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26th International Conference on Pattern Recognition, ICPR 2022

作者： Li, Shiqi Xiang, Xiang Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783031376597

Recent research on human pose estimation exploits complex structures to improve performance on benchmark datasets, ignoring the resource overhead and inference speed when the model is actually deployed. In this paper, we lighten the computation cost and parameters of the deconvolution head network in SimpleBaseline and introduce an attention mechanism that utilizes original, inter-level, and intra-level information to intensify the accuracy. Additionally, we propose a novel loss function called heatmap weighting loss, which generates weights for each pixel on the heatmap that makes the model more focused on keypoints. Experiments demonstrate our method achieves a balance between performance, resource volume, and inference speed. Specifically, our method can achieve 65.3 AP score on COCO test-dev, while the inference speed is 55 FPS and 18 FPS on the mobile GPU and CPU, respectively. © 2023, Springer Nature Switzerland AG.

关键词： Benchmarking

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A Generalized Subspace Distribution Adaptation Framework for Cross-Corpus Speech Emotion Recognition 48

A Generalized Subspace Distribution Adaptation Framework for...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Li, Shaokai Song, Peng Ji, Liang Jin, Yun Zheng, Wenming Yantai University School of Computer and Control Engineering China Jiangsu Normal University School of Physics and Electronic Engineering China Research Center for Learning Science Southeast University Key Laboratory of Child Development and Learning Science Southeast University Ministry of Education China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province The State Key Laboratory of Tibetan Intelligent Information Processing and Application China

ISBN: (纸本)9781728163277

In this paper, we propose a novel transfer learning framework, named generalized subspace distribution adaptation (GSDA), to tackle the challenging cross-corpus speech emotion recognition problem. First, we learn a common low-dimensional feature subspace by utilizing a generalized subspace learning method. Second, we develop a novel distance metric to reduce the divergence between the source and target corpora, which can efficiently explore the similarity and dissimilarity information in the process of knowledge transfer. Third, to demonstrate the effectiveness of our framework, we apply GSDA to the traditional subspace learning algorithms. Finally, we conduct extensive experiments by using the low-level features and deep features on three popular emotional databases, i.e., Berlin, IEMOCAP, and CVE. The results demonstrate that the proposed framework can achieve better performance than several state-of-the-art transfer learning approaches. © 2023 IEEE.

关键词： Speech recognition

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A Lightweight and key Escrow-Free Access control Scheme on Encrypted Data in Edge Computing 12

A Lightweight and Key Escrow-Free Access Control Scheme on E...

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： He, Heng Chen, Yuejia Lu, Jianfeng Zheng, Jiacheng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9798331511074

Attribute-based encryption provides fine-grained access control, ensuring data security in edge computing. However, existing access control schemes face issues such as high computational complexity due to bilinear pairing and key escrow with a single authority, which limit their effectiveness for lightweight terminal devices. To address these challenges, we propose a lightweight and key escrow-free access control scheme on encrypted data. This scheme replaces bilinear pairing with fast elliptic curve scalar multiplication, transferring most computational tasks to edge servers while preserving privacy, thus alleviating the computational burden on terminal devices. Additionally, we design a blockchain-based distributed key management method that allows multiple edge servers to distribute private keys collaboratively, resolving the key-escrow issue. Security analysis and performance evaluation demonstrate that our scheme effectively ensures data confidentiality, resists collusion attacks, and achieves high computational efficiency on the user side with low storage overheads, making it highly suitable for lightweight devices in edge computing. © 2024 IEEE.

关键词： Ciphertext

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Design and Implementation of Warning Signs for Vehicles Based on Automatic Navigation Algorithms 1

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13th International Conference on Computer Engineering and Networks, CENet 2023

作者： Wang, Fangyan Jiao, Ge Lu, Jinjia Yuan, Yiping College of Computer Science and Technology Hengyang Normal University Hunan Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hunan Hengyang421002 China

ISBN: (数字)9789819992393

ISBN: (纸本)9789819992386

Traditional warning signs use human retrograde placement;there are problems such as short warning distances, imprecise placement positions, and difficult positioning of accident points. According to statistics, the user's reaction time after facing a car accident is generally 1.05–1.28 s, and the reaction time to light is 0.225 s. The reaction time for the user to deal with a car accident independently is completely insufficient, and these reasons are the main reasons for secondary traffic accidents. In order to solve this social pain point, we designed a new intelligent triangle warning sign. The intelligent warning sign is installed with an intelligent base at the bottom, and it gets the traffic accident point precisely located when the intelligent warning sign is working, and the system gets the vehicle accident point and reminds the vehicles behind to pay attention to avoid, realizing the three-way interconnection of drivers and passengers, traffic control departments, and the personnel of the vehicles behind, and using the automatic navigation algorithm to realize the automatic navigation of the warning sign to protect the safety of drivers and passengers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Vehicles

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Reinforcement Learning-based Frequency control for Renewable Energy Microgrids 7

Reinforcement Learning-based Frequency Control for Renewable...

