Connection Science最新文献_第6页

A multi-factorial evolutionary algorithm concerning diversity information for solving the multitasking Robust Influence Maximization Problem on networks 基于多样性信息的多因子进化算法求解多任务鲁棒影响最大化问题

IF 5.3 4区计算机科学

Connection Science Pub Date : 2023-11-16 DOI: 10.1080/09540091.2023.2275534

Minghao Chen, Shuai Wang, Jiazhong Zhang

引用次数: 0

UAV mission scheduling with completion time, flight distance, and resource consumption constraints 具有完成时间、飞行距离和资源消耗约束的无人机任务调度

4区计算机科学

Connection Science Pub Date : 2023-11-10 DOI: 10.1080/09540091.2023.2281250

Keqin Li

{"title":"UAV mission scheduling with completion time, flight distance, and resource consumption constraints","authors":"Keqin Li","doi":"10.1080/09540091.2023.2281250","DOIUrl":"https://doi.org/10.1080/09540091.2023.2281250","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are widely used in various military and civilian applications. UAV mission scheduling is a key issue in UAV applications and a central topic in UAV research. UAV task scheduling should include several constraints into consideration, such as completion time constraint, flight distance constraint, and resource consumption constraint. Furthermore, UAV task scheduling should be studied within the traditional framework of combinatorial optimisation. In this paper, we consider optimal mission scheduling for heterogeneous UAVs with completion time, flight distance, and resource consumption constraints. The contributions of the paper are summarised as follows. We define two combinatorial optimisation problems, namely, the NFTM (number of finished tasks maximisation) problem and the RFTM (reward of finished tasks maximisation) problem. We construct an algorithmic framework for both NFTM and RFTM problems, so that our heuristic algorithms (four for NFTM and two for RFTM) can be presented in a unified way. We derive upper bounds for optimal solutions, so that our heuristic solutions can be compared with optimal solutions. We experimentally evaluate the performance of our heuristic algorithms. To the best of our knowledge, this is the first paper studying UAV mission scheduling with time, distance, and resource constraints as combinatorial optimisation problems.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":"68 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135093338","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

Evaluating machine and deep learning techniques in predicting blood sugar levels within the E-health domain 评估在电子健康领域预测血糖水平的机器和深度学习技术

4区计算机科学

Connection Science Pub Date : 2023-11-09 DOI: 10.1080/09540091.2023.2279900

Beniamino Di Martino, Antonio Esposito, Gennaro Junior Pezzullo, Tien-Hsiung Weng

引用次数: 0

ScTCN-LightGBM: a hybrid learning method via transposed dimensionality-reduction convolution for loading measurement of industrial material ScTCN-LightGBM:基于转置降维卷积的混合学习方法在工业材料载荷测量中的应用

4区计算机科学

Connection Science Pub Date : 2023-11-09 DOI: 10.1080/09540091.2023.2278275

Zihua Chen, Runmei Zhang, Zhong Chen, Yu Zheng, Shunxiang Zhang

{"title":"ScTCN-LightGBM: a hybrid learning method via transposed dimensionality-reduction convolution for loading measurement of industrial material","authors":"Zihua Chen, Runmei Zhang, Zhong Chen, Yu Zheng, Shunxiang Zhang","doi":"10.1080/09540091.2023.2278275","DOIUrl":"https://doi.org/10.1080/09540091.2023.2278275","url":null,"abstract":"Dynamic measurement via deep learning can be applied in many industrial fields significantly (e.g. electrical power load and fault diagnosis acquisition). Nowadays, accurate and continuous loading measurement is essential in coal mine production. The existing methods are weak in loading measurement because they ignore the symbol characteristics of loading and adjusting features. To address the problem, we propose a hybrid learning method (called ScTCN-LightGBM) to realize the loading measurement of industrial material effectively. First, we provide an abnormal data processing method to guarantee raw data accuracy. Second, we design a sided-composited temporal convolutional network that combines a novel transposed dimensionality-reduction convolution residual block with the conventional residual block. This module can extract symbol characteristics and values of loading and adjusting features well. Finally, we utilize the light-gradient boosting machine to measure loading capacity. Experimental results show that the ScTCN-LightGBM outperforms existing measurement models with high metrics, especially the stability coefficient R2 is 0.923. Compared to the conventional loading measurement method, the measurement performance via ScTCN-LigthGBM improves by 40.2% and the continuous measurement time is 11.28s. This study indicates that the proposed model can achieve the loading measurement of industrial material effectively.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":" 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135241417","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

The algorithm and implementation of an extension to LLVM for solving the blocking between instruction sink and division-modulo combine 解决指令集和除模组合间阻塞的LLVM扩展算法及实现

4区计算机科学

Connection Science Pub Date : 2023-10-30 DOI: 10.1080/09540091.2023.2273219

YungYu Zhuang, Ting-Wei Lin, Yin-Jung Huang

引用次数: 0

Accelerating AI performance with the incorporation of TVM and MediaTek NeuroPilot 结合TVM和联发科NeuroPilot，加速AI性能

4区计算机科学

Connection Science Pub Date : 2023-10-30 DOI: 10.1080/09540091.2023.2272586

Chao-Lin Lee, Chun-Ping Chung, Sheng-Yuan Cheng, Jenq-Kuen Lee, Robert Lai

引用次数: 0

Fidan: a predictive service demand model for assisting nursing home health-care robots Fidan:协助养老院医疗保健机器人的预测服务需求模型

