2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)最新文献_第2页

Smart Integrated Energy System Based on Digital Twin: Research, Application and Outlook 基于数字孪生的智能综合能源系统:研究、应用与展望

2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI) Pub Date : 2022-10-24 DOI: 10.1109/DTPI55838.2022.9998890

Xiaojie Lin, Wei Zhong, Yihui Mao, Nan Zhang

{"title":"Smart Integrated Energy System Based on Digital Twin: Research, Application and Outlook","authors":"Xiaojie Lin, Wei Zhong, Yihui Mao, Nan Zhang","doi":"10.1109/DTPI55838.2022.9998890","DOIUrl":"https://doi.org/10.1109/DTPI55838.2022.9998890","url":null,"abstract":"Carbon neutrality receives more and more attention in China due to the concerns about the energy crisis and global warming. The smart energy system plays a critical role in carbon reduction and sustainable development of the district. As a key part of the district energy system in China, the smart heating system (SHS) has a wide range of energy sources (including boilers, combined heat, and power plants (CHP plants), waste heat recovery systems, heat pumps, and so on). However, heating systems in China still make extensive use of technologies developed in the 1990s, desperately needing a new paradigm of system analysis, renovation, and operation. This paper will include the recent work in the clean heating technology review, data-driven modeling, flexibility analysis, operation decoupling optimization, substation data mining, and the transition from SHS to integrated energy system (IES). The highlights of this paper will be the introduction of the digital twin (DT) framework which incorporates energy system analysis, data-driven modeling, and system operation optimization. We will show the effectiveness of such a methodology in both SHS cases and future IES cases.","PeriodicalId":409822,"journal":{"name":"2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123730258","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

Design and application of parallel intelligent control system for denitration system in coal-fired power plant 燃煤电厂脱硝系统并联智能控制系统的设计与应用

2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI) Pub Date : 2022-10-24 DOI: 10.1109/DTPI55838.2022.9998946

Quan Li, F. Yin, Jiangfeng Zhang, Jiandong Sun, L. Dong, Ye Su

{"title":"Design and application of parallel intelligent control system for denitration system in coal-fired power plant","authors":"Quan Li, F. Yin, Jiangfeng Zhang, Jiandong Sun, L. Dong, Ye Su","doi":"10.1109/DTPI55838.2022.9998946","DOIUrl":"https://doi.org/10.1109/DTPI55838.2022.9998946","url":null,"abstract":"Based on the parallel control theory, a denitration parallel intelligent automation system is designed in this paper. It is composed of hardware system and software system. The hardware system has modules such as power supply, industrial controller and host, and real-time communication is realized between the control modules. The software system is based on the parallel control theory. Firstly, the pseudo-random sequence module, period data acquisition module, identification module, predictive control module and other modules are realized, and the specific basic functions are given;Secondly, the relevant modules and logic systems corresponding to manual system, calculation experiment and parallel execution are designed, the specific structure of denitration parallel control system is given, and the structure of parallel process controller is analyzed in detail;In this paper, it is proposed that the program-controlled signal flow is the key to realize the intelligent operation of parallel control, and the specific design method is given. At the same time, the virtual machine simulation operation in the hardware system is also an important part of parallel intelligent control, and the specific signal communication mode is given. Finally, an engineering application example of denitration parallel intelligent automation system is given, and the effectiveness of the system is verified by experiments, The design method proposed in this paper has strong practical significance for the application of parallel intelligent automation system.","PeriodicalId":409822,"journal":{"name":"2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130890142","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

Integrating Human Mobility and Infrastructure Design in Digital Twin to Improve Equity and Resilience of Cities 将人口流动和基础设施设计整合到数字孪生中，提高城市公平性和韧性

2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI) Pub Date : 2022-10-24 DOI: 10.1109/DTPI55838.2022.9998905

Chao Fan

{"title":"Integrating Human Mobility and Infrastructure Design in Digital Twin to Improve Equity and Resilience of Cities","authors":"Chao Fan","doi":"10.1109/DTPI55838.2022.9998905","DOIUrl":"https://doi.org/10.1109/DTPI55838.2022.9998905","url":null,"abstract":"The changing climate has amplified the intensities and scales of the environmental stresses with disproportional impacts on global cities. Building equity and resilience for cities to cope with disaster impacts it in an urgent need. Cities are composed of the built environment that shapes humans' mobility and capabilities to survive in disasters. Hence, integrating human mobility and infrastructure design may hold the key to building equity and resilience for our cities. However, a methodological framework is missing to analyze the interdependencies and interactions between human mobility and infrastructure systems to inform the design of equitable and resilient urban spaces. Here, we summarize our studies on characterizing complex urban environments, modeling human-infrastructure interactions, and proposing an optimal scenario that improves equity and resilience of population accessibility to infrastructures. Our work has broad implications for the integration of humans and infrastructure in digital space through smart city digital twins. We finally propose a design of the digital space to connect the societal challenges of equity and resilience with the technological innovations in the digital twin.","PeriodicalId":409822,"journal":{"name":"2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134555592","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

