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arXiv 2022年

作者： Chen, Weiling Lin, Rongfu Liao, Honggang Zhao, Tiesong Gu, Ke Le Callet, Patrick Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Peng Cheng Laboratory Shenzhen518000 China The Faculty of Information Technology Beijing University of Technology Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Équipe Image Perception et Interaction Laboratoire des Sciences du Numérique de Nantes Université de Nantes France

The widespread image applications have greatly promoted the vision-based tasks, in which the Image Quality Assessment (IQA) technique has become an increasingly significant issue. For user enjoyment in multimedia systems, the IQA exploits image fidelity and aesthetics to characterize user experience;while for other tasks such as popular object recognition, there exists a low correlation between utilities and perceptions. In such cases, the fidelity-based and aesthetics-based IQA methods cannot be directly applied. To address this issue, this paper proposes a utility-oriented IQA in object recognition. In particular, we initialize our research in the scenario of underwater fish detection, which is a critical task that has not yet been perfectly addressed. Based on this task, we build an Underwater Image Utility Database (UIUD) and a learning-based Underwater Image Utility Measure (UIUM). Inspired by the top-down design of fidelity-based IQA, we exploit the deep models of object recognition and transfer their features to our UIUM. Experiments validate that the proposed transfer-learning-based UIUM achieves promising performance in the recognition task. We envision our research provides insights to bridge the researches of IQA and computer vision. Copyright © 2022, The Authors. All rights reserved.

关键词： Multimedia systems

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Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach

arXiv

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arXiv 2022年

作者： Li, Yuanzheng He, Shangyang Li, Yang Ge, Leijiao Lou, Suhua Zeng, Zhigang School of Artificial Intelligence and Automation Key Laboratory on Image Information Processing and Intelligent Control of Ministry of Education Huazhong University of Science and Technology Wuhan430074 China China-Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering Northeast Electric Power University Jilin 132012 China The Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin 300072 China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China

The electric vehicle (EV) and electric vehicle charging station (EVCS) have been widely deployed with the development of large-scale transportation electrifications. However, since charging behaviors of EVs show large uncertainties, the forecasting of EVCS charging power is non-trivial. This paper tackles this issue by proposing a reinforcement learning assisted deep learning framework for the probabilistic EVCS charging power forecasting to capture its uncertainties. Since the EVCS charging power data are not standard time-series data like electricity load, they are first converted to the time-series format. On this basis, one of the most popular deep learning models, the long short-term memory (LSTM) is used and trained to obtain the point forecast of EVCS charging power. To further capture the forecast uncertainty, a Markov decision process (MDP) is employed to model the change of LSTM cell states, which is solved by our proposed adaptive exploration proximal policy optimization (AePPO) algorithm based on reinforcement learning. Finally, experiments are carried out on the real EVCSs charging data from Caltech, and Jet Propulsion laboratory, USA, respectively. The results and comparative analysis verify the effectiveness and outperformance of our proposed framework. Copyright © 2022, The Authors. All rights reserved.

关键词： Markov processes

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A Single-Cell Clustering Algorithm Based on Structure Perturbation Non-Negative Matrix Factorization

A Single-Cell Clustering Algorithm Based on Structure Pertur...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hanjing Jiang Meineng Wang Luping Zhang Yu-An Huang Yangyuan Li Yabing Huang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology China University of Mining and Technology Xuzhou China School of Mathematics and Computer Science Yichun University Yichun China School of Information Engineering East China University of Technology Nanchang China School of Computer Science Northwestern Polytechnical University Xi’an China Department of Pathology Renmin Hospital of Wuhan University Wuhan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The advent of single-cell RNA sequencing (scRNA-seq) has facilitated the acquisition of high-resolution data regarding cell heterogeneity across various tissues. A fundamental and critical step in the analysis of scRNA-seq data is cell type identification. An appropriate graph construction strategy can reflect the topological structure between cells and allows for detailed exploration of the relationships between cells and genes. Given the sparsity of scRNA-seq data and the pronounced noise generated by shallow sequencing, choosing an appropriate graph construction strategy has become a significant challenge. In this paper, we propose a single-cell clustering method, named Sc-PNNMF, based on structural perturbation of nonnegative matrix factorization. Sc-PNNMF employs a structural perturbation algorithm to optimize the gene expression matrix. The optimized matrix guides the matrix factorization process, effectively overcoming the impact of data sparsity and significant noise in gene expression data on the graph construction strategy. We compared the clustering abilities of Sc-PNNMF with five other methods on ten real datasets.

