2021 IEEE 19th International Conference on Industrial Informatics (INDIN)最新文献_第3页

Fault Detection in Railway Switches using Deformable Convolutional Neural Networks 变形卷积神经网络在铁路交换机故障检测中的应用

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557554

Robert F. Maack, Hasan Tercan, A. F. Solvay, Maximilian Mieth, Tobias Meisen

{"title":"Fault Detection in Railway Switches using Deformable Convolutional Neural Networks","authors":"Robert F. Maack, Hasan Tercan, A. F. Solvay, Maximilian Mieth, Tobias Meisen","doi":"10.1109/INDIN45523.2021.9557554","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557554","url":null,"abstract":"Recently, time series classification methods based on Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance outperforming former ensemble-based methods like HIVE-COTE on a multitude of time series datasets. Inspired by the current rise of Deep Neural Networks (DNNs) end-to-end classifiers for time series classification, we propose utilisation of Deformable Convolutional Neural Networks (Deformable CNNs), which have already proven to drastically enhance classification performance on image classification tasks. Our aim is to evaluate the applicability of such methods on the practical use-case of a German railway provider, in which sensory data from railway switches is employed to detect and classify faults in switching operation. Prior to any classification, we have to address two main issues, which is that the available data is in a raw, unlabelled format and the contained time series have vastly varying length. We cope by applying extensive pre-processing and semi-supervised labelling. As baseline classifier, we use a conventional KNN classifier that is tailored to enable handling of sensory data. Finally, we compare the baseline classifier against more advanced DNN classifiers and discuss their feasibility in general and in context of our use-case.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115053354","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

Stacked denoising autoencoder for infrared thermography image enhancement 用于红外热成像图像增强的层叠去噪自编码器

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557407

Ziang Wei, H. Fernandes, J. Tarpani, A. Osman, X. Maldague

引用次数: 0

Automated Pruning of Neural Networks for Mobile Applications 移动应用中神经网络的自动修剪

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557525

Andreas Glinserer, Martin Lechner, Alexander Wendt

{"title":"Automated Pruning of Neural Networks for Mobile Applications","authors":"Andreas Glinserer, Martin Lechner, Alexander Wendt","doi":"10.1109/INDIN45523.2021.9557525","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557525","url":null,"abstract":"Pruning is useful method to compress neural networks and further reduce the required computations and thus the inference speed. This work presents an automatic pruning workflow using an measurement based method to determine which portions of the network only contribute little to the total accuracy. Furthermore to increase the pruneability within networks containing residual blocks this work evaluates zero-padding as an useful complement to existing pruning methods. With zero-padding added to the pruning, we enable the automatic pruning process to also choose layers for pruning which would otherwise not be possible or only possible with removing additional filters which might contribute to the total accuracy. Zero-padding therefore adds the removed channels back into the original output feature map in a manner that the shapes remain identical, but the computations are saved. Using this method we achieved a speedup of up to 21% on CPU based platforms and 5-6% on GPU based execution on a MobileNetV2. The pruned network became comparable to an original network with an applied depth multiplier with only little additional retraining time.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116081266","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

Network Transparent Decrypting of Cryptographic Stream Considering Service Provision at the Edge 考虑边缘业务提供的加密流网络透明解密

