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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Lee, Jiyoon Kim, Heegwang Park, Chanyeong Jang, Junbo Paik, Joonki Chung-Ang University Image Processing and Intelligent System Laboratory Department of Artificial Intelligence Seoul Korea Republic of Chung-Ang University Image Processing and Intelligent System Laboratory Department of Image Seoul Korea Republic of

ISBN: (纸本)9798350324136

Infrared image-based object detection is challenging due to inherent low resolution and scarce details, especially when locating small objects that cover minimal region in the image. We present a methodology for detecting small objects in infrared imagery captured by UAVs, emphasizing the use of attention mechanisms to increase detection precision. Using YOLOX as our baseline network, we integrated a modified Recursive Feature Pyramid (RFP) to enhance vehicle detection in the VisDrone-Vehicle dataset. To amplify the spatial details of these smaller objects, we incorporated an attention module and sigmoid function, bridging the connection between the primary and secondary Feature Pyramid Networks (FPNs). The sigmoid function further refines the RFP outputs by filtering out background *** experiments demonstrate that the proposed network outperforms the baseline in detecting vehicles in infrared images. © 2024 IEEE.

关键词： infrared image object detection small object detection

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Spontaneous Recovery in Directed Dynamical Networks

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Engineering 2024年第6期37卷 208-214页

作者： Xueming Liu Xian Yan H.Eugene Stanley School of Artificial Intelligence and Automation&The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems&the Key Laboratory of Image Processing Huazhong University of Science and TechnologyWuhan 430074China Center for Polymer Studies Department of PhysicsBoston UniversityBostonMA 02215USA

Complex networked systems,which range from biological systems in the natural world to infrastructure systems in the human-made world,can exhibit spontaneous recovery after a failure;for example,a brain may spontaneously return to normal after a seizure,and traffic flow can become smooth again after a *** studies on the spontaneous recovery of dynamical networks have been limited to undirected ***,most real-world networks are *** fill this gap,we build a model in which nodes may alternately fail and recover,and we develop a theoretical tool to analyze the recovery properties of directed dynamical *** find that the tool can accurately predict the final fraction of active nodes,and the prediction accuracy decreases as the fraction of bidirectional links in the network increases,which emphasizes the importance of directionality in network *** to different initial states,directed dynamical networks may show alternative stable states under the same control parameter,exhibiting hysteresis *** addition,for networks with finite sizes,the fraction of active nodes may jump back and forth between high and low states,mimicking repetitive failure-recovery *** findings could help clarify the system recovery mechanism and enable better design of networked systems with high resilience.

关键词： Network resilience Directed dynamical networks Spontaneous recovery

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Cross-user Gesture Recognition Based on Deep Domain Adaptation Networks 43

Cross-user Gesture Recognition Based on Deep Domain Adaptati...

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43rd Chinese Control Conference, CCC 2024

作者： Fu, Zhongzheng He, Xinrun Wang, Haoyuan Chen, Xingjian Li, Yixuan Huang, Jian Huazhong University of Science and Technology Ministry of Education Key Laboratory of Image Processing and Intelligent Control Hubei Key Laboratory of Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Wuhan430074 China

ISBN: (纸本)9789887581581

This study introduces an innovative approach for gesture recognition in smart wearable devices using a deep domain adaptation model, focusing on the challenges posed by heterogeneous user environments and the need for precise, adaptable, and personalized gesture recognition systems. Traditional methods based on machine learning and deep learning techniques, while effective, struggle with the variance in data distribution among different users, leading to accuracy challenges. To address this, the study proposes the Domain Kernel-Alignment Adaptation Network (DKAN) model, utilizing a new Kernel-Aligned Multi-kernel Maximum Mean Discrepancy (KAMMD) method within deep networks. By calculating kernel alignments between source and target domains and adjusting weights of Gaussian kernels accordingly, DKAN emphasizes features conducive to transfer learning, enabling more accurate cross-user gesture recognition. The model achieves a significant average accuracy of 97.3% across 21 gestures, highlighting its potential in practical applications. This advancement addresses challenges in traditional gesture recognition methods and sets a new direction for future research in smart wearable technology. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Adversarial machine learning

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A Spatio-Temporal Scale Based Affective Neural Network for Emotion Detection in Conversation

A Spatio-Temporal Scale Based Affective Neural Network for E...

