Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557554
Robert F. Maack, Hasan Tercan, A. F. Solvay, Maximilian Mieth, Tobias Meisen
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.
{"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}
Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557407
Ziang Wei, H. Fernandes, J. Tarpani, A. Osman, X. Maldague
Pulsed thermography is one of the most popular thermography inspection methods. During an experiment of pulsed thermography, a specimen is quickly heated, and infrared images are captured to provide information about the specimen’s surface and subsurface conditions. Adequate transformations are usually performed to enhance the contrast of the thermal images and to highlight the abnormal regions before these thermal images are visually inspected. Given that deep neural networks have been a success in computer vision in the past few years, a data contrast enhancement approach with stacked denoising autoencoder (DAE) is proposed in this paper to enhance the abnormal regions in the thermal frames gathered by pulsed thermography. Compared to the direct principal component thermography, the proposed method can enhance the abnormalities evidently without weakening important details.
{"title":"Stacked denoising autoencoder for infrared thermography image enhancement","authors":"Ziang Wei, H. Fernandes, J. Tarpani, A. Osman, X. Maldague","doi":"10.1109/INDIN45523.2021.9557407","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557407","url":null,"abstract":"Pulsed thermography is one of the most popular thermography inspection methods. During an experiment of pulsed thermography, a specimen is quickly heated, and infrared images are captured to provide information about the specimen’s surface and subsurface conditions. Adequate transformations are usually performed to enhance the contrast of the thermal images and to highlight the abnormal regions before these thermal images are visually inspected. Given that deep neural networks have been a success in computer vision in the past few years, a data contrast enhancement approach with stacked denoising autoencoder (DAE) is proposed in this paper to enhance the abnormal regions in the thermal frames gathered by pulsed thermography. Compared to the direct principal component thermography, the proposed method can enhance the abnormalities evidently without weakening important details.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"1 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":"115631063","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}
Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557525
Andreas Glinserer, Martin Lechner, Alexander Wendt
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.
{"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}
Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557366
H. Hiraga, H. Nishi
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.
{"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}
Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557387
Marwin Züfle, Joachim Agne, Johannes Grohmann, Ibrahim Dörtoluk, Samuel Kounev
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.
{"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}
Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557538
Jiaqi Sun, Yong Jiang, Jian Lin
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.
{"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}
Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557389
Tianfan Xue, P. Zeng, Haibin Yu
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.
{"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}
Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557535
Niclas Ericsson, J. Åkerberg, M. Björkman, T. Lennvall, S. Larsson, Hongyu Pei Breivold
Most evaluations of industrial real-time software are conducted on real embedded systems. The use of simulators that provides easily reproducible evaluations is often limited, due to different levels of abstraction, e.g., programming languages and run-time contexts. This paper extends previous work on a flexible task design, enabling tasks to be agnostic to run-time context, with evaluations conducted on bare-metal and real-time operating systems. Based on the same design and experiments we extend the proof-of-concept implementation in a discrete event simulation context, executing on a Windows based simulation host. Our experiments show that the flexible task design can be driven in a simulation run-time context, and still support typical industrial constructs. The result indicates that improved code reuse between discrete event simulators and industrial embedded systems is feasible.
{"title":"Improving Code Reuse between Industrial Embedded Systems and Discrete Event Simulators","authors":"Niclas Ericsson, J. Åkerberg, M. Björkman, T. Lennvall, S. Larsson, Hongyu Pei Breivold","doi":"10.1109/INDIN45523.2021.9557535","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557535","url":null,"abstract":"Most evaluations of industrial real-time software are conducted on real embedded systems. The use of simulators that provides easily reproducible evaluations is often limited, due to different levels of abstraction, e.g., programming languages and run-time contexts. This paper extends previous work on a flexible task design, enabling tasks to be agnostic to run-time context, with evaluations conducted on bare-metal and real-time operating systems. Based on the same design and experiments we extend the proof-of-concept implementation in a discrete event simulation context, executing on a Windows based simulation host. Our experiments show that the flexible task design can be driven in a simulation run-time context, and still support typical industrial constructs. The result indicates that improved code reuse between discrete event simulators and industrial embedded systems is feasible.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"35 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":"125106560","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}
Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557476
Sérgio Teixeira, Rafael Arrais, G. Veiga
Continuous Integration and Deployment in the robotics domain is still underutilized when compared to other fields of software development. Also, conventional testing techniques used in CI/CD pipelines are usually not enough to fully test a robotic project in its integrity. In this paper, an analysis is made regarding the usage of CI/CD techniques in robotic related repositories to both verify the veracity of these statements, as well as finding their causes. Additionally, a novel approach in the scope of CI/CD is explored, making use of cloud-based technologies to add additional automated simulation tests to the pipeline and integrate them with ease in the development of robotic software. Finally, the proposed approach is showcased in an industrial application.
{"title":"Cloud Simulation for Continuous Integration and Deployment in Robotics","authors":"Sérgio Teixeira, Rafael Arrais, G. Veiga","doi":"10.1109/INDIN45523.2021.9557476","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557476","url":null,"abstract":"Continuous Integration and Deployment in the robotics domain is still underutilized when compared to other fields of software development. Also, conventional testing techniques used in CI/CD pipelines are usually not enough to fully test a robotic project in its integrity. In this paper, an analysis is made regarding the usage of CI/CD techniques in robotic related repositories to both verify the veracity of these statements, as well as finding their causes. Additionally, a novel approach in the scope of CI/CD is explored, making use of cloud-based technologies to add additional automated simulation tests to the pipeline and integrate them with ease in the development of robotic software. Finally, the proposed approach is showcased in an industrial application.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"225 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":"122043946","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}
Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557375
Yang Wei, Yucheng Liu, K. Tsang, Hao Wang
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.
{"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}
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