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arXiv 2024年

作者： Zhou, Junjie Wang, Lin Meng, Qiang Wang, Xiaofan Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Automation Shanghai Jiao Tong University School of Mechatronic Engineering and Automation Shanghai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai201418 China Department of Civil and Environmental Engineering National University of Singapore Singapore117576 Singapore

Generating realistic and diverse road scenarios is essential for autonomous vehicle testing and validation. Nevertheless, owing to the complexity and variability of real-world road environments, creating authentic and varied scenarios for intelligent driving testing is challenging. In this paper, we propose DiffRoad, a novel diffusion model designed to produce controllable and high-fidelity 3D road scenarios. DiffRoad leverages the generative capabilities of diffusion models to synthesize road layouts from white noise through an inverse denoising process, preserving real-world spatial features. To enhance the quality of generated scenarios, we design the Road-UNet architecture, optimizing the balance between backbone and skip connections for high-realism scenario generation. Furthermore, we introduce a road scenario evaluation module that screens adequate and reasonable scenarios for intelligent driving testing using two critical metrics: road continuity and road reasonableness. Experimental results on multiple real-world datasets demonstrate DiffRoad’s ability to generate realistic and smooth road structures while maintaining the original distribution. Additionally, the generated scenarios can be fully automated into the OpenDRIVE format, facilitating generalized autonomous vehicle simulation testing. DiffRoad provides a rich and diverse scenario library for large-scale autonomous vehicle testing and offers valuable insights for future infrastructure designs that are better suited for autonomous vehicles. Copyright © 2024, The Authors. All rights reserved.

关键词： Automobile testing

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Application of Fuzzy Neural Network in Chromatographic Separation 6th

Application of Fuzzy Neural Network in Chromatographic Separ...

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6th Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2019

作者： Xie, Chao-Fan Xu, Lu-Xiong Wang, Ruobin Zhang, Fuquan Department of Electrical Engineering I-Shou University Kaohsiung84001 Taiwan Electronic Information and Engineering Institute Fuqing Branch of Fujian Normal University FuJian FuQing FuZhou350300 China Key Laboratory of Nondestructive Testing Fuqing Branch of Fujian Normal University FuJian FuQing FuZhou350300 China School of Information Science and Technology North China University of Technology Beijing100144 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China School of Software Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789811650352

Chromatographic separation process is a continuous and discrete mixed system. It is a fixed bed consisting of chromatographic columns or similar chromatographic columns. The components are separated by valve switching. Therefore, many small and medium-sized enterprises need to control system parameters, so they are vulnerable to external interference. In the actual production process, it is often in the safety aspect, the system operation will deviate from the best operating conditions, so it is difficult to give full play to the separation ability of SMB chromatographic separation process. In order to make the separation system of small and medium-sized enterprises play its potential, the results based on simulation model are particularly important. This paper firstly digitalization the simulation process, then uses FNN to training the simulation data to the influence of observation parameters on the results, searching for the trend of complex parameter change from neural network learning, foundation for verifying the controllability of the system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy neural networks

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Ascl: Accelerating Semi-Supervised Learning Via Contrastive Learning

SSRN

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SSRN 2024年

作者： Liu, Haixiong Li, Zuoyong Wu, Jiawei Zeng, Kun Hu, Rong Zeng, Wei Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Data Science Minjiang University Fuzhou350121 China School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Guangdong Shenzhen518107 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Physics and Mechanical and Electrical Engineering Longyan University Longyan364012 China

