检索结果-内蒙古大学图书馆

Optimal parallel-distributed-compensation controller design for a class of time-varying Takagi-Sugeno fuzzy model-based time-delay systems by using the orthogonal function approach-assisted genetic algorithm

引用

JOURNAL OF VIBRATION AND CONTROL 2021年第9-10期27卷 1077-1086页

作者： Chou, Fu-, I Hsu, Ming-Ren Ho, Wen-Hsien Natl Formosa Univ Dept Automat Engn Huwei Township Yunlin Taiwan Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Kaohsiung Taiwan Kaohsiung Med Univ Dept Healthcare Adm & Med Informat 100 Shin Chuan 1st Rd Kaohsiung 807 Taiwan Kaohsiung Med Univ Hosp Dept Med Res Kaohsiung Taiwan

This study proposes a method of designing quadratic optimal fuzzy parallel-distributed-compensation controllers for a class of time-varying Takagi-Sugeno fuzzy model-based time-delay control systems used to solve the finite-horizon optimal control problem. The proposed method fuses the orthogonal function approach and the improved hybrid Taguchi-genetic algorithm. The Taguchi-genetic algorithm only requires algebraic computation to perform the algorithm used to solve time-varying Takagi-Sugeno fuzzy model-based time-delay feedback dynamic equations. The fuzzy parallel-distributed-compensation controller design problem is simplified by using the Taguchi-genetic algorithm to transform the static parameter optimization problem into an algebraic equation. The static optimization problem can then be solved easily by using the improved hybrid Taguchi-genetic algorithm to find the quadratic optimal parallel-distributed-compensation controllers of the time-varying Takagi-Sugeno fuzzy model-based time-delay control systems. The applicability of the proposed integrative method is demonstrated in a real-world design problem.

关键词： Finite horizon time-varying Takagi-Sugeno fuzzy model time delay orthogonal functions hybrid Taguchi-genetic algorithm quadratic-optimal control

来源：评论

学校读者我要写书评

暂无评论

distributed Model Predictive Control for Multiple Trains with Coupled Constraints 42

Distributed Model Predictive Control for Multiple Trains wit...

引用

42nd Chinese Control Conference, CCC 2023

作者： Zhang, Zixuan Song, Haifeng Bai, Weiqi Wang, Hongwei Dong, Hairong Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Beijing100044 China School of Electronic Information Engineering Beihang University Beijing100191 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9789887581543

This paper presents a distributed model predictive control of multiple trains, aiming at overcoming coupled constraints and achieving cooperative operation while reducing the computational burden. According to the coupled constraints associated with maintaining safety distance between adjacent trains, trains are divided into sub-systems and distributed optimal control problem is then formulated. To solve the problem, an alternating direction method of multipliers (ADMM) is employed with a fully distributed scheme, decomposing the original optimal control problem into smaller sub-problems that can be computed in parallel and meet the real-time control requirements. The proposed method effectively guarantees the cooperative operation of trains under local and coupled safety constraints. Numerical simulations are provided to demonstrate the feasibility and effectiveness of the proposed train control model and methods. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： real time control

来源：评论

学校读者我要写书评

暂无评论

Unrepairable system with single production unit and n failure-prone identical parallel storage units

引用

RELIABILITY ENGINEERING & SYSTEM SAFETY 2022年第0期222卷 108437-108437页

作者： Levitin, Gregory Xing, Liudong Dai, Yuanshun Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu Peoples R China NOGA Israel Independent Syst Operator Herzliyya Israel Univ Massachusetts Dartmouth MA 02747 USA

Despite the crucial role of product storage played in the operation of real-world systems, only very few reliability studies have considered this component in the system reliability modeling and analysis and the existing models assume only a single storage unit per system or subsystem. This paper makes advancements by modeling an unrepairable system with a production unit (PU) and multiple parallel storage units (SUs) of limited capacity. Both the PU and SUs are failure prone or imperfect with time-to-failure following arbitrary distributions. The surplus product in the case of the PU's performance exceeding the demand is evenly distributed among available SUs. The cumulated products in available SUs may subsequently compensate the deficiency when the PU fails or has insufficient performance. We put forward a numerical algorithm of evaluating the mission success probability (MSP) of the considered system with required demand and mission time. We conduct a case study on a water supply system with five SUs to illustrate the proposed model and investigate impacts of several model parameters (including initial SU filling, SU capacity, the number of SUs, PU productivity, reliabilities of PU and SU) as well as their interactions on the MSP.