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557366

H. Hiraga, H. Nishi

{"title":"Network Transparent Decrypting of Cryptographic Stream Considering Service Provision at the Edge","authors":"H. Hiraga, H. Nishi","doi":"10.1109/INDIN45523.2021.9557366","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557366","url":null,"abstract":"The spread of Internet of Things (IoT) devices and high-speed communications, such as 5G, makes their services rich and diverse. Therefore, it is desirable to perform functions of rich services transparently and use edge computing environments flexibly at intermediate locations on the Internet, from the perspective of a network system. When this type of edge computing environment is achieved, IoT nodes as end devices of the Internet can fully utilize edge computing systems and cloud systems without any change, such as switching destination IP addresses between them, along with protocol maintenance for the switching. However, when the data transfer in the communication is encrypted, a decryption method is necessary at the edge, to realize these transparent edge services. In this study, a transparent common key-exchanging method with cloud service has been proposed as the destination node of a communication pair, to transparently decrypt a secure sockets layer-encrypted communication stream at the edge area. This enables end devices to be free from any changes and updates to communicate with the destination node.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128897451","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 Predictive Maintenance Methodology: Predicting the Time-to-Failure of Machines in Industry 4.0 预测性维护方法:预测工业4.0中机器的故障时间

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557387

Marwin Züfle, Joachim Agne, Johannes Grohmann, Ibrahim Dörtoluk, Samuel Kounev

{"title":"A Predictive Maintenance Methodology: Predicting the Time-to-Failure of Machines in Industry 4.0","authors":"Marwin Züfle, Joachim Agne, Johannes Grohmann, Ibrahim Dörtoluk, Samuel Kounev","doi":"10.1109/INDIN45523.2021.9557387","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557387","url":null,"abstract":"Predictive maintenance is an essential aspect of the concept of Industry 4.0. In contrast to previous maintenance strategies, which plan repairs based on periodic schedules or threshold values, predictive maintenance is normally based on estimating the time-to-failure of machines. Thus, predictive maintenance enables a more efficient and effective maintenance approach. Although much research has already been done on time-to-failure prediction, most existing works provide only specialized approaches for specific machines. In most cases, these are either rotary machines (i.e., bearings) or lithium-ion batteries. To bridge the gap to a more general time-to-failure prediction, we propose a generic end-to-end predictive maintenance methodology for the time-to-failure prediction of industrial machines. Our methodology exhibits a number of novel aspects including a universally applicable method for feature extraction based on different types of sensor data, well-known feature transformation and selection techniques, adjustable target class assignment based on fault records with three different labeling strategies, and the training of multiple state-of-the-art machine learning classification models including hyperparameter optimization. We evaluated our time-to-failure prediction methodology in a real-world case study consisting of monitoring data gathered over several years from a large industrial press. The results demonstrated the effectiveness of the proposed methodology for six different time-to-failure pre-diction windows, as well as for the downscaled binary prediction of impending failures. In this case study, the multi-class feed-forward neural network model achieved the overall best results.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125680391","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

Convolutional LSTM Network for forecasting correlations between stocks based on spatiotemporal sequence 基于时空序列的卷积LSTM网络股票相关性预测

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557538

Jiaqi Sun, Yong Jiang, Jian Lin

{"title":"Convolutional LSTM Network for forecasting correlations between stocks based on spatiotemporal sequence","authors":"Jiaqi Sun, Yong Jiang, Jian Lin","doi":"10.1109/INDIN45523.2021.9557538","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557538","url":null,"abstract":"The correlation between stocks is important for investment portfolio pricing and evaluation, risk management, and formulating trading and hedging strategies. The COVID-19 has led to a general increase in the degree of correlation between stocks, the market-wide allocation has lost its meaning, and the hedging strategy has failed. It is more necessary and urgent to predict the correlation between stocks under the influence of the epidemic. However, previous studies mostly focused on traditional financial models. There are problems such as too many assumptions and restrictions, the dimensional disaster of the estimated parameters, and the poor effect of fitting nonlinearity and tail risk, which cannot provide reliable and accurate estimates. In this paper, the covariance matrix for stock return is considered as a sequence with both time and space characteristics, to transform the problem into the study of spatiotemporal sequence prediction. We Innovatively apply the end-to-end Convolutional LSTM (ConvLSTM) to the correlation prediction between stocks and use random matrix theory (RMT) to improve mean squared error (MSE) to eliminate the influence of noise. Experiments show that the performance of ConvLSTM on this problem is better than that of traditional financial model, especially after de-nosing by Random Matrix Theory (RMT). Compared with Fully Connected LSTM (FC-LSTM), ConvLSTM acquired a better out-of-sample MSE and RMT_MSE, which proves the effectiveness of the method. Finally, we repeat experiments with other stock dataset to verify the robustness of the model.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"os-42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127783717","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

