In this work, a deep residual network named GFresNet-2D is proposed for a point-focusing shear horizontal guided wave electromagnetic acoustic transducer, which can be used to quantify different types of defects, such as pinholes, cracks, and corrosion, in materials. As the traditional feature extraction and statistical machine learning methods are too complex and rely on artificial recognition, an automatic feature extraction model based on deep learning is applied for defect detection and quantification. Owing to their similarity with the ultrasonic guided wave signals, the measured 1D signals from the experiments cannot be directly applied to train neural networks. Therefore, we used the normalization, minimum suppression, and continuous wavelet transform methods to convert the initial measured 1D signals into processed 2D images, and constructed a data set containing 1,440,000,000 signal/image data. The performance of the proposed GFresNet-2D model for this new data set was also compared with those of traditional models, and sensitivity analyses were performed for some of the representative parameters. The results confirm that the proposed method can contribute to the development of deep-learning-based defect quantification using the ultrasonic guided wave focusing method.
{"title":"Quantification of Defects with Point-Focusing Shear Horizontal Guided Wave EMAT Using Deep Residual Network","authors":"Hongyu Sun, Songling Huang, Shen Wang, Wei Zhao, Lisha Peng","doi":"10.1109/INDIN45523.2021.9557567","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557567","url":null,"abstract":"In this work, a deep residual network named GFresNet-2D is proposed for a point-focusing shear horizontal guided wave electromagnetic acoustic transducer, which can be used to quantify different types of defects, such as pinholes, cracks, and corrosion, in materials. As the traditional feature extraction and statistical machine learning methods are too complex and rely on artificial recognition, an automatic feature extraction model based on deep learning is applied for defect detection and quantification. Owing to their similarity with the ultrasonic guided wave signals, the measured 1D signals from the experiments cannot be directly applied to train neural networks. Therefore, we used the normalization, minimum suppression, and continuous wavelet transform methods to convert the initial measured 1D signals into processed 2D images, and constructed a data set containing 1,440,000,000 signal/image data. The performance of the proposed GFresNet-2D model for this new data set was also compared with those of traditional models, and sensitivity analyses were performed for some of the representative parameters. The results confirm that the proposed method can contribute to the development of deep-learning-based defect quantification using the ultrasonic guided wave focusing method.","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":"129604266","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/INDIN45523.2021.9557386
Pedro Melo, Rafael Arrais, G. Veiga
There are significant difficulties in deploying and reusing application code within the robotics community. Container technology proves to be a viable solution for such problems, as containers isolate application code and all its dependencies from the surrounding computational environment. They are also light, fast and performant. Manual generation of configuration files required by orchestration tools such as Docker Compose is very time-consuming, especially for more complex scenarios. In this paper a solution is presented to ease the development and deployment of Robot Operating System (ROS) packages using containers, by automatically generating all files used by Docker Compose to both containerize and orchestrate multiple ROS workspaces, supporting multiple ROS distributions and multi-robot scenarios. The proposed solution also generates Dockerfiles and is capable of building new Docker images at run-time, given a list of desired ROS packages to be containerized. Integration with existing Docker images is supported, even if non-ROS-related. After an analysis of existing solutions and techniques for containerizing ROS nodes, the multi-stage pipeline adopted by the proposed solution for file generation is detailed. Then, a real usage example of the proposed tool is presented, showcasing how it an aid both the development and deployment of new ROS packages and features.