关键词： Production parallel storage Demand satisfaction Mission success probability

来源：评论

学校读者我要写书评

暂无评论

Demeter: Resource-Efficient distributed Stream Processing under Dynamic Loads with Multi-Configuration Optimization 24

Demeter: Resource-Efficient Distributed Stream Processing un...

引用

15th ACM/SPEC International Conference on Performance Engineering (ICPE)

作者： Geldenhuys, Morgan K. Scheinert, Dominik Kao, Odej Thamsen, Lauritz Tech Univ Berlin Berlin Germany Univ Glasgow Glasgow Lanark Scotland

ISBN: (纸本)9798400704444

distributed Stream Processing (DSP) focuses on the near real-time processing of large streams of unbounded data. To increase processing capacities, DSP systems are able to dynamically scale across a cluster of commodity nodes, ensuring a good Quality of Service despite variable workloads. However, selecting scaleout configurations which maximize resource utilization remains a challenge. This is especially true in environments where workloads change over time and node failures are all but inevitable. Furthermore, configuration parameters such as memory allocation and check-pointing intervals impact performance and resource usage as well. Sub-optimal configurations easily lead to high operational costs, poor performance, or unacceptable loss of service. In this paper, we present Demeter, a method for dynamically optimizing key DSP system configuration parameters for resource efficiency. Demeter uses time Series Forecasting to predict future workloads and Multi-Objective Bayesian Optimization to model runtime behaviors in relation to parameter settings and workload rates. Together, these techniques allow us to determine whether or not enough is known about the predicted workload rate to proactively initiate short-lived parallel profiling runs for data gathering. Once trained, the models guide the adjustment of multiple, potentially dependent system configuration parameters ensuring optimized performance and resource usage in response to changing workload rates. Our experiments on a commodity cluster using Apache Flink demonstrate that Demeter significantly improves the operational efficiency of long-running benchmark jobs.

关键词： distributed Stream Processing Cluster Resource Management Performance Profiling Performance Modeling Runtime Optimization time Series Forecasting Cloud Computing

来源：评论

学校读者我要写书评

暂无评论

Research on distributed PV Consumption Control Method

Research on Distributed PV Consumption Control Method

引用

CIRED 2024 Vienna workshop

作者： Liang, Ge Zhuo, Gao Fengqing, Zhao Xiaozhou, Song Beijing SifangAutomation Co. LTD. Beijing China

ISBN: (纸本)9781837242672

The traditional distribution network has been developed into a new distribution network with multi-power supply, bidirectional power current and a large number of new power electronic equipment access, after large number of distributed resources such as photovoltaic, wind power and energy storage are connected to the medium and low voltage distribution system. The active power of distributed generators such as photovoltaic wind generators is greatly affected by meteorological factors and has strong randomness and volatility, which brings difficulties for the safe and stable operation control of the active distribution networks. Sometimes photovoltaic or wind generators have to be decreased output or even given up, which reduces the economy incomes of DGs' owners and reliability of distribution system. To solve this problem, an operational control method has been proposed to improve the load-bearing capacity of the distribution system in this paper. The method aims to maximize the consumption ability of distributed generators, and establishes an active power coordination control model for distribution system meeting with the safe operation constraint. This model has the characteristics of multi spatiotemporal comprehensive optimization and it is a multidimensional comprehensive optimization method. In terms of time dimension, it is divided into day-ahead strategy optimization, intraday ultra short-term strategy optimization, and real-time control optimization. In terms of space dimension, it is divided into three levels: regional autonomy, inter regions collaboration, and global optimization. The specific implementation method is as follows: regional autonomy includes coordinated control of distributed energy sources such as photovoltaic generators, wind power generators, and energy storage systems within one MV/LV transformer supplying area to achieve power autonomy within the MV/LV transformer supplying area, flexible interconnection of the low-voltage areas power

关键词： Electrochemical electrodes

来源：评论

学校读者我要写书评

暂无评论

A distributed user-oriented IoT-based Air Pollution Monitoring 28

A distributed user-oriented IoT-based Air Pollution Monitori...

