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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 4826-4830页

作者： Jingqiu Guo Long Chen Lingxi Li Xiaoxiang Na Ljubo Vlacic Fei-Yue Wang Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University Shanghai China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering Purdue School of Engineering and Technology Indiana University–Purdue University Indianapolis (IUPUI) Indianapolis IN USA Department of Engineering University of Cambridge Cambridge U.K. Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith University Nathan QLD Australia State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Engineering Macau University of Science and Technology Macau China

Advanced Air Mobility (AAM) envisages a sustainable, safe, convenient, and affordable air transport system. In socio-technical transition of AAM, there are a number of trade-offs in ecosystem that need to be studied. Three perspectives on economic feasibility are explored: first, based on history of VTOL services and value of time estimates, we discuss whether AAM can provide customers with competitive mobility services; second, what are the stakeholders’ insights on the deployment of AAM; last, the experience in the development of autonomous driving technology, such as parallel intelligence, can inform future AAM research.

关键词： Active appearance model Costs Economics Transportation Helicopters Aircraft propulsion Public transportation

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A Ray-based Asynchronous Distributed Parallel K-SVD Method

A Ray-based Asynchronous Distributed Parallel K-SVD Method

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Yongkang Li Xiaodong Chen Xiaohui Wan Jin Sun Peng Zheng Yunchang Wang Zebin Wu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Department of Industry Development Quzhou Transportation Bureau Quzhou China Department of Quality Control and Testing Chang shan Inspection and Testing institute Quzhou China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

K-SVD (K-Singular Value Decomposition)is a com-monly used dictionary learning method that progressively opti-mizes dictionaries to better represent data by iterating sparse coding and dictionary update steps. However, with the increasing data size, the traditional K -SVD method faces significant challenges in terms of computational efficiency. Therefore, this paper proposes an asynchronous distributed parallel K-SVD method based on Ray framework to cope with the computational efficiency problem in large-scale data processing. This method achieves efficient parallel computation by decomposing the sparse coding and dictionary updating steps of the K-SVD method into multiple independent tasks and utilizing the distributed scheduling capability of the Ray framework. Since the zero elements of the coefficient matrix are unevenly distributed, we introduce a stride partitioning approach when performing the decomposition. In addition, in order to avoid the method falling into local optima and failing to find global optima due to parallelism, this paper synchronizes with the global at the same time as the atomic update of the dictionary. Meanwhile, for the purpose of avoiding the overhead caused by data synchronization, a parameter server is introduced to realize asynchronous updating of dictionary and coefficient matrix to reduce the overhead caused by communication. The experimental results show that, compared with the traditional K-SVD method, the method proposed in this paper has a significant advantage in computational efficiency and is able to efficiently process hyperspectral image datasets under the premise of guaranteeing the sparse representation accuracy. Compared with the synchronous parallel K-SVD method, the proposed method has faster error convergence and does not fall into local optima.

关键词： Dictionaries Image coding Parallel processing Data processing Computational efficiency Sparse matrices Synchronization Servers Hyperspectral imaging Convergence

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High Performance Distributed control Law for Large-Scale Linear Systems: A Partitioned Distributed Observer Approach

arXiv

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arXiv 2024年

作者： Xu, Haotian Liu, Shuai Shi, Ling Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

In recent years, the distributed-observer-based distributed control law has shown powerful ability to arbitrarily approximate the centralized control performance. However, the traditional distributed observer requires each local observer to reconstruct the state information of the whole system, which is unrealistic for large-scale scenarios. To fill this gap, this paper develops a greedy-idea-based large-scale system partition algorithm, which can significantly reduce the dimension of local observers. Then, the partitioned distributed observer for large-scale systems is proposed to overcome the problem that the system dynamics are difficult to estimate due to the coupling between partitions. Furthermore, the two-layer Lyapunov analysis method is adopted and the dynamic transformation lemma of compact errors is proved, which solves the problem of analyzing stability of the error dynamic of the partitioned distributed observer. Finally, it is proved that the distributed control law based on the partitioned distributed observer can also arbitrarily approximate the control performance of the centralized control law, and the dimension of the local observer is greatly reduced compared with the traditional method. The simulation results show that when the similarity between the physical network and the communication network is about 80%, the local observer dimension is greatly reduced by 90% and the relative error between the performance of the distributed control law and that of the centralized control law is less than 1%. © 2024, CC BY.

