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3rd China intelligent Robotics Annual Conference, CCF CIRAC 2022

作者： Ma, Yinxing Yang, Peihao Lv, Gang Deng, Shibin Jiao, Shangbin Li, Yujun Wu, Xiaohui Zhang, Jing Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Xi’an Thermal Power Research Institute Co. Ltd. Xi’an710054 China Huaneng Weihai Power Generation Co. Ltd. Wei’hai264200 China

ISBN: (纸本)9789819903009

The excitation unit is the weak link in the generator operation since it produces internal deterioration under long-term operation. To evaluate the health condition of the key excitation unit and generate warning information in advance, this work proposes a health assessment method based on the Gaussian mixture model (GMM) and LSTM neural network. Firstly, the weights of each type of data will be determined by obtaining the electrical parameter data of the generator excitation unit from the SIS system, using the infrared image data of carbon brush and excitation transformer based on infrared image acquisition system, and the correlation between each electrical parameter data and temperature on account of the MIC correlation analysis method. On the top of it, LSTM neural network prediction model is established. And establish a GMM-based health benchmark model based on the data collected in the field during healthy operation, and design the health decline index HDI using the Marxist distance for evaluating the health status and deterioration degree of the unit. Finally, the method of this paper is validated by the 2022 operation data of a thermal power plant in Weihai, which proved that this method can provide a basis for regular maintenance and early identification of faults in the power plan and quantifying the degradation degree of the excitation unit under long-term operation and making early warning before the abnormal occurrence of the unit. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deterioration

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Sparse Andrew's Sine Norm Promoting Adaptive Algorithm under Non-Gaussian Noises 5

Sparse Andrew's Sine Norm Promoting Adaptive Algorithm under...

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5th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2022

作者： Hadi, Abdul Huang, Xinqi Ali, Burhan Li, Yingsong College of Information and Communications Engineering Harbin Engineering University Harbin150001 China College of Intelligent System and Control Science Engineering Harbin Engineering University Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Hefei China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

Two sparse adaptive filtering (AF) algorithms based on Andrew's sine estimator (ASE) are presented to achieve improved performance for identifying sparse systems, where the ASE is derived within the least-square framework. Furthermore, zero-attracting (ZA) scheme is used in ASE to construct ZA-ASE and its re-weighting form (RZA-ASE) to combat non-Gaussian noises and use the sparse characteristics of the system. Their performance is investigated via simulations and compared with the least-mean square (LMS) and the maximum correntropy criterion (MCC) algorithms to show their superior performance. © 2022 IEEE.

关键词： Adaptive filtering

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Consensus of Linear Multi-agent Systems with Pure Relative Output Through Fully Distributed Reduced-Order Protocols

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IFAC-PapersOnLine 2023年第2期56卷 10162-10167页

作者： Yuwen Ma Xianwei Li Shaoyuan Li Department of Automation Shanghai JiaoTong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China

This paper considers fully distributed adaptive control for linear multi-agent systems with pure relative output information only. Two reduced-order protocols, namely, an edge-based protocol and a node-based protocol, are derived. For the edge-based protocol, each edge is adapted by a coupling weight which depends only on relative output information of the associated two agents, while the coupling weight in the node-based protocol is based on the relative output information of all neighboring agents. Sufficient conditions for the solvability of the consensus problem under the two protocols are derived. Compared with most of the existing related protocols, the main merits of the protocols are that only relative output information is needed, which helps reduce the communication burdens and protect the multi-agent systems from network attacks, and that the protocols are fully distributed. A simulation example is finally presented to illustrate the effectiveness of the proposed protocols.

