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NPGA: A Unified Algorithmic Framework for Decentralized Constraint-Coupled Optimization

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arXiv 2022年

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

This work focuses on a class of general decentralized constraint-coupled optimization problems. We propose a novel nested primal-dual gradient algorithm (NPGA), which can achieve linear convergence under the weakest known condition, and its theoretical convergence rate surpasses all known results. More importantly, NPGA serves not only as an algorithm but also as a unified algorithmic framework, encompassing various existing algorithms as special cases. By designing different network matrices, we can derive numerous versions of NPGA and analyze their convergences by leveraging the convergence results of NPGA conveniently, thereby enabling the design of more efficient algorithms. Finally, we conduct numerical experiments to compare the convergence rates of NPGA and existing algorithms, providing empirical evidence for the superior performance of NPGA. Copyright © 2022, The Authors. All rights reserved.

关键词： Consensus algorithm

GraphCFC: A Directed Graph Based Cross-Modal Feature Complementation Approach for Multimodal Conversational Emotion Recognition

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arXiv 2022年

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

Emotion Recognition in Conversation (ERC) plays a significant part in Human-Computer Interaction (HCI) systems since it can provide empathetic services. Multimodal ERC can mitigate the drawbacks of uni-modal approaches. Recently, Graph Neural Networks (GNNs) have been widely used in a variety of fields due to their superior performance in relation modeling. In multimodal ERC, GNNs are capable of extracting both long-distance contextual information and inter-modal interactive information. Unfortunately, since existing methods such as MMGCN directly fuse multiple modalities, redundant information may be generated and diverse information may be lost. In this work, we present a directed Graph based Cross-modal Feature Complementation (GraphCFC) module that can efficiently model contextual and interactive information. GraphCFC alleviates the problem of heterogeneity gap in multimodal fusion by utilizing multiple subspace extractors and Pair-wise Cross-modal Complementary (PairCC) strategy. We extract various types of edges from the constructed graph for encoding, thus enabling GNNs to extract crucial contextual and interactive information more accurately when performing message passing. Furthermore, we design a GNN structure called GAT-MLP, which can provide a new unified network framework for multimodal learning. The experimental results on two benchmark datasets show that our GraphCFC outperforms the state-of-the-art (SOTA) approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Emotion Recognition

Multimodal Representations Learning Based on Mutual Information Maximization and Minimization and Identity Embedding for Multimodal Sentiment analysis

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arXiv 2022年

作者： Zheng, Jiahao Zhang, Sen Wang, Xiaoping Zeng, Zhigang School of Artificial Intelligence and Automation The Key Laboratory of Image Processing Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan430074 China

Multimodal sentiment analysis (MSA) is a fundamental complex research problem due to the heterogeneity gap between different modalities and the ambiguity of human emotional expression. Although there have been many successful attempts to construct multimodal representations for MSA, there are still two challenges to be addressed: 1) A more robust multimodal representation needs to be constructed to bridge the heterogeneity gap and cope with the complex multimodal interactions, and 2) the contextual dynamics must be modeled effectively throughout the information flow. In this work, we propose a multimodal representation model based on Mutual information Maximization and Minimization and Identity Embedding (MMMIE). We combine mutual information maximization between modal pairs, and mutual information minimization between input data and corresponding features to mine the modal-invariant and task-related information. Furthermore, Identity Embedding is proposed to prompt the downstream network to perceive the contextual information. Experimental results on two public datasets demonstrate the effectiveness of the proposed model. Copyright © 2022, The Authors. All rights reserved.

关键词： Sentiment analysis

GNet: 3D Object Detection from Point Cloud with Geometry-Aware Network

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GNet: 3D Object Detection from Point Cloud with Geometry-Awa...

IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Hao Ruan Bingrong Xu Junbin Gao Lianguang Liu Junting Lv Yin Sheng Zhigang Zeng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China School of Automation Wuhan University of Technology Wuhan China

3D object detection based on point clouds in real-world applications has been a challenging task because the raw point cloud may be sparse or occluded. It may be caused by bad weather or limited sensor resolution, leading to decreasing detection performance. To this end, a novel highperformance Geometry-aware Network (GNet) is proposed and used to accurately detect 3D objects from sparse point clouds. Different from previous work that only regresses the offset of 3D bounding boxes numerically between ground truth (GT) and prediction, GNet considers the geometric information of 3D bounding boxes and combines the two effectively to achieve a better regression performance. In particular, Point Nets blocks learned voxel-wise features and the proposals are generated by the 3D voxel convolution neural network (voxel CNN). By utilizing the prior knowledge and the geometric properties of the 3D bounding boxes, the proposed novel OS-loss can improve detection performance and accelerate speed of inference. Furthermore, GNet takes advantage of prior geometry properties rather than only regressing the offset numerically. These speed up the convergence of the regression network. Finally, our proposed GNet was evaluated on the KITTI dataset for 3D objects and Bird’s Eye View (BEV) detection aspects. The results demonstrate that GNet can improve object detection performance for sparse point clouds.

关键词： Point cloud compression Location awareness Geometry Three-dimensional displays Neural networks Object detection Proposals

Energy Management Strategies for PEMFC-Lithium Battery Hybrid Power Systems Based on Pt Degradation

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Energy Management Strategies for PEMFC-Lithium Battery Hybri...

Chinese Control Conference (CCC)

作者： Ziang Guo Beijia Li Linghong Zeng Jun Fu Chuang Sheng Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Wuhan Hubei China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China

Proton Exchange Membrane Fuel Cells(PEMFCs) are prone to decreased lifespan due to the degradation of the plat-inum(Pt) catalyst during operation. In this study, we have established a one-dimensional model to investigate the effects of different voltage conditions on Pt degradation. High potential is found to be the most significant factor affecting the degradation. Prolonged exposure to high potentials will significantly reduce the PEMFC's lifespan, and frequent voltage changes will exac-erbate the problem. To mitigate this issue, we have designed three energy management strategies with different limitations and simulated their performance in the operating scenario of a fuel-cell hybrid electric vehicle(FCHEV). The results demonstrate that strategies restricting high potential occurrences and voltage variations can effectively alleviate Pt degradation.

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GA2MIF: Graph and Attention Based Two-Stage Multi-Source Information Fusion for Conversational Emotion Detection

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arXiv 2022年

作者： Li, Jiang Wang, Xiaoping Lv, Guoqing Zeng, Zhigang School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan430074 China

Multimodal Emotion Recognition in Conversation (ERC) plays an influential role in the field of human-computer interaction and conversational robotics since it can motivate machines to provide empathetic services. Multimodal data modeling is an up-and-coming research area in recent years, which is inspired by human capability to integrate multiple senses. Several graph-based approaches claim to capture interactive information between modalities, but the heterogeneity of multimodal data makes these methods prohibit optimal solutions. In this work, we introduce a multimodal fusion approach named Graph and Attention based Two-stage Multi-source Information Fusion (GA2MIF) for emotion detection in conversation. Our proposed method circumvents the problem of taking heterogeneous graph as input to the model while eliminating complex redundant connections in the construction of graph. GA2MIF focuses on contextual modeling and cross-modal modeling through leveraging Multi-head Directed Graph ATtention networks (MDGATs) and Multi-head Pairwise Cross-modal ATtention networks (MPCATs), respectively. Extensive experiments on two public datasets (i.e., IEMOCAP and MELD) demonstrate that the proposed GA2MIF has the capacity to validly capture intra-modal long-range contextual information and inter-modal complementary information, as well as outperforms the prevalent State-Of-The-Art (SOTA) models by a remarkable margin. Copyright © 2022, The Authors. All rights reserved.

关键词： Directed graphs

Incentive Mechanism and Path Planning for UAV Hitching over Traffic Networks

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arXiv 2022年

作者： Lu, Ziyi Yu, Na Wang, Xuehe The School of Artificial Intelligence Sun Yat-sen University Zhuhai519082 China The Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

