Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557584
Yu Zhang, Po-Sung Yang, V. Lanfranchi
Alzheimer's disease (AD) is a typical progressive neurodegenerative disease with insidious onset. Utilising various biomarkers to track and predict AD progression for supporting clinic decisions has recently received wide attentions. Accurate prediction of disease progression will help clinicians and patients make the best decisions on disease prevention and treatment. Typical prediction models focus on extracting biomarker morphological information of different regions of interest (ROIs) from magnetic resonance imaging (MRI) or positron emission tomography (PET), such as the average regional cortical thickness and regional volume. They are effective in modeling AD progression and understanding AD biomarkers, but cannot make full utilise of the internal temporal and spatial relationships between these biomarkers to improve the accuracy and stability of AD prediction. In this paper, we propose a new multi-task learning (MTL) method based on the tensor composed of the spatio-temporal similarity measure between brain biomarkers, using MRI data and cognitive scores of AD patients in different stages can effectively predict the progression of AD. Specifically, we define a temporal and spatial feature similarity measure to calculate the rate of change and velocity of each biomarker in MRI to form a vector, which represents the morphological changing trend of the biomarker, then we calculate the similarity of the changing trend between two biomarkers and encode the data to the third-order tensor, and extract interpretable biomarker latent factors from the original data. The prediction of each patient sample in the tensor is a task and all prediction tasks share a set of latent factors obtained from tensor decomposition to train the AD progression prediction model, which learns task correlation from the spatiotemporal tensor itself. We conducted extensive experiments utilising the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Experimental results show that compared with ROI-based traditional single feature regression methods, our proposed method has better accuracy and stability in disease progression prediction in terms of root mean square error exhibiting an average of 4.10 decrease compared to Ridge regression, 0.19 decrease compared to Lasso regression and 0.18 decrease compared to Temporal Group Lasso (TGL) in the Mini Mental State Examination (MMSE) questionnaire.
{"title":"Tensor Multi-Task Learning for Predicting Alzheimer’s Disease Progression using MRI data with Spatio-temporal Similarity Measurement","authors":"Yu Zhang, Po-Sung Yang, V. Lanfranchi","doi":"10.1109/INDIN45523.2021.9557584","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557584","url":null,"abstract":"Alzheimer's disease (AD) is a typical progressive neurodegenerative disease with insidious onset. Utilising various biomarkers to track and predict AD progression for supporting clinic decisions has recently received wide attentions. Accurate prediction of disease progression will help clinicians and patients make the best decisions on disease prevention and treatment. Typical prediction models focus on extracting biomarker morphological information of different regions of interest (ROIs) from magnetic resonance imaging (MRI) or positron emission tomography (PET), such as the average regional cortical thickness and regional volume. They are effective in modeling AD progression and understanding AD biomarkers, but cannot make full utilise of the internal temporal and spatial relationships between these biomarkers to improve the accuracy and stability of AD prediction. In this paper, we propose a new multi-task learning (MTL) method based on the tensor composed of the spatio-temporal similarity measure between brain biomarkers, using MRI data and cognitive scores of AD patients in different stages can effectively predict the progression of AD. Specifically, we define a temporal and spatial feature similarity measure to calculate the rate of change and velocity of each biomarker in MRI to form a vector, which represents the morphological changing trend of the biomarker, then we calculate the similarity of the changing trend between two biomarkers and encode the data to the third-order tensor, and extract interpretable biomarker latent factors from the original data. The prediction of each patient sample in the tensor is a task and all prediction tasks share a set of latent factors obtained from tensor decomposition to train the AD progression prediction model, which learns task correlation from the spatiotemporal tensor itself. We conducted extensive experiments utilising the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Experimental results show that compared with ROI-based traditional single feature regression methods, our proposed method has better accuracy and stability in disease progression prediction in terms of root mean square error exhibiting an average of 4.10 decrease compared to Ridge regression, 0.19 decrease compared to Lasso regression and 0.18 decrease compared to Temporal Group Lasso (TGL) in the Mini Mental State Examination (MMSE) questionnaire.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"48 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":"127234062","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/INDIN45523.2021.9557388
Dapeng Zhang, Zhiwei Gao
The great success of deep neural network (DNN) in image field stimulates its application in fault detection and diagnose. However due to the limitation of system security, it is impossible to obtain complete fault data as the training database for neural network, so that it is challenging to identify a fault that never occurred before. In this paper, an ensemble approach is proposed to adapt to a new fault by adding output branches of the neural network. Firstly, the time series are transferred to numerous imaging matrixes. The intrinsic characteristics of the matrixes are then extracted using deep neural network which are used to judge whether it is a new fault according to the distance criterion. For a new fault, the DNN will retrain by transferring learning in order to reduce the computation and training time. The effectiveness of the algorithm is demonstrated by a numerical simulation example based on a wind turbine benchmark model.
