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SSRN 2022年

作者： Wang, Yifan Ishibuchi, Hisao Er, Meng Joo Zhu, Jihua School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation China College of Marine Electrical Engineering Dalian Maritime University Dalian116026 China Institute of Artificial Intelligence Marine Robotics China

Nowadays, labeling a huge number of images is still a very challenging task. To tackle the problem of unlabeled data, unsupervised learning has been proposed. Among many unsupervised learning algorithms, K-means is the most popular algorithm. However, in a low-dimensional space, fuzzy c-means, which is more robust and less sensitive to initialization, has several advantages over K-means clustering. On the other hand, stacked convolutional pooling structures and manifold representation play pivotal roles in image clustering. In this paper, we proposed an unsupervised multilayer fuzzy neural network for image clustering, which unifies fuzzy systems, multilayer convolutional structures and manifold representation. The main contributions of this work are: First, we extend fuzzy systems to unsupervised tasks by introducing the manifold representation. Next, we proposed the idea of using only a small number of attributes with the largest variances to compute firing strengths. This is to prevent the firing strengths from falling to zero in clustering high-dimensional data. Then, randomly generated convolutional weights are used to extract features, which is a good choice for data without labels. It is demonstrated on a wide range of image datasets that the proposed approach is superior to existing fuzzy and non-fuzzy clustering algorithms. © 2022, The Authors. All rights reserved.

关键词： Fuzzy neural networks

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Research on capsule network optimization structure by variable route planning

Research on capsule network optimization structure by variab...

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2019 IEEE International Conference on Real-Time Computing and robotics, RCAR 2019

作者： Chu, Xin Xu, Ning Liu, Xiaofeng Yao, Xiao College of IoT Engineering Hohai University Changzhou213022 China Changzhou Key Laboratory of Robotics and Intelligent Technology Changzhou213022 China Jiangsu Key Laboratory of Special Robots Changzhou213022 China

ISBN: (纸本)9781728137261

Convolutional Neural Networks (CNN) perform quite good on image classification and object recognition tasks. To solve the problem that CNN is not robust to affine transformation and can not consider the spatial relationship between objects in images, Capsule Network (CapsNet) is proposed and the best accuracy of MNIST dataset has achieved. It shows developability on other datasets and its performance is significantly higher than CNN. However, the data categories in the existing routing algorithms of the CapsNet need to be given in advance, which is very difficult to estimate. In a complex dataset or a small sample dataset, calculating the maximum likelihood solution is more complicated, and it will lead to an inevitable singularity problem. For these shortcomings of the routing algorithm, this paper first proposes a variational routing algorithm. The algorithm does not bring additional computational burden, can automatically determine the most suitable data category, and can effectively avoid over-fitting problems. The algorithm uses the variational distribution to integrate the prior distribution information to increase the regularization limit, maximize the lower bound of the model evidence, and avoid the singularity problem caused by computing the maximum likelihood function. In this paper, the MNIST dataset is classified by a simple CapsNet architecture, and the result is encouraging. © 2019 IEEE.

关键词： Structural optimization

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Application of Graph-Curvature Features in Auxiliary Diagnosis for Histopathological Image Identification of Gastric Cancer

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SSRN 2023年

作者： He, Ruilin Li, Chen Yang, Xinyi Yang, Jinzhu Jiang, Tao Grzegorzek, Marcin Sun, Hongzan Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang China School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information Technology Chengdu China Institute for Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland Shengjing Hospital of China Medical University Shenyang China School of Computer Science and Engineering

Background: Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors, but it has some drawbacks. This paper explores a computer-aided diagnostic method that can identify benign and malignant gastric cancer with histopathology images. Method: This article obtains the most suitable process through multiple experiments, compared multiple methods and features for classification. Firstly, the U-net is applied to segment the image. Next, the nucleus is extracted from the segmented image and the Minimum Spanning Tree (MST) diagram structure is drawn. The third step is to extract the graph-curvature features of histopathology image according to the MST image. Finally, by inputting graph-curvature features into the classifier, the recognition results for benign or malignant can be obtained. Result: During the experiment, we use various methods for comparison. In the image segmentation stage, U-net, watershed algorithm and Otsu threshold segmentation methods are used respectively. Combined with multiple indicators, we find that the U-net method is the most suitable for segmentation of histopathology images. In the feature extraction stage, in addition to extracting graph-edge feature and graph-curvature feature, several basic image features are also extracted, including Red, Green, Blue feature, Gray-Level Co-occurrence Matrix feature, Histogram of Oriented Gradient feature, and Local Binary Pattern feature. In the classifier design stage, we experimented with various methods, such as Support vector machine (SVM), Random forest, Artificial Neural Network, K Nearest Neighbors, VGG-16 and Inception-V3. Through the comparison and analysis, the classification results with an accuracy of 98.57% can be obtained by inputting the graph-curvature feature into SVM classifier. Conclusion: This paper has created a unique feature, graph-curvature feature based on MST to represent and analyze histopathology images. This graph-based feature can be used