关键词： Continuous time systems

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Building Energy Performance Certificate Labelling Classification Based on Explainable Artificial Intelligence 1

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2nd International Conference on Neural Computing for Advanced Applications, NCAA 2021

作者： Tsoka, Thamsanqa Ye, Xianming Chen, YangQuan Gong, Dunwei Xia, Xiaohua Department of Electrical Electronic and Computer Engineering University of Pretoria Pretoria0002 South Africa Mechanical Engineering Department University of California MercedCA95340 United States School of Information and Control Engineering China University of Mining andTechnology Xuzhou221116 China

ISBN: (数字)9789811651885

ISBN: (纸本)9789811651878

In this study, the machine learning based classification approach is aptly applied to classify the building energy performance certificate (EPC) rating levels instead of applying the traditional approach of direct measurements of the annual building energy consumption and its effective floor areas. Accurately acquired EPC ratings deliver a successful building EPC programme which enables future regulatory actions for the buildings nationwide. Historical data and experiences from the countries who have implemented the building EPC programme can help to accelerate the development of such a programme in the developing countries like South Africa. With these concerns, an artificial neural network (ANN) classification model together with the explainable artificial intelligence (XAI) tools are adopted in this study to obtain the building EPC rating levels. In this study, an ANN model is trained from the historical registry of the building EPC best practices in Lombardy, Italy. The ANN classification model is calibrated and validated with optimal number of neurons. Results show that probability of detection for the ‘G’ labelled buildings is 0.9655 with a precision of 0.8547. In addition, the Local Interpretable Model-Agnostic Explanation (LIME) is adopted to explain the inherent properties of the identified ANN classification model. The LIME XAI shows that opaque surface and average U values for walls are the most influential features on the EPC rating classification while the degree day feature has the least influence during the ANN based EPC rating classification process. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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Dynamic Tracking of Ship Course with Limited Input Based on ZG control and Bivariate S-Function

Dynamic Tracking of Ship Course with Limited Input Based on ...

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第34届中国控制与决策会议

作者： Shengmian Xie Yunong Zhang Jinjin Guo Mengling Xiao School of Information and Communication Engineering Guangzhou Maritime University Guangdong Key Laboratory of Modern Control Technology School of Computer Science and Engineering Sun Yat-sen University

The nonlinear control system of ship is complex,which is a focus of research and key core of dynamic *** the input of the actual system is often restricted by the physical constraints of the actuator and energysaving control,the feasible region of the control input is often constrained by these ***,by combining Zhang-gradient(ZG) dynamics with S-function,in this paper,a novel controller for the limited input system is *** the fact that dynamic systems are generally nonlinear in practical applications,the nonlinear system study has more practical ***,this paper extends the ZG control method based on gradient dynamics and the bivariate S-function to the tracking control of nonlinear ship system,and designs a ship course tracking controller with a limited input,which can control the rudder angle within a certain feasible region while successfully tracking the ship course.

关键词： Dynamic tracking Zhang-gradient(ZG) dynamics Bivariate S-function Limited input Nonlinear ship system

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Mitigating the Negative Impact of Over-association for Conversational Query Production

arXiv

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arXiv 2024年

作者： Wang, Ante Song, Linfeng Min, Zijun Xu, Ge Wang, Xiaoli Yao, Junfeng Su, Jinsong School of Information Xiamen University Xiamen China Xiamen Key Laboratory of Intelligent Storage and Computing Xiamen China Tencent AI Lab BellevueWA United States College of Computer and Control Engineering Minjiang University Fuzhou China

Conversational query generation aims at producing search queries from dialogue histories, which are then used to retrieve relevant knowledge from a search engine to help knowledge-based dialogue systems. Trained to maximize the likelihood of gold queries, previous models suffer from the data hunger issue, and they tend to both drop important concepts from dialogue histories and generate irrelevant concepts at inference time. We attribute these issues to the over-association phenomenon where a large number of gold queries are indirectly related to the dialogue topics, because annotators may unconsciously perform reasoning with their background knowledge when generating these gold *** carefully analyze the negative effects of this phenomenon on pretrained Seq2seq query producers and then propose effective instance-level weighting strategies for training to mitigate these issues from multiple perspectives. Experiments on two benchmarks, Wizard-of-Internet and DuSinc, show that our strategies effectively alleviate the negative effects and lead to significant performance gains (2% ∼ 5% across automatic metrics and human evaluation). Further analysis shows that our model selects better concepts from dialogue histories and is 10 times more data efficient than the baseline. The code is available at https://***/DeepLearnXMU/QG-OverAsso. © 2024, CC BY.

关键词： Structured Query Language

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Raw Material Size Commonality Decision Tool: A Bi-Objective Integer Programming Model

SSRN

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SSRN 2024年

作者： Bernabei, Margherita Colabianchi, Silvia Costantino, Francesco De Santis, Marianna Patria, Daniele Department of Mechanical and Aerospace Engineering Sapienza University of Rome Via Eudossiana 18 Rome00184 Italy Department of computer control and management engineering Antonio Ruberti Sapienza University of Rome Via Ariosto 25 Rome00185 Italy Department of Information Engineering University of Florence Via di Santa Marta 3 Firenze50139 Italy

This paper addresses the industrial challenge of raw material size commonality. The Best Allocation of Resource Sizes (BARS) problem is introduced, wherein the same raw material is utilized across multiple products, and it is not possible to reuse the leftover raw material. The objective is to explore the cost implications of reducing the number of different raw materials sizes, considering several cost components, i.e., purchase, scrap, storage, safety stock, and set-up costs. The problem is formulated as a biobjective nonlinear integer program, seeking to simultaneously minimize the number of items and costs. The model is solved using an adapted version of the Ε-constraint method. The efficacy of the proposed model is demonstrated through validation in three real-world case studies spanning different industrial sectors. The developed model serves as a decision support tool for management, facilitating an understanding of the consequences associated with different sets of selected raw materials. Also, it permits the imposition of cost limits based on strategic objectives, whether in total or pertaining to specific cost items, and enables the analysis of feasible solutions with respect to the sets of items that can be purchased. © 2024, The Authors. All rights reserved.