{"title":"An Ensemble Approach for Fault Diagnosis via Continuous Learning","authors":"Dapeng Zhang, Zhiwei Gao","doi":"10.1109/INDIN45523.2021.9557388","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557388","url":null,"abstract":"The great success of deep neural network (DNN) in image field stimulates its application in fault detection and diagnose. However due to the limitation of system security, it is impossible to obtain complete fault data as the training database for neural network, so that it is challenging to identify a fault that never occurred before. In this paper, an ensemble approach is proposed to adapt to a new fault by adding output branches of the neural network. Firstly, the time series are transferred to numerous imaging matrixes. The intrinsic characteristics of the matrixes are then extracted using deep neural network which are used to judge whether it is a new fault according to the distance criterion. For a new fault, the DNN will retrain by transferring learning in order to reduce the computation and training time. The effectiveness of the algorithm is demonstrated by a numerical simulation example based on a wind turbine benchmark model.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"54 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":"125463097","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/INDIN45523.2021.9557472
Tiankai Jin, Zhiduo Ji, Shanying Zhu, Cailian Chen
Industrial cyber-physical systems (ICPS) refer to an emerging generation of intelligent systems, where distributed data acquisition is of great importance and is influenced by data transmission. In the improvement of the overall performance of sensing accuracy and energy efficiency, sensing and transmission are tightly coupled. Due to the unknown transmission channel states in the harsh industrial field environment, intelligently performing sensor scheduling for distributed sensing is challenging. In this paper, edge computing technology is utilized to enhance the level of intelligence at the edge side and deploy advanced scheduling algorithms. We propose a learning-based distributed edge sensing-transmission co-design (LEST) algorithm under the coordination of the sensors and the edge computing unit (ECU). Deep reinforcement learning is applied to perform real-time sensor scheduling under unknown channel states. The conditions for the existence of feasible scheduling policies are analyzed. The proposed algorithm is applied to estimate the slab temperature in the hot rolling process, which is a typical ICPS. The simulation results demonstrate that the overall performance of LEST is better than other suboptimal algorithms.