Package delivery via the UAVs is a promising transport mode to provide efficient and green logistic services, especially in urban areas or complicated topography. However, the energy storage limit of the UAV makes it difficult to perform long-distance delivery tasks. In this paper, we propose a novel multimodal logistics framework, in which the UAVs can call on ground vehicles to provide hitch services to save their own energy and extend their delivery distance. This multimodal logistics framework is formulated as a two-stage model to jointly consider the incentive mechanism design for ground vehicles and path planning for UAVs. In Stage I, to deal with the motivations for ground vehicles to assist UAV delivery, a dynamic pricing scheme is proposed to best balance the vehicle response time and payments to ground vehicles. It shows that a higher price should be decided if the vehicle response time is long to encourage more vehicles to offer a ride. In Stage II, the task allocation and path planning of the UAVs over traffic network is studied based on the vehicle response time obtained in Stage I. To address pathfinding with restrictions and the performance degradation of the pathfinding algorithm due to the rising number of conflicts in multi-agent pathfinding, we propose the suboptimal conflict avoidance-based path search (CABPS) algorithm, which has polynomial time complexity. Finally, we validate our results via simulations. It is shown that our approach is able to increase the success rate of UAV package delivery. Moreover, we estimate the delivery time of the UAV in a pessimistic case, it is still twice as fast as the delivery time of the ground vehicle only. Copyright © 2022, The Authors. All rights reserved.

关键词： Motion planning

Cooperative Target-Surrounding Control of Unmanned Surface Vessels Based on MADDPG

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Cooperative Target-Surrounding Control of Unmanned Surface V...

Annual Conference of Industrial Electronics Society

作者： Taoman Li Fei Zhu Zihan Gan Zexing Zhou Xiaotao Wang Renzhi Lu Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China China Ship Scientific Research Center Wuxi China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan China

This article proposes a multi-agent deep reinforce-ment learning algorithm to control a fleet of unmanned surface vessels (USVs) that encircle and capture sea targets. First, a simulation environment for USVs is established based on a dynamic model; two-dimensional control variables are used to control movements in three directions. Second, the multi-agent deep deterministic policy gradient (MADDPG) algorithm is employed to achieve intelligent control of the USVs, using a reward function based on certain prior knowledge. Finally, centralized training and decentralized execution are used to complete the offline learning of multiple agents, and continuous action decisions are made based on the observations of the USV sensors. Simulations demonstrate that the method can capture stationary moving sea targets with any number of multi-agents under disturbances, and exhibits strong robustness and practicality.

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Faster R-CNN railway foreign body detection algorithm combined with attention between channels

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Faster R-CNN railway foreign body detection algorithm combin...

2021 International Conference on Signal processing and Communication Technology, SPCT 2021

作者： Gao, Deyong Kang, Yihua Wang, Yangping School of Electronic & Information Engineering Lanzhou Jiaotong University Gansu Lanzhou730070 China Gansu Provincial Eng. Res. Center for Artificial Intelligence and Graphic & Image Processing Gansu Lanzhou730070 China Gansu Provincial Key Laboratory of System Dynamics and Reliability of Rail Transport Equipment Gansu Lanzhou730070 China

ISBN: (数字)9781510653344

ISBN: (纸本)9781510653337

The intrusion of foreign objects such as pedestrians and vehicles into the boundary of the railway has seriously threatened the safety of pedestrians and the safety of railway traffic. Aiming at the low utilization rate of the classic Faster R-CNN model for inter-channel information, in the feature extraction stage of the model, combined with the inter-channel attention module of SENet, the SE-Faster R-CNN model is proposed and used on this basis A more balanced loss function and adjustment of the size of the anchor improve the detection accuracy of the model. When the number of railway foreign body images is insufficient, first use VOC2007 to train the model, and then use part of the KITTI data set and a small amount of high-speed rail platform monitoring data and railway foreign body data to fine-tune the model, making the model better in the railway scene Performance. The experimental results show that although the algorithm speed has dropped slightly, the mAP of the improved Faster R-CNN model on the VOC data set has reached 80.3%, which is an increase of 6.9% compared to the original model. The image data on the railway platform and the railway foreign body The average accuracy on the image data reached 87%, especially for pedestrians and trains in the railway, the detection accuracy reached 89.61% and 90.9%, respectively. Studies have shown that the introduction of the SEnet structure into Faster R-CNN improves the feature extraction capabilities of the original model, which makes the model's detection accuracy for targets greatly improved, and can well complete the task of railway foreign body detection. © 2022 SPIE.

关键词： Feature extraction