Pub Date : 2022-07-25DOI: 10.1109/INDIN51773.2022.9976157
Quang-Duy Nguyen, S. Dhouib, K. Suri, Fadwa Rekik
Open Platform Communication Unified Architecture (OPC UA) has emerged as a highly-demanded standard in building industrial systems. One factor in this success is the concepts of OPC UA address space and OPC UA information model. OPC UA address space provides a mechanism to represent the resources of an OPC UA server and its field devices as OPC UA nodes. OPC UA information model structures OPC UA nodes as a schema. Once other devices and systems understand the schema, they can interact appropriately with the industrial system. Thus, designing the OPC UA information model is an undeniable and essential practice of OPC UA-based industrial system engineering. However, both academia and industry suffer from a lack of shared experiences on this subject. This gap motivated us to share our experiences on the development of an OPC UA information model. These experiences are based on an actual product assembly line monitoring case study developed at CEA LIST. Overall, in this paper, we aim to throw some light on the steps involved in the transformation of a requirement specification into an OPC UA information model.
{"title":"From Requirement Specification to OPC UA Information Model Design: A Product Assembly Line Monitoring Case Study","authors":"Quang-Duy Nguyen, S. Dhouib, K. Suri, Fadwa Rekik","doi":"10.1109/INDIN51773.2022.9976157","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976157","url":null,"abstract":"Open Platform Communication Unified Architecture (OPC UA) has emerged as a highly-demanded standard in building industrial systems. One factor in this success is the concepts of OPC UA address space and OPC UA information model. OPC UA address space provides a mechanism to represent the resources of an OPC UA server and its field devices as OPC UA nodes. OPC UA information model structures OPC UA nodes as a schema. Once other devices and systems understand the schema, they can interact appropriately with the industrial system. Thus, designing the OPC UA information model is an undeniable and essential practice of OPC UA-based industrial system engineering. However, both academia and industry suffer from a lack of shared experiences on this subject. This gap motivated us to share our experiences on the development of an OPC UA information model. These experiences are based on an actual product assembly line monitoring case study developed at CEA LIST. Overall, in this paper, we aim to throw some light on the steps involved in the transformation of a requirement specification into an OPC UA information model.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125214218","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 : 2022-07-25DOI: 10.1109/INDIN51773.2022.9976174
Jingfang Ding, M. Zheng
Industrial Wireless Networks (IWNs) are expected to guarantee the Ultra-Reliable Low-Latency Communication (URLLC) in future manufacturing systems. However, due to limited resources and unstable radio environment, it is an inherent challenge to improve transmission reliability while maintaining low latency. This paper considers the uplink URLLC of mixed traffic (deterministic traffic and sporadic traffic) in IWNs with multiple channels. For deterministic traffic, an Automatic On-demand Retransmission scheme based on NACK REpetitions (AOR-NRE) is proposed. For sporadic traffic, a Flexible Repetition Coding-based Contention scheme (FRCC) is proposed. Then, the adaptive decision for mixed traffic on a time slicing based scheme and a frequency slicing based scheme is explored based on reliability analysis of AOR-NRE and FRCC. Finally, the advantages of the proposed retransmission schemes over existing works and the significance of adaptive decision are demonstrated via numerical results.
{"title":"Resource Allocation and Retransmission Scheme for URLLC in Industrial Wireless Networks with Mixed Traffic","authors":"Jingfang Ding, M. Zheng","doi":"10.1109/INDIN51773.2022.9976174","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976174","url":null,"abstract":"Industrial Wireless Networks (IWNs) are expected to guarantee the Ultra-Reliable Low-Latency Communication (URLLC) in future manufacturing systems. However, due to limited resources and unstable radio environment, it is an inherent challenge to improve transmission reliability while maintaining low latency. This paper considers the uplink URLLC of mixed traffic (deterministic traffic and sporadic traffic) in IWNs with multiple channels. For deterministic traffic, an Automatic On-demand Retransmission scheme based on NACK REpetitions (AOR-NRE) is proposed. For sporadic traffic, a Flexible Repetition Coding-based Contention scheme (FRCC) is proposed. Then, the adaptive decision for mixed traffic on a time slicing based scheme and a frequency slicing based scheme is explored based on reliability analysis of AOR-NRE and FRCC. Finally, the advantages of the proposed retransmission schemes over existing works and the significance of adaptive decision are demonstrated via numerical results.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125444590","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 : 2022-07-25DOI: 10.1109/INDIN51773.2022.9976114
Shao-Jun Xu, Hongxin Huan, Y. Qi, Guoxiang Guo, J. Yen
In financial field, predicting the future price of an asset has always been a hot topic. There are mainly two existing methods: One is to model the trend of asset prices in price prediction. Therefore, this method inevitably has a lag at the inflection point of the asset sequence. The other is to mine market opinion information from the internet to predict the future direction of prices. The challenge with this approach is that unstructured data processing and analysis is difficult. Therefore, we propose a method for asset movement prediction based on SABR [3] model. On the one hand, the market’s prediction of asset trends implied in options can be used to solve the hysteresis problem. On the other hand, options data is easy to process and analyze. In this article, we try to use a neural network model to capture the market’s view of the future trend of assets hidden in the stochastic volatility surface generated by the stochastic volatility model and establish a mapping relationship with asset prices. The results show that our methods can effectively eliminate the lag of price prediction and improve the accuracy of the prediction.
