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Predictive coordinated control of fuel consumption and emissions for diesel engine vehicles under intelligent network environments

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Science China(Information Sciences) 2021年第9期64卷 255-257页

作者： Di LIU Hong CHEN Jinwu GAO Jinghua ZHAO Yunfeng HU State Key Laboratory of Automotive Simulation and Control Jilin University Department of Control Science and Engineering Jilin University Computer College Jilin Normal University

Dear editor,Diesel engines have been widely used in vehicles because of their excellent fuel efficiency and durability. Although they only represent a small percentage of vehicle ownership, they cause the majority of ... 详细信息

关键词： Predictive control Coordinated control Constraint Intelligent network information Diesel vehicle

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Crysformer:An attention-based graph neural network for properties prediction of crystals

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Chinese Physics B 2023年第9期32卷 15-20页

作者：王田陈家辉滕婧史金钢曾新华 Hichem Snoussi Institute of Artificial Intelligence SKLSDEBeihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100191China School of Aeronautic Science and Engineering Beihang UniversityBeijing 100191China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China School of Software Engineering Xi'an Jiaotong UniversityXi'an 710049China Academy for Engineering and Technology Fudan UniversityShanghai 200433China Charles Delaunay Institute University of Technology of TroyesTroyes Cedex 10004France

We present a novel approach for the prediction of crystal material properties that is distinct from the computationally complex and expensive density functional theory(DFT)-based ***,we utilize an attention-based graph neural network that yields high-accuracy *** approach employs two attention mechanisms that allow for message passing on the crystal graphs,which in turn enable the model to selectively attend to pertinent atoms and their local environments,thereby improving *** conduct comprehensive experiments to validate our approach,which demonstrates that our method surpasses existing methods in terms of predictive *** results suggest that deep learning,particularly attention-based networks,holds significant promise for predicting crystal material properties,with implications for material discovery and the refined intelligent systems.

关键词： deep learning property prediction crystal attention networks

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A Multi-robot Exploration Approach for Larger Maps with Unreliable Communications

A Multi-robot Exploration Approach for Larger Maps with Unre...

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2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Wu, Lingxin Luo, Haibo College of Computer and Data Science College of Software FuZhou University Fuzhou China College of Computer and Control Engineering Minjiang University Fuzhou China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

The issues of limited communication distance and task capability are rarely discussed in designing multi-robot cooperative methods. However, these are important problems that cannot be avoided in practical applications. In this paper, simulation experiments are carried out on some task maps that are larger than the team's task ability. The effect of the dispersion of members on the team's task performance is tested, and the existing methods are improved by introducing long-range communication to auxiliary data sharing. At the same time, we use the calculation of Energy-Benefit to improve the task selection method based on the minimum distance to the target. Compared with existing strategies, the improved scheme performs better on communication-constrained large-map exploration tasks. © 2022 IEEE.

关键词： Multipurpose robots

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Machine Learning based Design of Energy Management System using Non-Intrusive Load Monitoring Strategy

Machine Learning based Design of Energy Management System us...

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2024 International Conference on control, Automation and Diagnosis, ICCAD 2024

作者： Shahid, Kamal Gulzar, Muhammad Majid Raza, Muhammad Ali Habib, Salman Ahmad, Hasnain University of The Punjab Institute of Electrical Electronics and Computer Engineering Lahore Pakistan King Fahd University of Petroleum & Minerals Department of Control & Instrumentation Engineering Dhahran31261 Saudi Arabia Dhahran31261 Saudi Arabia Dhahran31261 Saudi Arabia Department of Electrical Engineering Islamabad Pakistan

ISBN: (纸本)9798350361025

In this work, a Non-Intrusive Load Monitoring (NILM) system is designed for smart homes based on smart energy meters. The proposed solution simplifies the monitoring process by using a single set of sensors, in contrast to conventional systems that call for several sensors. From different appliances and their combinations, crucial electrical information, such as voltage, current, active power, reactive power, apparent power, power factor, and frequency was gathered. Proposed intelligent data analysis employing machine learning methods, particularly Artificial Neural Networks (ANN) and Deep Neural Networks (DNN), is the key innovation. These methods accurately categorize various appliance kinds, with DNN performing best in real-time circumstances. Additionally, there is a built-in email alert system that activates whenever there are odd electrical surges. The streamlined user interface, offers accurate identification of individual appliances and combinations in real-time forecasts based on past data. Future developments include a larger appliance data-set, improved categorization methods, load forecasting, and the creation of a smartphone app for real-time energy consumption data and automatic control, among other things. In a net-shell, this study offers an effective, single-sensorbased solution for load monitoring in smart homes, representing a substantial development in NILM technology. This technology claims to improve resource utilization and energy management in both domestic and commercial settings. © 2024 IEEE.

关键词： Deep neural networks

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Sensor Fault Detection and Identification of a Photovoltaic System Based on Sliding Mode Observer

Sensor Fault Detection and Identification of a Photovoltaic ...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Maouadda Ismail Karim Dahech Laboratory of “sciences and techniques of automatic control & computer engineering ” University of Sfax Sfax Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

The growing emphasis on sustainability and green energy in electricity generation has led researchers to focus on improving photovoltaic systems. Consequently, a reliable fault diagnosis method is crucial for protecting and maintaining the performance of PV systems. This paper introduces a fault detection approach utilizing a sliding mode observer to detect sensor faults in a standalone photovoltaic system. The method is based on residual generation, which identifies faults by comparing residuals with specific threshold values. The standalone photovoltaic test bench includes a boost converter that facilitates maximum power point tracking using the perturb and observe method for the load. Simulations are carried out in MATLAB/Simulink under stable temperature and varying irradiance conditions to evaluate the effectiveness and robustness of the proposed fault detection technique.

