2022 IEEE 20th International Conference on Industrial Informatics (INDIN)最新文献_第9页

Less is More: Bitcoin Volatility Forecast Using Feature Selection and Deep Learning Models 少即是多:使用特征选择和深度学习模型预测比特币波动

2022 IEEE 20th International Conference on Industrial Informatics (INDIN) Pub Date : 2022-07-25 DOI: 10.1109/INDIN51773.2022.9976100

Haiping Wang, Xing Zhou

引用次数: 0

Partial Domain Intelligent Diagnosis Method for Rotor-Bearing System Based on Deep Learning 基于深度学习的转子-轴承系统局部域智能诊断方法

2022 IEEE 20th International Conference on Industrial Informatics (INDIN) Pub Date : 2022-07-25 DOI: 10.1109/INDIN51773.2022.9976132

Xiaoyue Liu, Cong Peng

{"title":"Partial Domain Intelligent Diagnosis Method for Rotor-Bearing System Based on Deep Learning","authors":"Xiaoyue Liu, Cong Peng","doi":"10.1109/INDIN51773.2022.9976132","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976132","url":null,"abstract":"Recently, deep transfer learning (TL) has successfully addressed the problem of fault diagnosis under variable operating conditions. Existing methods default that the source and target domains have the same label space, and solve distribution discrepancy problem under different working conditions by aligning their feature distributions. However, in the practical industry, is unlikely to guarantee the health conditions of the target domain data are consistent with the source domain. Therefore, industrial applications usually face the challenge of more difficult partial domain diagnosis scenarios. In this paper, a deep partial domain adaptation network based on a balanced alignment constraint strategy is proposed to realize cross-domain diagnosis. The proposed method combines balanced augmentation and subdomain alignment, which can effectively facilitate the positive transfer of shared categories. Meanwhile, the conditional entropy minimization is introduced to encourage the predictions of target domain samples with high confidence. The experimental results on the rolling bearing dataset verify the effectiveness and feasibility of the proposed method in handling the actual partial domain fault diagnosis problem.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127401206","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

Fundamental Quantitative Investment Theory and Technical System Based On Multi-Factor Models 基于多因素模型的基本定量投资理论与技术体系

2022 IEEE 20th International Conference on Industrial Informatics (INDIN) Pub Date : 2022-07-25 DOI: 10.1109/INDIN51773.2022.9976124

Li Zhao, Nathee Naktnasukanjn, Lei Mu, Haichuan Liu, Heping Pan

引用次数: 0

Learning-based Automatic Report Generation for Scheduling Performance in Time-Sensitive Networking 基于学习的时间敏感网络调度性能自动报表生成

2022 IEEE 20th International Conference on Industrial Informatics (INDIN) Pub Date : 2022-07-25 DOI: 10.1109/INDIN51773.2022.9976085

Lingzhi Li, Qimin Xu, Yanzhou Zhang, Lei Xu, Yingxiu Chen, Cailian Chen

{"title":"Learning-based Automatic Report Generation for Scheduling Performance in Time-Sensitive Networking","authors":"Lingzhi Li, Qimin Xu, Yanzhou Zhang, Lei Xu, Yingxiu Chen, Cailian Chen","doi":"10.1109/INDIN51773.2022.9976085","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976085","url":null,"abstract":"As the global industrial upgrading requires higher reliability and real-time performance of data communication, Time-sensitive Networking (TSN) has been widely studied. Al-though many TSN scheduling algorithms are designed, there is no standardized analysis report after scheduling and comprehensive scheduling performance evaluation. This paper presents a complete automatic report generation system to analyze the scheduling performance. To standardize various data in TSN-based manufacturing, a uniform auto-generated report model is defined based on the Open Platform Communication Unified Architecture (OPC UA). A learning-based performance evaluation (LPE) method is established to comprehensively analyze the performance of TSN scheduling. In LPE, analytical hierarchy process (AHP) and entropy weight method (EWM) is adopted to optimize the weight distribution of performance indexes objectively, and convolutional neural network (CNN) is used to get the final evaluation result rapidly. Compared with the previous evaluation methods, simulations show the training time of the evaluation method is significantly reduced.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129209665","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

