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Construction of the rat brain spatial cell firing model on a quadruped robot

CAAI Transactions on intelligence Technology 2022年第4期7卷 732-743页

作者： Naigong Yu Yishen Liao Hejie Yu Ouattara Sie Faculty of Information Technology Beijing University of TechnologyBeijingChina Beijing Key Laboratory of Computational Intelligence and Intelligent System BeijingChina Engineering Research Center of Digital Community Ministry of EducationBeijingChina College of Robotic UniversitéFélix Houphouet-BoignyAbidjanCote d'Ivoire

Physiological studies have shown that rats in a dark environment rely on the limbs and vestibule for their self-motion information,which can maintain the specific firing patterns of grid cells and hippocampal CA3 place *** the development stage of rats,grid cells are considered to come from place cells,and place cells can be encoded by hippocampal theta *** on these,the quadruped robot is used as a platform in this ***,the sensing information of the robot's limbs and inertial measurement unit is obtained to solve its position in the *** the position information is encoded by theta cells and mapped to place cells through a neural *** obtaining the place cells with singlepeak firing fields,Hebb learning is used to adjust the connection weight of the neural network between place cells and grid *** order to verify the model,3-D simulation experiments are designed in this *** experiment results show that with the robot exploring in space,the spatial cells firing effects obtained by the model are consistent with the physiological research facts,which lay the foundation for the bionic environmental cognition model.

关键词： robot neural firing

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Structural Connectivity Enhanced Anisotropic 3D Network for Brain Midline Delineation

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Journal of beijing Institute of Technology 2023年第5期32卷 562-578页

作者： Yufan Liu Kongming Liang Yinuo Jing Shen Wang Zhanyu Ma Yiming Li Yizhou Yu Yizhou Wang Jun Guo Pattern Recognition and Intelligent System Laboratory School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijing 100876China. Center on Frontiers of Computing Studies School of Computer SciencePeking UniversityBeijing 100871China AI Lab Deepwise HealthcareBeijing 100089China

Brain midline delineation can facilitate the clinical evaluation of brain midline shift,which has a pivotal role in the diagnosis and prognosis of various brain ***,there are still challenges for brain midline delineation:1)the largely deformed midline is hard to localize if mixed with severe cerebral hemorrhage;2)the predicted midlines of recent methods are not smooth and continuous which violates the structural *** overcome these challenges,we propose an anisotropic three dimensional(3D)network with context-aware refinement(A3D-CAR)for brain midline *** proposed network fuses 3D context from different two dimensional(2D)slices through asymmetric context *** exploit the elongated structure of the midline,an anisotropic block is designed to balance the difference between the adjacent pixels in the horizontal and vertical *** maintaining the structural priority of a brain midline,we present a novel 3D connectivity regular loss(3D CRL)to penalize the disconnectivity between nearby *** experiments on the CQ dataset and one in-house dataset show that the proposed method outperforms three state-of-the-art methods on four evaluation metrics without excessive computational burden.

关键词： brain midline delineation refinement network structure prior connectivity regular loss

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Mechanics of Soft-Body Rolling Motion without External Torque

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Physical Review Letters 2025年第19期134卷 198401-198401页

作者： Xudong Liang Yimiao Ding Zihao Yuan Yuxuan Han Yutang Zhou Junqi Jiang Zongling Xie Peng Fei Yixuan Sun Pan Jia Guoying Gu Zheng Zhong Feifei Chen Guangwei Si Zhefeng Gong School of Science Harbin Institute of Technology Shenzhen 518055 China Department of Neurology of the Fourth Hospital and School of Brain Science and Brain Medicine Zhejiang University School of Medicine Hangzhou 310058 China Robotics Institute and State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Cognitive Science and Mental Health Institute of Biophysics Chinese Academy of Sciences 15 Datun Road Beijing 100101 China University of Chinese Academy of Sciences 19A Yuquan Road Beijing 100049 China Huazhong University of Science and Technology Optical and Electronic Information Wuhan 430074 China Zhejiang Lab Hangzhou 311121 China Shanghai Key Laboratory of Intelligent Robotics Meta Robotics Institute Shanghai Jiao Tong University Shanghai 200240 China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-machine Integration State Key Laboratory of Brain-machine Intelligence Zhejiang University 1369 West Wenyi Road Hangzhou 311121 China NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang University Hangzhou 310058 China

The Drosophila larva, a soft-body animal, can bend its body and roll efficiently to escape danger. However, contrary to common belief, this rolling motion is not driven by the imbalance of gravity and ground reaction forces. Through functional imaging and ablation experiments, we demonstrate that the sequential actuation of axial muscles within an appropriate range of angles is critical for generating rolling. We model the interplay between muscle contraction, hydrostatic skeleton deformation, and body-environment interactions, and systematically explain how sequential muscle actuation generates the rolling motion. Additionally, we construct a pneumatic soft robot to mimic the larval rolling strategy, successfully validating our model. This mechanics model of soft-body rolling motion not only advances the study of related neural circuits, but also holds potential for applications in soft robotics.

