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arXiv 2022年

作者： Gao, Jiaqi Huang, Zhizhong Lei, Yiming Shan, Hongming Wang, James Z. Wang, Fei-Yue Zhang, Junping The Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China The Institute of Science and Technology for Brain-inspired Intelligence MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China The Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai201210 China The College of Information Sciences and Technology The Pennsylvania State University University ParkPA16802 United States The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Institute of Systems Engineering Macau University of Science and Technology China Qingdao Academy of Intelligent Industries Qingdao266109 China

Most conventional crowd counting methods utilize a fully-supervised learning framework to establish a mapping between scene images and crowd density maps. They usually rely on a large quantity of costly and time-intensive pixel-level annotations for training supervision. One way to mitigate the intensive labeling effort and improve counting accuracy is to leverage large amounts of unlabeled images. This is attributed to the inherent self-structural information and rank consistency within a single image, offering additional qualitative relation supervision during training. Contrary to earlier methods that utilized the rank relations at the original image level, we explore such rank-consistency relation within the latent feature spaces. This approach enables the incorporation of numerous pyramid partial orders, strengthening the model representation capability. A notable advantage is that it can also increase the utilization ratio of unlabeled samples. Specifically, we propose a Deep Rank-consistEnt pyrAmid Model (DREAM), which makes full use of rank consistency across coarse-to-fine pyramid features in latent spaces for enhanced crowd counting with massive unlabeled images. In addition, we have collected a new unlabeled crowd counting dataset, FUDAN-UCC, comprising 4,000 images for training purposes. Extensive experiments on four benchmark datasets, namely UCF-QNRF, ShanghaiTech PartA and PartB, and UCF-CC-50, show the effectiveness of our method compared with previous semi-supervised methods. The codes are available at https://***/bridgeqiqi/DREAM. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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Interdependence between individual and social learning shapes cooperation with asymmetric exploration

arXiv

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arXiv 2025年

作者： Hou, Zhihao She, Zhikun Liang, Quanyi Su, Qi Li, Daqing School of Mathematical Sciences Beihang University Beijing100191 China 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 School of Reliability and Systems Engineering Beihang University Beijing100083 China

Cooperation on social networks is crucial for understanding human survival and development. Although network structure has been found to significantly influence cooperation, some human experiments indicate that it cannot fully explain the evolutionary patterns of cooperation. While evidence suggests that this gap arises from human exploration, our understanding of its impact mechanisms and characteristics, such as asymmetry, remains limited. Here, we seek to formalize human exploration as an individual learning process involving trial and reflection, and integrate social learning to examine how their interdependence shapes cooperation. We find that individual learning can alter the imitative tendency of social learning, while its cooperative tendency in turn relies on social learning and players' decision preferences. By analyzing a series of key experiments, we show that the above coupled dynamics can explain human behavior in game interactions. Furthermore, we find that individual learning can promote cooperation when its probability is negatively correlated with payoffs, a mechanism rooted in the psychological tendency to avoid trial-and-error when individuals are satisfied with their current payoffs. These results link long-unexplained asymmetric exploration with the cooperation-promoting ability of social networks, helping to bridge the gap between theoretical research and reality. © 2025, CC BY.

关键词： Human engineering

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Output regulation for load frequency control

arXiv

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arXiv 2020年

作者： Silani, Amirreza Cucuzzella, Michele Scherpen, Jacquelien M.A. Yazdanpanah, Mohammad Javad Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

Motivated by the inadequacy of the existing control strategies for power systems affected by time-varying uncontrolled power injections such as loads and the increasingly widespread renewable energy sources, this paper proposes two control schemes based on the well-known output regulation control methodology. The first one is designed based on the classical output regulation theory and addresses the so-called Load Frequency control (LFC) problem in presence of time-varying uncontrolled power injections. Then, in order to also minimize the generation costs, we use an approximate output regulation method that solves numerically only the partial differential equation of the regulator equation and propose a controller based on this solution, minimizing an appropriate penalty function. An extensive case study shows excellent performance of the proposed control schemes in different and critical scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Scheduling

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Passivity properties for regulation of DC networks with stochastic load demand

arXiv

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arXiv 2020年

In this paper we present new (stochastic) passivity properties for Direct Current (DC) power networks, where the unknown and unpredictable load demand is modelled by a stochastic process. More precisely, the considered power network consists of distributed generation units supplying ZIP loads, i.e., nonlinear loads comprised of impedance (Z), current (I) and power (P) components. Differently from the majority of the results in the literature, where each of these components is assumed to be constant, we consider time-varying loads whose dynamics are described by a class of stochastic differential equations. Finally, we prove that an existing distributed control scheme achieving current sharing and (average) voltage regulation ensures the asymptotic stochastic stability of the controlled network. Copyright © 2020, The Authors. All rights reserved.