4区计算机科学

Connection Science Pub Date : 2023-10-27 DOI: 10.1080/09540091.2023.2267791

Feng Zhou, Xin Du, WenLi Li, Zhihui Lu, Shih-Chia Huang

{"title":"Fidan: a predictive service demand model for assisting nursing home health-care robots","authors":"Feng Zhou, Xin Du, WenLi Li, Zhihui Lu, Shih-Chia Huang","doi":"10.1080/09540091.2023.2267791","DOIUrl":"https://doi.org/10.1080/09540091.2023.2267791","url":null,"abstract":"While population aging has sharply increased the demand for nursing staff, it has also increased the workload of nursing staff. Although some nursing homes use robots to perform part of the work, such robots are the type of robots that perform set tasks. The requirements in actual application scenarios often change, so robots that perform set tasks cannot effectively reduce the workload of nursing staff. In order to provide practical help to nursing staff in nursing homes, we innovatively combine the LightGBM algorithm with the machine learning interpretation framework SHAP (Shapley Additive exPlanations) and use comprehensive data analysis methods to propose a service demand prediction model Fidan (Forecast service demand model). This model analyzes and predicts the demand for elderly services in nursing homes based on relevant health management data (including physiological and sleep data), ward round data, and nursing service data collected by IoT devices. We optimise the model parameters based on Grid Search during the training process. The experimental results show that the Fidan model has an accuracy rate of 86.61% in predicting the demand for elderly services.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136234124","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

On routing algorithms in the internet of vehicles: a survey 车联网中的路由算法研究

4区计算机科学

Connection Science Pub Date : 2023-10-27 DOI: 10.1080/09540091.2023.2272583

Arundhati Sahoo, Asis Kumar Tripathy

引用次数: 0

Counting and measuring the size and stomach fullness levels for an intelligent shrimp farming system 计算和测量智能虾养殖系统的大小和胃饱度

4区计算机科学

Connection Science Pub Date : 2023-10-18 DOI: 10.1080/09540091.2023.2268878

Yu-Kai Lee, Bo-Yi Lin, Tien-Hsiung Weng, Chien-Kang Huang, Chen Liu, Chih-Chin Liu, Shih-Shun Lin, Han-Ching Wang

{"title":"Counting and measuring the size and stomach fullness levels for an intelligent shrimp farming system","authors":"Yu-Kai Lee, Bo-Yi Lin, Tien-Hsiung Weng, Chien-Kang Huang, Chen Liu, Chih-Chin Liu, Shih-Shun Lin, Han-Ching Wang","doi":"10.1080/09540091.2023.2268878","DOIUrl":"https://doi.org/10.1080/09540091.2023.2268878","url":null,"abstract":"The penaeid shrimp farming industry is experiencing rapid growth. To reduce costs and labour, automation techniques such as counting and size estimation are increasingly being adopted. Feeding based on the degree of stomach fullness can significantly reduce food waste and water contamination. Therefore, we propose an intelligent shrimp farming system that includes shrimp detection, measurement of approximated shrimp length, shrimp quantity, and two methods for determining the degree of digestive tract fullness. We introduce AR-YOLOv5 (Angular Rotation YOLOv5) in the system to enhance both shrimp growth and the environmental sustainability of shrimp farming. Our experiments were conducted in a real shrimp farming environment. The length and quantity are estimated based on the bounding box, and the level of stomach fullness is approximated using the ratio of the shrimp´s digestive tract to its body size. In terms of detection performance, our proposed method achieves a precision rate of 97.70%, a recall rate of 91.42%, a mean average precision of 94.46%, and an F1-score of 95.42% using AR-YOLOv5. Furthermore, our stomach fullness determined method achieves an accuracy of 88.8%, a precision rate of 91.7%, a recall rate of 90.9%, and an F1-score of 91.3% in real shrimp farming environments.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135883657","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

Comparative relation mining of customer reviews based on a hybrid CSR method 基于混合CSR方法的顾客评论比较关系挖掘

4区计算机科学

Connection Science Pub Date : 2023-10-06 DOI: 10.1080/09540091.2023.2251717

Song Gao, Hongwei Wang, Yuanjun Zhu, Jiaqi Liu, Ou Tang

{"title":"Comparative relation mining of customer reviews based on a hybrid CSR method","authors":"Song Gao, Hongwei Wang, Yuanjun Zhu, Jiaqi Liu, Ou Tang","doi":"10.1080/09540091.2023.2251717","DOIUrl":"https://doi.org/10.1080/09540091.2023.2251717","url":null,"abstract":"Online reviews contain comparative opinions that reveal the competitive relationships of related products, help identify the competitiveness of products in the marketplace, and influence consumers’ purchasing choices. The Class Sequence Rule (CSR) method, which is previously commonly used to identify the comparative relations of reviews, suffers from low recognition efficiency and inaccurate generation of rules. In this paper, we improve on the CSR method by proposing a hybrid CSR method, which utilises dependency relations and the part-of-speech to identify frequent sequence patterns in customer reviews, which can reduce manual intervention and reinforce sequence rules in the relation mining process. Such a method outperforms CSR and other CSR-based models with an F-value of 84.67%. In different experiments, we find that the method is characterised by less time-consuming and efficient in generating sequence patterns, as the dependency direction helps to reduce the sequence length. In addition, this method also performs well in implicit relation mining for extracting comparative information that lacks obvious rules. In this study, the optimal CSR method is applied to automatically capture the deeper features of comparative relations, thus improving the process of recognising explicit and implicit comparative relations.","PeriodicalId":50629,"journal":{"name":"Connection Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135346338","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