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2021 3rd International Conference on Robotics, Intelligent control and Artificial Intelligence, ICRICA 2021

作者： Cheng, Xianpeng Lu, Fei Han, Xiangzu Yuan, Ying Tian, Guohui Wu, Hao School of Control Science and Engineering Shandong University Shandong China

For action recognition, 3D CNNs can achieve satisfactory performance, but they are parameter-rich and computationally intensive. Although conventional 2D CNNs have a small computational burden, they do not capture the channel relationships well. To mitigate this issue, we propose a plug-and-play channel attention module that is simple yet effective. Our CA module can perform adaptive refinement of the feature for the given input feature map by capturing channel-wise dependencies. Since the CA module is generic and lightweight, it can be easily integrated into regular convolutional neural networks. We embedded the CA module into TSM and evaluated it by conducting extensive experiments on the commonly used action recognition dataset: Kinetics-400 and gesture recognition dataset: Jester. For Kinetics-400, our CA module was able to improve the top-1 accuracy by 1.24%, 1.46%, 1.43% and 1.53% when using one crop, three crops, five crops and ten crops, respectively. Our experimental results showed that the CA module brings consistent performance improvements, proving the effectiveness of the CA module. © Published under licence by IOP Publishing Ltd.

关键词： Crops

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Effect of nitrite on hydrolysis-acidification, biogas production and microbial community in semi-continuous two-phase anaerobic digestion of sewage sludge

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Journal of Environmental sciences 2023年第4期126卷 434-444页

作者： Qian Sheng Yiqing Lu Shijie Yuan Xiaowei Li Xiaohu Dai Yali Guo Bin Dong State Key Laboratory of Pollution Control and Resource Reuse College of Environmental Science and EngineeringTongji UniversityShanghai 200092China School of Environmental and Chemical Engineering Organic Compound Pollution Control EngineeringMinistry of EducationShanghai UniversityShanghai 200444China Shanghai Investigation Design&Research Institute Co.Ltd. Shanghai 200335China YANGTZE Eco-Environment Engineering Research Center China Three Gorges CorporationBeijing 100038China

Previous study found that the pre-treatment of sewage sludge with nitrite improves the biogas production during the mono/two-phase anaerobic digestion (AD) using batch biochemical methane potential *** this study,the effects of nitrite on hydrolysisacidification,biogas production,volatile solids destruction and microbial composition in semi-continuous two-phase AD of sewage sludge were *** addition of nitrite promotes sludge organic matter solubilization (+484%) and VFAs production (+98.9%),and causes an increase in the VS degradation rate during the AD process (+8.7%).The comparison of biogas production from the acidogenic and methanogenic reactors with or without the addition of nitrite implies that the nitrite has no significant effect on the overall biogas production of two-phase sludge AD ***-throughput sequencing analysis shows that the microbial communities of bacteria and archaea in two-phase AD reactors significantly changes after the addition of *** (bacteria) and Candidatus Methanofastidiosum (archaea) become the dominant genera in the acidogenic and methanogenic reactors with the nitrite *** findings provide new insights about using nitrite to promote the organic matter degradation of sewage sludge in a semicontinuous two-phase AD system.

关键词： Sewage sludge Bioenergy Methane production Nitrite Semi-continuous two-phase anaerobic digestion

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Development of Mobile Manipulator System Providing Grounded Haptic Information During Gait Training for the Stroke

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Journal of Institute of control, Robotics and Systems 2025年第4期31卷 265-268页

作者： Lee, Junyeong Lee, Hosu Kim, Minkyung Han, Jaeyoung Yoon, Jungwon Department of AI Convergence Gwangju Institute of Science and Technology Korea Republic of Department of Control and Robot Engineering & School of Aerospace Engineering Gyeongsang National University Korea Republic of Department of Physical Medicine and Rehabilitation Chonnam National University Hospital Korea Republic of

Effective gait training of the stroke can be performed by leveraging grounded haptic information obtain by touching an external immobile object. Robotic systems with various preset curvatures allow users to undergo gait training without spatial restrictions, as well as straight-path gait training. We developed a mobile manipulator platform in which the handle is positioned along the preset gait-training path while the robot guides the user by providing grounded haptic information along the designated path as a rail. To verify the system’s functionality, experiments were conducted for various curvatures. Based on the results, the robot can guide the user along the defined track while providing grounded haptic information. Further investigation is required to verify the system’s effectiveness across a broad patient population. © ICROS 2025.

关键词： Electric grounding

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The super-twisting sliding mode control for half-bridge three-level LLC resonant converter 6

The super-twisting sliding mode control for half-bridge thre...

