2021 IEEE International Conference on Progress in Informatics and Computing (PIC)最新文献_第6页

Fault Detection on Bearings and Rotating Machines based on Vibration Sensors Data 基于振动传感器数据的轴承和旋转机械故障检测

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9686999

Thanasis Kotsiopoulos, T. Vafeiadis, Aristeidis Apostolidis, Alexandros Nizamis, Nikolaos Alexopoulos, D. Ioannidis, D. Tzovaras, P. Sarigiannidis

{"title":"Fault Detection on Bearings and Rotating Machines based on Vibration Sensors Data","authors":"Thanasis Kotsiopoulos, T. Vafeiadis, Aristeidis Apostolidis, Alexandros Nizamis, Nikolaos Alexopoulos, D. Ioannidis, D. Tzovaras, P. Sarigiannidis","doi":"10.1109/PIC53636.2021.9686999","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9686999","url":null,"abstract":"In this work a comparative study among the known fault detection techniques Local Outlier Factor and Isolation Forest as well as a proposed methodology called Standardised Mahalanobis Distance is presented. The study is focusing on the challenging problem of fault detection on bearings and rotating machines using vibration sensors’ data. During the first phase of the experiments, all models are applied and evaluated using cross-validation on a dataset created in lab by obtaining vibration signals of a rotating machine. In the second phase, the outlier detection techniques including the proposed one, are applied and evaluated on a popular, public dataset. In both phases, various parameters’ combinations are tested in order to find the most efficient set for each technique. As can been derived by the evaluation results, the Standardised Mahalanobis Distance methodology outperforms Local Outlier Factor and Isolation Forest on fault detection on voltage drop down of rotating machines in the case the voltage value of the abnormal condition is not close to the nominal. In addition, the evaluation results from the public dataset indicate that Standardised Mahalanobis Distance is able to identify outliers before an outer race fault on a bearing occurs, in a more efficient and solid way than Local Outlier Factor and Isolation Forest models. The proposed approach is applied also on a real world scenario in the premises of major lift manufacturer, using custom vibration sensors and it is currently under further evaluation.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132308771","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: 2

Enhanced Particle Swarm Optimization for Workflow Scheduling in Clouds 云环境下工作流调度的增强粒子群优化

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687073

Chang Lu, Dayu Feng, Jie Zhu, Haiping Huang

引用次数: 0

Weighted Best Linear Prediction and Its Randomized Acceleration for Poisson Image Denoising 泊松图像去噪的加权最佳线性预测及其随机加速

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687088

Qing Li, Jun Zhang

引用次数: 0

Path Planning for an Omnidirectional Mobile Robot Based on Modified A * Algorithm with Energy Model 基于能量模型改进A *算法的全向移动机器人路径规划

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687067

Cong Liu, Xiaobin Xu, Xinhong Li, Zhijie Pan, Kai Hu, You Shu

引用次数: 1

A Job Shop Scheduling Method Based on Ant Colony Algorithm 基于蚁群算法的作业车间调度方法

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687078

Junqing Li, Huawei Deng, Dawei Liu, Changqing Song, Ruiyi Han, Taiyuan Hu

{"title":"A Job Shop Scheduling Method Based on Ant Colony Algorithm","authors":"Junqing Li, Huawei Deng, Dawei Liu, Changqing Song, Ruiyi Han, Taiyuan Hu","doi":"10.1109/PIC53636.2021.9687078","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687078","url":null,"abstract":"The problem of job shop scheduling is a hot research topic nowadays. How to improve the production efficiency of the equipment and shorten the processing time of the workpieces has become an important research work. The parallelism and mechanism of distributed computing of Ant colony optimization (ACO) provide a good solution in solving job shop scheduling problems. In this paper, the ACO is applied to the job shop scheduling of industrial production. And the ACO is used to solve the scheduling problem, the pheromone update strategy in the ant colony algorithm has been modified, and roulettes wheel was introduced. On the basis of above modifications, a job shop scheduling method based on ant colony algorithm has been used in this paper. In addition, the disjunction graph model of the job shop problem has been also established in this paper, which turned the job shop scheduling problem into a solution to the traveling salesman problem and then redefined as a natural expression model suitable for ant colony algorithm. When solving the traveling salesman problem, virtual nodes were added as the super source and destination in the search process, the distance between cities and the shortest path in the traveling salesman were corresponded with the processing time and the shortest processing time in the job shop scheduling problem one by one. In this paper, C++ has been used for programming, and the FT06 data example was used as a test example. In the experiment, the scheme of job scheduling with minimum total completion time was obtained successfully, which verified the feasibility and effectiveness of this method in the shop scheduling problem.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127260600","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

