2021 IEEE 19th International Conference on Industrial Informatics (INDIN)最新文献_第2页

Chinese Value Investing Theory and Quantitative Technology 中国价值投资理论与定量技术

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557552

Heping Pan

{"title":"Chinese Value Investing Theory and Quantitative Technology","authors":"Heping Pan","doi":"10.1109/INDIN45523.2021.9557552","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557552","url":null,"abstract":"After nearly three decades of a hard journey, China's capital market has more and more clearly demonstrated the right value of value investing. A-share market participants - retail investors and institutions - are in urgent need of a value investing theory in line with China's national conditions. We realize that China's value investing system must be the joint value investing of China and the world. This paper proposes a value investing theory and quantitative realizing technology system with China as the main body and taking both China and the world conditions into account. The main contents include: 1) under the framework of big data, using the credit risk analysis for filtering out stocks with mediocre or poor credit; 2) multi-factor models of quantitative investment for selection of value and growth stocks; 3) deep learning financial market prediction model for capturing dynamic margin of safety and profit opportunities; 4) deep intelligent portfolio trading technology for implementing value investing into super intelligent systems of quantitative investment. The characteristics and innovations of the theory are: expanding the big data holographic credit risk analysis for Chinese enterprises to value investing analysis; developing comprehensive multi-factor models for selecting value and growth stocks into portfolios; developing big data-driven deep learning financial market prediction models; innovating and developing deep intelligent trading strategies and systems.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"5 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":"127882119","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

Dual Stationary Frame Control of Inverter-based Resources for Reliable Phase Selection 基于逆变器资源的可靠选相双静止帧控制

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557510

Abdallah A. Aboelnaga, M. Azzouz

{"title":"Dual Stationary Frame Control of Inverter-based Resources for Reliable Phase Selection","authors":"Abdallah A. Aboelnaga, M. Azzouz","doi":"10.1109/INDIN45523.2021.9557510","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557510","url":null,"abstract":"Phase selection is a significant protection element, which determines faulty phase(s). However, commercial phase selection methods (PSMs) are susceptible to failure in the presence of inverter-interfaced resources (IBRs) because their fault currents possess different characteristics than conventional generation sources. This paper develops a dual current controller (DCC) in the stationary frame to make the traditional current-angle-based PSM operate correctly at the relay location without frame transformation. First, the fault type is determined at the IBR. Then, the reference angle of the negative-sequence current is determined to achieve the current-angle-based PSM requirements. Subsequently, the negative-sequence current reference in the stationary frame is determined according to its current reference angle and its maximum current limit. Besides, the positive-sequence current is determined based on the active and reactive current references. Lastly, the sinusoidal reference currents are tracked accurately using a proportional-resonant (PR) control. Comprehensive time-domain simulations are used to validate the accuracy of the proposed DCC under different fault conditions.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"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":"129987835","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

Monitoring of Human-Intensive Assembly Processes Based on Incomplete and Indirect Shopfloor Observations 基于不完全和间接车间观察的人力密集型装配过程监控

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557551

Ouijdane Guiza, Christoph Mayr-Dorn, G. Weichhart, M. Mayrhofer, Bahman Bahman Zangi, Alexander Egyed, Björn Fanta, Martin Gieler

{"title":"Monitoring of Human-Intensive Assembly Processes Based on Incomplete and Indirect Shopfloor Observations","authors":"Ouijdane Guiza, Christoph Mayr-Dorn, G. Weichhart, M. Mayrhofer, Bahman Bahman Zangi, Alexander Egyed, Björn Fanta, Martin Gieler","doi":"10.1109/INDIN45523.2021.9557551","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557551","url":null,"abstract":"As manufacturing companies move towards producing highly customizable products in small lot sizes, assembly workers remain an integral part of production systems. However, with workers in the loop, it is necessary to monitor the production process for timely detection of deviations and timely provisioning of worker assistance. Grounded in an industrial case study describing the assembly of construction vehicles, we outline a generic heuristic-based approach for monitoring progress in human-intensive assembly systems. Specifically, we highlight the challenges in dealing with uncertainty stemming from the limitations in accurately, timely, and completely observing human physical assembly steps. We discuss a motivating example to showcase these challenges and present a set of heuristics that manages to accurately infer assembly progress from indirect and incomplete observations of deviating worker behavior. Validated against ground truth obtained from a real industrial assembly line, on average our approach correctly estimates completion times for steps that are associated with shopfloor observations within 14 seconds or less of their true value.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"22 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":"133989446","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

Reliable Real-time Destination Prediction 可靠的实时目的地预测

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557585

G. Meyers, Miguel Martínez-García, Yu Zhang, Yudong Zhang

{"title":"Reliable Real-time Destination Prediction","authors":"G. Meyers, Miguel Martínez-García, Yu Zhang, Yudong Zhang","doi":"10.1109/INDIN45523.2021.9557585","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557585","url":null,"abstract":"In this paper, a reliable online destination prediction methodology is presented. The destination prediction methodology consists of a novel sequential complete diameter distance limited clustering method and an ensemble of random forest classifiers employing a one-vs-rest binarization strategy. Through the use of a novel OvR Uncertainty metric, predictions with high uncertainty could be withheld, thus increasing the overall reliability of the predictions made. The methodology was validated on 778 journeys from two real non-commuter vehicles based in the UK. These datasets allowed the methodology to be tested on real, yet challenging-to-predict journeys and irregular driver behavior. The sequential complete diameter distance limited clustering method was found to be a fast and effective method for sequentially clustering GPS coordinates into clusters that correspond to geographical locations. Prediction results showed that while only an overall mean prediction accuracy of 52% and 34% could be achieved on the two datasets, mean prediction accuracy could be significantly increased to over 90% and 73% respectively by only providing predictions with low uncertainty.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"5 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":"124494714","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

