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20th IFAC symposium on System Identification (SYSID)

作者： Cestari, Raffaele G. Maroni, Gabriele Cannelli, Loris Piga, Dario Formentin, Simone Politecn Milan Dept Elect Informat & Bioengn Milan Italy Dalle Molle Inst Artificial Intelligence SUPSI DTI IDSIA Lugano Switzerland

The calibration and training of a neural network is a complex and time-consuming procedure that requires significant computational resources to achieve satisfactory results. Key obstacles are a large number of hyperparameters to select and the onset of overfitting in the face of a small amount of data. In this framework, we propose an innovative training strategy for feed-forward architectures - called split-boost - that improves performance and automatically includes a regularizing behaviour without modeling it explicitly. Such a novel approach ultimately allows us to avoid explicitly modeling the regularization term, decreasing the total number of hyperparameters and speeding up the tuning phase. The proposed strategy is tested on a real-world (anonymized) dataset within a benchmark medical insurance design problem. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Artificial intelligence deep learning machine learning neural networks hyperparameter tuning regularization

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Joint Optimization of Compression, Transmission and Computation for Cooperative Perception Aided Intelligent Vehicular Networks

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ieee Transactions on Vehicular Technology 2025年第5期74卷 8201-8214页

作者： Lu, Binbin Huang, Xumin Wu, Yuan Qian, Liping Zhou, Sheng Niyato, Dusit University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science Macau China Guangdong University of Technology School of Automation Guangzhou510006 China University of Macau State Key Lab of Internet of Things for Smart City Macau China University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer Information Science Macau China Zhuhai UM Science and Technology AQ2 Research Institute Zhuhai519301 China Zhejiang University of Technology Institute of Cyberspace Security Hangzhou310023 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Nanyang Technological University School of Computer Science and Engineering Singapore

Cooperative perception is a promising paradigm to tackle the perception limitations of a single intelligent vehicle (IV) to enhance the driving safety and efficiency in intelligent vehicular networks. However, the real-time transmission and computation-intensive fusion of raw sensing data raise new challenges for satisfying the stringent delay requirement of delay-sensitive applications. In this paper, we formulate a joint optimization problem of the cooperative IV selection, compression ratio selection, transmit power control, task offloading decision and computation allocation to minimize the end-to-end delay consisting of compression, transmission and computation. In particular, to meet the perception probability requirement, the quality and quantity-aware matching algorithm is proposed to optimize the cooperative IV selection. To guarantee the queue stability of offloading tasks, we develop the Lyapunov optimization algorithm to determine the upper bound of the offloading tasks and the corresponding optimal computation allocation. The Lyapunov aided deep reinforcement learning algorithm is further proposed to dynamically adjust the task offloading decision and compression ratio selection to minimize the end-to-end delay with the transmit power control being optimized by the successive convex approximation algorithm. Simulation results demonstrate that, compared to several benchmark algorithms, our proposed algorithm achieves the lowest end-to-end delay while guaranteeing the requirements on perception probability and queue stability effectively. © 1967-2012 ieee.

关键词： Deep reinforcement learning

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MA-YOLO: a Lightweight Vehicle Detection Framework Based on YOLO

MA-YOLO: a Lightweight Vehicle Detection Framework Based on ...

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2023 International Conference on Artificial intelligence and automation control, AIAC 2023

作者： Wang, Xiangjie Jing, Zhiyong Shi, Lei Cheng, Guozhen Gao, Yufei Hao, Bing Zhengzhou University School of Cyber Science and Engineering Zhengzhou China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou China Information Engineering University Institute of Information Technology Zhengzhou China Songshan Laboratory Zhengzhou China

ISBN: (纸本)9798350383805

The current vehicle detection models are inadequate for deployment on edge computing devices due to limited computing capabilities. Therefore, this paper proposes a novel lightweight vehicle detection method called MA-YOLO (MobileNet Attention YOLO). In order to reduce the number of parameters and computational requirements, the depthwise separable convolution and inverse residual structure is used to instead of conventional convolution. Additionally, the SELayer and CALayer module of attention mechanism is embedded into Backbone module, to improve the correlation of inter-channel and inter-space features, respectively. The experimental results demonstrate that the MA-YOLO has achieved a mean average precision (mAP) of 90.7% for vehicle detection under normal weather conditions. In comparison to YOLOv8s, MA-YOLO has compressed the number of parameters by 40.5% and computation by 52.4%, while experiencing only a decrease of 0.1% in mAP. © 2023 ieee.