关键词： Multi-agent systems consensus pure relative output fully distributed design

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Disentangling Spatial and Temporal Learning for Efficient image-to-Video Transfer Learning

arXiv

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arXiv 2023年

作者： Qing, Zhiwu Zhang, Shiwei Huang, Ziyuan Zhang, Yingya Gao, Changxin Zhao, Deli Sang, Nong Key Laboratory of Image Processing Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China ARC National University of Singapore Singapore

Recently, large-scale pre-trained language-image models like CLIP have shown extraordinary capabilities for understanding spatial contents, but naively transferring such models to video recognition still suffers from unsatisfactory temporal modeling capabilities. Existing methods insert tunable structures into or in parallel with the pre-trained model, which either requires back-propagation through the whole pre-trained model and is thus resource-demanding, or is limited by the temporal reasoning capability of the pre-trained structure. In this work, we present DiST, which disentangles the learning of spatial and temporal aspects of videos. Specifically, DiST uses a dual-encoder structure, where a pre-trained foundation model acts as the spatial encoder, and a lightweight network is introduced as the temporal encoder. An integration branch is inserted between the encoders to fuse spatio-temporal information. The disentangled spatial and temporal learning in DiST is highly efficient because it avoids the back-propagation of massive pre-trained parameters. Meanwhile, we empirically show that disentangled learning with an extra network for integration benefits both spatial and temporal understanding. Extensive experiments on five benchmarks show that DiST delivers better performance than existing state-of-the-art methods by convincing gaps. When pretraining on the large-scale Kinetics-710, we achieve 89.7% on Kinetics-400 with a frozen ViT-L model, which verifies the scalability of DiST. Codes and models can be found in https://***/alibaba-mmai-research/DiST. Copyright © 2023, The Authors. All rights reserved.

关键词： Backpropagation

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Autonomous Path Planning via Sand Cat Swarm Optimization With Multi-Strategy Mechanism for Unmanned Aerial Vehicles in Dynamic Environment

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IEEE Internet of Things Journal 2025年

作者： Deng, Wu Feng, Jiayi Zhao, Huimin Civil Aviation University of China College of Electronic Information and Automation Tianjin300300 China Civil Aviation University of China Tianjin Key Lab Intelligent Signal and Image Processing Tianjin300300 China State Key Laboratory of Rail Transit Vehicle System of Southwest Jiaotong University Chengdu610031 China

Unmanned Aerial Vehicles (UAVs) face critical challenges in path planning in dynamic environment. Sand Cat Swarm Optimization (SCSO) algorithm has demonstrated promise in addressing complex optimization challenges. However, SCSO is limited by slow convergence, susceptibility to local optima, and insufficient adaptability. To overcome these shortcomings, an enhanced SCSO with the spiral search, Livy flight, tent chaotic mapping, and adaptive sparrow alert mechanism, namely TSLS-SCSO is developed to propose an autonomous path planning method for UAVs in dynamic environment. In TSLS-SCSO, a new population initialization strategy with tent chaotic mapping is designed to achieve a large dynamic range and coverage capability. Then a new spiral search strategy is designed to broaden the search range in the search phase. And a new Livy flight strategy is employed to enrich the diversity of population in the attacking prey phase. A new sparrow alert mechanism with integrating the sand cat group with the sparrow alert is designed to obtain faster convergence speed and accuracy. The experiment results on CEC 2017 and CEC 2022 show that the TSLS-SCSO obtains higher accuracy and more stable solutions and exhibits better competitiveness. Furthermore, the proposed UAV path planning method successfully found effective paths with an obstacle avoidance effectiveness of 95.5%. The obtained results validate the effectiveness and competitiveness of TSLS-SCSO in UAV path planning in the dynamic environment. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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VideoLCM: Video Latent Consistency Model

arXiv

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arXiv 2023年

作者： Wang, Xiang Zhang, Shiwei Zhang, Han Liu, Yu Zhang, Yingya Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China Shanghai Jiao Tong University China

Consistency models have demonstrated powerful capability in efficient image generation and allowed synthesis within a few sampling steps, alleviating the high computational cost in diffusion models. However, the consi...