关键词： Photovoltaic systems Fault diagnosis Temperature sensors Maximum power point trackers Uncertainty Fault detection System performance Observers Robustness Sustainable development

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The ChatGPT After: Building Knowledge Factories for Knowledge Workers with Knowledge Automation

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2041-2044页

作者： Yutong Wang Xiao Wang Xingxia Wang Jing Yang Oliver Kwan Lingxi Li Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence Anhui UniversityHefei 230039China Motion G Inc068898Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndiana 46202-5132 USA IEEE

Briefing: This perspective introduces the concept and framework of knowledge factories with knowledge machines for knowledge workers to achieve knowledge automation for Industry 5.0 and intelligent *** The big hit of Chat GPT makes it imperative to contemplate the practical applications of big or foundation models [1]-[5]. However, as compared to conventional models, there is now an increasingly urgent need for foundation intelligence of foundation models for real-world industrial applications.

关键词： Automation template Chat GPT

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Early Functional Safety and PPA Evaluation of Digital Designs

Early Functional Safety and PPA Evaluation of Digital Design...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Michelangelo Bartolomucci David Kingston Teo Cupaiuolo Alessandra Nardi Riccardo Cantoro Department of Control and Computer Engineering Politecnico di Torino Italy Synopsys Inc.

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

The use of semiconductor devices in safety-critical scenarios is increasing in both quantity and complexity. This paper presents a novel approach to support safety requirements from RTL exploration through to implementation, with the aid of a Safety Specification Format (SSF), thereby minimizing costly development iterations and reducing the Time-To-Market. An assessment of the results is given for the CV32E40P open source RISC-V processor.

关键词： Measurement Degradation Runtime Semiconductor devices Time to market Europe Safety Complexity theory Digital circuits Physical design

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Multi-object Crowd Real-time Tracking in Dynamic Environment Based on Neural Network

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第2期7卷 386-394页

作者： Zhang, Fu-Quan Ma, Lin-Juan College of Computer and Control Engineering Minjiang University Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China School of Computer Science and Technology Beijing Institute of Technology Beijing China

In this paper, it proposes a new lightweight neural network detection model and tracking algorithm that enables us to perform multi-object crowd tracking. The technique applied in our study is known as object detection and tracking which is based on neural network and can be calculated and reproduced intelligently according to the movement and behavior of the tracked crowd. Also, a number of experiments have been carried out to test the validity of the accuracy in the algorithm. And it is indicated that the results of the experiments have better performance and can contribute to multi-object crowd real-time tracking with high accuracy, which is similar to the state-of-the-art. All our preliminary results throw light on the nature of detection and tracking and result in a solution of multi-object crowd tracking, and it proves to be fruitful and encouraging. © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Object detection

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Policy for Optimal Avoidance of Multiple Engagement Zones

Policy for Optimal Avoidance of Multiple Engagement Zones

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Milutinović, Dejan Von Moll, Alexander Weintraub, Isaac E. Casbeer, David Department of Electrical and Computer Engineering University of California Santa Cruz Santa CruzCA95064 United States Control Science Center Air Force Research Laboratory WPAFB OH45433 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This work formulates the feedback control strategies for vehicles to reach a goal point amongst a field of dynamic risk regions. Whereas previous work has considered deterministic versions of this problem, we consider the scenario in the context of uncertainty. This uncertainty could be due to unknown wind or other external disturbances. The risk regions’ dynamics are tightly coupled to the state of the vehicle and as a result the task of navigating through a field dynamically changes as the vehicle traverses through it. Rather than using a nonlinear program solver, the approach taken here is one of formulating the problem as a stochastic optimal control problem wherein a feedback control law is derived. This feedback control law allows a single or multiple vehicles to reach the desired goal location efficiently and with minimal risk incursion. When inside a risk region, there is a probability rate of the vehicle being neutralized. The stochastic dynamical system is modeled as a hybrid system, i.e., regime switching diffusion. The hybrid system is comprised of continuous spatial dynamics and discrete operational states corresponding to "normal" and "neutralized". The optimal control problem is discretized into a field of vehicle states and control. Using Value Iteration, the control signal at each discrete location is optimized, providing a feedback control strategy over the field. Following a discussion of the methods and approaches, this problem is formulated, simulations are performed, and conclusions and remarks are made © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Stochastic systems

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Research on Multivariable control of Boilers Based on Model Predictive control (MPC) and Genetic Algorithm

Research on Multivariable Control of Boilers Based on Model ...

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Advanced Algorithms and control engineering (ICAACE), International Conference on

作者： Jiale Wang School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

This paper proposes a model predictive control (MPC) method based on genetic algorithm optimization for the problem of multivariable control of boilers. By constructing a dynamic mathematical model and an optimization control strategy for the boiler system, the problems of high energy consumption, slow response and insufficient anti-interference in traditional control methods are solved. In the experimental design, a comparative analysis of traditional MPC and GA-MPC was conducted in combination with a dynamic simulation platform. The results show that GA-MPC is significantly superior to traditional MPC in steady-state energy consumption optimization, can effectively reduce energy consumption by about 28.5%, and maintain good control accuracy and rapid response capabilities in the dynamic adjustment stage. At the same time, GA-MPC shows higher robustness and stability when dealing with complex multivariable systems, proving that the global optimization capability of genetic algorithms significantly improves the performance of MPC. The research results of this paper provide an efficient optimization strategy for boiler control systems, which has important theoretical value and engineering application significance.

关键词： Energy consumption Heuristic algorithms Boilers Prediction algorithms Stability analysis Robustness Steady-state Optimization Genetic algorithms Predictive control

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