关键词： Local binary pattern

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Asymptotic Trajectory Tracking of Stratospheric Airships Based on RISE Controllers Under External Uncertainties

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Liu, Can Li, Baoquan Dai, Fengzhi Shi, Wuxi Zhang, Xuebo Tiangong University School of Control Science and Engineering Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tianjin300387 China Tianjin University of Science and Technology College of Electronic Information and Automation Tianjin300222 China Nankai University Institute of Robotics and Automatic Information Systems Tianjin Key Laboratory of Intelligent Robotics Tianjin300071 China

For a stratospheric airship under internal and external uncertainties, controller strategies with robust integral sign of the error (RISE) are proposed for asymptotic trajectory tracking control. Specifically, a fully connected neural network (FCNN) is constructed with adaptive weight updating to estimate internal uncertainties of the airship system. Then, a self-tuning RISE controller is designed to track trajectories with compensating external disturbance, which enables gain adjustment in realtime according to system feedback. Based on Lyapunov technique and LaSalle-Yoshizawa corollary extension, system stability is strictly analyzed, and asymptotic convergence performance is obtained for trajectory tracking by the designed controller. Moreover, a RISE controller is designed with fixed gain matrices for the airship based on classic control strategies, and asymptotic tracking is also proved. Finally, numerical simulations are conducted with spiral and hovering control to verify performance of the proposed strategies. © 1965-2011 IEEE.

关键词： Lyapunov methods

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Effects of depth of field on eye movement

Effects of depth of field on eye movement

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7th International Symposium on Test Automation and Instrumentation, ISTAI 2018

作者： Zhang, Tingting Xia, Ling Liu, Xiaofeng Kong, Weijie Wu, Xiaoli Changzhou Key Laboratory of Robotics and Intelligent Technology Jiangsu Key Laboratory of Special Robot Technology College of IoT Engineering Hohai University Changzhou China College of Mechanical and Electrical Engineering Hohai University Changzhou China

Depth of field is an important image feature, which can be used to enhance observers’ perception of stereopsis. To explore how the depth of field influenced observers’ attention in images, a within-subject experiment including two factors, two scenes and six levels of depth of field, was performed. A remote desktop eye tracker, Tobii X120 was used to record participants' eye movement during the experiment. The results showed that the limited depth of field could direct observers' attention on the sharp area. However, manipulating depth of field in the same scene may not influence the distribution of observers’ attention. In addition, it was concluded that depth from focus object influences observers' attention significantly, which means that the closer the object is, more attention the object obtains. © 2019 Institution of engineering and technology. All rights reserved.

关键词： Eye movements

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Analysis of Big Data Complex Network Structure Based on Fuzzy Clustering Algorithm

Analysis of Big Data Complex Network Structure Based on Fuzz...

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Networking, Communications and Information technology (NetCIT), International Conference on

作者： Fu Shanshan Ruan Zhiqiang Center of Modem Education Technology and Information Management Minjiang University Fujian China College of Computer and Control Engineering Minjiang University Fujian China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fujian China Digital Fujian IoT Laboratory of Intelligent Production Fujian China

ISBN: (纸本)9781665400718

In the era of artificial intelligence with big data as the background, the statistical analysis method based on complex network has become a research hotspot and actively promoted the development of big data visualization. Complex network is a new discipline developed by cross research with other disciplines. Its network topology has obvious clustering characteristics, that is, complex network cluster structure. Together with small world and scale-free, it is called the three most important and basic attributes of complex network topology. The fundamental purpose of complex network clustering algorithm is to detect the actual network cluster structure in complex network, which has very important practical value for mining the cluster structure in network and understanding its characteristics, and is conducive to understanding the functions of network, such as predicting the development of complex network, analyzing the behavior of social network, discovering hot topics on network, etc. This paper combines the methods and ideas of complex network analysis, carries out the method design and research of organization analysis, and tries to solve the relationship and characteristics of inter departmental cooperation within the organization.

关键词： Visualization Data analysis Heuristic algorithms Data visualization Clustering algorithms Complex networks Organizations

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Position Sensorless Control of Permanent Magnet Synchronous Motor Based on Adaptive Filtering Sliding Mode Observer

Position Sensorless Control of Permanent Magnet Synchronous ...