A Custom Brace Design to Connect a User Limb to an Exoskeleton Link with Minimal Discomfort 一种将用户肢体连接到外骨骼链路的定制支架设计，将不适降到最低

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557500

Suleyman Can Cevik, M. Derman, R. Unal, B. Ugurlu, O. Bebek

引用次数: 1

A Framework for Fault Diagnosis using Continuous Bayesian Network and Causal Inference 基于连续贝叶斯网络和因果推理的故障诊断框架

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557490

Asif Hanif, S. Ali, Ali Ahmed

引用次数: 1

Utilizing an Enterprise Architecture Framework for Model-Based Industrial Systems Engineering 在基于模型的工业系统工程中利用企业架构框架

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557460

C. Binder, Werner Leiter, Oliver Joebstl, Lukas Mair, C. Neureiter, A. Lüder

{"title":"Utilizing an Enterprise Architecture Framework for Model-Based Industrial Systems Engineering","authors":"C. Binder, Werner Leiter, Oliver Joebstl, Lukas Mair, C. Neureiter, A. Lüder","doi":"10.1109/INDIN45523.2021.9557460","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557460","url":null,"abstract":"Accompanied by the fourth industrial revolution, current and future manufacturing systems are undergoing a major transformation. Driven by the integration of cyber-physical systems and the thereby resulting autonomy of its single components, organizing the interplay of its physical counterparts becomes more and more challenging. In order to structure and realize such an industrial system in a collaborative manner, the Reference Architecture Model Industrie 4.0 (RAMI 4.0) has been developed. However, although being widely accepted by the community, the standardized architecture is missing actual industrial applications. A major reason for this issue might be missing specifications in the standard itself, hindering the mutual development of such a critical infrastructure at the right level of detail. Thus, in order to counteract the mentioned problem, this work delineates the alignment of RAMI 4.0 with a well-established framework for specifying enterprise architectures, named The Open Group Architecture Framework (TOGAF). By coalescing the system development concepts of RAMI 4.0 and TOGAF, both of the approaches could benefit from each other. The result is thereby evaluated with an actual industrial case study.","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":"129243771","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: 3

Using In-Browser Augmented Reality to Promote Knowledge-Based Engineering throughout the Product Life Cycle 在整个产品生命周期中使用浏览器内增强现实技术促进基于知识的工程

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557437

Anna Florea, A. Lobov, T. Minav

引用次数: 0

Platform Generation for Edge AI Devices with Custom Hardware Accelerators 具有自定义硬件加速器的边缘AI设备的平台生成

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557519

Leon Hielscher, Alexander Bloeck, A. Viehl, Sebastian Reiter, Marc Staiger, O. Bringmann

引用次数: 0

Application of Deep Neural Network on Net Photosynthesis Modeling 深度神经网络在净光合作用建模中的应用

2021 IEEE 19th International Conference on Industrial Informatics (INDIN) Pub Date : 2021-07-21 DOI: 10.1109/INDIN45523.2021.9557452

Y. Qu, A. Clausen, B. Jørgensen

{"title":"Application of Deep Neural Network on Net Photosynthesis Modeling","authors":"Y. Qu, A. Clausen, B. Jørgensen","doi":"10.1109/INDIN45523.2021.9557452","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557452","url":null,"abstract":"Photosynthesis is a crucial biochemical process for plant growth, which is determined by multiple environmental factors and other organic matter. In the horticultural industry, the environmental conditions in commercial greenhouses directly impact the quality of productions. Predicting the Net Photosynthesis (Pn) of plants based on the environmental parameters can help growers optimize the climate in greenhouse systems, thereby ensuring the quality of production. Meanwhile, due to the greenhouse climate can be controlled according to the prediction results, excess energy consumption can be avoided, so the production cost can be reduced. However, since the photosynthesis reaction is a highly nonlinear biochemical process, it is difficult for traditional algorithms to describe the hidden effects of individual elements. In previous related works, polynomial fitting was utilized for modeling the relation between Pn and environmental elements. In this paper, a Deep Learning (DL) method is explored to predict the Pn based on three inputs: light level, CO2 concentration and temperature. An exponential decay learning rate is applied in the training process to ensure convergence performance while increasing the convergence speed. Then, the performance of various Deep Neural Network (DNN) architectures is experimented and compared within this modeling problem. Finally, through a comprehensive analysis of the accuracy of individual architecture, a particular architecture is determined to solve this problem. According to the test results, the selected DNN can successfully predict the Pn based on the three environmental elements with high accuracy.","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":"131281676","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