Reinforcement Learning based Optimal Tracking Control for Hypersonic Flight Vehicle: A Model Free Approach 基于强化学习的高超声速飞行器最优跟踪控制:一种无模型方法

2022 IEEE 20th International Conference on Industrial Informatics (INDIN) Pub Date : 2022-07-25 DOI: 10.1109/INDIN51773.2022.9976071

Xiaoxiang Hu, Kejun Dong, Teng-Chieh Yang, Bing Xiao

引用次数: 1

Configuration of Parallel Real-Time Applications on Multi-Core Processors 多核处理器上并行实时应用的配置

2022 IEEE 20th International Conference on Industrial Informatics (INDIN) Pub Date : 2022-07-25 DOI: 10.1109/INDIN51773.2022.9976163

Mohammad Samadi Gharajeh, Tiago Carvalho, L. M. Pinho

{"title":"Configuration of Parallel Real-Time Applications on Multi-Core Processors","authors":"Mohammad Samadi Gharajeh, Tiago Carvalho, L. M. Pinho","doi":"10.1109/INDIN51773.2022.9976163","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976163","url":null,"abstract":"Parallel programming models (e.g., OpenMP) are more and more used to improve the performance of real-time applications in modern processors. Nevertheless, these processors have complex architectures, being very difficult to understand their timing behavior. The main challenge with most of existing works is that they apply static timing analysis for simpler models or measurement-based analysis using traditional platforms (e.g., single core) or considering only sequential algorithms. How to provide an efficient configuration for the allocation of the parallel program in the computing units of the processor is still an open challenge. This paper studies the problem of performing timing analysis on complex multi-core platforms, pointing out a methodology to understand the applications’ timing behavior, and guide the configuration of the platform. As an example, the paper uses an OpenMP-based program of the Heat benchmark on a NVIDIA Jetson AGX Xavier. The main objectives are to analyze the execution time of OpenMP tasks, specify the best configuration of OpenMP directives, identify critical tasks, and discuss the predictability of the system/application. A Linux perf based measurement tool, which has been extended by our team, is applied to measure each task across multiple executions in terms of total CPU cycles, the number of cache accesses, and the number of cache misses at different cache levels, including L1, L2 and L3. The evaluation process is performed using the measurement of the performance metrics by our tool to study the predictability of the system/application.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116225126","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

Multi-Agent Deep Reinforcement Learning For Real-World Traffic Signal Controls - A Case Study 现实世界交通信号控制的多智能体深度强化学习-一个案例研究

2022 IEEE 20th International Conference on Industrial Informatics (INDIN) Pub Date : 2022-07-25 DOI: 10.1109/INDIN51773.2022.9976109

Maxim Friesen, Tian Tan, J. Jasperneite, Jie Wang

{"title":"Multi-Agent Deep Reinforcement Learning For Real-World Traffic Signal Controls - A Case Study","authors":"Maxim Friesen, Tian Tan, J. Jasperneite, Jie Wang","doi":"10.1109/INDIN51773.2022.9976109","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976109","url":null,"abstract":"Increasing traffic congestion leads to significant costs, whereby poorly configured signaled intersections are a common bottleneck and root cause. Traditional traffic signal control (TSC) systems employ rule-based or heuristic methods to decide signal timings, while adaptive TSC solutions utilize a traffic-actuated control logic to increase their adaptability to real-time traffic changes. However, such systems are expensive to deploy and are often not flexible enough to adequately adapt to the volatility of today’s traffic dynamics. More recently, this problem became a frontier topic in the domain of deep reinforcement learning (DRL) and enabled the development of multi-agent DRL approaches that can operate in environments with several agents present, such as traffic systems with multiple signaled intersections. However, many of these proposed approaches were validated using artificial traffic grids. This paper presents a case study, where real-world traffic data from the town of Lemgo in Germany is used to create a realistic road model within VISSIM. A multi-agent DRL setup, comprising multiple independent deep Q-networks, is applied to the simulated traffic network. Traditional rule-based signal controls, modeled in LISA+ and currently employed in the real world at the studied intersections, are integrated into the traffic model and serve as a performance baseline. The performance evaluation indicates a significant reduction of traffic congestion when using the RL-based signal control policy over the conventional TSC approach with LISA+. Consequently, this paper reinforces the applicability of RL concepts in the domain of TSC engineering by employing a highly realistic traffic model.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123905730","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