关键词： Creatinine

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A Convolutional Neural Network-Based Complexity Reduction Scheme in 3D-HEVC 6th

A Convolutional Neural Network-Based Complexity Reduction Sc...

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6th International Conference on Artificial intelligence and Security, ICAIS 2020

作者： Liu, Chang Jia, Ke-bin Liu, Peng-yu Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

ISBN: (纸本)9783030578831

View synthesis optimization (VSO) is one of the core techniques for depth map coding in three dimensional high efficiency video coding (3D-HEVC). It improves the quality for synthesized views, while it also introduces heavy computational complexity caused by the calculation of synthesized view distortion change (SVDC) in practice. To reduce the complexity, this paper proposes a convolutional neural network-based VSO scheme in 3D-HEVC. First, the potential factors that may relate to the encoding complexity are explored. Then, based on this, a convolutional neural network (CNN) is embedded into the 3D-HEVC reference software HTM16.0 to predict the depth of coding units (CUs). The complexity of SVDC can be drastically reduced by avoiding the brute-force search for VSO in depth 0 and depth 1. Finally, for depth 2 and depth 3, the zero distortion area (ZDA) is determined based on texture smoothness and the SVDC calculation for that area is skipped. The experimental results show that the proposed scheme can reduce 76.7% encoding time without any significant loss for the 3D video quality. © 2020, Springer Nature Switzerland AG.

关键词： Convolution

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Adaptive Enhancement Technology for Screen Content Photos 17th

Adaptive Enhancement Technology for Screen Content Photos

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17th International Forum on Digital TV and Wireless Multimedia Communication, IFTC 2020

作者： Zhou, Yazhen Kyefondeme, Dakurah Collins Zhou, Huayi Zhao, Min Guo, Nan Wu, Li Faculty of Information Technology Beijing University of Technology Beijing China Key Laboratory of Artificial Intelligence Ministry of Education Shanghai China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China

ISBN: (纸本)9789811611933

Recent years have witnessed the widespread applications of mobiles and other portable electronic devices, involving a variety of screen content photos. Different from natural scene photos, screen content photos are composed of more lines, fewer colors and unique text messages. Thus, it is difficult to realize the satisfactory enhancement effect of screen content photos using traditional image enhancement technology since they are designed for the natural scene. In this paper, we develop a novel enhancement model for screen content photos by considering text and picture separately. To be specific, we first use a fully convolutional network to divide a screen content photo into three independent parts: picture region, foreground text region, and background. Second, an optimal modification of histogram is used to automatically enhance the picture region’s contrast, and the guided image filter is used to enhance the foreground text region. Third, the enhanced picture region, the enhanced foreground text region, and background are fused to obtain the final enhanced image. Experimental results show that our model has produced less noise and derived outstanding enhancement effect than the popular enhancement techniques. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image segmentation

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An improved method for cloth pattern cutting based on Holistically-nested Edge Detection 10

An improved method for cloth pattern cutting based on Holist...

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10th IEEE Data Driven Control and Learning systems Conference, DDCLS 2021

作者： Yu, Naigong Zhang, Zhen Xu, Qiao Firdaous, Essaf Lin, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent System Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China

ISBN: (纸本)9781665424233

Image edge detection is the basis for precise positioning and accurate cutting of cloth pattern contours. Compared with the commonly used traditional edge detection methods, the Holistically-nested Edge Detection has clearer and more continuous detection results including the reduction of the false detection rate. However, this method extracts a coarser thick outline. In order to extract a high-precision cloth pattern outline, clearly distinguish the main body of the pattern from the background, provide convenience for the follow-up cutting machine for accurate cutting, this paper proposes an improved method for edge detection of cloth pattern cutting based on the holistically-nested Edge Detection method. The edge refinement and smoothing process are added, where the edge detection, edge refinement, and edge smoothing of the clothes images are carried out in sequences, so that the extracted cloth pattern contour is continuous, smooth, and detailed, allowing the respect of the cutting requirements of the cutting machine and the requirements of the factory production. © 2021 IEEE.

关键词： Machinery

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Structure Design for RBF Neural Network Based on Improved K-means Algorithm

Structure Design for RBF Neural Network Based on Improved K-...