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6th CAA International Conference on Vehicular control and Intelligence, CVCI 2022

作者： Feng, Dexiang Zhang, Chenzhong Song, Jinqiu Duan, Bin Ding, Wenlong Zhang, Chenghui School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9781665453745

ISBN: (纸本)9781665453745

The zero-voltage switching capability of the half-bridge three-level LLC resonant converter(HBTHLLC) is possible, and the switch's maximum withstand voltage is just half the input voltage. High efficiency, minimal electromagnetic interferencehigh power density, and high power density are benefits of the converter. However, the HBTHLLC is a complex high-order system with dynamic characteristics drift problem, and its nonlinearity also brings great challenges to the design of the closed-loop controller. In this paper, a new super-twisting sliding mode(STSMC) control strategy is proposed for the HBTHLLC. This control strategy does not need to consider the internal parameters of the system, but only needs to solve the first derivative of the input, which is easy to calculate. The control strategy is simulated and verified in Simulink, and this method has good dynamic response ability and robustness. © 2022 IEEE.

关键词： Sliding mode control

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Curve-Suppression-Based Event-Triggered Mechanisms for Quasi-Synchronization of Fuzzy Delayed Neural Networks on Time Scales

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3174-3187页

作者： Wan, Peng Zhou, Yufeng Zeng, Zhigang Lai, Jingang Wuhan University of Science and Technology School of Artificial Intelligence and Automation Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430081 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent control of Education Ministry of China Wuhan430074 China

The vast majority of published event-triggered mechanisms (ETMs) are constructed based on measurement errors, which introduces a problem naturally that they are updated when the measurement errors exceed the thresholds although the current obtained sampling states can make systems converge well. With this problem in mind, we redesign ETMs for quasi-synchronization of T-S fuzzy neural networks (FNNs) with time delays on time scales. First, a novel ETM is designed for continuous-time FNNs with time-varying delays to achieve quasi-synchronization, with which synchronization errors is suppressed to globally exponentially converge to a ball. Second, we introduce the ETM for continuous-time FNNs to discrete-time FNNs, owing to the existence of discrete-time states, the Lypunov function of synchronization errors run over the exponentially decay curve, but it can be suppressed to evolve under another exponentially decay curve. Third, for FNNs on time scales with constant and time-varying delays, we estimate the forward jump operator of the Lyapunov functions and design ETMs to guarantee that the Lypunov functions evolve under the exponentially decay curves, so quasi-synchronization can be achieved. Last but not least, we prove that Zeno behavior will not happen and four numerical examples are introduced to verify the validity and the superiority of the proposed ETMs in reducing information transmission. © 2013 IEEE.

关键词： Continuous time systems

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Auto-Associative LSTM for Multivariate Time Series Imputation 41

Auto-Associative LSTM for Multivariate Time Series Imputatio...

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第41届中国控制会议

作者： Jianming Yang Xiaochen Lai Liyong Zhang School of Software Dalian University of Technology School of Control Science and Engineering Dalian University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The existence of missing values increases the difficulty of data *** this paper,we design an auto-associative long short-term memory network(AALSTM) for handling time series,and proposed an AALSTM based imputation method(AALSTM-I) to estimate missing values of multivariate time *** output of AALSTM is the estimation of its *** not only history information but also current information will be utilized to produce *** network of AALSTM is divided into several groups in case of identity mapping,and these networks share one group of weights to reduce the number of *** handling missing values,an imputation unit is added to AALSTM to form *** imputation unit,history information and current observed values are both used to estimate missing ***-I can directly be trained with incomplete data without setting the initial values of missing *** experiment results on several datasets verify the effectiveness of AALSTM and AALSTM-I.

关键词： Incomplete multivariate time series Missing value Imputation LSTM Auto-associative

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State-of-charge estimation for lithium-ion battery pack based on Unscented Kalman Filter considering cell inconsistency 6

State-of-charge estimation for lithium-ion battery pack base...

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6th CAA International Conference on Vehicular control and Intelligence, CVCI 2022

作者： Xiao, Kun Cui, Zhongrui Cui, Naxin School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9781665453745

ISBN: (纸本)9781665453745

The accuracy of battery state of charge (SOC) estimation has tremendous value in the safe use of battery. However, inconsistencies between batteries will affect the power and usable capacity. When vehicle battery pack contains hundreds of single cell, more efforts should be devoted to develop a high-precision and low-complexity method of SOC estimation. In the article, a mean-difference model (MDM) has been established to precisely characterize the battery characteristics, in which the thevenin model is chosen to be mean model (MM) to characterize the whole battery pack features, and the rint model is used to evaluate the inconsistency between batteries. The mean difference model parameters are identified by means of forgetting factor least square (FFRLS), and then the SOC were estimated by the unscented kalman filter (UKF). The validity of MDM and the UKF is verified on a small series battery pack at last. © 2022 IEEE.

