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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： He, Yuesheng Zhang, Wei Yang, Ming The Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Shenzhen Deeproute.ai Co. Ltd China

ISBN: (纸本)9781665481090

Estimating the pose information on moving vehi-cles is one of the most fundamental functions of autonomous driving for detecting and tracking moving objects. The current methods are often based on the prior information of the point cloud distribution, however, the distribution in the actual scene changes dynamically, and is easy not to meet a priori. This paper proposes a 3D pose estimation method based on heuristic rules. In a single frame point cloud, the edge length and edge visibility are used to heuristically screen the candidate orientations established by the convex hull sampling method. On consecutive frame point clouds, tracking heuristics are employed to obtain smoother orientation estimation results. The experimental results on the KITTI dataset and the data from the actual scene of vehicles show that the proposed method reduces the average object-orientation error and improves the accuracy of estimation effectively. © 2022 IEEE.

关键词： Autonomous vehicles

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Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion Recognition

arXiv

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arXiv 2024年

作者： Li, Jiang Wang, Xiaoping Zeng, Zhigang The School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan430074 China The Hubei Key Laboratory of Brain-Inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China

Multimodal emotion recognition in conversation (MERC) has garnered substantial research attention recently. Existing MERC methods face several challenges: (1) they fail to fully harness direct inter-modal cues, possibly leading to less-than-thorough cross-modal modeling;(2) they concurrently extract information from the same and different modalities at each network layer, potentially triggering conflicts from the fusion of multi-source data;(3) they lack the agility required to detect dynamic sentimental changes, perhaps resulting in inaccurate classification of utterances with abrupt sentiment shifts. To address these issues, a novel approach named GraphSmile is proposed for tracking intricate emotional cues in multimodal dialogues. GraphSmile comprises two key components, i.e., GSF and SDP modules. GSF ingeniously leverages graph structures to alternately assimilate inter-modal and intra-modal emotional dependencies layer by layer, adequately capturing cross-modal cues while effectively circumventing fusion conflicts. SDP is an auxiliary task to explicitly delineate the sentiment dynamics between utterances, promoting the model’s ability to distinguish sentimental discrepancies. Furthermore, GraphSmile is effortlessly applied to multimodal sentiment analysis in conversation (MSAC), forging a unified multimodal affective model capable of executing MERC and MSAC tasks. Empirical results on multiple benchmarks demonstrate that GraphSmile can handle complex emotional and sentimental patterns, significantly outperforming baseline models. Copyright © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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Learning Knowledge Evolution with Time Duration from Finer-Time-Granularity Temporal Knowledge Graph

Learning Knowledge Evolution with Time Duration from Finer-T...

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Chinese Automation Congress (CAC)

作者： Shaochong Du Kang Chen Shijie Yang Hong Huo Tao Fang Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Shanghai Engineering Research Centre of Intelligent Control and Management Shanghai China

How to represent the temporal information corresponding to each fact in temporal knowledge graphs (TKGs) effectively is always challenging. Most existing representation learning methods usually map the timelines of knowledges into multiple points and ignore the underlying continuous time property and periodic characteristics behind those dense timestamps in finer-time granularity temporal knowledge graphs. A novel Knowledge Evolution with Time Duration (KETD) model is proposed for knowledge representation of temporal knowledge graphs with finer-time granularities. It represents the entity embedding as a time-varying nonlinear function to find the specific continuous time property from dense timestamps. To the best of our knowledge, it is the first attempt to learn embeddings of time durations and combines them into embeddings of entities and relations to predict potential periodic facts. It also uses the temporal point process to capture the impact of historical facts on the current fact. The experiments on self-built finer-time granularity TKG of Satellite-to-Earth Communication (STEC) and two public datasets have demonstrated the superiority of KETD compared to some baseline approaches.

