Date : 2023-10-04 DOI: 10.1080/09540091.2023.2264537

Venkateswara Rao Kagita, Sanjaya Kumar Panda, Ram Krishan, P. Deepak Reddy, Jabba Aswanth

{"title":"High-performance computing for static security assessment of large power systems","authors":"Venkateswara Rao Kagita, Sanjaya Kumar Panda, Ram Krishan, P. Deepak Reddy, Jabba Aswanth","doi":"10.1080/09540091.2023.2264537","DOIUrl":"https://doi.org/10.1080/09540091.2023.2264537","url":null,"abstract":"Contingency analysis (CA) is one of the essential tools for the optimal design and security assessment of a reliable power system. However, its computational requirements rise with the growth of distributed generations in the interconnected power system. As CA is a complex and computationally intensive problem, it requires a fast and accurate calculation to ensure the secure operation. Therefore, efficient mathematical modelling and parallel programming are key to efficient static security analysis. This paper proposes a parallel algorithm for static CA that uses both central processing units (CPUs) and graphical processing units (GPUs). To enhance the accuracy, AC load flow is used, and parallel computation of load flow is done simultaneously, with efficient screening and ranking of the critical contingencies. We perform extensive experiments to evaluate the efficacy of the proposed algorithm. As a result, we establish that the proposed parallel algorithm with high-performance computing (HPC) computing is much faster than the traditional algorithms. Furthermore, the HPC experiments were conducted using the national supercomputing facility, which demonstrates the proposed algorithm in the context of N−1 and N−2 static CA with immense power systems, such as the Indian northern regional power grid (NRPG) 246-bus and the polish 2383-bus networks.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135591121","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

Neighbor interaction-based personalised transfer for cross-domain recommendation 基于邻居交互的跨域推荐个性化转移

4区计算机科学

Connection Science Pub Date : 2023-09-29 DOI: 10.1080/09540091.2023.2263664

Kelei Sun, Yingying Wang, Mengqi He, Huaping Zhou, Shunxiang Zhang

{"title":"Neighbor interaction-based personalised transfer for cross-domain recommendation","authors":"Kelei Sun, Yingying Wang, Mengqi He, Huaping Zhou, Shunxiang Zhang","doi":"10.1080/09540091.2023.2263664","DOIUrl":"https://doi.org/10.1080/09540091.2023.2263664","url":null,"abstract":"Mapping-based cross-domain recommendation (CDR) can effectively tackle the cold-start problem in traditional recommender systems. However, existing mapping-based CDR methods ignore data-sparse users in the source domain, which may impact the transfer efficiency of their preferences. To this end, this paper proposes a novel method named Neighbor Interaction-based Personalized Transfer for Cross-Domain Recommendation (NIPT-CDR). This proposed method mainly contains two modules: (i) an intra-domain item supplementing module and (ii) a personalised feature transfer module. The first module introduces neighbour interactions to supplement the potential missing preferences for each source domain user, particularly for those with limited observed interactions. This approach comprehensively captures the preferences of all users. The second module develops an attention mechanism to guide the knowledge transfer process selectively. Moreover, a meta-network based on users' transferable features is trained to construct personalised mapping functions for each user. The experimental results on two real-world datasets show that the proposed NIPT-CDR method achieves significant performance improvements compared to seven baseline models. The proposed model can provide more accurate and personalised recommendation services for cold-start users.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135193121","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

An efficiency control strategy of dual-motor multi-gear drive algorithm 双电机多齿轮驱动算法的高效控制策略

4区计算机科学

Connection Science Pub Date : 2023-09-27 DOI: 10.1080/09540091.2023.2249264

Lijun Xiao, Wei Liang, Jiahong Cai, Ming Wang, Jiahong Xiao, Yinyan Gong, Weigang Zhang

引用次数: 0

CPW-DICE: a novel center and pixel-based weighting for damage segmentation CPW-DICE:一种新的基于中心和像素的损伤分割加权方法

4区计算机科学

Connection Science Pub Date : 2023-09-26 DOI: 10.1080/09540091.2023.2259115

Yunus Abdi, Ömer Küllü, Mehmet Kıvılcım Keleş, Berk Gökberk

{"title":"CPW-DICE: a novel center and pixel-based weighting for damage segmentation","authors":"Yunus Abdi, Ömer Küllü, Mehmet Kıvılcım Keleş, Berk Gökberk","doi":"10.1080/09540091.2023.2259115","DOIUrl":"https://doi.org/10.1080/09540091.2023.2259115","url":null,"abstract":"Reliable evaluation of damage in vehicles is a primary concern in the insurance industry. Consequently, solutions enhanced with Artificial Intelligence (AI) have become the norm. During the assessment, precise damage segmentation plays a crucial role. Dent is a type of damage that can commonly occur in vehicles. It is difficult to pinpoint and tends to blend in with the background. This paper proposes a novel loss function to improve dent segmentation accuracy in vehicle insurance claims. Centre and Pixel-based Weighted DICE (CPW-DICE) is a loss function that performs pixel-based weighting. The CPW-DICE aims to concentrate on the centre of the dent damage to lessen faulty segmentations. CPW-DICE generates a weight mask during training by employing ground truth (GT) and prediction masks. Simultaneously, the weight mask is incorporated into DICE loss. Experiments conducted on our comprehensive internal dataset show a 3% improvement in Intersection over Union (IoU) score for three state-of-the-art (SOTA) approaches compared to DICE loss. Finally, CPW-DICE is evaluated in similar tasks to demonstrate its benefits beyond car damage segmentation.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134885411","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

Detecting susceptible communities and individuals in hospital contact networks: a model based on social network analysis 检测医院接触网络中的易感社区和个人:基于社会网络分析的模型

IF 5.3 4区计算机科学

Connection Science Pub Date : 2023-08-22 DOI: 10.1080/09540091.2023.2236810

Yixuan Yang, Sony Peng, Sophort Siet, Sadriddinov Ilkhomjon, Vilakone Phonexay, Seok-Hoon Kim, Doosoon Park

引用次数: 0

Design of advanced intrusion detection systems based on hybrid machine learning techniques in hierarchically wireless sensor networks 分层无线传感器网络中基于混合机器学习技术的高级入侵检测系统设计

IF 5.3 4区计算机科学

Connection Science Pub Date : 2023-08-22 DOI: 10.1080/09540091.2023.2246703

Gebrekiros Gebreyesus Gebremariam, J. Panda, S. Indu

引用次数: 0

Real-time reading system for pointer meter based on YolactEdge 基于YolactEdge的指针式仪表实时读数系统