{"title":"Asset Movement Forcasting with the Implied Volatility Surface Analysis Based on SABR Model","authors":"Shao-Jun Xu, Hongxin Huan, Y. Qi, Guoxiang Guo, J. Yen","doi":"10.1109/INDIN51773.2022.9976114","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976114","url":null,"abstract":"In financial field, predicting the future price of an asset has always been a hot topic. There are mainly two existing methods: One is to model the trend of asset prices in price prediction. Therefore, this method inevitably has a lag at the inflection point of the asset sequence. The other is to mine market opinion information from the internet to predict the future direction of prices. The challenge with this approach is that unstructured data processing and analysis is difficult. Therefore, we propose a method for asset movement prediction based on SABR [3] model. On the one hand, the market’s prediction of asset trends implied in options can be used to solve the hysteresis problem. On the other hand, options data is easy to process and analyze. In this article, we try to use a neural network model to capture the market’s view of the future trend of assets hidden in the stochastic volatility surface generated by the stochastic volatility model and establish a mapping relationship with asset prices. The results show that our methods can effectively eliminate the lag of price prediction and improve the accuracy of the prediction.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123609643","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 : 2022-07-25DOI: 10.1109/INDIN51773.2022.9976076
Guanwen Cui, Zhezhuang Xu, Xuchao Gao, Songbing Lin, Yi Guo
Single twisted pair Ethernet becomes popular in the industrial internet of thing (IIoT), since it can use only one twisted pair to provide high speed data transmission while the cables of the field bus can be reused. However, since its transmission medium is inferior to traditional Ethernet, it is easier to generate delay jitter that greatly impacts the accuracy of time synchronization. To solve this problem, in this paper, an offset estimation method based on AutoRegressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) is proposed to estimate the clock offset when the delay jitter appears. The offset estimation model is firstly obtained by training the ARIMA-LSTM with offline offset data. When the delay jitter is detected, the offset can be estimated by the model to replace the unreliable offset obtained by the time synchronization protocol. Experiments are executed in the testbed, and the results prove that the proposed method can improve the time synchronization accuracy in the single twisted pair Ethernet.
{"title":"Offset Estimation Based on ARIMA-LSTM for Time Synchronization in Single Twisted Pair Ethernet","authors":"Guanwen Cui, Zhezhuang Xu, Xuchao Gao, Songbing Lin, Yi Guo","doi":"10.1109/INDIN51773.2022.9976076","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976076","url":null,"abstract":"Single twisted pair Ethernet becomes popular in the industrial internet of thing (IIoT), since it can use only one twisted pair to provide high speed data transmission while the cables of the field bus can be reused. However, since its transmission medium is inferior to traditional Ethernet, it is easier to generate delay jitter that greatly impacts the accuracy of time synchronization. To solve this problem, in this paper, an offset estimation method based on AutoRegressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) is proposed to estimate the clock offset when the delay jitter appears. The offset estimation model is firstly obtained by training the ARIMA-LSTM with offline offset data. When the delay jitter is detected, the offset can be estimated by the model to replace the unreliable offset obtained by the time synchronization protocol. Experiments are executed in the testbed, and the results prove that the proposed method can improve the time synchronization accuracy in the single twisted pair Ethernet.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114457557","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 : 2022-07-25DOI: 10.1109/INDIN51773.2022.9976175
Y. Qi, Guoxiang Guo, Yang Wang, Jerome Yen
Nowadays, people are showing growing attention to the market movements. With more demand for market sentiment analysis and risk management, advanced investment tools are needed to assist the high frequency trading activities. Machine learning as a fast-growing tool provides people a new perspective to handle complex problems. Although financial data contains various information and is usually regarded as hard to concentrate into one unified dimension, our research aims to fuse the image processing method with the high frequency implied-volatility-based market sentiment analysis. In this way, our research implemented the real-time processing of the market data and proposes an innovative idea, applying the machine learning method to regress the market price using the two-dimensional discrete financial data, which is traditionally viewed as images. The proposed method shows satisfying performance in testing with tick-level S&P500 option dataset containing around 1.5 million trading record. To go further with the improvement of the economic image classification and represent the momentum factors of the implied volatility surface images, we also introduce the speed and acceleration of sequence images. Overall, we have reached 61.23% accuracy for implied volatility image classification, and 63.22% & 65.52% accuracy for financial image considering velocity and acceleration.