Modeling Methodology and In-field Measurement Setup to Develop Empiric Weather Models for Solid-State LiDAR Sensors 建模方法和现场测量设置，以开发固态激光雷达传感器的经验天气模型

2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI) Pub Date : 2022-10-24 DOI: 10.1109/DTPI55838.2022.9998918

M. Kettelgerdes, G. Elger

{"title":"Modeling Methodology and In-field Measurement Setup to Develop Empiric Weather Models for Solid-State LiDAR Sensors","authors":"M. Kettelgerdes, G. Elger","doi":"10.1109/DTPI55838.2022.9998918","DOIUrl":"https://doi.org/10.1109/DTPI55838.2022.9998918","url":null,"abstract":"With the automotive industry's dedicated roadmap towards partly automated driving, the responsibility for reliable environmental perception moves from the driver to the vehicle's advanced driving assistance systems (ADAS). However, with steadily growing system complexity, the required test mileage to certify new driving functions increases to an unworkably high level. In order to validate ADAS functions like lane change assist (LCA), automated emergency breaking (AEB), or even path planning virtually, there is a strong demand for high fidelity sensor models which are capable of simulating automotive Radar, LiDAR as well as camera sensor perception in real time while providing realistic, artificial sensor raw data. Yet, especially LiDAR models mostly lack the capability of replicating the impact of specific weather characteristics, although optical sensors in particular are heavily influenced by precipitation, fog and sun irradiance. Furthermore, there is - in contrast to numerous publicly available LiDAR datasets in differing driving situations - a strong lack of datasets which are annotated with quantitative weather data such as particle size and velocity distribution in order to develop and validate such models. Hence, within this work, an automated infrastructure setup for targeted measurement of time-correlated LiDAR and weather data is presented with the aim to develop and calibrate weather models, which can eventually be used to augment virtual LiDAR data from raytracing capable driving simulation suits as well as real data, recorded under ideal weather conditions. In addition to that, the considerable effect of varying precipitation rates on an automotive Flash LiDAR system was demonstrated based on first measurements and quantified by calculating the pixel-wise temporal coefficient of variation for measured depth and intensity, reaching up to approximately 50% and 350%, respectively.","PeriodicalId":409822,"journal":{"name":"2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123862610","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

Human and Building Digital Twins for Virtual Reality Based Building Emergency Training 基于虚拟现实的建筑应急训练的人与建筑数字孪生

2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI) Pub Date : 2022-10-24 DOI: 10.1109/DTPI55838.2022.9998934

Ruying Liu, B. Becerik-Gerber

引用次数: 1

An Artificial Intelligence Driven Method for Power System Control Based on the Cloud-Edge Collaboration Architecture 基于云边缘协同架构的电力系统控制人工智能驱动方法

2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI) Pub Date : 2022-10-24 DOI: 10.1109/DTPI55838.2022.9998943

Wenchen Li, Yanhao Huang, Chunjiang He, Chenglong Xu, Peidong Xu, Tianlu Gao, Jun Zhang

{"title":"An Artificial Intelligence Driven Method for Power System Control Based on the Cloud-Edge Collaboration Architecture","authors":"Wenchen Li, Yanhao Huang, Chunjiang He, Chenglong Xu, Peidong Xu, Tianlu Gao, Jun Zhang","doi":"10.1109/DTPI55838.2022.9998943","DOIUrl":"https://doi.org/10.1109/DTPI55838.2022.9998943","url":null,"abstract":"The new-type power system with the high penetration of renewable energy accessed is of strong uncertainty and complexity, which can be challenging for the traditional methods to control. It's significant to introduce artificial intelligence to meet the challenge. This paper proposes a cloud-edge collaborative framework based on multi-agent deep reinforcement learning for power system regulation. Using an unsupervised clustering algorithm, the power grid is decomposed into several sub-networks according to the geographical relationship. Then, edge computing platforms are set up on each sub-network, where agents are deployed. The distributed control problem of each subnetwork can be modeled as a Markov decision model. The global observation information of the system is delivered to each edge platform through the cloud computing center, and all agents are trained to learn the best regulation strategy according to the global information. The proposed method can effectively decompose the centralized tasks and transfer them to the edge side, alleviating the pressure on the cloud center and enhancing the robustness of system operation.","PeriodicalId":409822,"journal":{"name":"2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129422464","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