关键词： Convolution

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InstruPro: Instrumenting Programmable Logic controller (PLC) based on Model Checking

InstruPro: Instrumenting Programmable Logic Controller (PLC)...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Al Farooq, Abdullah Sarker, Manash Wentworth Inst Technol Sch Comp & Data Sci Boston MA 02115 USA Potuakhali Sci & Technol Univ Dept Comp & Commun Engn Potuakhali Bangladesh

ISBN: (纸本)9781665487689

Programmable Logic controller (PLC) suffers from various types of attacks due to misconfiguration in its logic system. These misconfigurations cannot be found by looking at the logic/rules only. Therefore, we propose a methodology where temporal logic is used to find the misconfigurations in the subsequent rules. Our framework is capable of finding the codes in PLC that are responsible for abnormal behavior in PLC. Having accomplished that, an automated approach to instrument the code. We run our methodology on some real world PLC program applications and found that our approach can eradicate a good number of abnormal behaviors successfully.

关键词： Programmable Logic controller formal verification dynamic analysis model checking Industrial control System

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Adaptive control of 3D Gantry Crane Considering Rope Length Variation 8

Adaptive Control of 3D Gantry Crane Considering Rope Length ...

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8th International symposium on Computer Science and Intelligent control, ISCSIC 2024

作者： Zhang, Wenjie Qian, Yuzhe Hebei University of Technology School of Artificial Intelligence Control Engineering Technology Innovation Center of Hebei Province Tianjin China

ISBN: (纸本)9798350380286

In recent years, with the development of the economy, the control of harbor container cranes has attracted the attention of researchers. Tianjin Port is the first large comprehensive port in the north of China, the efficiency and work stability of port container cranes will directly affect the development of the port economy. In the face of market demand continues to large-scale, automation, and intelligent development, there is an urgent need to design a set of control methods suitable for port container cranes. To this end, this paper proposes a three-dimensional gantry crane system based on a sliding mode surface adaptive control method under the case of time-varying rope length and theoretically proves that the method can make the trolley arrive at the specified position quickly and accurately, and can effectively inhibit the load and the swing angle of the trolley through Lyapunov stability and Barbalat's Lemma The effectiveness of the method is proven. Finally, the good performance of the method is proved by setting up a Simulink platform for a series of simulations and comparing it with the PD control method. The design concept of this control method can be widely applied to the design of other adaptive control methods. © 2024 ieee.

关键词： Gantry cranes

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Real-time Determination and Correction of Multi-source Multi-modal Heterogeneous Data Quality 10

Real-time Determination and Correction of Multi-source Multi...

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10th ieee Smart World Congress, SWC 2024

作者： Fan, Bo Liu, Jiahao Qin, Youbao Kuang, Wenbin Liu, Zhihong Automation Research Institute of China South Industries Group Corporation Mianyang China

ISBN: (纸本)9798331520861

With the rapid development of industrial informatization, digital twins have achieved comprehensive dynamic simulation in the production and manufacturing processes in digital form. They cover the entire product lifecycle and the entire industrial chain, which is of significant importance in promoting the development of future intelligent manufacturing. Digital twin workshops measure the multidimensional attributes of the production process by deploying multiple independent heterogeneous data sources, resulting in multi-source, multi-modal heterogeneous perception data. However, due to inherent limitations of equipment and environmental noise, perception equipment inevitably encounters issues such as missed reads, multiple reads, and incorrect reads, leading to common data quality problems. Therefore, this paper focuses on the twin workshop as the main subject and multi-source, multi-modal heterogeneous perception data as the *** explores critical technologies such as real-time data collection and data quality control during the production process. These technologies enable the assessment and optimization of real-time data quality, ensuring the generation of high-quality digital twin *** allows the workshop to operate accurately and efficiently, providing a reference for advancing the research and application of digital twin technology in future manufacturing industries. © 2024 ieee.

关键词： Smart manufacturing

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Attention-Aware Based Differentiable Architecture Search Network for Waste Recognition 13

Attention-Aware Based Differentiable Architecture Search Net...

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13th ieee International Conference on CYBER Technology in automation, control, and Intelligent Systems, CYBER 2023

作者： Zhang, Qiyu Han, Honggui Li, Fangyu Du, Yongping Engineering Research Center of Digital Community Ministry of Education Beijing Artificial Intelligence Institute Beijing University of Technology Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (纸本)9798350315196

Manually designed deep neural networks have successfully forwarded waste recognition tasks in the resource recycling field. However, due to the diversity of waste samples, the feature extraction ability of inherently designed models fails to fully satisfy the requirements of real world applications. In this article, an attention-aware based differentiable architecture search (ADAS) network for waste recognition is proposed, which self-organizes to generate an optimal network structure according to diverse waste data. First, a structured search space with attention-aware modules is designed to enhance the diverse feature representations of waste data. Secondly, an efficient and differentiable structure search is achieved by continuously relaxing the representation of the network architecture and search space. Finally, the optimal architecture search process is evaluated by a bi-level optimization algorithm. Experimental results show that the proposed method achieves more satisfactory classification performances than the manually designed ResNet, DenseNet networks in the TrashNet dataset and the self-built household waste dataset. © 2023 ieee.