关键词： Sequential analysis Perturbation methods Clustering methods RNA Noise Clustering algorithms Sparse matrices Gene expression Unsupervised learning Periodic structures

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Motion Projection Consistency Based 3D Human Pose Estimation with Virtual Bones from Monocular Videos

arXiv

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arXiv 2021年

作者： Wang, Guangming Zeng, Honghao Wang, Ziliang Liu, Zhe Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment and System Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FD United Kingdom

Real-time 3D human pose estimation is crucial for human-computer interaction. It is cheap and practical to estimate 3D human pose only from monocular video. However, recent bone splicing based 3D human pose estimation method brings about the problem of cumulative error. In this paper, the concept of virtual bones is proposed to solve such a challenge. The virtual bones are imaginary bones between non-adjacent joints. They do not exist in reality, but they bring new loop constraints for the estimation of 3D human joints. The proposed network in this paper predicts real bones and virtual bones, simultaneously. The final length of real bones is constrained and learned by the loop constructed by the predicted real bones and virtual bones. Besides, the motion constraints of joints in consecutive frames are considered. The consistency between the 2D projected position displacement predicted by the network and the captured real 2D displacement by the camera is proposed as a new projection consistency loss for the learning of 3D human pose. The experiments on the Human3.6M dataset demonstrate the good performance of the proposed method. Ablation studies demonstrate the effectiveness of the proposed inter-frame projection consistency constraints and intra-frame loop constraints. Copyright © 2021, The Authors. All rights reserved.

关键词： E-learning

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SPP-CNN: An Efficient Framework for Network Robustness Prediction

arXiv

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arXiv 2023年

作者： Wu, Chengpei Lou, Yang Wang, Lin Li, Junli Li, Xiang Chen, Guanrong The College of Computer Science Sichuan Normal University Chengdu610066 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks. This kind of network robustness is typically measured by the time-consuming attack simulation, which returns a sequence of values that record the remaining connectivity and controllability after a sequence of node- or edge-removal attacks. For improvement, this paper develops an efficient framework for network robustness prediction, the spatial pyramid pooling convolutional neural network (SPP-CNN). The new framework installs a spatial pyramid pooling layer between the convolutional and fully-connected layers, overcoming the common mismatch issue in the CNN-based prediction approaches and extending its generalizability. Extensive experiments are carried out by comparing SPP-CNN with three state-of-the-art robustness predictors, namely a CNN-based and two graph neural networks-based frameworks. Synthetic and real-world networks, both directed and undirected, are investigated. Experimental results demonstrate that the proposed SPP-CNN achieves better prediction performances and better generalizability to unknown datasets, with significantly lower time-consumption, than its counterparts. © 2023, CC BY.

关键词： Complex networks

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NMF: an Efficient Method for Detecting the Fallen Leaves Using Cleaning Robots on the Road

NMF: an Efficient Method for Detecting the Fallen Leaves Usi...

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IEEE International Conference on Robotics and Biomimetics

作者： Yanzi Miao Zongwei Zhang Hesheng Wang Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China China University of Mining and Technology Xuzhou China Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665405362

The improvement of urban greening level makes sanitary work more complicated and difficult, especially, for the seasonal leaf cleaning on roads in the park, campus etc.. With the rapid development of computer vision, it is a feasible and innovative way to detect and clean the fallen leaves effectively with intelligent robot. However, since the irregular shape and size, uneven distribution of fallen leaves and complex outdoor conditions, especially the densely stacked leaves, even current advanced object detection algorithm has no satisfactory effect on detection of fallen leaves. To deal with the dense leaves detection problem and improve navigation efficiency, we propose a Non-Maximum Fusion(NMF) algorithm. NMF scales the high-confidence box with the pre-defined δ to fuse intersecting and adjacent boxes instead of supressing these boxes. The experiments on the fallen leaves data set shows that NMF improves the fallen leaves detection coverage significantly and the coverage reaches to 95%. Also, NMF greatly reduces the number of goal nodes for path planning. Since NMF functions at the back end of detector to process detection boxes without training, it can be intergrated into any fallen leaves detection pipeline easily.

关键词： Training Navigation Shape Roads Pipelines Green products Object detection

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Fully Circuit Implementation of a two-layer Memristive Neural Network for Pattern Recognition

Fully Circuit Implementation of a two-layer Memristive Neura...

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International Conference on Information Science and Technology (ICIST)

作者： Mian Li Xiaoping Wang Zhanfei Chen School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665412667;9781665429412

In this paper, a fully circuit implementation of Memristive Neural Network (MNN) is proposed. The forward calculation of the network is based on winner-take-all (WTA) mechanism. The weight updating is achieved through the difference of pre-spike and post-spike, which is more close to the biological weight adjustment mechanism. The network is implemented in a full circuit without additional control units. The designed circuit consists of four modules. The memristive crossbar array module with one-memristor (1M) unit structure can effectively calculate the vector-matrix multiplication with only one step. The switch S is replaced by the transistor in the designed leaky-integrate-and-fire (LIF) module, which can control the integration and leakage of the membrane voltage and realize the lateral inhibition between output neurons. Connecting the integrated monostable trigger and the difference circuit, the post-spike generating module can output the required post-spike. The signal switch module realizes the switching of signals connected to memristors by using voltage-controlled switches. The combination of two modules validly realizes weight updating. The functions of four modules are verified separately. We performed a simulation experiment of 5×3 pixels image classification based on the designed circuit in PSPICE. The circuit output results and the high classification accuracy prove the circuit can be effectively applied in pattern recognition. The noise experiment shows the robustness of the designed circuit.