Learning-based Edge Computing Architecture for Regional Scheduling in Manufacturing System 基于学习的制造系统区域调度边缘计算体系结构

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557389

Tianfan Xue, P. Zeng, Haibin Yu

{"title":"Learning-based Edge Computing Architecture for Regional Scheduling in Manufacturing System","authors":"Tianfan Xue, P. Zeng, Haibin Yu","doi":"10.1109/INDIN45523.2021.9557389","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557389","url":null,"abstract":"This paper proposes a novel edge-computing based structure to support learning-based decision-making in industry manufacturing field. This structure consists of four functional layers, respectively realizing model establishment, task allocation and task processing work. In order to take full advantage of the distributed computing resources at the edge, the manufacturing computing task can be further decomposed into several sub-tasks, separating the complex computing problem with large problem size into regional scheduling ones with much smaller problem size. All the sub-tasks are allocated to the edges, accomplished by the algorithm deployed on computing devices of region-related edge node, which contributes to faster data-processing and problem-solving speed. A simulation test has been performed in which a multi-AGV scheduling problem was solved according to a distributed reinforcement learning method configured in such edge computing architecture. The objective of each edge node is to acquire AGV schedule of related region that minimizes the makespan. Simulation results demonstrate that this distributed edge computing system can be enabled to learn satisfying solution and converge much faster when it is compared with conventional method applied in centralized architecture.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117093036","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

Improving Code Reuse between Industrial Embedded Systems and Discrete Event Simulators 改进工业嵌入式系统和离散事件模拟器之间的代码重用

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557535

Niclas Ericsson, J. Åkerberg, M. Björkman, T. Lennvall, S. Larsson, Hongyu Pei Breivold

引用次数: 0

Cloud Simulation for Continuous Integration and Deployment in Robotics 机器人持续集成与部署的云模拟

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557476

Sérgio Teixeira, Rafael Arrais, G. Veiga

引用次数: 2

Modeling of IEEE1451-Standardized Low Power Wide Area Networks ieee1451标准低功耗广域网的建模

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557375

Yang Wei, Yucheng Liu, K. Tsang, Hao Wang

{"title":"Modeling of IEEE1451-Standardized Low Power Wide Area Networks","authors":"Yang Wei, Yucheng Liu, K. Tsang, Hao Wang","doi":"10.1109/INDIN45523.2021.9557375","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557375","url":null,"abstract":"Internet of Things (IoT) has become one of the most popular technologies in recent years, covering from citywide services to industrial applications, which enlarges the smart life for human beings. Through IoT, billions of IoT end devices can be interconnected to support various applications. The emergence of low-power wide-area network (LPWAN) technologies provides a great opportunity to support such an enormous network with their kilometer-level coverage and uA-level power consumption. To improve the efficiency of network resources of LPWANs, the cooperated IoT is proposed by researchers. However, the current LPWAN consists of diverse protocols, equipment, and design standards, rendering the increasing development effort on designing a compliance network by developers. To address this issue, the IEEE 1451, developed by Instrumentation and Measurement Society, is proposed. The IEEE 1451 standardized the wireless IoT systems with wireless transducer interface module (WTIM), network capable application processor server (NCAP Server) and NCAP Client. Besides, the application programming interfaces (APIs) and transducer electronic data sheet (TEDS) are also standardized. Based on the IEEE 1451, a standardized structure for LPWANs, namely IEEE1451-LPWAN is introduced. In addition, an M/M/1/N based queueing model is built to analyze the queueing performance of IEEE1451-LPWAN, which provides guidance for adopters in the future.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"188 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123255977","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