关键词： Decision support systems

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A Formation control for Multi-Agent Systems for Event-Trigger-Based Variable Gain controllers

A Formation Control for Multi-Agent Systems for Event-Trigge...

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Annual Conference of Industrial Electronics Society

作者： Daiki Asada Takuya Nakagawa Yoshikatsu Hoshi Hidetoshi Oya Shunya Nagai Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan The Department of Computer Science Tokyo City University Tokyo Japan Department of Electronics and Control Engineering National Institute of Technology (KOSEN) Niihama College Ehime Japan

This paper deals with a design problem for a new event-trigger-based variable gain controller which achieves consensus for multi-agent systems (MASs) with the leader-follower structure. The proposed variable gain controller consists of stabilizing state feedback laws, state feedback inputs for consensus, and compensation inputs with adjustable parameters, and is designed by taking relative positions into account explicitly. In this paper, we show that sufficient conditions for the existence of the proposed formation control system are given in terms of Linear Matrix Inequalities (LMIs).

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Enhancing online industrial quality index prediction with a general deep temporal feature extraction and incremental ensemble modeling framework

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： Zhao, Yuchen Yang, Rui Ying, Jie Wang, Song Zeng, Zhenyu College of Control Science and Engineering Zhejiang University Zheda Road 38 Hangzhou310027 China School of Automotive and Transportation Engineering Hefei University of Technology Tunxi Road 193 Hefei230000 China School of Computer Science and Engineering Nanyang Technological University Nanyang Avenue 50 Singapore639798 Singapore Alibaba Cloud Computing Co. Ltd. Alibaba Group Dengcai Road 1008 Hangzhou310030 China

Data-driven modeling methods for industrial quality index prediction often face the challenge of limited data representation. Using process variable snapshots from a single time step is insufficient for building high-performance soft sensors. Moreover, during online prediction, the performance of soft sensors is affected by diverse operating conditions and concept drift in industrial data streams, leading to performance degradation. To address these challenges, this paper proposes a Temporal Feature Extraction and Incremental Variational Bayesian Regression Ensemble (TFE-IVBRE) framework, which provides a general solution for various online industrial quality index prediction tasks. The TFE-IVBRE framework combines the temporal feature extraction capabilities of deep neural networks, the ability of ensemble learning to handle diverse operating conditions, and the online learning features of variational Bayesian models. An incremental update strategy is also incorporated to maintain consistent prediction performance. Experiments in the Debutanizer Column and Sulfur Recovery Unit scenarios show that the prediction performance of TFE-IVBRE significantly outperforms other static and online comparison models, with the effectiveness of its components validated through ablation studies. Finally, the overall model also demonstrates good robustness. These results offer valuable insights for the advancement of industrial soft sensor development. © 2025 Elsevier Ltd

关键词： Deep neural networks

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Gnss-Onboard Inertial Sensor Integration for Accurate Autonomous Vehicle Localization in Gnss-Denied Scenarios

SSRN

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SSRN 2023年

作者： Yusefi, Abdullah Toy, İbrahim Sungur, Cemil Durdu, Akif Computer Engineering Konya Technical University Konya Turkey Inc. Co. Konya Turkey Electrical and Electronics Engineering Konya Technical University Konya Turkey Robotics Automation Control Laboratory Konya Technical University Konya Turkey

This letter proposes a global navigation satellite system (GNSS) and inertial navigation system (INS)-based, pose estimation method for autonomous vehicles. We focus on the issue of localization in situations where GNSS signals are blocked, where localization must proceed using alternate sensors such as INS. Our methodology uses two filtering mechanisms to filter out the noise and obtain velocity and attitude of the vehicle from inertial sensor measurements. The two mechanism are the sigmoid filtering for angular velocity, and the sliding window filtering for linear acceleration measurements. The filtered velocity and attitude are then loosely coupled with GNSS data to produce a highly accurate pose estimation. Our techniques are deployed and tested on a real-world autonomous vehicle. When determining the pose solely from inertial dead-reckoning measurements during GNSS outages, the highest translational and rotational RPE drifts are less than 0.3 meters and 5.0 degrees of the entire 5500 meters’ distance traveled. © 2023, The Authors. All rights reserved.

关键词： Inertial navigation systems

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