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2023 China Automation Congress, CAC 2023

作者： Li, Jiang Wang, Xiaoping Lei, Shanglin Zeng, Zhigang Wuhan430074 China Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China Key Laboratory of Brain-inspired Intelligent Systems Hubei Province Wuhan430074 China

ISBN: (纸本)9798350303759

Emotion Detection in Conversation (EDC) has been implemented in numerous domains and is under increasing attention. The EDC task is highly challenging due to the need to incorporate both the context of the utterance and the identity of the participant. Some existing EDC approaches employ redundant network structures and inadequately model contextual utterances, resulting in limited performance gains. In this work, we devise a Spatio-Temporal scale based Affective neural Network (STANet) in a simplistic and efficacious manner. STANet consists of two main components, i.e., spatial scale network and temporal scale network, which are applied to extract spatial scale and temporal scale information in the conversation, respectively. Prior to spatio-temporal scale modeling, we encode the identity information of participants to examine its impact on the performance of emotion detection. We conduct substantial comparative experiments and ablation studies on three conversational emotion datasets, and the results demonstrate that our STANet realizes superior performance without taking advantage of external knowledge. © 2023 IEEE.

关键词： Emotion Recognition

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Maintenance dynamic scheduling for a Tunisian railway transport system

Maintenance dynamic scheduling for a Tunisian railway transp...

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

作者： Mellouli, Sarra Mhalla, Anis Dutilleul, Simon Collart Messaoud, Hassani Laboratory of Automatic Signal and Image Processing Laratsi Monastir Tunisia Laboratory of Automation Electrical Systems Environment Laese Monastir Tunisia Estas Laboratory University Gustave Eiffel Cosys Department Lille France

ISBN: (纸本)9798350361025

The developments reported in this paper are devoted to the dynamic scheduling of rail transport systems. In this context, our study focuses on implementing an algorithm for solving railway planning with predictable or unexpected demands. The system modeling process uses P-time stochastic Petri nets. In order to improve the efficiency and punctuality of rail transport. A method for minimizing disruptions by managing maintenance is proposed. The paper's contribution is a scheduling algorithm that prioritizes maintenance tasks according to a dynamic criticality. This dynamic criterion incorporates real-time information provided by a supervisory rail system layer (a SCADA system). The STPN is used for modeling system operation. It supplies a mathematical framework for disturbance detection and prognosis. The presented work constitutes a first step towards adapting the digital twin concepts for the maintenance of weakly instrumented railway infrastructures. © 2024 IEEE.

关键词： Petri nets

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Small Object Detection in Infrared images Using Attention Mechanism and Sigmoid Function

Small Object Detection in Infrared Images Using Attention Me...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Jiyoon Lee Heegwang Kim Chanyeong Park Junbo Jang Joonki Paik Department of Artificial Intelligence Image Processing and Intelligent System Laboratory Chung-Ang University Seoul Korea Department of Image Image Processing and Intelligent System Laboratory Chung-Ang University Seoul Korea

Infrared image-based object detection is challenging due to inherent low resolution and scarce details, especially when locating small objects that cover minimal region in the image. We present a methodology for detecting small objects in infrared imagery captured by UAVs, emphasizing the use of attention mechanisms to increase detection precision. Using YOLOX as our baseline network, we integrated a modified Recursive Feature Pyramid (RFP) to enhance vehicle detection in the VisDrone-Vehicle dataset. To amplify the spatial details of these smaller objects, we incorporated an attention module and sigmoid function, bridging the connection between the primary and secondary Feature Pyramid Networks (FPNs). The sigmoid function further refines the RFP outputs by filtering out background noise. Comparative experiments demonstrate that the proposed network outperforms the baseline in detecting vehicles in infrared images.

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USV Path Planning Based on ACO-SA Algorithm 42

USV Path Planning Based on ACO-SA Algorithm

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42nd Chinese Control Conference, CCC 2023

作者： Luo, Chuanshang Tang, Yifeng Huang, Zhihui Xu, Yifan Liu, Bin Zhang, Hai-Tao School of Artificial Intelligence and Automation Huazhong University of Science and Technology The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Wuhan430074 China

ISBN: (纸本)9789887581543

This paper solves USV path planning problem constrained by multiple factors via ant-colony optimization algorithm. First, this paper uses the ways of multi-objective optimization to model the USV path planning problem. An improved ant colony algorithm named ACO-SA is put forward afterwards to effectively solve the problem. The algorithm is a combination of ACO algorithm(ant colony algorithm) and SA algorithm(simulated annealing algorithm), which has three improments: change the initial distribution of pheromone to guide the search when the algorithm has just started running;change the heuristic function and state transition probability taking three factors into consideration;change the pheromone update rule and make the ants compete for the right to update pheromone by simulated annealing algorithm, and update the best solution by the same algorithm. Finally, simulation experiment and field experiment are conducted to check the validity of ACO-SA algorithm. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Simulated annealing

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Koopman Operator Learning Control of the Piezoelectric Actuator 42

Koopman Operator Learning Control of the Piezoelectric Actua...