关键词： Battery Pack

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Visual Positioning System of Bag Removal Robot Based on Deep Learning 9

Visual Positioning System of Bag Removal Robot Based on Deep...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Liu, Bo Tang, Ruiyin College of electrical engineering North China University of Science and Technology Tangshan China School of electronic and control engineering North China Institute of Aerospace Engineering Langfang065000 China

ISBN: (纸本)9798350376548

In industrial production, the dumping of raw material packaging is mostly done manually, which not only affects production efficiency but also endangers the health of workers. This article proposes an improved algorithm based on YOLOV5, which uses Mobiletv3 instead of the YOLOV5 backbone network, adjusts parameters using an improved SE attention mechanism, and optimizes the model using the LeakyReLU activation function. The entire system uses an improved algorithm to detect the target bag rope, uses a depth camera to obtain the target coordinates, and finally calculates the target coordinates in the robot coordinate system. Compared with Fast RCNN and YOLOV5 algorithms, the improved YOLOV5 MobileNetV3 algorithm has a knot detection accuracy of 97.5%, a recall rate of 92.43%, and an F1 score of 94.8. The model has a small volume, fast detection speed, and a single image detection time of only 187ms, with a mapping rate of 97.5%. The error between the coordinates obtained after hand eye calibration and the actual coordinates of the robotic arm in the XYZ axis direction does not exceed 3mm, and the actual coordinates are much smaller than the target size. © 2024 IEEE.

关键词： Robotic arms

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Tunnel crack detection method based on Crack-Aware Vision Transformer

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Journal of Traffic and Transportation engineering(English Edition) 2025年

作者： Xuanran Liu Liqiang Zhu Yaodong Wang Zujun Yu School of Mechanical， Electronic and Control Engineering， Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System， Beijing Jiaotong University

The occurrence of cracks as a common surface defect in tunnel linings is influenced by a combination of internal and external factors. In recent years， computer vision methods have been widely applied in the field of crack detection. Deep learning has the capability to handle large-scale crack image data and automatically extract crack features. However， owing to numerous difficulties， including low-quality images， intricate cracks， and complex background environments， it is challenging to solve these tricky issues in tunnel crack detection. Therefore， this paper proposes a tunnel lining crack detection model based on Crack-aware Vision Transformer (CAViT)， which includes a perceptual vision transformer backbone network and an improved decoding network. The vision Transformer model has a stronger contextual modeling ability and is suitable for the task of extracting features from tunnel cracks with long， fine， and irregular characteristics. Due to the limitations of the attention mechanism based on a fixed window in modeling long-range relationships， we propose Crack-aware self-attention with the ability to increase the attention to relevant features and extract long-distance feature dependencies. This attention mechanism focuses on the crack area by learning the transformation parameters of the sampled window and reference points from the prediction module， and the position bias generated by the transformation compensate in the position encoding. Subsequently， we employ recurrent convolution layers to enhance the fusion network's ability to integrate contextual information. Our proposed CAViT is evaluated on the Crack500 dataset and a self-made tunnel crack dataset. Experimental results show that our network achieves the highest scores in terms of crack intersection-over-union (IoU) and accuracy (Acc) compared to other transformer models in both datasets. These results demonstrate the suitability of our CAViT for pavement crack and tunnel crack detection tasks， sho

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GRU combined model based on multi-objective optimization for short-term residential load forecasting

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10423-10440页

作者： Yi, Lingzhi Peng, Xinlong Fan, Chaodong Wang, Yahui Li, Yunfan Liu, Jiangyong College of Automation and Electronic Engineering Xiangtan University Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Hunan Xiangtan China School of Computer Science and Technology Hainan University Hainan China College of Electrical and Information Engineering Hunan University Changsha China

Reliable and accurate short-term forecasting of residential load plays an important role in DSM. However, the high uncertainty inherent in single-user loads makes them difficult to forecast accurately. Various traditional methods have been used to address the problem of residential load forecasting. A single load forecast model in the traditional method does not allow for comprehensive learning of data characteristics for residential loads, and utilizing RNNs faces the problem of long-term memory with vanishing or exploding gradients in backpropagation. Therefore, a gated GRU combined model based on multi-objective optimization is proposed to improve the short-term residential load forecasting accuracy in this paper. In order to demonstrate the effectiveness, GRUCC-MOP is first experimentally tested with the unimproved model to verify the model performance and forecasting effectiveness. Secondly the method is evaluated experimentally with other excellent forecasting methods: models such as DBN, LSTM, GRU, EMD-DBN and EMD-MODBN. By comparing simulation experiments, the proposed GRU combined model can get better results in terms of MAPE on January, April, July, and November load data, so this proposed method has better performance than other research methods in short-term residential load forecasting. © 2024 - IOS Press. All rights reserved.

关键词： Multiobjective optimization

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