{"title":"Image Processing Based Implied Volatility Surface Analysis for Asset movement Forecasting","authors":"Y. Qi, Guoxiang Guo, Yang Wang, Jerome Yen","doi":"10.1109/INDIN51773.2022.9976175","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976175","url":null,"abstract":"Nowadays, people are showing growing attention to the market movements. With more demand for market sentiment analysis and risk management, advanced investment tools are needed to assist the high frequency trading activities. Machine learning as a fast-growing tool provides people a new perspective to handle complex problems. Although financial data contains various information and is usually regarded as hard to concentrate into one unified dimension, our research aims to fuse the image processing method with the high frequency implied-volatility-based market sentiment analysis. In this way, our research implemented the real-time processing of the market data and proposes an innovative idea, applying the machine learning method to regress the market price using the two-dimensional discrete financial data, which is traditionally viewed as images. The proposed method shows satisfying performance in testing with tick-level S&P500 option dataset containing around 1.5 million trading record. To go further with the improvement of the economic image classification and represent the momentum factors of the implied volatility surface images, we also introduce the speed and acceleration of sequence images. Overall, we have reached 61.23% accuracy for implied volatility image classification, and 63.22% & 65.52% accuracy for financial image considering velocity and acceleration.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125276662","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 : 2022-07-25DOI: 10.1109/INDIN51773.2022.9976099
Xuan-Thuy Vo, T. Tran, Duy-Linh Nguyen, K. Jo
Accurate instance segmentation requires high-resolution features for performing a dense pixel-wise prediction task. However, using high-resolution feature maps results in highly expensive model complexity and ineffective receptive fields. To overcome the problems of high-resolution features, conventional methods explore multi-level feature fusion that exchanges the information between low-level features at earlier layers and high-level features at top layers. Both low and high information is extracted by the hierarchical backbone network where high-level features contain more semantic cues and low-level features encompass more specific patterns. Thus, adopting these features to the training segmentation model is necessary, and designing a more efficient multi-level feature fusion is crucial. Existing methods balance such information by using top-down and bottom-up pathway connections with more inefficient convolution layers to produce richer multi-scale features. In this work, we contribute two folds: (1) a simple but effective multilevel feature reweighting layer is proposed to strengthen deep high-level features based on channel reweighting generated from multiple features of the backbone, and (2) an efficient fusion block is proposed to process low-resolution features in a depth-to-spatial manner and combine enhanced multi-level features together. These designs enable the segmentation models to predict instance kernels for mask generation on high-level feature maps. To verify the effectiveness of the proposed method, we conduct experiments on the challenging benchmark dataset MS-COCO. Surprisingly, our simple network outperforms the baseline in both accuracy and inference speed. More specifically, we achieve 35.4% APmask at 19.5 FPS on a GPU device, becoming a state-of-the-art instance segmentation method.