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7th International Conference on intelligent Robotics and control Engineering, IRCE 2024

作者： He, Jing Zhang, Junpeng Jiang, Haiping Ma, Zongfang College of Information and Control Engineering Xi'an University of Architecture and Technology The Moe Key Laboratory of Intelligent Network and Network Security Xi'an Jiaotong University China Xi'an University of Architecture and Technology College of Information and Control Engineering Xi'an China

ISBN: (纸本)9798350352399

With extensive access to renewables, the inertia of microgrid is significantly reduced, which in turn puts forward higher requirements for the frequency stability of the system. In this paper, a frequency control method based on reinforcement learning is proposed for the intermittent and unstable characteristics of renewables. First, the microgrid frequency control principle and reinforcement learning theory are introduced. On this basis, a microgrid frequency control strategy based on Q-learning algorithm is proposed, and the design process is described in detail, including the design of the action set, state set, and reward function. Finally, the Q-learning algorithm is pretrained offline through simulation experiments, and tested in several scenarios for online operation. The effectiveness and adaptability of the proposed algorithm are verified through comparative analysis with other strategies. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Resilience of Microgrid-Integrated Power Systems in Disaster Events Using Reinforcement Learning 1

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Chinese intelligent Automation Conference, CIAC 2023

作者： Zha, Zhongyi Wang, Bo Liu, Lei Fan, Huijin Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Artificial Intelligence and Automation School Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9789819961870

ISBN: (纸本)9789819961863

Enhancing the resilience of power systems has become increasingly important to mitigate the negative impact of risk attacks. Integrating microgrids, which are small-scale power grids that can operate independently or in conjunction with the main grid, has emerged as an effective energy source for emergency situations and can avoid power outages. Typhoons, which are inevitable and relatively easy to predict risk attacks, have become one of the contexts to examine power system resilience. In this paper, we propose a reinforcement learning algorithm that combines graph theory knowledge to make decisions for optimizing power system resilience with microgrids under the Typhoon event. The algorithm effectively utilizes graph theory knowledge and reduces the number of decisions required in the optimization process. Numerical experimental results show that compared to traditional methods, the reinforcement learning algorithm can more effectively respond to typhoon changes, achieve flexible economic control, and enhance the resilience of power systems with microgrids. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Numerical methods

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Online Optimization in Power Systems With High Penetration of Renewable Generation:Advances and Prospects

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 839-858页

作者： Zhaojian Wang Wei Wei John Zhen Fu Pang Feng Liu Bo Yang Xinping Guan Shengwei Mei Key Laboratory of System Control and Information Processing Ministry of Education of ChinaDepartment of AutomationShanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Power System and the Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Institute of High Performance Computing(IHPC) Agency for Science Technology and Research(A*STAR)Singapore 138632Singapore

Traditionally,offline optimization of power systems is acceptable due to the largely predictable loads and reliable *** increasing penetration of fluctuating renewable generation and internet-of-things devices allowing for fine-grained controllability of loads have led to the diminishing applicability of offline optimization in the power systems domain,and have redirected attention to online optimization ***,online optimization is a broad topic that can be applied in and motivated by different settings,operated on different time scales,and built on different theoretical *** paper reviews the various types of online optimization techniques used in the power systems domain and aims to make clear the distinction between the most common techniques *** particular,we introduce and compare four distinct techniques used covering the breadth of online optimization techniques used in the power systems domain,i.e.,optimization-guided dynamic control,feedback optimization for single-period problems,Lyapunov-based optimization,and online convex optimization techniques for multi-period ***,we recommend some potential future directions for online optimization in the power systems domain.

