2021 IEEE 19th International Conference on Industrial Informatics (INDIN)最新文献_第4页

Promela Formal Modelling and Verification of IEC 61499 Systems with comparison to SMV IEC 61499系统的Promela形式化建模和验证，并与SMV进行比较

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557513

V. Shatrov, V. Vyatkin

引用次数: 2

Automatic classification of EEG signals via deep learning 基于深度学习的脑电信号自动分类

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557473

Tao Wu, X. Kong, Yiwen Wang, Xue Yang, Jingxuan Liu, Jun Qi

{"title":"Automatic classification of EEG signals via deep learning","authors":"Tao Wu, X. Kong, Yiwen Wang, Xue Yang, Jingxuan Liu, Jun Qi","doi":"10.1109/INDIN45523.2021.9557473","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557473","url":null,"abstract":"Electroencephalogram (EEG) is widely used to diagnose many neurological and psychiatric brain disorders. The correct interpretation of EEG data is critical to avoid misdiagnosis. However, the analysis of EEG data requires trained specialists and may vary from expert to expert. Meanwhile, it can be challenging and time-consuming to assess the EEG data since these signals may last several hours or days. Therefore, rapid and accurate classification of EEG data may be a key step towards interpreting EEG records. In this study, a novel deep learning model with an end-to-end structure is proposed to distinguish normal and abnormal EEG signals automatically. For this purpose, we investigate the possibility of combining the core ideas of inception and residual architectures into a hybrid model to improve classification performance. We evaluated the proposed method through extensive experiments on a real-world dataset, and it shows feasibility and effectiveness. Compared to previous studies on the same data, our method outperforms other existing EEG signal methods. Thus, the proposed method can aid clinicians to automatically detect brain activity.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"63 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":"129775398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Dynamic Multi-Loss Weighting for Multiple People Tracking in Video Surveillance Systems 视频监控系统中多人跟踪的动态多损失加权

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557515

Xuan-Thuy Vo, T. Tran, Duy-Linh Nguyen, K. Jo

{"title":"Dynamic Multi-Loss Weighting for Multiple People Tracking in Video Surveillance Systems","authors":"Xuan-Thuy Vo, T. Tran, Duy-Linh Nguyen, K. Jo","doi":"10.1109/INDIN45523.2021.9557515","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557515","url":null,"abstract":"Multiple people tracking is a fundamental yet challenging task in the computer vision field, which served as a primary process for high-level tasks such as human behaviors, action recognition, pose estimation. Person tracking is decomposed into detection and re-identification (re-ID) sub-tasks. Conventionally, the detection learns classification and regression objectives simultaneously; and the re-ID sub-task is treated as a classification task. Therefore, person tracking is multiple task learning corresponding to multiple loss functions (multiple objectives) with one bounding box regression and two classifications. The difference between various tasks is as follows: the ranges of each objective are inconsistent, the contribution of each task to the overall gradient is altered, and the learning pace of each task is different (level of difficulty). It leads to an objective imbalance in multi-task learning. Previous methods proposed weighting factors as new hyper-parameters to balance the ranges of each task. The dimension of search space for manually tuning these hyper-parameters is high, which depends on the number of tasks. Accordingly, selecting reasonable weighting factors is difficult and complicated. This paper introduces dynamic multi-loss weighting (DMW) with simple but effective in which the weighting factors are dynamically changed during training without introducing any hyper-parameters. The dynamic weights are optimized to balance regression and classification objectives, which depend on the difficulty level of each task and the correlation between each task. Additionally, the general convolution operations are spatially invariant to some degree, which hinders the network’s performance. Hence, this work employs the position-sensitive operation improving feature extraction. The proposed method is conducted on the MOT17 challenging benchmark, which outperforms the online multiple people trackers without using additional data.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"18 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":"130646572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Fine-grained Access Control for Time-Series Databases using NGAC 使用NGAC的时间序列数据库的细粒度访问控制

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557414

Alex Chiquito, Ulf Bodin, O. Schelén

{"title":"Fine-grained Access Control for Time-Series Databases using NGAC","authors":"Alex Chiquito, Ulf Bodin, O. Schelén","doi":"10.1109/INDIN45523.2021.9557414","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557414","url":null,"abstract":"Industrial Internet of Things (IIoT) and Industry 4.0 rely heavily on data for reasons such as production follow-up, planning and optimization. Industrial data come in large volumes from production logs and sensors whereof some data carries business and strategic value, sensitive information, or a combination of both. Such data must be protected from unauthorized access, but also be easy to access for authorized users to facilitate work to gain business and operational values from the data. The efficient creation and maintenance of access policies for secure data sharing is hence essential, but unfortunately also challenging in terms of the complexity and administrative effort for fine-grained such. Attribute-based access control (ABAC) such as the Next Generation Access Control (NGAC) provides efficient models for handling access policies. Existing access control models fail however to provide a simple and easy-to-maintain policy language capable of efficiently enforcing fine-grained access control policies for large volumes of time-series data. In this paper, we propose extensions to NGAC based on filter strings that facilitates efficient enforcement of row-level value and time constraint policies for time-series data. We evaluate two approaches for storing and retrieving these filter strings and provide a qualitative and quantitative discussion of the results.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"45 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":"132231828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fault Classification for Wind Turbine Benchmark Model Based on Hilbert-Huang Transformation and Support Vector Machine Strategies 基于Hilbert-Huang变换和支持向量机策略的风电标杆模型故障分类

