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arXiv 2022年

作者： Rehman, Abdul Liu, Zhen-Tao Wu, Min Cao, Wei-Hua Jiang, Cheng-Shan The School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

Speech emotion recognition systems have high prediction latency because of the high computational requirements for deep learning models and low generalizability mainly because of the poor reliability of emotional measurements across multiple corpora. To solve these problems, we present a speech emotion recognition system based on a reductionist approach of decomposing and analyzing syllable-level features. Mel-spectrogram of an audio stream is decomposed into syllable-level components, which are then analyzed to extract statistical features. The proposed method uses formant attention, noise-gate filtering, and rolling normalization contexts to increase feature processing speed and tolerance to adversity. A set of syllable-level formant features is extracted and fed into a single hidden layer neural network that makes predictions for each syllable as opposed to the conventional approach of using a sophisticated deep learner to make sentence-wide predictions. The syllable level predictions help to achieve the real-time latency and lower the aggregated error in utterance level cross-corpus predictions. The experiments on IEMOCAP (IE), MSP-Improv (MI), and RAVDESS (RA) databases show that the method archives real-time latency while predicting with state-of-the-art cross-corpus unweighted accuracy of 47.6% for IE to MI and 56.2% for MI to IE. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Accurate Identification and Localization of Transmission Lines Under Complex Lighting for Distribution Network Power Supply Operation Robots

Accurate Identification and Localization of Transmission Lin...

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Chinese automation Congress (CAC)

作者： Tianhe Liu Xin Chen Chenyu Hu Xu Jian Shengzu Li Gang Wang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Equipment Technology Center State Grid Hubei Electric Power Co. Ltd. Electric Power Science Research Institute Wuhan China Distribution Department State Grid Hubei Electric Power Co. Ltd. Wuhan Power Supply Company China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Accurate identification and position of transmission lines is critical for robots to safely and effectively perform distribution network operations. However, such tasks are often challenged by complex lighting conditions, where depth information can be easily lost. Transmission lines have limited texture features and reflective surfaces, making it difficult to match image feature points. To address these challenges, this paper proposes a visual perception system for weak texture targets under complex lighting, based on an “eye-on-hand” configuration and a depth complementation algorithm. Furthermore, a high-precision identification and localization method for transmission line grasping points is developed, utilizing the fusion of binocular visual color maps and depth maps. The proposed method has been shown to effectively solve the problem of identifying and localizing the grasping points of transmission lines under complex lighting conditions. Experiments demonstrated that the method can improve the localization accuracy to within $\pm 4\text{mm}$ , outperforming other algorithms. The method is characterized as simple and efficient, and it enhances the environmental adaptability of distribution network robots.

关键词： Location awareness Visualization Power transmission lines Accuracy Image color analysis Lighting Distribution networks Grasping Wire Robots

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Recurrent Attentional Reinforcement Learning for Machinery Fault Diagnosis

SSRN

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SSRN 2024年

作者： Tang, Zhenhui Wang, Jingcheng Wu, Shunyu The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University No.800 Dongchuan Road Shanghai200240 China The SJTU Sanya Yazhou Bay Institute of Deepsea Science and Technology Sanya572024 China The Autonomous Systems and Intelligent Control International Joint Research Center Xi’an Technological University Xi’an710021 China

Recognizing fault types of machinery system is a fundamental but challenging task in industrial application. Although remarkable progress has been attained by learning fault features and predicting the corresponded fault type with deep neural networks, the existing fault diagnosis pipelines usually learn non-discriminative information. Contextual sequence dependency between the localized time fragments is often ignored. To deal with these problem, a recurrent attention framework with reinforcement learning is proposed to find a series of informative features of local time fragments related to different faults in an iterative manner. Fault labels are further predicted on these time fragments. The proposed framework also explicitly models long-term dependencies among these attentional time fragments to capture historical fault information. Experiments on two typical machinery systems, bearing system and hydraulic system, show that the proposed framework achieves superior diagnosis performance and increases no computation burden on CPU. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Multi-objective Optimization of Production Scheduling Using Particle Swarm Optimization Algorithm for Hybrid Renewable Power Plants with Battery Energy Storage System

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Journal of Modern Power systems and Clean Energy 2021年第2期9卷 285-294页