Control of Battery Storage Systems in Residential Grids: Model-based vs. Data-Driven Approaches 住宅电网中电池存储系统的控制:基于模型与数据驱动的方法

2022 IEEE 20th International Conference on Industrial Informatics (INDIN) Pub Date : 2022-07-25 DOI: 10.1109/INDIN51773.2022.9976136

S. Sajjadi, N. Bazmohammadi, A. Amani, M. Jalili, J. Guerrero, Xinghuo Yu

{"title":"Control of Battery Storage Systems in Residential Grids: Model-based vs. Data-Driven Approaches","authors":"S. Sajjadi, N. Bazmohammadi, A. Amani, M. Jalili, J. Guerrero, Xinghuo Yu","doi":"10.1109/INDIN51773.2022.9976136","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976136","url":null,"abstract":"In this paper, control of Battery Storage Systems (BSS) in power distribution grids with residential consumers as well as prosumers equipped with rooftop photovoltaic (PV) solar panels and Electric Vehicles (EV) is addressed. Different features of these Distributed Energy Resources (DERs), such as intermittent behaviour and the difference between the maximum generation time and the maximum demand, have caused several issues for electricity distributors in delivering high quality power. Smart control and scheduling of ESS and EVs is a promising approach to protect the grid against extra power injection from prosumers during day times while the benefit of household owners from DERs are still achieved. In this context, the performance of model-based controllers such as model predictive controllers (MPC) is compared with model-free data driven controllers (DDC) considering different complex scenarios that may happen in a distribution grid. The control objective is to minimize the difference between the net power exchanged with the main grid from the estimated average net load of prosumers. Our study on the real consumption data of about 40 residential consumers/prosumers in Victoria, Australia, demonstrates the strength of data-driven control approaches to deal with the complex environment of power distribution grids in the presence of DERs.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127875936","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

Orthoimage Super-Resolution via Deep Convolutional Neural Networks 基于深度卷积神经网络的正射影超分辨率

2022 IEEE 20th International Conference on Industrial Informatics (INDIN) Pub Date : 2022-07-25 DOI: 10.1109/INDIN51773.2022.9976074

V. Berezovsky, Yunfeng Bai, Ivan Sharshov, R. Aleshko, K. Shoshina, I. Vasendina

引用次数: 0

Network Calculus-based Routing and Scheduling in Software-defined Industrial Internet of Things 软件定义工业物联网中基于网络演算的路由与调度

2022 IEEE 20th International Conference on Industrial Informatics (INDIN) Pub Date : 2022-07-25 DOI: 10.1109/INDIN51773.2022.9976177

Luyue Ji, Wen-Ruey Wu, Chaojie Gu, Jichao Bi, Shibo He, Zhiguo Shi

{"title":"Network Calculus-based Routing and Scheduling in Software-defined Industrial Internet of Things","authors":"Luyue Ji, Wen-Ruey Wu, Chaojie Gu, Jichao Bi, Shibo He, Zhiguo Shi","doi":"10.1109/INDIN51773.2022.9976177","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976177","url":null,"abstract":"With the emergence of Industry 5.0, it is significant to enable efficient cooperation between humans and machines in the Industrial Internet of Things (IIoT). However, achieving real-time and reliable transmission of data flows deriving from time-sensitive applications in IIoT remains an open challenge. In this paper, we propose a three-layer software-defined IIoT (SDIIoT) architecture to enable multiple industrial services and flexible network configuration. In particular, when network services change frequently in SDIIoT, the delay of the control plane has a great influence on the end-to-end delay of data flows. To address this issue, we portray two different service curves of OpenFlow switches to adapt to dynamic network status based on Network Calculus (NC). To elevate resource efficiency and comply with friendly environments, we minimize the total worst-case network cost under strict resource constraints and transmission requirements by exploiting the joint flow routing and scheduling algorithm (JFRSA). Our numerical simulation results demonstrate the effectiveness and efficiency of our solution.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124405780","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