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Operator-Adapted Evolutionary Large-Scale Multiobjective Optimization for Voltage Transformer Ratio Error Estimation 11th

Operator-Adapted Evolutionary Large-Scale Multiobjective Opt...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Huang, Changwu Li, Lianghao He, Cheng Cheng, Ran Yao, Xin Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783030720612

Large-scale multiobjective optimization problems (LSMOPs) exist widely in real-world applications, and they are challenging for existing evolutionary algorithms due to their massive volume of search space. Despite that a number of large-scale multiobjective evolutionary algorithms (LSMOEAs) have been proposed in recent years, their effectiveness in solving LSMOPs remains unsatisfactory. One main reason is that most existing LSMOEAs may fail to balance convergence enhancement and diversity maintenance, especially for solving real-world problems. To address this issue, we propose to use a hybridized LSMOEA with adaptive operator selection (AOS) to handle real-world LSMOPs. Specifically, the proposed hybridized LSMOEA with AOS (AOS-LSMOEA) includes multiple different offspring generation and environmental selection strategies extracted from some existing LSMOEAs. Then it uses the AOS to adaptively determine the application rates of different offspring generation and environmental selection operators in an online manner. The proposed approach is capable of taking advantage of existing LSMOEAs, and the AOS enables the algorithm to choose suitable operators for solving different LSMOPs. In this study, the proposed algorithm is expected to solve the voltage transformer ratio error estimation (TREE) problems effectively. Experimental results show that AOS-LSMOEA achieves significant performance improvement due to the hybridization of different operators and the adoption of AOS method. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Manifold Learning Inspired Mating Restriction for Evolutionary Constrained Multiobjective Optimization 11th

Manifold Learning Inspired Mating Restriction for Evolutiona...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Li, Lianghao He, Cheng Cheng, Ran Pan, Linqiang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783030720612

Mating restriction strategies are capable of restricting the distribution of parent solutions for effective offspring generation in evolutionary algorithms (EAs). Studies have shown the importance of these strategies in improving the performance of EAs for multiobjective optimization. Our previous study proposed a specific manifold learning inspired mating restriction (MLMR) strategy. It has shown promising capability of solving multiobjective optimization problems (MOPs) with complicated Pareto set shapes. However, the effect of mating restriction strategies in solving constrained MOPs is yet to be well studied. Here, we investigate the effectiveness of MLMR for solving constrained MOPs. The MLMR strategy is embedded into some representative multiobjective EAs and tested on various benchmark constrained MOPs. Experimental results indicate the encouraging performance of MLMR in constrained multiobjective optimization. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Energy management of solid oxide fuel cell/lithium battery hybrid system

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CSEE Journal of Power and Energy Systems 2022年 1-14页

作者： Li, Dong Zhong, Xiao Bo Xu, Yuan Wu Wei, Chong Yang Zhao, Song Jiang, Jianhua Deng, Zhonghua Fu, Xiaowei Li, Xi School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan430081 China

The research of fuel cell and lithium battery hybrid system has attracted more and more researchers because of its advantages of low emission. However, the lower efficiency of energy management has been a critical factor that obstruct the commercialization of hybrid system. In this work, based on finite element ideas, a solid oxide full cell node model is developed to accurately estimate the operating status of the fuel cell. As for lithium battery, previous energy management studies generally only focused on state of charge, however, state of health is also a key parameter for lithium battery. In this paper, considering the strong coupling between variables we use particle filter algorithm to jointly estimate the state of charge and state of health. To reasonably distribute and manage the energy in the hybrid system, a T-S fuzzy controller for the hybrid system is designed by controlling the state variables in the SOFC model and the state of lithium battery. Finally, our algorithm is verified by DSPACE simulation platform. The result shows that the hybrid system with the energy management strategy not only meets the real-time sharply changing demand but also ensures the hybrid system working efficiently and safely. © 2022 China Electric Power Research Institute. All rights reserved.

关键词： Hybrid systems

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Vehicle Scheduling with Exact Minimum Fleet-Size

SSRN

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SSRN 2022年

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

Vehicle scheduling plays a profound role in public transit planning. Vehicle scheduling problem (VSP) addresses the task of assigning vehicles to cover timetabled trips to minimize the fleet-size as well as operating costs. The fixed cost of acquiring and maintaining vehicles is much more expensive than the operating cost. It is of great theoretical and practical significance to construct the vehicle schedules with the exact minimum fleet-size (EMF). The theorem that the EMF is equal to the maximum incompatible degree of the timetabled trips is first proposed and proved. Based on the theorem and time compatibility between any two trips, this paper theoretically examines vehicle scheduling with EMF. Specifically, construction procedures of schedules with EMF are provided for VSP in a specific case as well as the general case. Furthermore, sensitivity analysis of the EMF and the schedule with EMF are carried out. The EMF is possible to decrease by 1 if a trip is deleted or the start/end time of a trip is changed in the timetable. © 2022, The Authors. All rights reserved.