SSL(Semi-supervised learning) is widely used in machine learning, which leverages labeled and unlabeled data to improve model performance. SSL aims to optimize class mutual information, but noisy pseudo-labels introduce false class information due to the scarcity of labels. Therefore, these algorithms often need significant training time to refine pseudo-labels for performance improvement iteratively. To tackle this challenge, we propose a novel plug-and-play method named ASCL(Accelerating Semi-Supervised Learning via Contrastive Learning). This method combines contrastive learning with uncertainty-based selection for performance improvement and accelerates the convergence of SSL algorithms. Contrastive learning initially emphasizes the mutual information between samples as a means to decrease dependence on pseudo-labels. Subsequently, it gradually turns to maximizing the mutual information between classes, aligning with the objective of semi-supervised learning. Uncertainty-based selection provides a robust mechanism for acquiring pseudo-labels. The combination of the contrastive learning module and the uncertainty-based selection module forms a virtuous cycle to improve the performance of the proposed model. Extensive experiments demonstrate that ASCL outperforms state-of-the-art methods in terms of both convergence efficiency and performance. In the experimental scenario where only one label is assigned per class in the CIFAR-10 dataset, the application of ASCL to Pseudo-label, UDA (Unsupervised Data Augmentation for Consistency Training), and Fixmatch benefits substantial improvements in classification accuracy. Specifically, the results demonstrate notable improvements in respect of 16.32%, 6.9%, and 24.43% when compared to the original outcomes. Moreover, the required training time is reduced by almost 50%. © 2024, The Authors. All rights reserved.

关键词： Image classification

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TTAGO-COUPLED-MULTIPLEX-DIGITAL-RPA-CRISPR/CAS12A FOR EGFR MUTATIONS DETECTION 26

TTAGO-COUPLED-MULTIPLEX-DIGITAL-RPA-CRISPR/CAS12A FOR EGFR M...

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26th International Conference on Miniaturized systems for Chemistry and Life Sciences, MicroTAS 2022

作者： Zhuang, Jianjian Xia, Liping Zou, Zheyu Yin, Juxin Mu, Ying Department of Clinical Pharmacology Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province Affiliated Hangzhou First People's Hospital Cancer Center Zhejiang University School of Medicine Hangzhou310006 China School of information and Electrical Engineering Zhejiang University City College Hangzhou China Research Centre for Analytical Instrumentation Institute of Cyber-Systems and Control State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)9781733419048

Herein, Ttago-Coupled-Multiplex-digital-RPA-CRISPR/Cas12a (TCMDC) was proposed for the enrichment and multiplex detection of EGFR mutations. TCMDC realizes the fast enrichment of mutant nucleic acid templates by using Ttago to cut the wild-type template efficiently. The RPA-dual-crRNA Cas12a without need of PAM sequence realizes the universal one-tube RPA-Cas12a detection. Moreover, a multiplex digital PCR chip was developed and TCMDC was used to achieve absolute quantitative detection of multiplex EGFR mutations. As a mutation detection method, TCMDC not only has extremely high sensitivity (0.001%) and specificity, but also is very fast (1h), simple and convenient, which has versatility in point-of-care cancer diagnosis and precision medicine. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized systems for Chemistry and Life Sciences. All rights reserved.

关键词： Diagnosis

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An Improved Machine Learning Technique with Effective Heart Disease Prediction System

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Computers, Materials & Continua 2021年第12期69卷 4169-4181页

作者： Mohammad Tabrez Quasim Saad Alhuwaimel Asadullah Shaikh Yousef Asiri Khairan Rajab Rihem Farkh Khaled Al Jaloud College of Computing and Information Technology University of BishaBisha67714Saudi Arabia King Abdulaziz City for Science and Technology P.O.Box 6086Riyadh11442Saudi Arabia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Department of Electrical Engineering Laboratory for AnalysisConception and Control of SystemsLR-11-ES20National Engineering School of TunisTunis El Manar University1002Tunisia

Heart disease is the leading cause of death *** heart disease is challenging because it requires substantial experience and *** research studies have found that the diagnostic accuracy of heart disease is *** coronary heart disorder determines the state that influences the heart valves,causing heart *** indications of coronary heart disorder are strep throat with a red persistent skin rash,and a sore throat covered by tonsils or strep *** work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial *** first,we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization(CSPSO)*** this perception measure,characterization and accuracy were improved,while the execution time of the proposed model was *** CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods *** proposed method offers an illustrative framework that helps predict heart attacks with high *** proposed technique demonstrates the model accuracy,which reached 0.97 with the applied dataset.

