Transactions on Machine Learning and Artificial Intelligence最新文献_第7页

The Importance of High-Bandwidth Low-Latency Network Systems in the Modern Age 高带宽低延迟网络系统在现代的重要性

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2023-02-12 DOI: 10.14738/tecs.111.13967

引用次数: 0

Evaluating the Influence of Passive Design Strategies on Cooling Energy Demand in Local Adobe, Stone and Concrete Dwellings in Wadi Hadramout, Yemen 评估被动式设计策略对也门瓦迪哈德拉穆当地土坯、石头和混凝土住宅冷却能源需求的影响

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2023-02-07 DOI: 10.14738/tecs.113.14815

引用次数: 0

Standardization of Criteria across Multiple Evaluators to Detect Objects 跨多个评估器的标准标准化以检测对象

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2023-01-31 DOI: 10.14738/tecs.111.13876

引用次数: 0

Scrutinizing UML Teaching and Learning Modeling Tools 仔细检查UML教学和学习建模工具

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2023-01-20 DOI: 10.14738/tecs.111.13820

引用次数: 0

Developing a Neural Network Based Fault Prediction Tool for a Solar Power Plant in Uganda 基于神经网络的乌干达太阳能电站故障预测工具开发

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2022-12-28 DOI: 10.14738/tmlai.106.13645

Salmah Nansamba, Hadi Harb

{"title":"Developing a Neural Network Based Fault Prediction Tool for a Solar Power Plant in Uganda","authors":"Salmah Nansamba, Hadi Harb","doi":"10.14738/tmlai.106.13645","DOIUrl":"https://doi.org/10.14738/tmlai.106.13645","url":null,"abstract":"Solar photovoltaic (PV) systems are one of the fastest growing renewable energy technologies and plenty of research has been and continues to be carried out in this domain. Maximization of solar PV power plant production, efficiency and return on investment can only be achieved by having adequate and effective maintenance systems in place. Of the various maintenance schemes, predictive maintenance is popular for its effectiveness and minimization of resource wastage. Maintenance activities are scheduled based on the real time condition of the system with priority being given to the system components with the highest likelihood of failure. A good predictive maintenance system is based on the premise of being able to anticipate faults before they occur. In this study therefore, a fault prediction tool for a solar plant in Uganda is proposed. The hybrid tool is developed using both feed forward and long short term memory neural networks for power prediction, in conjunction with a mean chart statistical process control tool for final fault prediction. Results from the study demonstrate that the feed forward and long short term memory neural network modules of the proposed tool attain mean absolute errors of 4.2% and 6.9% respectively for power production predictions. The fault prediction capability of the tool is tested under both normal and abnormal operating conditions. Results show that the tool satisfactorily discriminates against the fault and non-fault conditions thereby achieving successful solar PV system fault prediction.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114094148","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

Self-Supervised Learning in Hebrew–Model to Practice Framework 希伯来语的自我监督学习——从模型到实践框架

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2022-12-07 DOI: 10.14738/tmlai.106.13515

O. Gal, Rafi Michaeli, Y. Doytsher

引用次数: 0

Comparison of Machine Learning Algorithms for Ball Velocity Prediction in Baseball Pitcher using a Single Inertial Sensor 基于惯性传感器的投球速度预测的机器学习算法比较

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2022-12-02 DOI: 10.14738/tmlai.106.13492

Kodai Kitagawa

{"title":"Comparison of Machine Learning Algorithms for Ball Velocity Prediction in Baseball Pitcher using a Single Inertial Sensor","authors":"Kodai Kitagawa","doi":"10.14738/tmlai.106.13492","DOIUrl":"https://doi.org/10.14738/tmlai.106.13492","url":null,"abstract":"Ball velocity of pitching is an important factor in baseball players. Commonly, ball velocity measurement requires specific devices such as radar gun. On the other hand, Gomaz et al. developed the accurate ball velocity measurement using two inertial sensors on pelvis and trunk. Recently, smartphone installed inertial sensor is popular device in daily life. Therefore, if ball velocity can be measured by only a single inertial sensor, baseball players can measure own ball velocity by only smartphone in daily life and various situations. Thus, the objective of this study is to propose and evaluate the ball velocity prediction method using the only a single inertial sensor. The proposed method predicts ball velocity using by a single inertial sensor and machine learning technique. Five machine learning algorithms (linear regression, support vector machine, gaussian process, artificial neural network, and M5P) predicted ball velocity by data of single inertial sensor, body height, and body weight. In this study, Gomaz et al.’s public data for ball velocity and inertial data during pitching of baseball players were used for this investigation. Sensor placement was either sternum or pelvis. Accuracy of prediction was evaluated by root mean square error (RMSE) between actual and predicted value via leave-one-out cross-validation. The results showed that greatest algorithm (M5P) could accurately predict ball velocity by only single inertial sensor and body parameters (RMSE < 2.0 mph). These results suggest that ball velocity can be measured by only single inertial sensor such as smartphone.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116477774","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: 1

Real-time Virtual Machine Energy-Efficient Allocation in Cloud Data Centers Using Interval-packing Methods 基于间隔打包方法的云数据中心实时虚拟机能效分配

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2022-12-02 DOI: 10.14738/tmlai.106.13419

S. Jason

{"title":"Real-time Virtual Machine Energy-Efficient Allocation in Cloud Data Centers Using Interval-packing Methods","authors":"S. Jason","doi":"10.14738/tmlai.106.13419","DOIUrl":"https://doi.org/10.14738/tmlai.106.13419","url":null,"abstract":"The reduction of power consumption, which can lower the operation costs of Cloud providers, lengthen the useful life of a machine, as well as lessen the environmental effect caused by power consumption, is one of the critical concerns for large-scale Cloud applications. To satisfy the needs of various clients, Virtual Machines (VMs) as resources (Infrastructure as a Service (IaaS)) can be dynamically allocated in cloud data centers. In this research, we study the energy-efficient scheduling of real-time VMs by taking set processing intervals into account, with the providers' goal of lowering power consumption. Finding the best solutions is an NP-complete problem when virtual machines (VMs) share arbitrary amounts of a physical machine's (PM) total capacity, as demonstrated in numerous open-source resources. Our strategy treats the issue as a modified interval partitioning problem and takes into account configurations with dividable capacities to make the problem formulation easier and assist save energy. There are presented both exact and approximate solutions. The proposed systems consume 8–30% less power than the existing algorithms, according to simulation data.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127848581","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

The Entrepreneurial Orientation of Agricultural SMEs in the Fez-Meknes Region: A Qualitative Study 非斯-梅克内斯地区农业中小企业创业取向的定性研究

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2022-11-14 DOI: 10.14738/tmlai.106.13359

Abderrahman Lakbir, Amale Laaraussi, A. Bouayad

引用次数: 0

Optimum Sampling Plan on Quality Indices AOQL and MAPD 质量指标AOQL和MAPD的最优抽样方案

Transactions on Machine Learning and Artificial Intelligence Pub Date : 2022-10-18 DOI: 10.14738/tmlai.105.13168

R. Balan, E. Massawe

引用次数: 0