{"title":"Development and Deployment of Complex Robotic Applications using Containerized Infrastructures","authors":"Pedro Melo, Rafael Arrais, G. Veiga","doi":"10.1109/INDIN45523.2021.9557386","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557386","url":null,"abstract":"There are significant difficulties in deploying and reusing application code within the robotics community. Container technology proves to be a viable solution for such problems, as containers isolate application code and all its dependencies from the surrounding computational environment. They are also light, fast and performant. Manual generation of configuration files required by orchestration tools such as Docker Compose is very time-consuming, especially for more complex scenarios. In this paper a solution is presented to ease the development and deployment of Robot Operating System (ROS) packages using containers, by automatically generating all files used by Docker Compose to both containerize and orchestrate multiple ROS workspaces, supporting multiple ROS distributions and multi-robot scenarios. The proposed solution also generates Dockerfiles and is capable of building new Docker images at run-time, given a list of desired ROS packages to be containerized. Integration with existing Docker images is supported, even if non-ROS-related. After an analysis of existing solutions and techniques for containerizing ROS nodes, the multi-stage pipeline adopted by the proposed solution for file generation is detailed. Then, a real usage example of the proposed tool is presented, showcasing how it an aid both the development and deployment of new ROS packages and features.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"58 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":"129952003","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/INDIN45523.2021.9557545
Yunchuan Sun, Zixiu Ma, Xiaoping Zeng, Yao Guo
Accounting fraud, usually difficult to detect, can cause significant harm to stakeholders and serious damage to the market. Effective methods of accounting fraud detection are needed for the prevention and governance of accounting fraud.In this study, we develop a novel accounting fraud prediction model using XGBoost, a powerful ensemble learning approach. We respectively select 12 financial ratios, 28 raw accounting numbers and 99 raw accounting numbers available from Chinese listed firms’ financial statements, as the model input. To assess the performance of fraud prediction models, we select two evaluation metrics - AUC and NDCG@k, and two benchmark models - the Dechow et al. (2011) logistic regression model based on financial ratios, and the Bao et al. (2020) AdaBoost model based on raw accounting numbers.Results show that: 1) our XGBoost-based prediction model outperforms two benchmark models by a large margin whatever model inputs and evaluation metrics; 2) the XGBoost-based prediction model with raw accounting numbers input outperforms the one with financial ratios input; 3) the XGoost-based prediction model with 99 raw accounting numbers input outperforms the one with 28 raw accounting numbers input.
{"title":"A Predicting Model For Accounting Fraud Based On Ensemble Learning","authors":"Yunchuan Sun, Zixiu Ma, Xiaoping Zeng, Yao Guo","doi":"10.1109/INDIN45523.2021.9557545","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557545","url":null,"abstract":"Accounting fraud, usually difficult to detect, can cause significant harm to stakeholders and serious damage to the market. Effective methods of accounting fraud detection are needed for the prevention and governance of accounting fraud.In this study, we develop a novel accounting fraud prediction model using XGBoost, a powerful ensemble learning approach. We respectively select 12 financial ratios, 28 raw accounting numbers and 99 raw accounting numbers available from Chinese listed firms’ financial statements, as the model input. To assess the performance of fraud prediction models, we select two evaluation metrics - AUC and NDCG@k, and two benchmark models - the Dechow et al. (2011) logistic regression model based on financial ratios, and the Bao et al. (2020) AdaBoost model based on raw accounting numbers.Results show that: 1) our XGBoost-based prediction model outperforms two benchmark models by a large margin whatever model inputs and evaluation metrics; 2) the XGBoost-based prediction model with raw accounting numbers input outperforms the one with financial ratios input; 3) the XGoost-based prediction model with 99 raw accounting numbers input outperforms the one with 28 raw accounting numbers input.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"27 20 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":"132072830","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/INDIN45523.2021.9557584
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/INDIN45523.2021.9557582
F. Harrou, B. Taghezouit, Benamar Bouyeddou, Ying Sun, A. Arab
The demand for solar energy has rapidly increased throughout the world in recent years. However, anomalies in photovoltaic (PV) plants can reduce performances and result in serious consequences. Developing reliable statistical approaches able to detect anomalies in PV plants is vital to improving the management of these plants. Here, we present a statistical approach for detecting anomalies in the DC part of PV plants and partial shading. Firstly, we model the monitored PV plant. Then, we employ a generalized likelihood ratio test, which is a powerful anomaly detection tool, to check the residuals from the model and reveal anomalies in the supervised PV array. The proposed strategy is illustrated via actual measurements from a 9.54 PV plant.