{"title":"Learning-based Co-Design of Distributed Edge Sensing and Transmission for Industrial Cyber-Physical Systems","authors":"Tiankai Jin, Zhiduo Ji, Shanying Zhu, Cailian Chen","doi":"10.1109/INDIN45523.2021.9557472","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557472","url":null,"abstract":"Industrial cyber-physical systems (ICPS) refer to an emerging generation of intelligent systems, where distributed data acquisition is of great importance and is influenced by data transmission. In the improvement of the overall performance of sensing accuracy and energy efficiency, sensing and transmission are tightly coupled. Due to the unknown transmission channel states in the harsh industrial field environment, intelligently performing sensor scheduling for distributed sensing is challenging. In this paper, edge computing technology is utilized to enhance the level of intelligence at the edge side and deploy advanced scheduling algorithms. We propose a learning-based distributed edge sensing-transmission co-design (LEST) algorithm under the coordination of the sensors and the edge computing unit (ECU). Deep reinforcement learning is applied to perform real-time sensor scheduling under unknown channel states. The conditions for the existence of feasible scheduling policies are analyzed. The proposed algorithm is applied to estimate the slab temperature in the hot rolling process, which is a typical ICPS. The simulation results demonstrate that the overall performance of LEST is better than other suboptimal algorithms.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"37 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":"121802926","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/INDIN45523.2021.9557446
Riccardo Colelli, Chiara Foglietta, Roberto Fusacchia, S. Panzieri, F. Pascucci
Critical Infrastructures (CIs) such as power grid, water and gas distribution are controlled by Industrial Control Systems (ICS). Sensors and actuators of a physical plant are managed by the ICS. Data and commands transmitted over the network from the Programmable Logic Controllers (PLCs) are saved and parsed within the Historian. Generally, this architecture guarantees to check for any process anomalies that may occur due to component failures and cyber attacks. The other use of this data allows activities such as forensic analysis. To secure the network is also crucial to protect the communication between devices. A cyber attack on the log devices could jeopardize any forensic analysis be it for maintenance, or discovering an attack trail. In this paper is proposed a strategy to secure plant operational data recorded in the Historian and data exchange in the network. An integrity checking mechanism, in combination with blockchain, is used to ensure data integrity. Data redundancy is achieved by applying an efficient replication mechanism and enables data recovery after an attack.
{"title":"Blockchain application in simulated environment for Cyber-Physical Systems Security","authors":"Riccardo Colelli, Chiara Foglietta, Roberto Fusacchia, S. Panzieri, F. Pascucci","doi":"10.1109/INDIN45523.2021.9557446","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557446","url":null,"abstract":"Critical Infrastructures (CIs) such as power grid, water and gas distribution are controlled by Industrial Control Systems (ICS). Sensors and actuators of a physical plant are managed by the ICS. Data and commands transmitted over the network from the Programmable Logic Controllers (PLCs) are saved and parsed within the Historian. Generally, this architecture guarantees to check for any process anomalies that may occur due to component failures and cyber attacks. The other use of this data allows activities such as forensic analysis. To secure the network is also crucial to protect the communication between devices. A cyber attack on the log devices could jeopardize any forensic analysis be it for maintenance, or discovering an attack trail. In this paper is proposed a strategy to secure plant operational data recorded in the Historian and data exchange in the network. An integrity checking mechanism, in combination with blockchain, is used to ensure data integrity. Data redundancy is achieved by applying an efficient replication mechanism and enables data recovery after an attack.","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":"129674933","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/INDIN45523.2021.9557363
Michela Zaccaria, M. Giorgini, Riccardo Monica, J. Aleotti
In this work a multi-robot system is presented for people detection and tracking in automated warehouses. Each Automated Guided Vehicle (AGV) is equipped with multiple RGB cameras that can track the workers’ current locations on the floor thanks to a neural network that provides human pose estimation. Based on the local perception of the environment each AGV can exploit information about the tracked people for self-motion planning or collision avoidance.Additionally, data collected from each robot contributes to a global people detection and tracking system. A warehouse central management software fuses information received from all AGVs into a map of the current locations of workers. The estimated locations of workers are sent back to the AGVs to prevent potential collision. The proposed method is based on two-level hierarchy of Kalman filters. Experiments performed in a real warehouse show the viability of the proposed approach.
