2019 4th International Conference on Measurement, Information and Control (ICMIC)最新文献

A Multi-Agent Formation Control Method Based on Bearing Measurement 基于方位测量的多智能体编队控制方法

2019 4th International Conference on Measurement, Information and Control (ICMIC) Pub Date : 2019-08-01 DOI: 10.1109/ICMIC48233.2019.9068562

Xiaotan Zhang, Wenshan Su, Lei Chen

引用次数: 0

ICMIC 2019 Table of Contents ICMIC 2019目录

2019 4th International Conference on Measurement, Information and Control (ICMIC) Pub Date : 2019-08-01 DOI: 10.1109/icmic48233.2019.9068529

引用次数: 0

Acquisition Algorithm of Spatial Control Parameter Relative Distance for On-line Insulation 在线绝缘空间控制参数相对距离的获取算法

2019 4th International Conference on Measurement, Information and Control (ICMIC) Pub Date : 2019-08-01 DOI: 10.1109/ICMIC48233.2019.9068570

Xi-zhen Zhang, Xiaoyan Sun, Chen-hong Yao, Zhihui Wang

引用次数: 0

The Forecasting of Water Resource Based on Neural Network 基于神经网络的水资源预测

2019 4th International Conference on Measurement, Information and Control (ICMIC) Pub Date : 2019-08-01 DOI: 10.1109/icmic48233.2019.9068558

Kai Yu, L. Han

引用次数: 1

Study of Magnetostrictive Guided Wave Detection of Defects in Steel Strip for Elevator Traction 磁致伸缩导波检测电梯牵引钢带缺陷的研究

2019 4th International Conference on Measurement, Information and Control (ICMIC) Pub Date : 2019-08-01 DOI: 10.1109/ICMIC48233.2019.9068594

W. Gao, Donglai Zhang, Enchao Zhang, Xiaolan Yan

{"title":"Study of Magnetostrictive Guided Wave Detection of Defects in Steel Strip for Elevator Traction","authors":"W. Gao, Donglai Zhang, Enchao Zhang, Xiaolan Yan","doi":"10.1109/ICMIC48233.2019.9068594","DOIUrl":"https://doi.org/10.1109/ICMIC48233.2019.9068594","url":null,"abstract":"The traction technology used for elevators in the current market has gradually changed from wire rope-type to steel strip technology. The traditional nondestructive testing of elevator wire ropes mainly involves manual observation. The steel strip is composed of both rubber and multiple wire ropes. Therefore, manual observation can only detect wear of the rubber surface or wire leakage and cannot detect internal defects in the wire ropes. The wire ropes in the steel strip are thus generally electrified to determine whether or not any of these wire ropes have broken. This approach cannot detect whether the wire ropes contain small defects. In this paper, different modes are designed for magnetostrictive guided wave sensors according to the structural characteristics of elevator steel strips. The different sensor modes are determined by comparing the detection effects of the various sensors. Finally, the magnetostrictive guided wave sensors are used to detect defects in the steel strip. The experiments show that the sensor designed in this paper can detect defects within the steel strip effectively.","PeriodicalId":404646,"journal":{"name":"2019 4th International Conference on Measurement, Information and Control (ICMIC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128405183","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

Nondestructive Classification of Potatoes Based on HSI and Clustering 基于HSI和聚类的马铃薯无损分类

2019 4th International Conference on Measurement, Information and Control (ICMIC) Pub Date : 2019-08-01 DOI: 10.1109/ICMIC48233.2019.9068564

Yamin Ji, Laijun Sun

{"title":"Nondestructive Classification of Potatoes Based on HSI and Clustering","authors":"Yamin Ji, Laijun Sun","doi":"10.1109/ICMIC48233.2019.9068564","DOIUrl":"https://doi.org/10.1109/ICMIC48233.2019.9068564","url":null,"abstract":"A rapid classification method of potatoes based on the combination of hyperspectral imaging(HSI) technology and integrated learning algorithm was proposed in this paper. Here, potatoes were divided into six types: intact ones, green skin, germination, dry rot, wormhole and damage. Firstly, visible-near infrared (VNIR) hyperspectral imaging system with the band range of 400-1000nm was used to collect the potato hyperspectral image information in the experiment. Further, after the image masked, K-means clustering method was used to segmentate images. Extract the average spectrum of the defect areas and the intact areas as the classification data set. Based on the traditional machine learning algorithm (support vector machine, decision tree) and the integrated learning algorithm (random forest, gradient promotion decision tree), the classification model of potato defects was established and compared. The results show that among all classification algorithms, the classification accuracy of potato defects can be significantly improved by using the decision tree of gradient lifting. By comparing the feature importance of each band, the model accuracy was maintained above 80%. Furthermore, in order to improve the discrimination ability of data and reduce the dimension of data, linear discrimination analysis method was used to process spectral data, and the accuracy of the established model was finally improved to 84.62%.","PeriodicalId":404646,"journal":{"name":"2019 4th International Conference on Measurement, Information and Control (ICMIC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123788418","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