关键词： optimization Lyapunov optimization online convex optimization online optimization optimization-guided control

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Improved Double Deep Q Network Algorithm Based on Average Q-Value Estimation and Reward Redistribution for Robot Path Planning

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Computers, Materials & Continua 2024年第11期81卷 2769-2790页

作者： Yameng Yin Lieping Zhang Xiaoxu Shi Yilin Wang Jiansheng Peng Jianchu Zou Key Laboratory of Advanced Manufacturing and Automation Technology Guilin University of TechnologyEducation Department of Guangxi Zhuang Autonomous RegionGuilin541006China Guangxi Key Laboratory of Special Engineering Equipment and Control Guilin University of Aerospace TechnologyGuilin541004China Guilin Mingfu Robot Technology Company Limited Guilin541199China Key Laboratory of AI and Information Processing Education Department of Guangxi Zhuang Autonomous RegionHechi UniversityYizhou546300China

By integrating deep neural networks with reinforcement learning,the Double Deep Q Network(DDQN)algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile ***,the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality *** those problems,an improved DDQN algorithm based on average Q-value estimation and reward redistribution was ***,to enhance the precision of the target Q-value,the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value from the current target Q ***,a reward redistribution mechanism is designed to overcome the sparse reward problem by adjusting the final reward of each action using the round reward from trajectory ***,a reward-prioritized experience selection method is introduced,which ranks experience samples according to reward values to ensure frequent utilization of high-quality ***,simulation experiments are conducted to verify the effectiveness of the proposed algorithm in fixed-position scenario and random *** experimental results show that compared to the traditional DDQN algorithm,the proposed algorithm achieves shorter average running time,higher average return and fewer average *** performance of the proposed algorithm is improved by 11.43%in the fixed scenario and 8.33%in random *** not only plans economic and safe paths but also significantly improves efficiency and generalization in path planning,making it suitable for widespread application in autonomous navigation and industrial automation.

关键词： Double Deep Q Network path planning average Q-value estimation reward redistribution mechanism reward-prioritized experience selection method

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Leveraging Catastrophic Forgetting to Develop Safe Diffusion Models against Malicious Finetuning 38

Leveraging Catastrophic Forgetting to Develop Safe Diffusion...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Pan, Jiadong Gao, Hongcheng Wu, Zongyu Hu, Taihang Su, Li Huang, Qingming Li, Liang Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China University of Chinese Academy of Sciences China The Pennsylvania State University United States Nankai University China

Diffusion models (DMs) have demonstrated remarkable proficiency in producing images based on textual prompts. Numerous methods have been proposed to ensure these models generate safe images. Early methods attempt to incorporate safety filters into models to mitigate the risk of generating harmful images but such external filters do not inherently detoxify the model and can be easily bypassed. Hence, model unlearning and data cleaning are the most essential methods for maintaining the safety of models, given their impact on model parameters. However, malicious fine-tuning can still make models prone to generating harmful or undesirable images even with these methods. Inspired by the phenomenon of catastrophic forgetting, we propose a training policy using contrastive learning to increase the latent space distance between clean and harmful data distribution, thereby protecting models from being fine-tuned to generate harmful images due to forgetting. The experimental results demonstrate that our methods not only maintain clean image generation capabilities before malicious fine-tuning but also effectively prevent DMs from producing harmful images after malicious fine-tuning. Our method can also be combined with other safety methods to maintain their safety against malicious fine-tuning further. WARNING: This paper contains offensive images generated by models. © 2024 Neural information processing systems foundation. All rights reserved.

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ORB image feature extraction algorithm based on fuzzy control 3

ORB image feature extraction algorithm based on fuzzy contro...

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3rd International Conference on Machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Li, Ruilong College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9781510681880

With the increasing demands for real-time accuracy in visual SLAM (Simultaneous Localization and Mapping), the critical front-end component of SLAM systems has become the extraction and matching of image features to estimate camera motion pose. Existing feature extraction methods are susceptible to local feature point density and even overlap due to uncertainties in lighting intensity and surrounding environments, leading to uneven distribution of overall feature points. This, in turn, affects the accuracy of feature matching, thereby impacting the performance of the SLAM process. To address these challenges, this paper proposes a novel ORB image feature extraction algorithm based on fuzzy control. Initially, a Gaussian pyramid of the image is constructed, and the Intensity Centroid is calculated to ensure scale and rotation invariance. Subsequently, candidate feature points are extracted using ORB at each pyramid level, then the candidate feature points are selectively filtered through fuzzy control based on the number of candidate feature points and the magnitude of their response values. Finally, a BRIEF descriptor is established for feature matching. The proposed Fuzzy-controlled ORB Feature Extraction (OFFC) algorithm was experimentally validated on open-source TUM and Mikolajczyk datasets. Results indicate that OFFC successfully avoids excessive density and overlap of extracted feature points, reduces mismatches in image feature points, and enhances the accuracy of feature matching. Notably, OFFC demonstrates adaptability and maintains robust performance in three challenging scenarios: image blurring, varying brightness, and dense similarity of image blocks, highlighting its significant practical value for visual SLAM. © 2024 SPIE.

关键词： Fuzzy control

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