作者： Jon Martinez-Rico Ekaitz Zulueta Ismael Ruiz de Argandona Unai Femandez-Gamiz Mikel Armendia The Automation and Control Unit Fundacion TeknikerBasque Research and Technology Alliance(BRTA)Ifiaki Goenaga520600 the University of the Basque Country Ing.Torres Quevedo148013 BilbaoSpain the Department of Systems Engineering and Control College of Engineering at Vitoria-GasteizUniversity of the Basque CountryNieves Cano1201006 Vitoria-GasteizSpain the Department of Nuclear and Fluid Mechanics College of Engineering at Vitoria-GasteizUniversity of the Basque CountryNieves Cano1201006 Vitoria-GasteizSpain

Considering the increasing integration of renewable energies into the power grid,batteries are expected to play a key role in the challenge of compensating the stochastic and intermittent nature of these energy ***,the deployment of batteries can increase the benefits of a renewable power *** way to increase the profits with batteries studied in this paper is performing energy *** strategy is based on storing energy at low electricity price moments and selling it when electricity price is *** this paper,a hybrid renewable energy system consisting of wind and solar power with batteries is studied,and an optimization process is conducted in order to maximize the benefits regarding the dayahead production scheduling of the plant.A multi-objective cost function is proposed,which,on the one hand,maximizes the obtained profit,and,on the other hand,reduces the loss of value of the battery.A particle swarm optimization algorithm is developed and fitted in order to solve this non-linear multi-objective *** the aim of analyzing the importance of considering both the energy efficiency of the battery and its loss of value,two more simplified cost functions are *** show the importance of including the energy efficiency in the cost function to ***,it is proven that the battery lifetime increases substantially by using the multi-objective cost function,whereas the profitability is similar to the one obtained in case the loss of value is not ***,due to the small difference in price among hours in the analyzed Iberian electricity market,it is observed that low profits can be provided to the plant by using batteries just for arbitrage purposes in the day-ahead market.

关键词： Battery energy storage system energy arbitrage hybrid renewable energy system particle swarm optimization

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SC-NeRF: Self-Correcting Neural Radiance Field with Sparse Views

arXiv

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arXiv 2023年

作者： Song, Liang Wang, Guangming Liu, Jiuming Fu, Zhenyang Miao, Yanzi Wang, Hesheng Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China Department of Automation 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 Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

In recent studies, the generalization of neural radiance fields for novel view synthesis task has been widely explored. However, existing methods are limited to objects and indoor scenes. In this work, we extend the generalization task to outdoor scenes, trained only on object-level datasets. This approach presents two challenges. Firstly, the significant distributional shift between training and testing scenes leads to black artifacts in rendering results. Secondly, viewpoint changes in outdoor scenes cause ghosting or missing regions in rendered images. To address these challenges, we propose a geometric correction module and an appearance correction module based on multi-head attention mechanisms. We normalize rendered depth and combine it with light direction as query in the attention mechanism. Our network effectively corrects varying scene structures and geometric features in outdoor scenes, generalizing well from object-level to unseen outdoor scenes. Additionally, we use appearance correction module to correct appearance features, preventing rendering artifacts like blank borders and ghosting due to viewpoint changes. By combining these modules, our approach successfully tackles the challenges of outdoor scene generalization, producing high-quality rendering results. When evaluated on four datasets (Blender, DTU, LLFF, Spaces), our network outperforms previous methods. Notably, compared to MVSNeRF, our network improves average PSNR from 19.369 to 25.989, SSIM from 0.838 to 0.889, and reduces LPIPS from 0.265 to 0.224 on Spaces outdoor scenes. © 2023, CC BY.

关键词： Rendering (computer graphics)

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Anti-disturbance filter design for a class of stochastic systems with fading channels

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Science China(Information Sciences) 2020年第11期63卷 327-329页

作者： Yang LIU Zidong WANG Hongli DONG Hongjian LIU College of Electrical Engineering and Automation Shandong University of Science and Technology Department of Computer Science Brunel University London Institute of Complex Systems and Advanced Control Northeast Petroleum University School of Mathematics and Physics Anhui Polytechnic University

Dear editor,As a significant topic in control and signal processing communities, the filtering problem has been attracting persistent research attention in the past few decades [1]. In practical engineering, the parameter perturbations are unavoidable due mainly to exogenous disturbances, environmental changes and some other phenomena.