Study on Detection Methods for Frying Times of Soybean Oil Based on NIRS 基于近红外光谱的大豆油煎炸次数检测方法研究

2019 4th International Conference on Measurement, Information and Control (ICMIC) Pub Date : 2019-08-01 DOI: 10.1109/ICMIC48233.2019.9068555

Jinlong Li, Laijun Sun

{"title":"Study on Detection Methods for Frying Times of Soybean Oil Based on NIRS","authors":"Jinlong Li, Laijun Sun","doi":"10.1109/ICMIC48233.2019.9068555","DOIUrl":"https://doi.org/10.1109/ICMIC48233.2019.9068555","url":null,"abstract":"In the process of deep frying, oil can produce deleterious compounds which are harmful to human health. On the basis of analyzing the changing mechanisms of chemistries in repeatedly used oil, the study proposed a method for rapid detecting the frying times of oil based on near infrared spectroscopy (NIRS) technology. First derivative (D1), second derivative (D2) and standard normal variable transformation (SNV) were served as pretreatment methods, and characteristic wavelengths which sensitive to frying times were extracted by correlation coefficient (CC) method. Support vector machine (SVM), partial least squares regression (PLSR) and radial basis function neural network (RBFNN) were utilized to establish qualitative and quantitative analysis models. It turned out that the qualitative and quantitative analysis models had the best performance when D2 was used to pretreatment spectra and six characteristic wavelengths were extracted. More precisely, classification accuracy of the best SVM model reached 94%. Also, the performance of the best PLSR model was superior to the best RBFNN model, in which the values of correlation coefficient (R2), root mean square error of prediction (RMSEP), residual predictive deviation (RPD) were 0.9937, 0.3477 and 12.5803 respectively. The overall results indicated that the proposed method had a great potential to accurate detect frying times of oil.","PeriodicalId":404646,"journal":{"name":"2019 4th International Conference on Measurement, Information and Control (ICMIC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124507559","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

A Domain-Oriented Double Dictionary Forward Traversal Method for Fine Corpus Extraction 面向领域的双字典前向遍历精细语料库抽取方法

2019 4th International Conference on Measurement, Information and Control (ICMIC) Pub Date : 2019-08-01 DOI: 10.1109/ICMIC48233.2019.9068578

Gang Liu, Sennan Zhang, Wangyang Liu, Yang Cao, Xue-feng Li

引用次数: 0

Rapid and Non-destructive Detecting Frying Times of Peanut Oil Based on Near Infrared Reflectance Spectroscopy 基于近红外反射光谱的花生油油炸时间快速无损检测

2019 4th International Conference on Measurement, Information and Control (ICMIC) Pub Date : 2019-08-01 DOI: 10.1109/ICMIC48233.2019.9068530

Zhiyong Ran, Laijun Sun, Jinlong Li

{"title":"Rapid and Non-destructive Detecting Frying Times of Peanut Oil Based on Near Infrared Reflectance Spectroscopy","authors":"Zhiyong Ran, Laijun Sun, Jinlong Li","doi":"10.1109/ICMIC48233.2019.9068530","DOIUrl":"https://doi.org/10.1109/ICMIC48233.2019.9068530","url":null,"abstract":"Aiming at the food safety problem of edible oil, this paper proposes a new method based on Near Infrared Reflectance Spectroscopy (NIRS) for rapid non-destructive testing of the frying times of frying oils. Peanut oil is used as a research object, and frozen French fries are used as a frying medium. Peanut oil is subjected to 10 experiments, and each experiment is fried 15 times in the same batch, and the samples are collected in the near-infrared original spectra at 400 nm-2500 nm. The original spectra are preprocessed and combined with the data dimensionality reduction algorithm to establish the classification model and the regression model of frying times of peanut oil, and the accuracy of the model prediction is tested. Choose the first derivative as the pretreatment method and the Linear Discriminant Analysis (LDA) algorithm is used to reduce the dimensionality of the preprocessed spectral data to establish a K-Nearest-Neighbors (KNN) classification model for peanut oil. The prediction effect of the Random Forest Regression (RFR) regression model based on the spectral data after dimensionality reduction is slightly better than that of the Partial Least Squares Regression (PLSR) regression model. The Determination Coefficient (R2), Root Means Square Error (RMSEP), and Relative Analysis Error (RPD) of the peanut oil in RFR regression models are 0.9978, 0.1823, 21.2776. Therefore, the method used in this study can effectively detect the frying times of peanut oil and provide a technical guarantee for the rapid detection of food safety.","PeriodicalId":404646,"journal":{"name":"2019 4th International Conference on Measurement, Information and Control (ICMIC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129342048","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

Germination Prediction of Sugar Beet Seeds Based on HSI and SVM-RBF 基于HSI和SVM-RBF的甜菜种子发芽预测

2019 4th International Conference on Measurement, Information and Control (ICMIC) Pub Date : 2019-08-01 DOI: 10.1109/ICMIC48233.2019.9068534

Shuang Zhou, Laijun Sun, Yamin Ji

引用次数: 4