引用

IEEE 28th International workshop on Computer-Aided Modeling and Design of Communication Links and Networks (CAMAD)

作者： Figueroa, Jose Jorge Figueroa Shen, Hao Ahmadi, Hamed Canberk, Berk Univ York Sch Phys Engn & Technol York N Yorkshire England Edinburgh Napier Univ Edinburgh Midlothian Scotland

ISBN: (纸本)9798350303490

Environmental health services are paramount for ensuring the Quality of Life (QoL) in cities, with urban design playing a pivotal role. The rapid urbanization of cities globally has exacerbated air pollution, posing severe health risks to residents. Air quality is closely tied to human health, and elevated pollutant levels can lead to various illnesses, particularly affecting vulnerable populations. Indoor and outdoor air pollution is a leading cause of premature deaths in some regions. To address these challenges, governments and organizations are investing in air quality monitoring. This paper presents an IoT-based Air Pollution Monitor (APMIoT) comprising Raspberry Pico W embedded wireless systems and multiple sensors. These APMIoT modules transmit real-time data to a cloud server architecture, enabling the computation of pollutant indices and an Air Quality Index (AQI). Users can monitor and compare air quality data through an intuitive dashboard interface. The APMIoT includes key sensor modules for measuring particle matter, humidity, temperature, GPS coordinates, total volatile organic compounds (TVOC), and CO2 levels. It utilizes FreeRTOS for modular programming, ensuring independent handling of various processes. ThingSpeak serves as the cloud server, collecting, analysing, and visualizing data from single and multiple APMIoT devices. Custom analytics provide in-depth pollutant analysis and AQI representation. Testing demonstrates system reliability and alignment with external AQI data sources. This paper underscores the importance of open-source, customizable air quality monitoring systems for both indoor and outdoor settings. It offers a robust tool for environmental monitoring and air quality assessment, essential for managing urban health and well-being effectively.

关键词： real-time Insights Air Pollution Monitoring ThingSpeak Adaptability MQTT Collective IoT

来源：评论

学校读者我要写书评

暂无评论

HIL testing of a real-time Propulsion, Thermal and Flight Controller for Hybrid-Electric Regional Aircraft Applications

HIL testing of a Real-Time Propulsion, Thermal and Flight Co...

引用

AIAA Aviation Forum

作者： Misley, Anna Ramesh, Prashanth D'Arpino, Matilde Ohio State Univ Dept Mech & Aerosp Engn Columbus OH 43210 USA Ohio State Univ Ctr Automot Res Columbus OH 43210 USA

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

Hybrid turboelectric distributed propulsion systems are one the most promising solutions to reduce fuel consumption and emissions in aviation. In general, hybrid architectures require a higher number of components, which may increase the aircraft weight, so it is important to accurately design the energy and thermal management system of the powertrain to enable an optimal use of the electrical and fuel energy throughout the mission. This works describes the development of a real-time flight, thermal and propulsion control for a series-parallel hybrid turboelectric architecture, which guarantees the aircraft to follow the desired mission profile and to determine the control variables for optimally using the battery and the engine in terms of electrical power and cooling requirements. The developed controller is tested and validated in a Controller Hardware In the Loop test setup that includes a realistic model of the airframe, the propulsion system and the physical communication interface between the controller and the propulsor. The robustness of the controller is tested considering signal discretization effects and communication delays.

关键词： distributed Propulsion System Aircrafts Thermal Management System Airframes Fuel Consumption Aviation Thermal Propulsion Environmental Control System Pontryagin's Minimum Principle Turbogenerators

来源：评论

学校读者我要写书评

暂无评论

A Locality-aware Cooperative distributed Memory Caching for parallel Data Analytic Applications 36

A Locality-aware Cooperative Distributed Memory Caching for ...