关键词： Stochastic systems fault detection model uncertainty anti-disturbance fading channel

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CushionCatch: A Compliant Catching Mechanism for Mobile Manipulators via Combined Optimization and Learning

arXiv

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arXiv 2024年

作者： Chen, Bingjie Fan, Keyu Yang, Qi Cheng, Yi Liu, Houde Dong, Kangkang Xia, Chongkun Han, Liang Liang, Bin Center for Artificial Intelligence and Robotics Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Jianghuai Advanced Technology Center Hefei230000 China University of Science and Technology of China Hefei23000 China School of Advanced Manufacturing Sun Yat-Sen University Shenzhen518055 China School of Electrical and Automation Engineering Hefei University of Technology Hefei230009 China Navigation and Control Research Center Department of Automation Tsinghua University Beijing100084 China

Catching flying objects with a cushioning process is a skill commonly performed by humans, yet it remains a significant challenge for robots. In this paper, we present a framework that combines optimization and learning to achieve compliant catching on mobile manipulators (CCMM). First, we propose a high-level capture planner for mobile manipulators (MM) that calculates the optimal capture point and joint configuration. Next, the pre-catching (PRC) planner ensures the robot reaches the target joint configuration as quickly as possible. To learn compliant catching strategies, we propose a network that leverages the strengths of LSTM for capturing temporal dependencies and positional encoding for spatial context (P-LSTM). This network is designed to effectively learn compliant strategies from human demonstrations. Following this, the post-catching (POC) planner tracks the compliant sequence output by the P-LSTM while avoiding potential collisions due to structural differences between humans and robots. We validate the CCMM framework through both simulated and real-world ball-catching scenarios, achieving a success rate of 98.70% in simulation, 92.59% in real-world tests, and a 28.7% reduction in impact torques. The open source code will be released for the reference of the community1 Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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Oscillation Trajectory Optimization and control Method for Two-Link Underactuated Manipulators

Oscillation Trajectory Optimization and Control Method for T...

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Chinese control Conference (CCC)

作者： Lejun Wang Huiming Wang Xianlun Tang Zixin Huang Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University Wuhan China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China

This paper provides an oscillation trajectory optimization and control method for two-link underactuated manipulators (UMs) in a vertical plane. The proposed method solves the problem that the UMs cannot always enter the balance region in the partitioning method. First, we establish the system dynamic model, and analyze the system couple characteristics. Then, we program an oscillation trajectory for the active link, and use the intelligent method to obtain the trajectory parameters, so ensuring the system can reach the area adjacent to the target position through tracking control. Next, we design the controller to realize the stable control at the target position. Finally, the simulation results show the effectiveness and generality of the control strategy.

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Hand Detection and Gesture Classification based on Transformer

Hand Detection and Gesture Classification based on Transform...

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Chinese automation Congress (CAC)

作者： Genghui Pei Chi Xu Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geosciences Engineering Research Center of Intelligent Technology for Geo-Exploration Ministy of Education Wuhan China Engineering Research Center of Intelligent Technology for Geo-Geo-Exploration Ministy of Education Wuhan China

Hand detection and gesture recognition are an important branch of artificial intelligence, which are widely used in daily life, such as interaction between human and machines, communication using signals, virtual reality and so on. In order to realize gesture recognition in unconstrained situations, we propose a novel end-to-end model based on Transformer, which can detect multiple hands in a picture. Our network can be roughly divided into the following three stages. In the first stage, we will extract feature maps of different layers by convolutional network from monocular RGB images. In the second stage, features are sent into the codec module based on Transformer, and hand detection is further carried out through multi-layer perceptrons with the decoding vectors as the input. In the third stage, we combine the attention weight maps obtained in the second step with the features of different layers in the first step to complete the gesture recognition task. From the experiment results, our network accurately identifies the positions of multiple hands in the picture and correctly recognizes hand gestures.

关键词： Solid modeling Codecs automation Gesture recognition Virtual reality Transformers Feature extraction

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An Adaptive Distributed Observer for A Class of Discrete-Time Uncertain Linear systems over Acyclic Digraphs

An Adaptive Distributed Observer for A Class of Discrete-Tim...

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IEEE Conference on Decision and control

作者： Tao Liu Jie Huang Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen China Shenzhen Research Institute and Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong

This paper proposes an adaptive distributed observer for a class of discrete-time uncertain linear leader systems. The leader system is assumed to be neutrally stable with unknown parameters in the system matrix. Such a leader system can produce multi-tone sinusoidal signals with unknown frequencies, magnitudes, and phases. Under the assumption that the digraph of the communication network is a spanning tree with the leader system as the root, the proposed adaptive distributed observer is shown to be capable of estimating over the communication network not only the leader's state, but also the unknown parameters of the leader's system matrix.

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