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42nd Chinese Control Conference, CCC 2023

作者： Huang, Xiang Zhang, Hai-Tao School of Artificial Intelligence and Automation Huazhong University of Science and Technology Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Wuhan430074 China

ISBN: (纸本)9789887581543

The piezoelectric actuator is one kind of device that can drive nanoscale motion. However, the nonlinear hysteresis effect induced by its natural material greatly degrades its positioning accuracy. To handle this challenging issue, this work develops a Koopman model predict control (Koopman- MPC) framework for the piezoelectric actuator. Specifically, the Koopman operator theory is adapted for modeling the piezoelectric actuator dynamics. A simple yet powerful linear model spanned in a high-dimensional space is thus constructed to characterize the hysteresis dynamics. Subsequently, upon the established Koopman model, an MPC scheme is put forward for tracking control of piezoelectric actuators. Therein, by sustained optimizing a cost function containing future outputs and control increments, the control input is obtained. Moreover, extensive tracking simulations are carried out on a simulated piezoelectric actuator for verifying the feasibility and effectiveness of the Koopman- Mpc scheme. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Model predictive control

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WDNet: A Water Depth Perception Network with Global Information 42

WDNet: A Water Depth Perception Network with Global Informat...

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42nd Chinese Control Conference, CCC 2023

作者： Huang, Zhihui Tang, Yifeng Tang, Chuancong Xu, Yifan Luo, Chuanshang Liu, Bin Zhang, Hai-Tao School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789887581543

Deep perception of the unmanned surface vehicle's surroundings is an inaccessible part of its fully autonomous navigation mission. The existing methods, whether based on traditional stereo matching or deep learning, do not fully consider the characteristics of water environment, resulting in severe error depths in weak textures (sky, calm lake) and water reflections regions, that increases the risk of running aground or collision. What is worse that there is not a public dataset for depth estimation in the water environment. Therefore, this work proposes a self-supervised model for depth estimation named Water Depth Perception Network (WDNet) to address these problems. The decoder of this network has a wider receptive field and can effectively handle the depth error in the weak texture region. Besides, the WDNet is trained with a novel and effective loss function which assist the network to reduce errors in sky and water region, and some indexes are proposed to evaluate the model's performances in sky and water region. Finally, our proposed WDNet achieves a 0.1056 absolute relative error in ranging, the average number of error pixels in the sky area drops from 15803.87 to 580.91, which only accounted for 0.29% of the image, and the error in water region drops from 51.04 to 6.75, all of them are superior to the performance of baseline model. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Errors

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Dynamic Graph Dismantling with Reinforcement Supervised Learning 42

Dynamic Graph Dismantling with Reinforcement Supervised Lear...

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42nd Chinese Control Conference, CCC 2023

作者： Wang, Yumei Tang, Chuancong Zhang, Hai-Tao School of Artificial Intelligence and Automation Huazhong University of Science and Technology The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Wuhan430074 China

ISBN: (纸本)9789887581543

There are always some 'key' nodes in a big complex network, which can joint the most connected subgraphs. How to identify these nodes, finding a minimum set of nodes to attack for reducing the size of residual network's Largest Connected Component(LCC) to break up the original network, has become a research hotspot. Therefore, a method for determining the 'key' nodes based on reinforcement learning framework and supervised learning model is proposed. This algorithm can not only utilize the dynamic exploration ability of reinforcement learning to collect a rich training dataset, but also take advantage of the characteristics that supervised learning is adaptive and has strong generalization ability to possess high efficiency and strong robustness. In order to further improve the algorithm's performance, ϵ-greedy mechanism is used to explore more network states. The experiment results show that given the same fraction of removed nodes, our algorithm can make the residual LCC smaller in various networks which is superior to the state-of-the-art algorithms in terms of effectiveness and generalization. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Complex networks

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