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557362

Yichuan Fu, Zhiwei Gao, A. Zhang, Xiaoxu Liu

引用次数: 3

Social Economy Association Analysis for the 2020 Presidential Election with Semi-Covariance 基于半协方差的2020年总统大选社会经济关联分析

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557577

Yaqian Qi, Yu Andy Li, JiaminMoran Huang, J. Huang, Heping Pan

{"title":"Social Economy Association Analysis for the 2020 Presidential Election with Semi-Covariance","authors":"Yaqian Qi, Yu Andy Li, JiaminMoran Huang, J. Huang, Heping Pan","doi":"10.1109/INDIN45523.2021.9557577","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557577","url":null,"abstract":"The expectation of an engineering observation random quantity is the first order origin moment. Variance is a special case of covariance, when two variables are the same. The variance is the second-order central moment, its root is standard deviation. The normalization of covariance to standard deviation is called Pearson correlation coefficient. The covariance for the region above or below the average is called semi-covariance (upper or down). Here we present semi-covariance, an accurate ReLU (Rectified Linear Unit) way of measuring the non-linear correlation between variables. Our framework is applied to successfully analyze the association between alternative factors and the poll response. The result of our analyses of the 2020 USA presidential election suggest that stock, pandemic, funding, culture, and mental health have different impacts on presidential candidates: Biden vs Trump. The voters care about the economy (stock and funding situations), pandemic impacted voter’s culture fairness income, and voters’ stress leading to mental issue. That’s why we picked above five factors. Whoever win more number of strong correlations is predicted as the winner.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"54 3 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":"124861403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards the generic integration of agent-based AASs and Physical Assets: a four-layered architecture approach 面向基于代理的AASs和物理资产的通用集成:四层体系结构方法

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557568

Alejandro López, O. Casquero, E. Estévez, P. Leitão, M. Marcos

{"title":"Towards the generic integration of agent-based AASs and Physical Assets: a four-layered architecture approach","authors":"Alejandro López, O. Casquero, E. Estévez, P. Leitão, M. Marcos","doi":"10.1109/INDIN45523.2021.9557568","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557568","url":null,"abstract":"The I4.0 Component is a key concept of the Platform Industrie 4.0 initiative. The I4.0 Component is made up of an asset performing services and an Asset Administration Shell (AAS) representing the asset in the system. I4.0 Components must meet the requirements of interoperability, identification, representation, information management, integration, and asset management. Industrial agents are a suitable approach to implement I4.0 Components since they meet these requirements and provide additional features such as distributed decision-making capabilities. Nevertheless, the development of generic approaches for the integration of AASs and assets is not straightforward due to the diversity of physical assets in the factories. This paper proposes a four-layered architecture for the implementation of I4.0 Components based on industrial agents, two in the agent-based AAS and the other two in the physical asset. The division into layers enhances the separation of concerns, encapsulating the different integration aspects at different abstraction levels. This approach is complementary to the standard IEEE 2660.1-2020 on recommended practices for industrial agents, which focuses on the deployment of the interface between the agent and the physical asset. As a proof of concept, the proposed architecture will be applied for the integration of a software agent and a robot from an assembly cell, aiming to create an I4.0 Component.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"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":"129778545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

The Correction of the Nozzle-Bed-Distance in Robotic Fused Deposition Modeling 机器人熔敷建模中喷嘴-床距的校正

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557518

G. Mewes, A. Fay

引用次数: 2

Diagnosis for IGBT Open-circuit Faults in Photovoltaic Inverters: A Compressed Sensing and CNN based Method 光伏逆变器IGBT开路故障诊断:基于压缩感知和CNN的方法

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557384

Xinyi Wang, Bo Yang, Qi Liu, Jingzheng Tu, Cailian Chen

{"title":"Diagnosis for IGBT Open-circuit Faults in Photovoltaic Inverters: A Compressed Sensing and CNN based Method","authors":"Xinyi Wang, Bo Yang, Qi Liu, Jingzheng Tu, Cailian Chen","doi":"10.1109/INDIN45523.2021.9557384","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557384","url":null,"abstract":"The inverter is the most vulnerable module of photovoltaic (PV) systems. The insulated gate bipolar transistor (IGBT) is the core part of inverters and the root source of PV inverter failures. How to effectively diagnose the IGBT faults is critical for reliability, high efficiency, and safety of PV systems. Recently, deep learning (DL) methods are widely used for fault detection and diagnosis. Different from traditional diagnosis methods, DL methods use deep neural networks which can automatically extract the useful representative features from raw data. However, DL methods require large amounts of data, which leads to the high cost of communication, storage, and computation. To tackle these issues, a data-driven fault detection and diagnosis method for IGBT open-circuit faults based on compressed sensing (CS) and convolutional neural networks (CNN) is proposed in this paper. CS is adopted to compress raw signals, and the optimal value of compression ratio (CR) is determined by considering the trade-off between classification accuracy and model training time. The overlap sampling method is adopted for data segmentation. Meanwhile, overlap sampling can also increase the number of training samples and improve the sample correlation. The compressed signals are segmented and reconstructed into two-dimensional feature maps for model training. Finally, compared with CNN of the same structure, the developed CS-CNN model can compress 85% of data without accuracy loss. The performance comparison with the state-of-the-art networks demonstrates that the test accuracy is 98.68% and the model training time is much shorter than other methods.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"19 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":"122462112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Constraint Checking of Skills using SHACL 使用SHACL的技能约束检查

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557549

Aljosha Köcher, Luis Miguel Vieira da Silva, A. Fay

引用次数: 3