{"title":"Multi-Robot Multiple Camera People Detection and Tracking in Automated Warehouses","authors":"Michela Zaccaria, M. Giorgini, Riccardo Monica, J. Aleotti","doi":"10.1109/INDIN45523.2021.9557363","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557363","url":null,"abstract":"In this work a multi-robot system is presented for people detection and tracking in automated warehouses. Each Automated Guided Vehicle (AGV) is equipped with multiple RGB cameras that can track the workers’ current locations on the floor thanks to a neural network that provides human pose estimation. Based on the local perception of the environment each AGV can exploit information about the tracked people for self-motion planning or collision avoidance.Additionally, data collected from each robot contributes to a global people detection and tracking system. A warehouse central management software fuses information received from all AGVs into a map of the current locations of workers. The estimated locations of workers are sent back to the AGVs to prevent potential collision. The proposed method is based on two-level hierarchy of Kalman filters. Experiments performed in a real warehouse show the viability of the proposed approach.","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":"126947747","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/INDIN45523.2021.9557392
Yimin Yang, Min Wu
Model explainability has become an important objective when developing machine learning algorithms, especially in highly regulated industries. However, it is difficult to achieve both prediction accuracy and intrinsic explainability as the two objectives usually conflict with each other. Recent development regarding Explainable Neural Network, or xNN, shed some lights on resolving the trade-off between accuracy and explainability for neural network. In this paper, we propose an xNN approach to develop or improve logistic regressions, which can be useful in credit risk modeling and money-laundering or fraud detection. Our data experiment shows that the proposed xNN model keeps the flexibility of pursuing high prediction accuracy while attaining improved explainability.
{"title":"Explainable Machine Learning for Improving Logistic Regression Models","authors":"Yimin Yang, Min Wu","doi":"10.1109/INDIN45523.2021.9557392","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557392","url":null,"abstract":"Model explainability has become an important objective when developing machine learning algorithms, especially in highly regulated industries. However, it is difficult to achieve both prediction accuracy and intrinsic explainability as the two objectives usually conflict with each other. Recent development regarding Explainable Neural Network, or xNN, shed some lights on resolving the trade-off between accuracy and explainability for neural network. In this paper, we propose an xNN approach to develop or improve logistic regressions, which can be useful in credit risk modeling and money-laundering or fraud detection. Our data experiment shows that the proposed xNN model keeps the flexibility of pursuing high prediction accuracy while attaining improved explainability.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"80 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":"134215885","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/INDIN45523.2021.9557468
Tobias Striffler, H. Schotten
Deterministic communication across integrated wired and wireless networks is currently one of the big topics in research and standardization. 5G and TSN integration efforts are at the forefront of enabling the convergence of wired and wireless networks for Industry 4.0.In this paper, we investigate how synchronization and syntonization errors affect the achievable end-to-end time synchronization accuracy in integrated 5G and TSN networks. We specifically focus on the impact of the 5G System modeling a TSN transparent clock according to 3GPP Release 17.
{"title":"The 5G Transparent Clock: Synchronization Errors in Integrated 5G-TSN Industrial Networks","authors":"Tobias Striffler, H. Schotten","doi":"10.1109/INDIN45523.2021.9557468","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557468","url":null,"abstract":"Deterministic communication across integrated wired and wireless networks is currently one of the big topics in research and standardization. 5G and TSN integration efforts are at the forefront of enabling the convergence of wired and wireless networks for Industry 4.0.In this paper, we investigate how synchronization and syntonization errors affect the achievable end-to-end time synchronization accuracy in integrated 5G and TSN networks. We specifically focus on the impact of the 5G System modeling a TSN transparent clock according to 3GPP Release 17.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"23 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":"129880298","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/INDIN45523.2021.9557461
V. Huang, H. Nishi, A. Espírito-Santo, Allen C. Chen, D. Bruckner
With the active development of IES in standards since the mid-2010s, the society has made considerable progress with multiple standards’ developments. This paper presents the results of engaging in standards’ development within and across borders of an IEEE society. In particular, the hands-on INTEROP Plugfests, coupled with the CoEs, provide platforms to create ideas for standards, develop standards, initiate interoperability among multiple vendors, providing competitive time-to-market advantage for involved industry partners.
