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MULTI-SCALE RESNET INTEGRATED WITH ATTENTION MECHANISM FOR ENHANCED WHEAT FRESHNESS DIAGNOSIS BASED ON BIOPHOTONICS

Acta Scientiarum Polonorum, Technologia Alimentaria 2024年第4期23卷 525-535页

作者： Shi, Weiya Chen, Liang Key Laboratory of Grain Information Processing and Control Ministry of Education Henan 450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Henan 450001 China College of Artificial Intelligence and Big Data Henan University of Technology Henan 450001 China

Background. Accurate detection of wheat freshness is important for ensuring the quality and safety of wheat products, thereby protecting the health and interests of consumers. Material and methods. This study integrates biophoton emission technology with advanced deep learning frameworks to transform the process of wheat freshness assessment. Leveraging the powerful feature extraction capabilities of the ResNet architecture, we employ a multi-scale framework integrated with the Gaussian Context Transformer (GCT) attention mechanism. The MS-GCT-ResNet method presents a groundbreaking approach that not only enhances the accuracy and efficiency of wheat freshness discrimination but also demonstrates the potential for combining biophysical phenomena with cutting-edge Artificial intelligence (AI) technologies for precision agriculture and food quality control. Results. This model enhances detection accuracy and adaptability, offering a powerful technique for the rapid and precise evaluation of wheat freshness. Its effectiveness is validated using years of wheat sample data. Based on the experimental results, MS-GCT-ResNet achieves a recognition accuracy of 93.6%. Compared with traditional CNN and ResNet models, the recognition accuracy increases by 1.9% and 1.1%, respectively. Conclusion. MS-GCT-ResNet is a highly promising, non-invasive, and efficient technological advancement capable of quickly and accurately assessing wheat freshness. This method holds immense potential to revolutionize the agriculture and food processing industries. © (2024), (Wydawnictwo Uniwersytetu Przyrodniczego w Poznaniu). All rights reserved.

关键词： attention mechanism biophoton emission detection multi-scale wheat freshness

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Lightweight Human Pose Estimation Using Loss Weighted by Target Heatmap 26th

Lightweight Human Pose Estimation Using Loss Weighted by Ta...

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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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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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Deployment of Heterogeneous Multi-Agent Systems via a PDE Approach

Deployment of Heterogeneous Multi-Agent Systems via a PDE Ap...

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2023 China Automation Congress, CAC 2023

作者： Man, Jingtao Zeng, Zhigang Xiao, Qiang Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan China

ISBN: (纸本)9798350303759

Spatial deployment of large-scale heterogeneous multi-agent systems (HMASs) over desired 2D or 3D curves is investigated in this paper. With assumption that HMASs consist of numerous first-order agents (FOAs) and second-order agents (SOAs) that could obtain local information of desired curves and their positions relative to their closest neighbors, the collective dynamics of large-scale HMASs are modeled as heterogeneous partial differential equations (PDEs). In particular, this paper introduces series-dependent topological weights between neighboring agents, which are more versatile and practical than constant topological weights commonly used in previous studies. A novel single-point control scheme is proposed, where an informed agent is situated between the last FOA and first SOA. This operation could not only ensure successful implementation of spatial deployment, but also guarantee well-posedness of the constructed heterogeneous error PDEs. By utilizing inequality techniques, sufficient conditions for exponential convergence of error system are derived. A numerical example is presented to demonstrate effectiveness of the proposed approaches. © 2023 IEEE.

关键词： Multi agent systems

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Task Allocation and Path Planning for Multiple Destinations Oriented Towards Task Updates 6

Task Allocation and Path Planning for Multiple Destinations ...

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6th International Conference on Industrial Artificial intelligence, IAI 2024

作者： Yang, Yong He, Dingxin Mao, Huantao Chen, Hong Wu, Hongtao Liu, Zhi-Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation China Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China

ISBN: (纸本)9798350356618

This study addresses the complexities of orchestrating multi-target transportation tasks within multi-agent systems, constrained by load capacity. The primary objective is to engineer an advanced path planning framework that assures collision avoidance and optimal load distribution. Diverging from conventional single-path strategies, this research incorporates load factors and requires continuous dynamic multi-target point and path planning to ensure agents can efficiently navigate through a series of predetermined key points. To address this complex requirement, a two-stage multi-agent task allocation and path planning method is proposed. First, an initial solution is obtained using a suboptimal algorithm, followed by optimization iterations using the large neighborhood search algorithm to improve task allocation. In the second stage, an accelerated algorithm based on priority search is used to plan optimal paths for each agent in a predetermined order. The proposed algorithm's effectiveness is comprehensively evaluated through a series of experimental evaluations and comparisons with the commercial solver Gurobi within a limited time. The results show that the proposed method achieves optimality in both running time and minimum path cost while ensuring the load balance of agents. © 2024 IEEE.

关键词： Motion planning

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Emotion Prediction in Conversation Based on Relationship Extraction

Emotion Prediction in Conversation Based on Relationship Ext...

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2022 IEEE International Conference on Cyborg and Bionic Systems, CBS 2022

作者： Yingjian, Liu Xiaoping, Wang Shanglin, Lei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China

ISBN: (纸本)9781665490283

With the development of human-computer interaction systems, emotion recognition in conversation (ERC) has attracted increasing interest in recent years. Motivated by recent studies which have proven that generating emotional conversation responses can effectively improve the performance of the ERC model. However, accurate emotional response is complicated due to the limited number of dialogue samples. Therefore, we propose a simple and effective framework for emotion prediction in conversation based on relationship extraction (DiaRP), consisting of two curricula: (1) Dialogue Relationship Capture (DRC);and (2) Next Emotion Prediction (NEP). In DRC, we capture the current emotional self-dependence and interpersonal dependence according to the influence of self and others on the current moment emotion in the conversation. We integrate self-dependence and interpersonal dependence for NEP to predict their emotional state without current utterance. We also measure the similarity between recognized emotion distribution and predicted emotion distribution by the KL divergence. With the proposed model-agnostic DiaRP strategy, we observe a significant performance improvement over a wide range of existing ERC models and achieve new state-of-the-art results on three public ERC datasets. © 2023 IEEE.