关键词： Machine learning deep recurrent neural network effective heart disease prediction framework

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Minimal control Placement of Turing's Model Using Symmetries

Minimal Control Placement of Turing's Model Using Symmetries

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IEEE Conference on Decision and control

作者： Yuexin Cao Yibei Li Zhixin Liu Lirong Zheng Xiaoming Hu Department of Mathematics KTH Royal Institute of Technology Sweden School of Electrical and Electronic Engineering Nanyang Technological University Singapore Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences China School of Information Science and Technology Fudan University China

In this paper, the minimal control placement prob-lem for Turing's reaction-diffusion model is studied. Turing's model describes the process of morphogens diffusing and reacting with each other and is considered as one of the most fundamental models to explain pattern formation in a devel-oping embryo. controlling pattern formation artificially has gained increasing attention in the field of development biology, which motivates us to investigate this problem mathematically. In this work, the two-dimensional Turing's reaction-diffusion model is discretized into square grids. The minimal control placement problem for the diffusion system is investigated first. The symmetric control sets are defined based on the symmetry of the network structure. A necessary condition is provided to guarantee controllability. Under certain circumstances, we prove that this condition is also sufficient. Then we show that the necessary condition can also be applied to the reaction-diffusion system by means of suitable extension of the symmetric control sets. Under similar circumstances, a sufficient condition is given to place the minimal control for the reaction-diffusion system.

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Joint Optimization of Energy Consumption and Truck Transfer by Stack Allocation in Steel Warehouses

Joint Optimization of Energy Consumption and Truck Transfer ...

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Chinese Automation Congress (CAC)

作者： Hongwei Wang Zhezhuang Xu Jinlong Wang Boyu Chen Qingdong Zhang Xiuren Wu Yuxiong Xia Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing College of Electrical Engineering and Automation Fuzhou University Fuzhou China Sansteel Minguang Co. Ltd. Sanming China Fujian Huading Intelligent Manufacturing Technology Co. Ltd. Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Due to the continuous growth in steel market demand and the shift towards small batches and multiple varieties, efficient storage management has become a key factor in increasing the profitability of logistics parks. However, the current storage methods, due to the unreasonable allocation of stack positions, still result in energy waste during the dispatch phase and the need for trucks to pick up goods across multiple yards. To address these challenges, this paper proposes a comprehensive optimization scheme to reduce truck transfers and energy consumption in steel product dispatch. First, we constructed an optimization problem model that integrates the spatial layout of the logistics park and order data, aiming to maximize the correlation between steel products within the same yard and reduce the total energy consumption during the dispatch phase. Subsequently, a new algorithm called Enhanced Greedy and Tabu-Assisted Adaptive Large Neighborhood Search (EGTA-ALNS) is introduced. Experimental results demonstrate that the proposed algorithm outperforms existing ones in terms of solving speed and scale. It effectively optimizes energy consumption during steel dispatch and reduces the number of truck transfers.

关键词： Lead acid batteries Energy consumption Profitability Storage management Loading Layout Steel Resource management Optimization Logistics

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The Emerging Intelligent Vehicles and Intelligent Vehicle Carriers Collaborative systems

The Emerging Intelligent Vehicles and Intelligent Vehicle Ca...