{"title":"Multi-level Feature Reweighting and Fusion for Instance Segmentation","authors":"Xuan-Thuy Vo, T. Tran, Duy-Linh Nguyen, K. Jo","doi":"10.1109/INDIN51773.2022.9976099","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976099","url":null,"abstract":"Accurate instance segmentation requires high-resolution features for performing a dense pixel-wise prediction task. However, using high-resolution feature maps results in highly expensive model complexity and ineffective receptive fields. To overcome the problems of high-resolution features, conventional methods explore multi-level feature fusion that exchanges the information between low-level features at earlier layers and high-level features at top layers. Both low and high information is extracted by the hierarchical backbone network where high-level features contain more semantic cues and low-level features encompass more specific patterns. Thus, adopting these features to the training segmentation model is necessary, and designing a more efficient multi-level feature fusion is crucial. Existing methods balance such information by using top-down and bottom-up pathway connections with more inefficient convolution layers to produce richer multi-scale features. In this work, we contribute two folds: (1) a simple but effective multilevel feature reweighting layer is proposed to strengthen deep high-level features based on channel reweighting generated from multiple features of the backbone, and (2) an efficient fusion block is proposed to process low-resolution features in a depth-to-spatial manner and combine enhanced multi-level features together. These designs enable the segmentation models to predict instance kernels for mask generation on high-level feature maps. To verify the effectiveness of the proposed method, we conduct experiments on the challenging benchmark dataset MS-COCO. Surprisingly, our simple network outperforms the baseline in both accuracy and inference speed. More specifically, we achieve 35.4% APmask at 19.5 FPS on a GPU device, becoming a state-of-the-art instance segmentation method.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129692170","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 : 2022-07-25DOI: 10.1109/INDIN51773.2022.9976108
Inês Lino, J. Cecílio
The Internet of Things has been applied in different application contexts, ranging from industrial, healthcare or simple home life applications. It allows adding a new level of automation to objects, making them appealing to everyone. Since several applications involve data, and in some cases, it is sensitive, those applications are more vulnerable to be attacked. As a result, researchers are constantly exploring secure mechanisms to keep the data and its owners safe. For the resource-constrained nature of these IoT applications, the design of cryptography solutions becomes a challenging task because IoT nodes should be cheap and require low power, which means lower computational performance. Lightweight cryptography algorithms are attractive solutions to reduce computation complexity, keeping the desired level of security. This work provides an experimental performance analysis of several cryptography solutions targeted for embedded platforms to help choose better algorithms for IoT systems in terms of efficiency. Execution time, code efficiency and communication influences are evaluated for a set of cryptography algorithms. The results of this work intend to help IoT developers to choose a suitable cryptography algorithm for their IoT applications.
{"title":"A Comparative Analysis of the Impact of Cryptography in IoT LoRa Applications","authors":"Inês Lino, J. Cecílio","doi":"10.1109/INDIN51773.2022.9976108","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976108","url":null,"abstract":"The Internet of Things has been applied in different application contexts, ranging from industrial, healthcare or simple home life applications. It allows adding a new level of automation to objects, making them appealing to everyone. Since several applications involve data, and in some cases, it is sensitive, those applications are more vulnerable to be attacked. As a result, researchers are constantly exploring secure mechanisms to keep the data and its owners safe. For the resource-constrained nature of these IoT applications, the design of cryptography solutions becomes a challenging task because IoT nodes should be cheap and require low power, which means lower computational performance. Lightweight cryptography algorithms are attractive solutions to reduce computation complexity, keeping the desired level of security. This work provides an experimental performance analysis of several cryptography solutions targeted for embedded platforms to help choose better algorithms for IoT systems in terms of efficiency. Execution time, code efficiency and communication influences are evaluated for a set of cryptography algorithms. The results of this work intend to help IoT developers to choose a suitable cryptography algorithm for their IoT applications.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130165287","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 : 2022-07-25DOI: 10.1109/INDIN51773.2022.9976087
Jaekwon Lee, Jooyoung Kim, K. Toh
In this paper, we propose to extract the intersection points of the palmprint and the palm-vein lines from multi-spectral images and use them as reliable features for identity verification. Essentially, by utilizing a sum of cardinal directional image difference operation, the palmprint and palm-vein line features are respectively extracted from palm images of the Blue channel and the NIR channel of image spectrums based on simple matrix projection. Subsequently, the intersection locations of the two biometric line features are extracted and utilized to compute a set of keypoint descriptors. After calculating the match scores based on the extracted keypoint descriptors, a score level fusion of the matching results obtained from the Blue channel and the NIR channel is adopted to enhance the verification performance. The proposed method has been experimented on a public domain multispectral palm database where encouraging results in terms of verification accuracy have been obtained.