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3D Cinemagraphy from a Single image

3D Cinemagraphy from a Single Image

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xingyi Li Zhiguo Cao Huiqiang Sun Jianming Zhang Ke Xian Guosheng Lin Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology S-Lab Nanyang Technological University Adobe Research

We present 3D Cinemagraphy, a new technique that mar-ries 2D image animation with 3D photography. Given a single still image as input, our goal is to generate a video that contains both visual content animation and camera motion. We empirically find that naively combining existing 2D image animation and 3D photography methods leads to obvious artifacts or inconsistent animation. Our key insight is that representing and animating the scene in 3D space offers a natural solution to this task. To this end, we first convert the input image into feature-based layered depth images using predicted depth values, followed by unprojecting them to a feature point cloud. To animate the scene, we perform motion estimation and lift the 2D motion into the 3D scene flow. Finally, to resolve the problem of hole emer-gence as points move forward, we propose to bidirectionally displace the point cloud as per the scene flow and synthe-size novel views by separately projecting them into target image planes and blending the results. Extensive experiments demonstrate the effectiveness of our method. A user study is also conducted to validate the compelling rendering results of our method.

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

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

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Cyber-Energy Systems and intelligent Energy (ICCSIE), International Conference on

作者： Junnan Li Xiaoping Wang the School of Artificial Intelligence and Automation and the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan P. R. China

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.

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Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs

arXiv

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arXiv 2024年

作者： Chen, Xiaoqing Wang, Ziwei Wu, Dongrui The Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China

Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI studies focused on improving the decoding accuracy, with only a few considering the adversarial security. Although many adversarial defense approaches have been proposed in other application domains such as computer vision, previous research showed that their direct extensions to BCIs degrade the classification accuracy on benign samples. This phenomenon greatly affects the applicability of adversarial defense approaches to EEG-based BCIs. To mitigate this problem, we propose alignment-based adversarial training (ABAT), which performs EEG data alignment before adversarial training. Data alignment aligns EEG trials from different domains to reduce their distribution discrepancies, and adversarial training further robustifies the classification boundary. The integration of data alignment and adversarial training can make the trained EEG classifiers simultaneously more accurate and more robust. Experiments on five EEG datasets from two different BCI paradigms (motor imagery classification, and event related potential recognition), three convolutional neural network classifiers (EEGNet, ShallowCNN and DeepCNN) and three different experimental settings (offline within-subject cross-block/-session classification, online cross-session classification, and pre-trained classifiers) demonstrated its effectiveness. It is very intriguing that adversarial attacks, which are usually used to damage BCI systems, can be used in ABAT to simultaneously improve the model accuracy and robustness. © 2024, CC BY.

关键词： Adversarial machine learning

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A Novel Brain-Inspired Hierarchical Perception-Association Circuit Based on Memristor Arrays

SSRN

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SSRN 2023年

作者： Chen, Liangyu Wang, Xiaoping Chen, Zhanfei Yang, Chao Zeng, Zhigang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Education Ministry of China Wuhan430074 China Hubei Key Laboratory of Brain-inspired Intelligent Systems Wuhan430074 China

In the brain, primary sensory cells can efficiently perceive multimodal stimuli, and then associative memory cells perform an advanced bidirectional associative memory function with perceived information. Here, a brain-inspired hierarchical perception-association circuit based on memristor arrays is proposed. Firstly, a memristive reservoir computing circuit is designed to simulate a low-level perceptual function, which mainly comprises dynamic analog reservoirs, memristor arrays, and analog integrators, enabling efficient spatio-temporal information processing. Secondly, a spiking bidirectional associative memory memristive circuit, as the core of the whole circuit, is proposed to mimick a high-level associative function, which mainly consists of memristor arrays, current amplifiers, and leaky integrate-and-fire neuron circuits, implementing multimodal associative learning. The simulation results in LTspice show that the modified neuron circuit exhibits 66× improvement in energy efficiency, and the proposed circuit can precisely perform the letter association and vision-audio association tasks in a spiking fashion with an average firing energy consumption of 230 pJ/spike, which has a great potential to be embedded in mobile robotic platforms. © 2023, The Authors. All rights reserved.

关键词： Memristors

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