关键词： Network architecture

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Parallel Manufacturing for Industrial Metaverses:A New Paradigm in Smart Manufacturing

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ieee/CAA Journal of Automatica Sinica 2022年第12期9卷 2063-2070页

作者： Jing Yang Xiaoxing Wang Yandong Zhao The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Beijing SANBODY Technology Company LtdBeijing 214000China North Automatic Control Technology Institute Taiyuan 030006China

To tackle the complexity of human and social factors in manufacturing systems, parallel manufacturing for industrial metaverses is proposed as a new paradigm in smart manufacturing for effective and efficient operations of those systems, where Cyber-Physical-Social Systems(CPSSs) and the Internet of Minds(Io M) are regarded as its infrastructures and the "Artificial systems", "computational experiments"and "Parallel execution"(ACP) method is its methodological foundation for parallel evolution, closed-loop feedback, and collaborative optimization. In parallel manufacturing, social demands are analyzed and extracted from social intelligence for product R&D and production planning, and digital workers and robotic workers perform the majority of the physical and mental work instead of human workers, contributing to the realization of low-cost, high-efficiency and zero-inventory manufacturing. A variety of advanced technologies such as Knowledge automation(KA), blockchain, crowdsourcing and Decentralized Autonomous Organizations(DAOs) provide powerful support for the construction of parallel manufacturing, which holds the promise of breaking the constraints of resource and capacity, and the limitations of time and space. Finally, the effectiveness of parallel manufacturing is verified by taking the workflow of customized shoes as a case,especially the unmanned production line named Flex Vega.

关键词： Parallel Manufacturing Digital Workers CPSS Smart Manufacturing Industrial Metaverses

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Real-time Object Detection Based on Mamba and YOLOv8 4

Real-time Object Detection Based on Mamba and YOLOv8

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4th International Conference on Industrial automation, Robotics and control Engineering, IARCE 2024

作者： Wang, Jie Zhao, Wenyi Liu, Caixi Yang, Huihua Xu, Wei Research Institute of Science and Technology Co. Ltd China Electrical Equipment Shanghai China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350380323

Amidst the swift progress in deep learning, the YOLO series has redefined the standards for real-time object detection. The incorporation of the Transformer architecture into YOLO models has significantly broadened their receptive fields and enhanced their feature extraction capabilities, thereby markedly improving detection performance. Nonetheless, this enhancement also leads to increased computational demands. To address this, our manuscript proposes an improved YOLOv8 object detection model that integrates a State Space Model (SSM) to overcome its previous constraints in accuracy and speed. Specifically, we've proposed the MambaCSP module and embedded it into the neck and decoupled modules. By employing depthwise separable convolutions and channel shuffle operations, we've tailored branches for both classification and regression tasks. These innovations not only bolster the model's feature extraction prowess but also maintain its computational efficiency. Our proposed method has demonstrated an average improvement of 1 % on the COCO dataset compared to the YOLOv8 model. © 2024 ieee.

关键词： Object recognition

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Collaborative Optimization Algorithm of Deep Learning and Compliant control in Online Trajectory Planning 14

Collaborative Optimization Algorithm of Deep Learning and Co...

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14th ieee International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Liao, Junting Zhao, Chunjian Xie, Hui School of Computer and Artificial Intelligence Xiangnan University Hunan Chenzhou423000 China China Telecom Hunan Chenzhou Branch Hunan Chenzhou423000 China

ISBN: (纸本)9798331531935

With the rapid development of robotics and automation, online trajectory planning faces problems such as poor adaptability to dynamic environments and low computational efficiency. This study aims to propose a collaborative optimization algorithm that combines deep learning and compliant control to improve the efficiency and accuracy of online trajectory planning. First, CNN is used for feature extraction and a prediction model is established. Then a compliant control strategy is designed to deal with dynamic obstacles to ensure the flexibility and stability of the robot in trajectory planning. The trained CNN model is then used to generate real-time trajectory predictions. The robot motion is adjusted through a fuzzy logic controller to optimize the relative position of the trajectory and obstacles. The average obstacle avoidance success rate of the algorithm in dynamic environments reaches 97%. The collaborative optimization algorithm of deep learning and compliant control effectively improves the real-time and accuracy of online trajectory planning, providing a new solution for the application of intelligent robots in complex environments. © 2025 ieee.

关键词： Intelligent robots

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