关键词： Training Neurons Switches Robustness Pattern recognition Integrated circuit modeling Task analysis

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MMTrans-MT: A Framework for Multimodal Emotion Recognition Using Multitask Learning

MMTrans-MT: A Framework for Multimodal Emotion Recognition U...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Jinrui Shen Jiahao Zheng Xiaoping Wang the School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China 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 Wuhan China

With the development of deep learning, emotion recognition tasks are more inclined to use multimodal data and adequate supervised information to improve accuracy. In this work, MMTrans-MT (Multimodal Transformer-Multitask), the framework for multimodal emotion recognition using multitask learning is proposed. It has three modules: modalities representation module, multimodal fusion module, and multitask output module. Three modalities, i.e, words, audio and video, are comprehensively utilized to carry out emotion recognition by a simple but efficient fusion model based on Transformer. As for multitask learning, the two tasks are defined as categorical emotion classification and dimensional emotion regression. Considering a potential mapping relationship between two kinds of emotion model, multitask learning is adopted to make the two tasks promote each other and improve recognition accuracy. We conduct experiments on CMU-MOSEI and IEMOCAP datasets. Comprehensive experiments show that the accuracy of recognition using multimodal information is higher than that using unimodal information. Adopting multitask learning promotes the performance of emotion recognition.

关键词： Learning systems Deep learning Emotion recognition Correlation Computational modeling Task analysis Computational intelligence

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Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video

Real-time Multi-person Eyeblink Detection in the Wild for Un...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wenzheng Zeng Yang Xiao Sicheng Wei Jinfang Gan Xintao Zhang Zhiguo Cao Zhiwen Fang Joey Tianyi Zhou Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Biomedical Engineering Southern Medical University Guangzhou China Department of Rehabilitation Medicine Zhujiang Hospital Southern Medical University Guangzhou China Centre for Frontier AI Research Agency for Science Technology and Research (A*STAR) Singapore Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore

Real-time eyeblink detection in the wild can widely serve for fatigue detection, face anti-spoofing, emotion analysis, etc. The existing research efforts generally focus on single-person cases towards trimmed video. However, multi-person scenario within untrimmed videos is also important for practical applications, which has not been well concerned yet. To address this, we shed light on this research field for the first time with essential contributions on dataset, theory, and practices. In particular, a large-scale dataset termed MPEblink that involves 686 untrimmed videos with 8748 eyeblink events is proposed under multi-person conditions. The samples are captured from uncon-strainedfilms to reveal “in the wild“ characteristics. Meanwhile, a real-time multi-person eyeblink detection method is also proposed. Being different from the existing counter-parts, our proposition runs in a one-stage spatio-temporal way with end-to-end learning capacity. Specifically, it simultaneously addresses the sub-tasks of face detection, face tracking, and human instance-level eyeblink detection. This paradigm holds 2 main advantages: (1) eyeblink features can be facilitated via the face's global context (e.g., head pose and illumination condition) with joint optimization and interaction, and (2) addressing these sub-tasks in parallel instead of sequential manner can save time remarkably to meet the real-time running requirement. Experiments on MPEblink verify the essential challenges of real-time multi-person eyeblink detection in the wild for untrimmed video. Our method also outperforms existing approaches by large margins and with a high inference speed.

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Distributed Online Optimization for Charging and Discharging of Plug-in Electric Vehicles

Distributed Online Optimization for Charging and Discharging...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Haiming Xiong Ming Chi Zhi-Wei Liu Dandan Hu Liang-Liang Zhu Cheng Jiang Jun-Ping Gui Yao Rao School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan China School of Management South-Central University for Nationalities Wuhan China Nari Group Corporation State Grid Electric Power Research Institute Nanjing China State Grid Electric Power Research Institute Wuhan Efficiency Evaluation Company Limited Wuhan China

The increasing number of uncoordinated charging and discharging for plug-in electric vehicles (PEVs) poses new challenges to the operation of the distribution network. We introduce a dynamic price mechanism so that charging at peak load will lead to an increase in cost. Then, a distributed online charging and discharging scheduling scheme for PEVs based on the dynamic price mechanism is proposed, considering the minimization of charging cost and battery degradation cost. Unlike most of the existing studies that require the future information of PEVs to schedule charging, the proposed algorithm can solve the scheduling problem for PEVs by taking advantage of a distributed alternating direction method of multipliers (ADMM). Simulation results demonstrate the effectiveness and correctness of the proposed scheduling scheme.

关键词： Plug-in electric vehicles Schedules Costs Heuristic algorithms Simulation Dynamic scheduling Minimization

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