2016 10th International Conference on Sensing Technology (ICST)最新文献_第5页

Development of molecular imprinted polymer interdigital sensor for C-terminal telopeptide of type I collagen I型胶原c端末端肽分子印迹聚合物数字间传感器的研制

2016 10th International Conference on Sensing Technology (ICST) Pub Date : 2016-11-01 DOI: 10.1109/ICSENST.2016.7796240

Nasrin Afsarimanesh, M. Alahi, S. Mukhopadhyay, M. Kruger, P. Yu

引用次数: 3

Machine health monitoring with LSTM networks 使用LSTM网络进行机器运行状况监视

2016 10th International Conference on Sensing Technology (ICST) Pub Date : 2016-11-01 DOI: 10.1109/ICSENST.2016.7796266

Rui Zhao, Jinjiang Wang, Ruqiang Yan, K. Mao

{"title":"Machine health monitoring with LSTM networks","authors":"Rui Zhao, Jinjiang Wang, Ruqiang Yan, K. Mao","doi":"10.1109/ICSENST.2016.7796266","DOIUrl":"https://doi.org/10.1109/ICSENST.2016.7796266","url":null,"abstract":"Effective machine health monitoring systems are critical to modern manufacturing systems and industries. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, sensory data that is a kind of sequential data can not serve as direct meaningful representations for machine conditions due to its noise, varying length and irregular sampling. A majority of previous models focus on feature extraction/fusion methods that involve expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, representation learning from raw data has been redefined. Among deep learning models, Long Short-Term Memory networks (LSTMs) are able to capture long-term dependencies and model sequential data. Therefore, LSTMs is able to work on the sensory data of machine condition. Here, the first study about a empirical evaluation of LSTMs-based machine health monitoring systems is presented. A real life tool wear test is introduced. Basic and deep LSTMs are designed to predict the actual tool wear based on raw sensory data. The experimental results have shown that our models, especially deep LSTMs, are able to outperform several state-of-arts baseline methods.","PeriodicalId":297617,"journal":{"name":"2016 10th International Conference on Sensing Technology (ICST)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124025002","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: 140

An improved gray weighted method for sub-pixel center extraction of structured light stripe 一种改进的结构光条纹亚像素中心提取的灰色加权方法

2016 10th International Conference on Sensing Technology (ICST) Pub Date : 2016-11-01 DOI: 10.1109/ICSENST.2016.7796313

Li Yuehua, Zhou Jingbo, Huang Fengshan, L. Lijian

{"title":"An improved gray weighted method for sub-pixel center extraction of structured light stripe","authors":"Li Yuehua, Zhou Jingbo, Huang Fengshan, L. Lijian","doi":"10.1109/ICSENST.2016.7796313","DOIUrl":"https://doi.org/10.1109/ICSENST.2016.7796313","url":null,"abstract":"Center extraction of structured light stripe is an essential problem for the development of line structured light sensors (LSLS). To obtain the sub-pixel center coordinates precisely, an improved gray weighted method (IGWM) is proposed with an adaptive sampling region. Firstly, the center of the stripe is computed using gray weighted method (GWM) for each pixel column. Then these center points are fitted using moving least squares algorithm to estimate the tangential vector, the normal vector and the radius of curvature. For each center point, a rectangular region is defined with two sides parallel with the normal vector. The other two sides that parallel with the tangential vector alter their length automatically according to the radius of curvature. After that, the center coordinate at this point is recalculated based on the GWM, but in the normal vector direction and only takes into account the pixels within the rectangular region. The experimental results show that this method is not only suited for the center extraction of smooth laser stripes, but also the ones with sharp corners. The noise can also be obviously suppressed than that of the traditional GWM.","PeriodicalId":297617,"journal":{"name":"2016 10th International Conference on Sensing Technology (ICST)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129169805","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: 4

A SVM-based adaptive stance detection method for pedestrian inertial navigation 基于支持向量机的行人惯性导航自适应姿态检测方法

2016 10th International Conference on Sensing Technology (ICST) Pub Date : 2016-11-01 DOI: 10.1109/ICSENST.2016.7796271

Zhechen Zhang, Hongyu Wang, Zhonghua Zhao, Zhejun Wu

引用次数: 2

AC magnetic nanothermometry: The influence of particle size distribution 交流磁纳米测温:粒度分布的影响

2016 10th International Conference on Sensing Technology (ICST) Pub Date : 2016-11-01 DOI: 10.1109/ICSENST.2016.7796321

Wenzhong Liu, Shiqiang Pi

引用次数: 2

Multi-channel features fitted 3D CNNs and LSTMs for human activity recognition 多通道特征拟合三维cnn和lstm进行人体活动识别

2016 10th International Conference on Sensing Technology (ICST) Pub Date : 2016-11-01 DOI: 10.1109/ICSENST.2016.7796232

Y. Qin, L. Mo, Jing Ye, Z. Du

引用次数: 2

High performance combustible gas monitoring system 高性能可燃气体监测系统

2016 10th International Conference on Sensing Technology (ICST) Pub Date : 2016-11-01 DOI: 10.1109/ICSENST.2016.7796340

Xu Jun, Tang Ya-nan, Li Xin

{"title":"High performance combustible gas monitoring system","authors":"Xu Jun, Tang Ya-nan, Li Xin","doi":"10.1109/ICSENST.2016.7796340","DOIUrl":"https://doi.org/10.1109/ICSENST.2016.7796340","url":null,"abstract":"In order to realize remote monitoring and centralized management of combustible gas. A monitoring system of high performance fuel gas based on RS485 bus communication mode was presented in this paper. The system was composed of center controller and unit controller. The hardware design of unit controller and center controller was introduced, and the software design scheme of the control and communication between each controller were given in this paper. The center controller used Modbus protocol to communicate with the unit controller, to realize the display, storage and query of alarm signal of combustible gas, and complete the automatic zero adjustment, calibration and fault detection of the sensor signal of the unit controller. The unit controller collected the concentration value and temperature value of the combustible gas in real time, but due to effect of temperature, so I used RBF neural network to solve the problem of measuring deviation. The experimental results show that the measurement error is less than 2% LEL, and the alarm error is less than 3% LEL. The presented system has advantages such as easiness of operation, real-time fixing, relativity low cost and a wide prospect of applications.","PeriodicalId":297617,"journal":{"name":"2016 10th International Conference on Sensing Technology (ICST)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115152604","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

A Monte Carlo approach to determining bessel beam source parameters 确定贝塞尔光束源参数的蒙特卡罗方法

2016 10th International Conference on Sensing Technology (ICST) Pub Date : 2016-11-01 DOI: 10.1109/ICSENST.2016.7796332

I. Platt, A. Tan, I. Woodhead, K. Eccleston

引用次数: 1

A novel vehicle dynamics identification method utilizing MIMU sensors based on support vector machine 一种基于支持向量机的MIMU传感器车辆动力学识别新方法

2016 10th International Conference on Sensing Technology (ICST) Pub Date : 2016-11-01 DOI: 10.1109/ICSENST.2016.7796252

Lei Jiang, Yu Wang, Xin-hua Zhu, Yan Su

引用次数: 1

A study of the impact of smoothing on parallel factor model of fluorescence emission excitation matrix 平滑对荧光发射激发矩阵平行因子模型影响的研究

2016 10th International Conference on Sensing Technology (ICST) Pub Date : 2016-11-01 DOI: 10.1109/ICSENST.2016.7796274

Xu Jing, Wang Yutian, Zhang Lijuan, Zhao Xu, Wu Xijun, Pan Zhao

引用次数: 0