引用

36th IEEE International parallel and distributed Processing Symposium (IEEE IPDPS)

作者： Hung, Chia-Ting Chou, Jerry Chen, Ming-Hung Chung, I-Hsin Natl Tsing Hua Univ Comp Sci Dept Hsinchu Taiwan IBM TJ Watson Res Ctr New York NY USA

ISBN: (纸本)9781665497473

Memory caching has long been used to fill up the performance gap between processor and disk for reducing the data access time of data-intensive computations. Previous studies on caching mostly focus on optimizing the hit rate of a single machine. But in this paper, we argue that the caching decision of a distributed memory system should be performed in a cooperative manner for the parallel data analytic applications, which are commonly used by emerging technologies, such as Big Data and AI (Artificial Intelligence), to perform data mining and sophisticated analytics on larger data volume in a shorter time. A parallel data analytic job consists of multiple parallel tasks. Hence, the completion time of a job is bounded by its slowest task, meaning that the job cannot benefit from caching until all inputs of its tasks are cached. To address the problem, we proposed a cooperative caching design that periodically rearranges the cache placement among nodes according to the data access pattern while taking the task dependency and network locality into account. Our approach is evaluated by a trace-driven simulator using both synthetic workload and real-world traces. The results show that we can reduce the average completion times up to 33% compared to a non-collaborative caching polices and 25% compared to other start-of-the-art collaborative caching policies.

关键词： parallel Data Processing Caching Algorithm Performance distributed systems

来源：评论

学校读者我要写书评

暂无评论

A distributed, Asynchronous Algorithm for Large-Scale Internet Network Topology Analysis 24

A Distributed, Asynchronous Algorithm for Large-Scale Intern...

引用

24th IEEE/ACM International Symposium on Cluster, Cloud, and Internet Computing (CCGrid)

作者： Elmougy, Youssef Hayashi, Akihiro Sarkar, Vivek Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9798350395679;9798350395662

With the growing complexity of modern internet networks, we introduce a distributed, asynchronous, and scalable algorithm tailored for determining the centrality and importance of various levels of the global internet network topology at a massive scale. By utilizing triangle formations and neighborhood densities in these complex networks, our algorithm provides valuable insights into the structural importance of individual nodes and the intricate relationships among interconnected entities. Focusing on the global internet's prime components routers, IP addresses, and Autonomous systems - the algorithm provides a scalable solution extending to the global internet infrastructure, datacenters, cloud networks, and private networks. By taking advantage of the parallelism, distributed processing, efficient communication, and fine-grained asynchronous execution through an Actor-based programming system, our algorithm is capable of efficiently processing and performing rapid computations on large-scale internet networks. We perform scalability studies on the NERSC Perlmutter supercomputer and the Georgia Tech HPC PACE cluster using three large-scale real-world network datasets and one large-scale synthetic network dataset, achieving up to 91.7% parallel efficiency scaling out to 2K cores and reducing execution time from 5.7 hours to 18.3 seconds, while performing 105.4x better on average compared to related approaches. Our research contributes significantly to facilitating enhanced network management, fault tolerance and resilience, and security, but as well to improving overall network stability, reducing latency, and optimizing energy consumption across the vast internet network devices environment.

关键词： Network Topology Internet Analysis Actors Selectors distributed systems Scalable Computing Centrality Shared Memory PGAS OpenSHMEM

来源：评论

学校读者我要写书评

暂无评论

real-time Deep-Learning-Driven parallel MPC 62

Real-Time Deep-Learning-Driven Parallel MPC

引用

62nd IEEE Conference on Decision and Control (CDC)

作者： Kohut, Roman Pavlovicova, Erika Fedorova, Kristina Oravec, Juraj Kvasnica, Michal Slovak Univ Technol Bratislava Inst Informat Engn Automat & Math Fac Chem & Food Technol Radlinskeho 9 SK-81237 Bratislava Slovakia

ISBN: (纸本)9798350301243

A novel real-time approximated MPC control policy based on deep learning is proposed to address the high computational burden of model predictive control (MPC) for large-scale systems and those with fast dynamics. This control method approximates the optimal solution of the distributed optimization problems in the ALADIN-based parallel MPC design framework, resulting in a highly effective approach that outperforms other well-known methods for solving the MPC design problem. The numerical case study shows promising results, demonstrating the potential of this approach for realtime implementation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：