Application of Improved YOLOV4 in Intelligent Driving Scenarios 改进型YOLOV4在智能驾驶场景中的应用

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687039

Zicheng Zhang, Quan Liang, Zhihui Feng, W. Ji, Hansong Wang, Jinjing Hu

{"title":"Application of Improved YOLOV4 in Intelligent Driving Scenarios","authors":"Zicheng Zhang, Quan Liang, Zhihui Feng, W. Ji, Hansong Wang, Jinjing Hu","doi":"10.1109/PIC53636.2021.9687039","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687039","url":null,"abstract":"With the development of unmanned technology, the technical innovation of invehicle vision detection system is also getting faster and faster, while the improvement of algorithm accuracy often brings an increase in the number of parameters and poor real-time performance. In his paper, the optimization of the algorithm structure of YoLoV4 target detection is achieved by using MobileNet-v3 instead of the CspDarkNet53 master Network, which has the inverse residual structure of linear bottleneck, while the lightweight attention mechanism is added to the feature extraction process, and the learning degree of feature channels is enhanced; due to the long computation time of sigmoid, it also uses ReLU6(x+3)/6 is used to approximate the original activation function due to the long computation time of sigmoid; the system parameters are reduced by constructing a depth-separable convolution instead of the normal convolution in PaNet. Meanwhile, this paper improves the original upsampling method by using dual cubic interpolation, which makes the image more smooth, less image loss and more accurate feature extraction during he upsampling method. The map% is improved from 79.1% to 81.2% on the voc dataset, reaching 58.14 FPS.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114562658","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 kNN Based Voyage’s Containers’ Entering Time Distribution Prediction System 基于kNN的航次集装箱进港时间分布预测系统

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687057

Shitong Shen, Jian Cao, Yinyue Yang, Yameng Guo

引用次数: 1

Network Situation Risk Assessment Based on Vulnerability Correlation Analysis 基于漏洞关联分析的网络态势风险评估

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687007

X. Nan, R. Chen, Hongtao Tian, Yupeng Liu

引用次数: 1

Development of a 1-DOF Elbow Power Assisting System Based on Mechanomyogram Signals 基于肌力图信号的1自由度弯头助力系统的研制

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687051

Bin Zhang, Kenji Isobe, Hun-ok Lim

引用次数: 0

Single Dendritic Neural Classification with Functional Weight-enhanced Differential Evolution 单树突神经分类与功能权重增强的差异进化

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687059

Ziqian Wang, Kaiyu Wang, Jiaru Yang, Zheng Tang

{"title":"Single Dendritic Neural Classification with Functional Weight-enhanced Differential Evolution","authors":"Ziqian Wang, Kaiyu Wang, Jiaru Yang, Zheng Tang","doi":"10.1109/PIC53636.2021.9687059","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687059","url":null,"abstract":"As current mainstream deep learning models based on neural networks have been long criticized because of their complex structures, attempts in formulating a single neural model have raised much attention. Owing to the nonlinear information processing ability, dendritic neuron model (DNM) has shown its great potential in classification problems over the past decades. However, designing an effective learning algorithm for training DNM is still an open question due to the issues of local optima trapping and overfiting caused by traditional back-propagation (BP) algorithm. In this study, a novel functional weight-enhanced differential evolutionary algorithm (termed FWDE) is proposed to solve the aforementioned problems. By introducing Gaussian distribution function into weight generation of fitness-distance balance selection strategy, FWDE obtains significantly better classification accuracy with faster convergence speed compared with other representative non-BP and BP algorithms. The experimental results verify the great performance of FWDE, indicating that DNM with an powerful learning algorithm is considerably more effective.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125491259","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