关键词： Electric power transmission networks

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An oracle bone recognition method based on eigenvalue-based preprocessing and deep learning

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Signal, Image and Video processing 2025年第8期19卷

作者： Zhou, Chun-Yang Liu, Zhen-Tao Lumo Road Hubei Wuhan430074 China Lumo Road Hubei Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Lumo Road Hubei Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Lumo Road Hubei Wuhan430074 China

Recognition of oracle bone characters is essential for accurately interpreting Chinese cultural history. However, existing recognition techniques are significantly challenged by severe noise present in the original images, leading to low recognition accuracy. To address this issue, this paper proposes an oracle bone recognition method based on eigenvalue-based preprocessing and deep learning. The preprocessing method computes four tailored eigenvalues-equivalent connectivity domain diameter, minimum connectivity domain distance, point feature analysis matrix, and point eigenvalue-based on the unique structural characteristics of oracle bone characters, enabling more effective noise reduction. Subsequently, an improved model, DC-YOLOv8, is introduced, which replaces the standard convolution module with a deformable convolution module in the high-level layers of the backbone to enhance adaptability to highly deformed characters. Additionally, the Convolutional Block Attention Module is incorporated into the head component to improve the model’s ability to extract key features in critical regions. Experimental results demonstrate that YOLOv8 achieves accuracy rates of 90.14% and 90.97% on two datasets after applying the proposed preprocessing method. Furthermore, the accuracy of DC-YOLOv8 reaches 95.11%, 91.74%, and 93.75% across three datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature Selection

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Experimental Study on Neural Network-ARX and ARMAX Actuation Identification of a 3-DoF Delta Parallel Robot for Accurate Motion controller Design

Experimental Study on Neural Network-ARX and ARMAX Actuation...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Saeed Rahimi Hasan Jalali Mohammad Reza Hairi Yazdi Ahmad Kalhor Mehdi Tale Masouleh Human and Robot Interaction Laboratory School of Mechanical Engineering University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mechanical Engineering College of Engineering University of Tehran Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781665420952

This paper investigates the problem of actuator identification of a 3-DoF Delta parallel robot, by means of linear AutoRegressive Moving Average with eXogenous input (ARMAX) and nonlinear dynamic Neural Network AutoRegressive with eXogenous input (NN-ARX) methods. To this end, the ARMAX and NN-ARX approaches are used to develop a scheme which is capable of identifying a model for each actuator. Based on the ARMAX structure, an accurate model of the actuation system is derived. The model is then trained and tested using the data collected from a real robotic setup. Using a dynamic neural network capabilities, an identification and prediction scheme is designed for modeling the nonlinear dynamic behavior of the system. The NN-ARX is trained based on the collected data from the system, and the new trajectory data is used to validate both methods. By considering the results of experimental implementations, three servo motors are demonstrated to have different dynamical behavior which was expected to happen from the outset, due to uncertainty in fabrication of motors component and gearbox. In the identification and prediction stages, the Root Mean Square Errors (RMSE) index is used to validate and analyze the performance of each method using the validation data from new trajectories. In terms of predicting the output of the system, NN-ARX performed better than ARMAX with RMSE of 0.001441, compared to ARMAX with RMSE of 0.0886. Due to the high accuracy of the obtained models. thus they can be used in the design of motion controllers and modelling disturbances in the system.

关键词： Actuators Parallel robots Artificial neural networks Predictive models Mathematical models Trajectory Nonlinear dynamical systems

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Automatic visual inspection of rare defects: A framework based on GP-WGAN and enhanced faster R-CNN

arXiv

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arXiv 2021年

作者： Jalayer, Masoud Jalayer, Reza Kaboli, Amin Orsenigo, Carlotta Vercellis, Carlo Department of Management Economics and Industrial Engineering Politecnico di Milano Via Lambruschini 24/b Milan20156 Italy Advanced Control & Intelligent Systems Laboratory School of Engineering University of British Columbia KelownaBC Canada Center of Excellence in Experimental Solid Mechanics and Dynamics School of Mechanical Engineering Iran University of Science and Technology Narmak 16846 Tehran Iran Lausanne Switzerland