{"title":"Fault Detection in Solar PV Systems Using Hypothesis Testing","authors":"F. Harrou, B. Taghezouit, Benamar Bouyeddou, Ying Sun, A. Arab","doi":"10.1109/INDIN45523.2021.9557582","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557582","url":null,"abstract":"The demand for solar energy has rapidly increased throughout the world in recent years. However, anomalies in photovoltaic (PV) plants can reduce performances and result in serious consequences. Developing reliable statistical approaches able to detect anomalies in PV plants is vital to improving the management of these plants. Here, we present a statistical approach for detecting anomalies in the DC part of PV plants and partial shading. Firstly, we model the monitored PV plant. Then, we employ a generalized likelihood ratio test, which is a powerful anomaly detection tool, to check the residuals from the model and reveal anomalies in the supervised PV array. The proposed strategy is illustrated via actual measurements from a 9.54 PV plant.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"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":"127741509","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/INDIN45523.2021.9557372
Flávia Pires, B. Ahmad, A. Moreira, P. Leitão
The research about the digital twin concept is growing worldwide, especially in the industrial sector, due to the increasing digitisation level associated to Industry 4.0. The application of the digital twin concept improves performance of a system by implementing monitoring, diagnosis, optimisation, and decision support actions. In particular, the decision-making process is very time consuming since the decision-maker is presented with hundreds of different scenarios that can be simulated and assessed in a what-if perspective. Bearing this in mind, this paper proposes to integrate a digital twin-based what-if simulation with a recommendation system to improve the decision-making cycle. The recommendation system is based on a reinforcement learning technique and takes user knowledge of the system into consideration and trust in the system recommendation. The applicability of the proposed approach is presented in an assembly line case study for recommending the best configurations for the system operation, in terms of the optimal number of AGVs (Autonomous Guided Vehicles) in various scenarios. The achieved results show its successful application and highlight the benefits of using AI-based recommendation systems for what-if simulation in digital twin systems.
{"title":"Recommendation System using Reinforcement Learning for What-If Simulation in Digital Twin","authors":"Flávia Pires, B. Ahmad, A. Moreira, P. Leitão","doi":"10.1109/INDIN45523.2021.9557372","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557372","url":null,"abstract":"The research about the digital twin concept is growing worldwide, especially in the industrial sector, due to the increasing digitisation level associated to Industry 4.0. The application of the digital twin concept improves performance of a system by implementing monitoring, diagnosis, optimisation, and decision support actions. In particular, the decision-making process is very time consuming since the decision-maker is presented with hundreds of different scenarios that can be simulated and assessed in a what-if perspective. Bearing this in mind, this paper proposes to integrate a digital twin-based what-if simulation with a recommendation system to improve the decision-making cycle. The recommendation system is based on a reinforcement learning technique and takes user knowledge of the system into consideration and trust in the system recommendation. The applicability of the proposed approach is presented in an assembly line case study for recommending the best configurations for the system operation, in terms of the optimal number of AGVs (Autonomous Guided Vehicles) in various scenarios. The achieved results show its successful application and highlight the benefits of using AI-based recommendation systems for what-if simulation in digital twin systems.","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":"127882729","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/INDIN45523.2021.9557363
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/INDIN45523.2021.9557517
Anna-Kristin Behnert, Felix Rinker, A. Lüder, S. Biffl
Efficient and effective engineering data exchange is increasingly considered a key success factor in the life cycle of production systems, leading to the intensified development of data logistic solutions. As small and medium-size companies (SMEs) play important roles in modern engineering organization structures, SMEs have to improve their capabilities to take part in these data logistics solutions. Unfortunately, SMEs have strong human and financial resource limitations. In this paper, we introduce a modular and easy-to-use data logistics architecture that aims at enabling SMEs to implement proof-of-concept software structures, applicable to validate benefits and challenges of data logistic solutions. This data logistics architecture provides a migration path towards the full participation of SMEs in data logistic solutions for engineering data exchange. We demonstrate the application of the architecture on use cases in automotive, steel, and machining industries.
{"title":"Migrating Engineering Tools Towards an AutomationML-Based Engineering Pipeline","authors":"Anna-Kristin Behnert, Felix Rinker, A. Lüder, S. Biffl","doi":"10.1109/INDIN45523.2021.9557517","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557517","url":null,"abstract":"Efficient and effective engineering data exchange is increasingly considered a key success factor in the life cycle of production systems, leading to the intensified development of data logistic solutions. As small and medium-size companies (SMEs) play important roles in modern engineering organization structures, SMEs have to improve their capabilities to take part in these data logistics solutions. Unfortunately, SMEs have strong human and financial resource limitations. In this paper, we introduce a modular and easy-to-use data logistics architecture that aims at enabling SMEs to implement proof-of-concept software structures, applicable to validate benefits and challenges of data logistic solutions. This data logistics architecture provides a migration path towards the full participation of SMEs in data logistic solutions for engineering data exchange. We demonstrate the application of the architecture on use cases in automotive, steel, and machining industries.","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":"115484196","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：