{"title":"Standards and Interoperability in Industrial Electronics – A Trending View","authors":"V. Huang, H. Nishi, A. Espírito-Santo, Allen C. Chen, D. Bruckner","doi":"10.1109/INDIN45523.2021.9557461","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557461","url":null,"abstract":"With the active development of IES in standards since the mid-2010s, the society has made considerable progress with multiple standards’ developments. This paper presents the results of engaging in standards’ development within and across borders of an IEEE society. In particular, the hands-on INTEROP Plugfests, coupled with the CoEs, provide platforms to create ideas for standards, develop standards, initiate interoperability among multiple vendors, providing competitive time-to-market advantage for involved industry partners.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"122 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":"126697400","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/INDIN45523.2021.9557555
Hsien-I Lin, A. Singh
Force measurement and control for the automatic process are crucial in automation, especially in the insertion (mating) task. This fragile task is needs to be automated for safety and economical purposes. One small mistake and misjudgement by operators could damage the fragile component, and also cause the company material loss. In this paper, the mating process is implemented by an articulated robot with a force sensor mounted on it. We propose a data-driven approach for the procedure to automate the mating process of the slimstack Board-to-Board (BtB) insertion process. The force data is recorded and encoded to a recurrence 2D plot. Then the 2D image is used to predict the position and alignment of the male and female Board-toBoard connector. By using the encoding approach, the system can classify each corresponding force based on its status of BtB insertion and provide a safety procedure in the insertion process. The proposed model is compared with the efficient time series LSTM model.
{"title":"Board-to-Board connector mating using data-driven approach","authors":"Hsien-I Lin, A. Singh","doi":"10.1109/INDIN45523.2021.9557555","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557555","url":null,"abstract":"Force measurement and control for the automatic process are crucial in automation, especially in the insertion (mating) task. This fragile task is needs to be automated for safety and economical purposes. One small mistake and misjudgement by operators could damage the fragile component, and also cause the company material loss. In this paper, the mating process is implemented by an articulated robot with a force sensor mounted on it. We propose a data-driven approach for the procedure to automate the mating process of the slimstack Board-to-Board (BtB) insertion process. The force data is recorded and encoded to a recurrence 2D plot. Then the 2D image is used to predict the position and alignment of the male and female Board-toBoard connector. By using the encoding approach, the system can classify each corresponding force based on its status of BtB insertion and provide a safety procedure in the insertion process. The proposed model is compared with the efficient time series LSTM model.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"39 10 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":"133494277","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/INDIN45523.2021.9557445
A. Pakonen
For over a decade, model checking has been successfully used to formally verify the instrumentation and control (I&C) logic design in Finnish nuclear power plant projects. One of the practical challenges is that the model checker NuSMV forces the user to abstract the way analog signals are processed in the model, which causes extra manual work, and could mask actual design issues. In this paper, we experiment with the newer tool nuXmv, which supports infinite-state modelling. Using actual models from practical industrial projects, we show that after changing the analog signal processing to be based on real number math, the analysis times are still manageable. The disadvantage is that certain useful types of formal properties are not supported by the infinite-state algorithms. We also discuss the nuclear industry specific features of I&C programming languages, which cause significant constraints on domain-specific formal verification method and tool development.
{"title":"Model-checking infinite-state nuclear safety I&C systems with nuXmv","authors":"A. Pakonen","doi":"10.1109/INDIN45523.2021.9557445","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557445","url":null,"abstract":"For over a decade, model checking has been successfully used to formally verify the instrumentation and control (I&C) logic design in Finnish nuclear power plant projects. One of the practical challenges is that the model checker NuSMV forces the user to abstract the way analog signals are processed in the model, which causes extra manual work, and could mask actual design issues. In this paper, we experiment with the newer tool nuXmv, which supports infinite-state modelling. Using actual models from practical industrial projects, we show that after changing the analog signal processing to be based on real number math, the analysis times are still manageable. The disadvantage is that certain useful types of formal properties are not supported by the infinite-state algorithms. We also discuss the nuclear industry specific features of I&C programming languages, which cause significant constraints on domain-specific formal verification method and tool development.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"12 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":"116508749","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：