Int. J. Comput. Commun. Control最新文献

IoT Embedded Smart Monitoring System with Edge Machine Learning for Beehive Management 用于蜂巢管理的边缘机器学习物联网嵌入式智能监控系统

Int. J. Comput. Commun. Control Pub Date : 2024-07-01 DOI: 10.15837/ijccc.2024.4.6632

Mihai Doinea, Ioana Trandafir, Cristian Toma, Marius Popa, Alin Zamfiroiu

引用次数: 0

DICOMIST: An methodology for Performing Distributed Computing in Heterogeneous ad hoc Networks DICOMIST：在异构特设网络中执行分布式计算的方法论

Int. J. Comput. Commun. Control Pub Date : 2024-07-01 DOI: 10.15837/ijccc.2024.4.6526

Alejandro Velazquez-Mena, Hector Benitez-Perez, Rita C. Rodríguez-Martínez, Ricardo F. Villarreal-Martínez

{"title":"DICOMIST: An methodology for Performing Distributed Computing in Heterogeneous ad hoc Networks","authors":"Alejandro Velazquez-Mena, Hector Benitez-Perez, Rita C. Rodríguez-Martínez, Ricardo F. Villarreal-Martínez","doi":"10.15837/ijccc.2024.4.6526","DOIUrl":"https://doi.org/10.15837/ijccc.2024.4.6526","url":null,"abstract":"The Internet of Things (IoT) has emerged as a cornerstone technology, transforming how we interact with our surroundings. Despite their widespread adoption, IoT devices encounter challenges related to processing capabilities and connectivity, frequently necessitating the delegation of tasks to remote cloud servers. This offloading, essential for enhancing user experience, poses challenges, particularly for latency-sensitive applications. Edge-centric paradigms like fog and mist computing have emerged to address these challenges, bringing computational resources closer to end-users. However, efficiently managing task offloading in dynamic IoT environments remains a complex issue. This paper introduces DIstributed COmputing for MIST (dicomist), a methodology designed to facilitate task offloading in IoT settings. Dicomist utilizes wireless mesh networks to organize mobile nodes, employing clustering and classification techniques. Tasks are treated as consensus problems, enabling distributed computation among selected nodes. Real-world experiments demonstrate dicomist’s effectiveness, underscoring its potential to enhance task offloading in IoT environments.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"80 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141714996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multi-attribute Decision-making Method for Interval Rough Number Information System Considering Distribution Types 考虑分布类型的区间粗略数信息系统多属性决策方法

Int. J. Comput. Commun. Control Pub Date : 2024-07-01 DOI: 10.15837/ijccc.2024.4.6633

Hongmei Liu, Shizhou Weng

引用次数: 0

Fault Diagnosis and Localization of Transmission Lines Based on R-Net Algorithm Optimized by Feature Pyramid Network 基于特征金字塔网络优化的 R 网算法的输电线路故障诊断与定位

Int. J. Comput. Commun. Control Pub Date : 2024-07-01 DOI: 10.15837/ijccc.2024.4.6608

Chunmei Zhang, Xingque Xu, Silin Liu, Yongjian Li, Jiefeng Jiang

{"title":"Fault Diagnosis and Localization of Transmission Lines Based on R-Net Algorithm Optimized by Feature Pyramid Network","authors":"Chunmei Zhang, Xingque Xu, Silin Liu, Yongjian Li, Jiefeng Jiang","doi":"10.15837/ijccc.2024.4.6608","DOIUrl":"https://doi.org/10.15837/ijccc.2024.4.6608","url":null,"abstract":"Timely fault diagnosis and localization of transmission lines is crucial for ensuring the reliable operation of increasingly complex power systems. This study proposes an optimized R-Net algorithm based on a feature pyramid network (FPN) and densely connected convolutional network (D-Net) for transmission line fault diagnosis and localization. The R-Net network is enhanced by reshaping the anchor points using an improved K-means algorithm and incorporating an FPN for multi-scale feature extraction. The backbone network is further optimized using D-Net to strengthen inter-layer connections and improve feature reuse. Experimental results demonstrate that the optimized R-Net achieves an overall average accuracy of 0.64, outperforming the original network by 1.30%. The accuracy improvement is particularly significant for ground wire defects (2.40%). The D-Net-based R-Net, despite having fewer parameters, maintains high accuracy (0.6502). Compared to other object detection algorithms, such as YOLO-v3 and Faster R-CNN, the optimized R-Net exhibits superior performance in terms of mean average precision (15.58% and 2.45% higher, respectively) and parameter efficiency (17M vs. 38M and 81M). Considering both performance and speed, the optimized R-Net achieves a processing rate of 10.5 frames per second. This study provides an efficient and accurate tool for transmission line fault diagnosis and localization, with significant practical implications for power system operation and maintenance.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"57 S8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141697890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Holiday Peak Load Forecasting Using Grammatical Evolution-Based Fuzzy Regression Approach 使用基于语法进化的模糊回归方法预测节假日高峰负荷

Int. J. Comput. Commun. Control Pub Date : 2024-07-01 DOI: 10.15837/ijccc.2024.4.6611