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Chinese Control and Decision Conference, CCDC

作者： Anna Wang Yingjie Xiong Tao Zhang College Of Information Science and Engineering Northeastern University Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665478977

In the position sensorless control strategy of permanent magnet synchronous motor (PMSM), the back-EMF method based on sliding mode observer (SMO) is favored because of its robustness. In this paper, a SMO based on the piecewise sigmoid function is designed. Based on the back-EMF observation error, the gain adaptation of the SMO is realized, which effectively reduces the high-frequency chattering problem of the conventional SMO. The low-pass filter of the conventional SMO is improved, and an adaptive filter based on the back-EMF low-pass filter is proposed. According to the back-EMF observation error, the cut-off frequency of the low-pass filter is changed in real-time, and the estimation of rotor position is improved. After Matlab/Simulink simulation verification, the control strategy is feasible and achieves a better control effect.

关键词： Adaptation models Low-pass filters Adaptive filters Rotors Sensorless control Observers Permanent magnet motors

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On Embeddings and Inverse Embeddings of Input Design for Regularized System Identification

arXiv

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arXiv 2022年

作者： Mu, Biqiang Chen, Tianshi Kong, He Jiang, Bo Wang, Lei Wu, Junfeng Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China Key Laboratory for NSLSCS of Jiangsu Province School of Mathematical Sciences Nanjing Normal University Nanjing China College of Control Science and Engineering Zhejiang University Hangzhou China

Input design is an important problem for system identification and has been well studied for the classical system identification, i.e., the maximum likelihood/prediction error method. For the emerging regularized system identification, the study on input design has just started, and it is often formulated as a non-convex optimization problem that minimizes a scalar measure of the Bayesian mean squared error matrix subject to certain constraints, and the state-of-art method is the so-called quadratic mapping and inverse embedding (QMIE) method, where a time domain inverse embedding (TDIE) is proposed to find the inverse of the quadratic mapping. In this paper, we report some new results on the embeddings/inverse embeddings of the QMIE method. Firstly, we present a general result on the frequency domain inverse embedding (FDIE) that is to find the inverse of the quadratic mapping described by the discrete-time Fourier transform. Then we show the relation between the TDIE and the FDIE from a graph signal processing perspective. Finally, motivated by this perspective, we further propose a graph induced embedding and its inverse, which include the previously introduced embeddings as special cases. This deepens the understanding of input design from a new viewpoint beyond the real domain and the frequency domain viewpoints. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

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A Soft Gripper Driven by Bellow Actuators and Twist Actuators for Dexterous Grasping

A Soft Gripper Driven by Bellow Actuators and Twist Actuator...

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IEEE International Conference on robotics and Biomimetics

作者： Diancheng Li Liangmin Zhou Renjie Zhu Jintao Yin Zhengbai Liu Han Yuan Hongqiang Wang Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China Innovation and Entrepreneurship College Southern University of Science and Technology Shenzhen China School of Mechanical Engineering and Automation Harbin Institute of Technology (Shenzhen) Shenzhen China

ISBN: (纸本)9781665405362

Human hands are dexterous and capable of grasping objects of various shapes and manipulating the objects within the hands. Conventional soft grippers are able to grasp, but few can manipulate objects within the grippers. To fill this gap, this work designs a soft gripper with soft bellow actuators and soft twisting actuators, which can adaptively clutch objects and handle them in different postures in the gripper. Compared with the previous rigid grippers, this soft gripper possesses a higher adaption to complex shapes and fragile objects and greater reliability against a strong impact due to its compliance. We demonstrate its successful grasping on more than 15 daily items of different profiles and surface textures, rolling and picking up a piece of thin plastic film, and reversing the posture of a ball within the gripper. It has also survived brutal hammer attacks and robustly grabbed an object while experiencing strong impacts.

关键词： Actuators Bellows Torque Shape Grasping End effectors Robustness

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Lighting effects, light distribution matters

Lighting effects, light distribution matters

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International Conference on Display technology, ICDT 2019

作者： Xia, Ling Zhang, Tingting Liu, Xiaofeng Pont, Sylvia C. College of IoT Engineering Hohai University Changzhou Key Laboratory of Robotics and Intelligent Technology Jiangsu Key laboratory of Special Robot Technology Changzhou China Department of Industrial Design Perceptual Intelligence lab Delft University of Technology Delft Netherlands

ISBN: (纸本)9781510896161

The interaction between light and its surroundings (i.e. the geometry and materials) shapes the lighting distribution and determines the appearance of scenes and its visual experiences. In this study, we introduced the concept of light distribution solids by distinguishing luminance distribution solids and illuminance distribution solids. It is helpful to understand the spatial and formgiving lighting effects on objects’ appearance. Furthermore, this study together with our previous study (Xia, Pont et al. 2016, Xia, Pont et al. 2016) provide theoretical basis for the exploration of extracting photometry parameters out of HDR panoramic photographs of scenes. © 2019, John Wiley and Sons Inc. All rights reserved.

关键词： Luminance

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