关键词： Sensitivity analysis

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3D Instances as 1D Kernels

arXiv

引用

arXiv 2022年

作者： Wu, Yizheng Shi, Min Du, Shuaiyuan Lu, Hao Cao, Zhiguo Zhong, Weicai Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of AIA Huazhong University of Science and Technology China Huawei CBG Consumer Cloud Service Search & Maps BU China

We introduce a 3D instance representation, termed instance kernels, where instances are represented by one-dimensional vectors that encode the semantic, positional, and shape information of 3D instances. We show that instance kernels enable easy mask inference by simply scanning kernels over the entire scenes, avoiding the heavy reliance on proposals or heuristic clustering algorithms in standard 3D instance segmentation pipelines. The idea of instance kernel is inspired by recent success of dynamic convolutions in 2D/3D instance segmentation. However, we find it non-trivial to represent 3D instances due to the disordered and unstructured nature of point cloud data, e.g., poor instance localization can significantly degrade instance representation. To remedy this, we construct a novel 3D instance encoding paradigm. First, potential instance centroids are localized as candidates. Then, a candidate merging scheme is devised to simultaneously aggregate duplicated candidates and collect context around the merged centroids to form the instance kernels. Once instance kernels are available, instance masks can be reconstructed via dynamic convolutions whose weights are conditioned on instance kernels. The whole pipeline is instantiated with a dynamic kernel network (DKNet). Results show that DKNet outperforms the state of the arts on both ScanNetV2 and S3DIS datasets with better instance localization. Code is available: https://***/W1zheng/DKNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Pipelines

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Cross-Modal De-Deviation for Enhancing Few-Shot Classification

SSRN

引用

SSRN 2023年

作者： Pan, Mei-Hong Shen, Hong-Bin School of Electronic Information and Electrical Engineering Shanghai Jiaotong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Few-shot learning poses a critical challenge due to the deviation problem caused by the scarcity of available samples. In this work, we aim to address deviations in both feature representations and prototypes. To achieve this, we propose a cross-modal de-deviation framework that leverages class semantic information to provide robust prior knowledge for the samples. This framework begins with a visual-to-semantic autoencoder trained on the labeled samples to predict semantic features for the unlabeled samples. Then, we devise a binary linear programming model to incorporate the initial prototypes with the cluster centers of the unlabeled samples. To avoid mismatch between the cluster centers and the initial prototypes, we conduct the label assignment process in the semantic space using the class ground truth semantic features as the reference points, with the cluster centers transformed into semantic representations. Moreover, we model a linear classifier with the concatenation of the refined prototypes and the class ground truth semantic features serving as the initial weights. Then we propose a novel optimization strategy based on the alternating least squares (ALS) model. From the ALS model, we can derive two closed-form solutions regarding to the features and weights, facilitating alternative optimization of them. Extensive experiments conducted on three standard benchmarks demonstrate the competitive advantages of our CMDD method over the state-of-the-art few-shot classification methods, confirming its effectiveness in reducing deviation. © 2023, The Authors. All rights reserved.

关键词： Semantics

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Short-Term Hybrid Prognostics of Fuel Cells: A Comparative and Improvement Study

SSRN

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SSRN 2024年

作者： Sheng, Chuang Fu, Jun Zu, YanMin Deng, ZhongHua Li, Xi School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Education Ministry Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China School of Electronic Information Engineering Jingchu University of Technology Jingmen448000 China

Accurately predicting the performance degradation trend of fuel cells helps take measures in advance and prolong the service life of the stack, which led to the creation of a novel hybrid forecasting approach. The first grey model prediction method based on residual exponential smoothing optimization (ES-R-GM) can capture the voltage deterioration trend. The integration of two different ES techniques, specifically the double ES (ES2) and cubic ES (ES3), is explored to investigate their effect on improving the predictive accuracy of the GM model. The second method of the adaptive network fuzzy inference system (ANFIS) can characterize local nonlinear behaviour. The simulated annealing (SA) algorithm is utilized to optimize ANFIS results under different fuzzy rule selection strategies. The outcomes of the two types of prediction methods mentioned above are combined to create a hybrid prediction using the data fusion method and the moving window technique. Different hybrid methods are evaluated under general conditions and further detailed optimization. The proposed hybrid framework is verified by the data obtained from the experimental platform. Moreover, when applied to consistent public datasets, the hybrid approach maintains its robustness and accuracy compared with other prognostic methods. © 2024, The Authors. All rights reserved.

关键词： Simulated annealing

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