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IEEE Symposium on Intelligent Vehicle

作者： Zhipeng Shen Chao Huang Hailong Huang Yutong Wang Fei-Yue Wang Abbas Jamalipour Duc Truong Pham Ljubo Vlacic Andrey V. Savkin Department of Aeronautical and Aviation Engineering the Hong Kong Polytechnic University Hong Kong Department of Industrial and Systems Engineering the Hong Kong Polytechnic University Hong Kong 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 School of Electrical and Information Engineering University of Sydney Sydney Australia Department of Mechanical Engineering University of Birmingham Birmingham UK Institute of Intelligent & Integrated Systems and the School of Engineering and Built Environment Griffith University Brisbane Australia School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

In this paper, we propose the innovative use of Intelligent Vehicle Carriers (IVCs) as a key solution to address the energy constraints of small-scale unmanned Intelligent Vehicles (IVs). IVCs function as both transporters and charging stations, significantly boosting the operational range and efficiency of IVs. Our research delves into the IV-IVC collaborative framework, highlighting the existing challenges, exploring potential solutions, and examining a range of applications. This study offers a visionary approach to revolutionizing intelligent transportation systems by leveraging the synergistic relationship between IVs and IVCs.

关键词： Intelligent vehicles Collaboration Charging stations Boosting

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Asynchronous Tag-based Physical-Layer Authentication in Wireless Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Tan, Haijun Xu, Yuntao Du, Jiewei Xie, Ning Li, Hongbin Peng Cheng Laboratory Control and Network Foundation Laboratory Frontier Research Center Shenzhen518107 China Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration The Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen518060 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China National Innovation Center of High Speed Train Qingdao266111 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

Tag-based physical-layer authentication (PLA) has gained significant research interest due to its high security and low complexity compared to traditional upper-layer authentication mechanisms. However, conventional tag-based PLA schemes often require strict synchronization between the tag and the source message before transmission, which may limit their practicality and effectiveness. To address this limitation, we propose an asynchronous tag-based PLA scheme, where the tag and the source message are transmitted asynchronously with an intentional time delay introduced at the transmitter. The proposed scheme enhances both compatibility and security without compromising robustness. We provide a theoretical analysis of the proposed scheme, and derive closed-form expressions to characterize its performance. Furthermore, theoretical comparisons with existing methods yield insightful conclusions regarding the advantages of the proposed approach. The scheme is implemented and extensively evaluated through simulations, where the theoretical results exhibit strong agreement with the simulation outcomes, thereby validating its effectiveness. © 2025 IEEE.

关键词： asynchronous closed form intentional timing delay Physical-layer authentication tag-based

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Computationally Expensive High-Dimensional Multiobjective Optimization via Surrogate-Assisted Reformulation and Decomposition

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Pan, Linqiang Lin, Jianqing Wang, Handing He, Cheng Tan, Kay Chen Jin, Yaochu School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence Xidian University Xi’an China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong School of Engineering Trustworth and General Artificial Intelligence Laboratory Westlake University Hangzhou China

In recent decades, various surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to solve computationally expensive multiobjective optimization problems (EMOPs). Nevertheless, designing an SAEA to handle high-dimensional EMOPs and balance convergence, diversity, and computational complexity remains challenging. Here, we propose a two-phase SAEA (TP-SAEA), which follows the idea of convergence first and diversity second, for solving high-dimensional EMOPs. In Phase I, a surrogate-assisted problem reformulation method is proposed to fast-track the Pareto optimal set in association with some reference solutions. Specifically, the high-dimensional EMOP is reformulated into an expensive single-objective one with low-dimensional decision space. Then, the surrogate-assisted optimization is utilized to obtain well-converged solutions. In Phase II, the high-dimensional EMOP is decomposed into two subproblems to explore subregions of the decision space that can effectively promote the diversity of the solutions. The two subproblems are optimized independently via surrogate-assisted optimization, aiming to push the population towards different regions of the Pareto optimal front. Experiments are conducted on EMOPs with 100 to 500 decision variables compared with four state-of-the-art SAEAs. The proposed TP-SAEA obtains well-converged and diverse solutions with only 509 real function evaluations. Moreover, its superiority is examined in six real-world instances with up to 12,000 decision variables. IEEE

关键词： Iron

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