STFL:Spatio-temporal Federated Learning for Vehicle Trajectory Prediction 基于时空联邦学习的车辆轨迹预测

2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI) Pub Date : 2022-10-24 DOI: 10.1109/DTPI55838.2022.9998967

Xuehan Zhou, Ruimin Ke, Zhiyong Cui, Qiang Liu, Wenxing Qian

{"title":"STFL:Spatio-temporal Federated Learning for Vehicle Trajectory Prediction","authors":"Xuehan Zhou, Ruimin Ke, Zhiyong Cui, Qiang Liu, Wenxing Qian","doi":"10.1109/DTPI55838.2022.9998967","DOIUrl":"https://doi.org/10.1109/DTPI55838.2022.9998967","url":null,"abstract":"Vehicle trajectory data is critical in the field of transportation. Its privacy needs to be protected, but not much attention has been paid to this. Federated learning (FL) has emerged as a useful technique to deal with privacy concerns in a distributed learning manner. Regarding large-scale vehicle trajectory data mining in the intelligent transportation systems (ITS) field, spatio-temporal characteristics are helpful to achieving better model performances; but there is a conflict concerning data sharing between privacy protection and the exploration of the spatio-temporal relationship. To better understand this problem, this paper designs a trajectory spatio-temporal prediction method based on FL named STFL. Different FL clients are trained together without sharing raw data while leveraging the spatio-temporal characteristics. In the overall solution, this paper proposes and integrates two different FL methods, i.e., space trajectory FL (s-FedWvg) and time trajectory FL (t-FedWvg) to form STFL. Several physical characteristics are extracted before training, and the weighted average algorithm is used to enhance the training process. Validation and analysis are conducted with the GAIA Open Dataset, demonstrating promising results using FL on vehicle trajectory data mining.","PeriodicalId":409822,"journal":{"name":"2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124787021","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: 5

A Novel Recurrent Neural Network for Dynamic Process Modeling with Inertia and Delay 一种新的递归神经网络用于具有惯性和延迟的动态过程建模

2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI) Pub Date : 2022-10-24 DOI: 10.1109/DTPI55838.2022.9998948

Lin Gao, Pan Zhao, Lin Wang, Haidong Gao, Yaokui Gao, Ming Liu

引用次数: 0

Analytical Design of Optimal Fractional Order PID Control for Industrial Robot based on Digital Twin 基于数字孪生的工业机器人分数阶PID最优控制解析设计

2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI) Pub Date : 2022-10-24 DOI: 10.1109/DTPI55838.2022.9998968

Xuan Liu, Ying Luo

{"title":"Analytical Design of Optimal Fractional Order PID Control for Industrial Robot based on Digital Twin","authors":"Xuan Liu, Ying Luo","doi":"10.1109/DTPI55838.2022.9998968","DOIUrl":"https://doi.org/10.1109/DTPI55838.2022.9998968","url":null,"abstract":"This paper proposes a fractional order PID analytical design framework for industrial robot based on digital twin. The effectiveness of the digital twin real-time optimization framework is verified by the simulation of PMSM motor speed control. The digital twin framework implemented in this paper consists of three following parts: multi-domain modeling, behavioral matching and control optimization. Firstly, the physical system is modeled to realize the omni-directional monitoring of robot and the kinematic simulation of single-axis motor. A model similar to the real physical system can be obtained in the step of behavioral matching, which is conducive to the next step of controller parameter optimization. The analytical design method of the five-parameter fractional order PID controller is then presented to optimize the parameters based on the accurate model after behavioral matching, so that the control system can meet the given frequency domain specifications and the required tracking performance. The simulation results show that the fractional order PID controller is robust to loop gain changes. In terms of time domain performance specifications like rise time, overshoot, and adjustment time, fractional order PID controller performs better than integer order PID controller.","PeriodicalId":409822,"journal":{"name":"2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126319609","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

Digital Twin Network based Network Slice Security Provision 基于数字双网的网络切片安全配置

2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI) Pub Date : 2022-10-24 DOI: 10.1109/DTPI55838.2022.9998964

Ke Wang, Haitao Du, Li Su

引用次数: 0