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第28届中国控制与决策会议

作者： Sun Yuqing Qiao Junfei Han Honggui College of Electronic Information & Control Engineering Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467397155

Aiming at the disadvantages of the traditional K-means clustering algorithm,a new algorithm based on density is proposed to remove the noises and outliers in this *** algorithm determines whether a point is a noise or not according to the density of the *** show that this algorithm can effectively eliminate the influence of the noises when the K-means algorithm searches cluster centers in the *** the subtractive clustering algorithm is used to initialize the clustering centers of the K-means algorithm,meanwhile the number of cluster centers is *** improved K-means algorithm is taken to optimize the structure of RBF neural network,and the results of experiments on the typical function approximation show that the proposed algorithm has the better approximation ability.

关键词： K-means algorithm Density Subtractive clustering algorithm RBF neural network

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Data cleaning method for the process of acid production with flue gas based on improved random forest

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Chinese Journal of Chemical Engineering 2023年第7期59卷 72-84页

作者： Xiaoli Li Minghua Liu Kang Wang Zhiqiang Liu Guihai Li Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital CommunityMinistry of EducationBeijing 100124China Guixi Smelter Jiangxi Copper Co.LtdGuixi 335400China Beijing RTlink Technology Co. LtdBeijing 100102China

Acid production with flue gas is a complex nonlinear process with multiple variables and strong *** operation data is an important basis for state monitoring,optimal control,and fault ***,the operating environment of acid production with flue gas is complex and there is much *** data obtained by the detection equipment is seriously polluted and prone to abnormal phenomena such as data loss and ***,to solve the problem of abnormal data in the process of acid production with flue gas,a data cleaning method based on improved random forest is ***,an outlier data recognition model based on isolation forest is designed to identify and eliminate the outliers in the ***,an improved random forest regression model is *** algorithm is used to optimize the hyperparameters of the random forest regression *** the optimal parameter combination is found in the search space and the trend of data is ***,the improved random forest data cleaning method is used to compensate for the missing data after eliminating abnormal data and the data cleaning is *** show that the proposed method can accurately eliminate and compensate for the abnormal data in the process of acid production with flue *** method improves the accuracy of compensation for missing *** the data after cleaning,a more accurate model can be established,which is significant to the subsequent temperature *** conversion rate of SO_(2) can be further improved,thereby improving the yield of sulfuric acid and economic benefits.

关键词： Acid production Data cleaning Isolation forest Random forest Data compensation

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A PSO-RBF neural network for BOD multi-step prediction in wastewater treatment process

A PSO-RBF neural network for BOD multi-step prediction in wa...

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2021 China Automation Congress, CAC 2021

作者： Liu, Yonglei Li, Wenjing Faculty of Information Technology Beijing University of Technology Beijing Institute of Artificial Intelligence Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China

ISBN: (纸本)9781665426473

The single-step prediction of the effluent biochemical oxygen demand (BOD) in wastewater treatment process (WWTP) cannot provide the information for future trends, which will affect the decision-making for subsequent control strategies in WWTP. To solve this problem, a radial basis function (RBF) neural network soft-sensing model based on PSO algorithm (PSO-RBF) is proposed for multi-step prediction of the biochemical oxygen demand (BOD) in WWTP. First, we propose an RBF model for multi-step prediction based on MIMO strategy. Then, the PSO algorithm is used to optimize the RBF parameters. Finally, the proposed model is applied to a four-step BOD prediction in WWTP. Experimental results show that this model can better predict BOD in multiple steps compared to traditional models. © 2021 IEEE

关键词： Forecasting

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An Improved NSGA2 Algorithm with the Adaptive Differential Mutation Operator

An Improved NSGA2 Algorithm with the Adaptive Differential M...

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第三十四届中国控制会议

作者： WEI Jing QIAO Junfei Meng Qinchao College of Electronic Information and Control Engineering Beijing University of TechnologyBeijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467374439

This paper proposes an improved non-dominated sorting genetic algorithm(NSGA2)-DNSGA2, with the aim of preserving diversity of obtained optimal solution and avoiding the original NSGA2 algorithm falling into local optimal. The proposed DNSGA2 algorithm which introduces a differential mutation operator to replace the original polynomial mutation because the method of differential local search is helpful to the uniformity of Pareto optimal solution set. The performance of the proposed DNSGA2, NSGA2 and W-LRCD-NSGA2(Based on left-right crowding distance non-dominated sorting genetic algorithm) are compared via four benchmark functions. Simulation results indicate that the diversity and uniformity of Pareto optimal solution obtained by DNSGA2 are better than the other two algorithms.

关键词： Multi-objective,NSGA2,Differential mutation,Pareto optimal solution

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