关键词： Emotion Recognition

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Cognitive Navigation for Intelligent Mobile Robots:A Learning-Based Approach With Topological Memory Configuration

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1933-1943页

作者： Qiming Liu Xinru Cui Zhe Liu Hesheng Wang Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China MoE Key Laboratory of Artificial Intelligence AI InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationKey Laboratory of Marine Intelligent Equipment and System of Ministry of EducationShanghai Engineering Research Center of Intelligent Control and ManagementShanghai Jiao Tong UniversityShanghai 200240China

Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial *** this paper,we propose a learning-based visual navigation pipeline that uses topological maps as memory *** introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity *** tackles the issues of topological node redundancy and incorrect edge connections,which stem from the distribution gap between the spatial and perceptual ***,we propose a differentiable graph extraction structure,the topology multi-factor transformer(TMFT).This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy *** from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory *** validation through behavior visualization,interpretability tests,and real-world deployment further underscore the adapt-ability and efficacy of our method.

关键词： Graph neural networks(GNNs) spatial memory topological map visual navigation

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Four development stages of collective intelligence

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Frontiers of Information Technology & Electronic Engineering 2024年第7期25卷 903-916页

作者： Renbin XIAO School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Image Information Processing and Intelligent Control Ministry of EducationWuhan 430074China

The new generation of artificial intelligence(AI)research initiated by Chinese scholars conforms to the needs of a new information environment changes,and strives to advance traditional artificial intelligence(AI 1.0)to a new stage of AI *** one of the important components of AI,collective intelligence(CI 1.0),i.e.,swarm intelligence,is developing to the stage of CI 2.0(crowd intelligence).Through in-depth analysis and informative argumentation,it is found that an incompatibility exists between CI 1.0 and CI ***,CI 1.5 is introduced to build a bridge between the above two stages,which is based on biocollaborative behavioral *** 1.5 is the transition from CI 1.0 to CI 2.0,which contributes to the compatibility of the two ***,a new interpretation of the meta-synthesis of wisdom proposed by Qian Xuesen is *** meta-synthesis of wisdom,as an improvement of crowd intelligence,is an advanced stage of bionic intelligence,i.e.,CI *** is pointed out that the dual-wheel drive of large language models and big data with deep uncertainty is an evolutionary path from CI 2.0 to CI 3.0,and some elaboration is *** a result,we propose four development stages(CI 1.0,CI 1.5,CI 2.0,and CI 3.0),which form a complete framework for the development of *** different stages are progressively improved and have good *** to the dominant role of cooperation in the development stages of CI,three types of cooperation in CI are discussed:indirect regulatory cooperation in lower organisms,direct communicative cooperation in higher organisms,and shared intention based collaboration in *** division is the main form of achieving cooperation and,for this reason,this paper investigates the relationship between the complexity of behavior and types of labor ***,based on the overall understanding of the four development stages of CI,the future development direction and research issues of CI are explored.

关键词： Collective intelligence Meta-synthesis of wisdom Incompatibility Labor division Cooperative behavior Collective intelligence emergence Large language model

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A Physiological Signal Emotion Recognition Method Based on Domain Adaptation and Incremental Learning 1

A Physiological Signal Emotion Recognition Method Based on D...

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1st International Conference on Cyber-Energy Systems and Intelligent Energy, ICCSIE 2022

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

ISBN: (纸本)9781665452151

Temporal concept shift (TCS) is an unavoidable problem in physiological signal-based emotion recognition tasks, i.e., the data distribution of physiological signals is constantly changing over time, which gradually degrades the model accuracy. To this end, we propose a method based on a combination of domain adaptation and incremental learning to reduce the impact of temporal concept drift. In this paper, domain adaptation is used to reduce the distribution differences and incremental learning is used to prevent the learned knowledge from being forgotten. Finally, we validate the effectiveness of our approach on two real datasets. © 2023 IEEE.

关键词： Emotion Recognition

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Multiple Unmanned Surface Vehicle Coverage control in Dynamic Environments

Multiple Unmanned Surface Vehicle Coverage Control in Dynami...

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2023 International Conference on Neuromorphic Computing, ICNC 2023

作者： Chen, Hong He, Dingxin Mao, Huantao Yang, Yong Liu, Zhi-Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China

ISBN: (纸本)9798350316889

This paper investigates the area coverage problem for multiple Unmanned Surface Vehicles (USVs) in dynamic environments. Multiple USVs are employed to execute the coverage task using the Voronoi Partition method for task allocation. Parameter adaptive laws are employed to estimate the parameters of the sensing function and the Voronoi cell centroids for updating the target positions in dynamic environments. Additionally, considering the influence of water surface disturbances, a nonholonomic underactuated model analysis is conducted for the USV under non-full constraints, resulting in a motion strategy for USVs. This enables the multiple USVs to effectively cover the task area. Finally, simulation results are presented to validate the effectiveness of the coverage scheme designed in this paper and the ability of USVs to effectively cover dynamic environments. © 2023 IEEE.

关键词： Unmanned surface vehicles

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