Guo Li, Xiang Hu, Shuyi Chen, Kaixuan Chang, Peiqi Li, Yujue Wang

{"title":"Holiday Peak Load Forecasting Using Grammatical Evolution-Based Fuzzy Regression Approach","authors":"Guo Li, Xiang Hu, Shuyi Chen, Kaixuan Chang, Peiqi Li, Yujue Wang","doi":"10.15837/ijccc.2024.4.6611","DOIUrl":"https://doi.org/10.15837/ijccc.2024.4.6611","url":null,"abstract":"Peak load forecasting plays an important role in electric utilities. However, the daily peak load forecasting problem, especially for holidays, is fuzzy and highly nonlinear. In order to address the nonlinearity and fuzziness of the holiday load behaviors, a grammatical evolution-based fuzzy regression approach is proposed in this paper. The proposed hybrid approach is based on the theorem that fuzzy polynomial regression can model all fuzzy functions. It employs the rules of the grammatical evolution to generate fuzzy nonlinear structures in polynomial form. Then, a two-stage fuzzy regression approach is used to determine the coefficients and calculate the fitness of the fuzzy functions. An artificial bee colony algorithm is used as the evolution system to update the elements of the grammatical evolution system. The process is repeated until a fuzzy model that best fits the load data is found. After that, the developed fuzzy nonlinear model is applied to forecast holiday peak load. Considering that different holidays possess different load patterns, a separate forecaster model is built for each holiday. Test results on real load data show that an averaged absolute percent error less than 2% can be achieved, which significantly outperforms existing methods involved in the comparison.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"47 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141698326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning-based Intelligent Fault Diagnosis for Power Distribution Networks 基于深度学习的配电网络智能故障诊断

Int. J. Comput. Commun. Control Pub Date : 2024-07-01 DOI: 10.15837/ijccc.2024.4.6607

Jingzhi Liu, Quanlei Qu, Hongyi Yang, Jianming Zhang, Zhidong Liu

{"title":"Deep Learning-based Intelligent Fault Diagnosis for Power Distribution Networks","authors":"Jingzhi Liu, Quanlei Qu, Hongyi Yang, Jianming Zhang, Zhidong Liu","doi":"10.15837/ijccc.2024.4.6607","DOIUrl":"https://doi.org/10.15837/ijccc.2024.4.6607","url":null,"abstract":"Power distribution networks with distributed generation (DG) face challenges in fault diagnosis due to the high uncertainty, randomness, and complexity introduced by DG integration. This study proposes a two-stage approach for fault location and identification in distribution networks with DG. First, an improved bald eagle search algorithm combined with the Dijkstra algorithm (D-IBES) is developed for fault location. Second, a fusion deep residual shrinkage network (FDRSN) is integrated with IBES and support vector machine (SVM) to form the FDRSN-IBS-SVM model for fault identification. Experimental results showed that the D-IBES algorithm achieved a CPU loss rate of 0.54% and an average time consumption of 1.70 seconds in complex scenarios, outperforming the original IBES algorithm. The FDRSN-IBS-SVM model attained high fault identification accuracy (99.05% and 98.54%) under different DG output power levels and maintained robustness (97.89% accuracy and 97.54% recall) under 5% Gaussian white noise. The proposed approach demonstrates superior performance compared to existing methods and provides a promising solution for intelligent fault diagnosis in modern distribution networks.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"14 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141700221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resource manager for heterogeneous processors 异构处理器的资源管理器

Int. J. Comput. Commun. Control Pub Date : 2024-07-01 DOI: 10.15837/ijccc.2024.4.6606

Jose Alberto Aparicio, Héctor Benítez-Pérez, Luis Agustin Alvarez-Icaza Longoria, Luis Mendoza Rodríguez

引用次数: 0

A Collaborative Control Protocol with Artificial Intelligence for Medical Student Work Scheduling 医学生工作安排的人工智能协同控制协议

Int. J. Comput. Commun. Control Pub Date : 2024-07-01 DOI: 10.15837/ijccc.2024.4.6686

Puwadol Oak Dusadeerungsikul, S. Nof

{"title":"A Collaborative Control Protocol with Artificial Intelligence for Medical Student Work Scheduling","authors":"Puwadol Oak Dusadeerungsikul, S. Nof","doi":"10.15837/ijccc.2024.4.6686","DOIUrl":"https://doi.org/10.15837/ijccc.2024.4.6686","url":null,"abstract":"Effective work scheduling for clinical training is essential for medical education, yet it remains challenging. Creating a clinical training schedule is a difficult task, due to the complexity of curriculum requirements, hospital demands, and student well-being. This study proposes the Collaborative Control Protocol with Artificial Intelligence for Medical Student Work Scheduling (CCP-AI-MWS) to optimize clinical training schedules. The CCP-AI-MWS integrates the Collaborative Requirement Planning principle with Artificial Intelligence (AI). Two experiments have been conducted comparing CCP-AI-MWS with current practice. Results show that the newly developed protocol outperforms the current method. CCP-AI-MWS achieves a more equitable distribution of assignments, better accommodates student preferences, and reduces unnecessary workload, thus mitigating student burnout and improving satisfaction. Moreover, the CCP-AI-MWS exhibits adaptability to unexpected situations and minimizes disruptions to the current schedule. The findings present the potential of CCP-AI-MWS to transform scheduling practices in medical education, offering an efficient solution that could benefit medical schools worldwide.","PeriodicalId":179619,"journal":{"name":"Int. J. Comput. Commun. Control","volume":"2015 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141706768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and Development of an Efficient Demographic-based Movie Recommender System using Hybrid Machine Learning Techniques 利用混合机器学习技术设计和开发基于人口统计的高效电影推荐系统

Int. J. Comput. Commun. Control Pub Date : 2024-07-01 DOI: 10.15837/ijccc.2024.4.5840

Vishal Paranjape, Neelu Nihalani, Nishchol Mishra

引用次数: 0

A Data-Driven Assessment Model for Metaverse Maturity 数据驱动的元数据成熟度评估模型

Int. J. Comput. Commun. Control Pub Date : 2024-07-01 DOI: 10.15837/ijccc.2024.4.6498

Mincong Tang, Jie Cao, Dalin Zhang, Ionut Pandelica

引用次数: 0