A current trend in industries such as semiconductors and foundry is to shift their visual inspection processes to Automatic Visual Inspection (AVI) systems, to reduce their costs, mistakes, and dependency on human experts. This paper proposes a two-staged fault diagnosis framework for AVI systems. In the first stage, a generation model is designed to synthesize new samples based on real samples. The proposed augmentation algorithm extracts objects from the real samples and blends them randomly, to generate new samples and enhance the performance of the image processor. In the second stage, an improved deep learning architecture based on Faster R-CNN, Feature Pyramid Network (FPN), and a Residual Network is proposed to perform object detection on the enhanced dataset. The performance of the algorithm is validated and evaluated on two multi-class datasets. The experimental results performed over a range of imbalance severities demonstrate the superiority of the proposed framework compared to other solutions. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Authors' Reply

引用

IEEE Sensors Journal 2018年第3期18卷 1336-1337页

作者： Ghanbari, Milad Yazdanpanah, Mohammad Javad School of Electrical and Computer Engineering University of Tehran Tehran*** Iran Control and Intelligent Processing Center of Excellence Advanced Control Systems Laboratory College of Engineering School of Electrical and Computer Engineering University of Tehran Tehran*** Iran

The experimental results of the paper titled 'Delay compensation of tilt sensors based on mems accelerometer using data fusion technique' was commented by Bodnicki and Luczak. In this note, we address the raised comments by the mentioned authors. Moreover, appropriate explanations are provided in order to make the procedure of the experimental section more clear. © 2001-2012 IEEE.

关键词： Kalman filters

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A Bioinspired approach-sensitive neural network for collision detection in cluttered and dynamic backgrounds

arXiv

引用

arXiv 2021年

作者： Huang, Xiao Qiao, Hong Li, Hui Jiang, Zhihong School of Mechatronical Engineering Advanced Innovation Center for Intelligent Robots and Systems Key Laboratory of Biomimetic Robots and Systems of Chinese Ministry of Education Beijing Institute of Technology Beijing100081 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Beijing Key Laboratory of Research and Application for Robotic Intelligence of’Hand-Eye-Brain’ Interaction Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

—Rapid, accurate and robust detection of looming objects in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform collision detection and avoidance tasks. Inspired by the neural circuit of elementary motion vision in the mammalian retina, this paper proposes a bioinspired approach-sensitive neural network (ASNN) that contains three main contributions. Firstly, a direction-selective visual processing module is built based on the spatiotemporal energy framework, which can estimate motion direction accurately via only two mutually perpendicular spatiotemporal filtering channels. Secondly, a novel approach-sensitive neural network is modeled as a push-pull structure formed by ON and OFF pathways, which responds strongly to approaching motion while insensitivity to lateral motion. Finally, a method of directionally selective inhibition is introduced, which is able to suppress the translational backgrounds effectively. Extensive synthetic and real robotic experiments show that the proposed model is able to not only detect collision accurately and robustly in cluttered and dynamic backgrounds but also extract more collision information like position and direction, for guiding rapid decision making. © 2021, CC BY.

关键词： Mammals

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A hybrid model based on stacking and multi-correction mechanisms for urban water demand prediction ⁎

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IFAC-PapersOnLine 2020年第2期53卷 16685-16690页

作者： Yang Lan Jingcheng Wang Miaoshun Bai Ibrahim Brahmia Haotian Xu Piao Hu Yuhao Long Yeming Zhang Dept. of Automation Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of education of China Shanghai 200240 China. Autonomous Systems and Intelligent Control International Joint Research Center Xi’an Technological University Xi’an Shaanxi710021 China. Shanghai Municipal engineering Design Institute (Group) Co. Ltd.

Water demand prediction is the key link for the effective operation of urban intelligent water supply system. Since the non-linearity and complex variability of water consumption, it is difficult for traditional water demand prediction models to guarantee high accuracy for a long period. Different holiday types and even tiny changes in temperatures can affect urban water demand seriously. This paper proposes a Stacking-based hybrid model which integrates multi-correction mechanisms to address these problems. A better stacking model is proposed to minimize the generalization error. The stability and reliability of predictions are improved through the design of multi-correction mechanisms such as high temperature weather compensation feature, holiday-type correction model and water quantity fluctuation correction model. Comparing different models before and after stacking also before and after correcting, the prediction accuracy of the proposed hybrid model is much higher and the predictions are more stable and reliable.

关键词： Water supply system Prediction methods Hybrid models Stacking methods Multi-correction mechanisms Neural-network models Time-series analysis

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