{"title":"Identity Verification based on the RGB and NIR Images of the Palm","authors":"Jaekwon Lee, Jooyoung Kim, K. Toh","doi":"10.1109/INDIN51773.2022.9976087","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976087","url":null,"abstract":"In this paper, we propose to extract the intersection points of the palmprint and the palm-vein lines from multi-spectral images and use them as reliable features for identity verification. Essentially, by utilizing a sum of cardinal directional image difference operation, the palmprint and palm-vein line features are respectively extracted from palm images of the Blue channel and the NIR channel of image spectrums based on simple matrix projection. Subsequently, the intersection locations of the two biometric line features are extracted and utilized to compute a set of keypoint descriptors. After calculating the match scores based on the extracted keypoint descriptors, a score level fusion of the matching results obtained from the Blue channel and the NIR channel is adopted to enhance the verification performance. The proposed method has been experimented on a public domain multispectral palm database where encouraging results in terms of verification accuracy have been obtained.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129175320","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 : 2022-07-25DOI: 10.1109/INDIN51773.2022.9976117
Mercedes Chacón Vásquez
A control over the network for an activated sludge wastewater treatment plant (WWTP) is presented. A predictive PID control algorithm is applied to the dissolved oxygen concentration in the aerated tank and to the nitrate level. To increase the reliability and performance of the networked solution, the effect of dropouts in the transmission was also investigated. The control strategy is evaluated using a networked wastewater control system simulator, which is an extension of the Benchmark Simulation Model No. 1 (BSM1) and a network simulator. The results showed that the approach can be used effectively for control in WWTP, since it achieves satisfactory dissolved oxygen control and dropout compensation.
{"title":"Control over networks: A case study of Wastewater Treatment Plants","authors":"Mercedes Chacón Vásquez","doi":"10.1109/INDIN51773.2022.9976117","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976117","url":null,"abstract":"A control over the network for an activated sludge wastewater treatment plant (WWTP) is presented. A predictive PID control algorithm is applied to the dissolved oxygen concentration in the aerated tank and to the nitrate level. To increase the reliability and performance of the networked solution, the effect of dropouts in the transmission was also investigated. The control strategy is evaluated using a networked wastewater control system simulator, which is an extension of the Benchmark Simulation Model No. 1 (BSM1) and a network simulator. The results showed that the approach can be used effectively for control in WWTP, since it achieves satisfactory dissolved oxygen control and dropout compensation.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127211013","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 : 2022-07-25DOI: 10.1109/INDIN51773.2022.9976106
Steve Yuwono, Andreas Schwung, Dorothea Schwung
In this paper, we discuss the impact of communication and memory-based learners on distributed self-optimization of smart and flexible manufacturing units. Specifically, we employ the recently proposed framework of state-based potential games, which has proven to be successful in allowing distributed optimization in multi-agent systems. We first augment the framework with additional communication capabilities for the individual players and analyze the efficacy of state and action communications within the different players. Second, we incorporate memory states within the learning dynamics of the players and analyze their impact on the learning performance. The proposed method is inspired by the promising results of memory-based reinforcement learning. However, previous studies have rarely dealt with distributed manufacturing control. We believe that it will be important to explore the potential use of the communication and memory-based approaches in manufacturing control with multi-agent settings. Hence, the proposed method is applied to a bulk good laboratory plant providing a thorough experimental analysis of the effect of the various improvements with very encouraging results.
{"title":"The Impact of Communication and Memory in State-Based Potential Game-based Distributed Optimization","authors":"Steve Yuwono, Andreas Schwung, Dorothea Schwung","doi":"10.1109/INDIN51773.2022.9976106","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976106","url":null,"abstract":"In this paper, we discuss the impact of communication and memory-based learners on distributed self-optimization of smart and flexible manufacturing units. Specifically, we employ the recently proposed framework of state-based potential games, which has proven to be successful in allowing distributed optimization in multi-agent systems. We first augment the framework with additional communication capabilities for the individual players and analyze the efficacy of state and action communications within the different players. Second, we incorporate memory states within the learning dynamics of the players and analyze their impact on the learning performance. The proposed method is inspired by the promising results of memory-based reinforcement learning. However, previous studies have rarely dealt with distributed manufacturing control. We believe that it will be important to explore the potential use of the communication and memory-based approaches in manufacturing control with multi-agent settings. Hence, the proposed method is applied to a bulk good laboratory plant providing a thorough experimental analysis of the effect of the various improvements with very encouraging results.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133478024","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：