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Seventh IASTED International Conference on Signal and Image Processing, SIP 2005

作者： Batmanghelich, Nematollah Soltanian-Zadeh, Hamid Araabi, Babak Najaar Elec. and Comp. Eng. Dept. Control and Intelligent Processing Center of Excellence University of Tehran Tehran Iran School of Cognitive Sciences Institute for Studies in Theoretical Physics and Mathematics Tehran Iran Image Analysis Lab. Radiology Dept. Henry Ford Health System Detroit MI United States

ISBN: (纸本)0889865183

In this paper, we propose a new method integrating both a priori shape information and our knowledge about gray levels of the desired structure. We describe an approach inspired from tracking to deal with non-uniform gray levels. We define focus region to consider both interior and exterior of the desired object. We utilize signed distance function to consider shape information. Embedding a priori shape and gray level knowledge in a statistical platform, we use correlation between changes in shape and histogram to improve the results. Our method successfully segments Thalamus and other brain structures.

关键词： Image segmentation

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Noise suppression of fMRI time-series in wavelet domain

Noise suppression of fMRI time-series in wavelet domain

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Seventh IASTED International Conference on Signal and Image Processing, SIP 2005

作者： Jahanian, Hesamoddin Soltanian-Zadeh, Hamid Hossein-Zadeh, Gholam Ali Elec. and Comp. Eng. Dept. Control and Intelligent Processing Center of Excellence University of Tehran Tehran 14399 Iran School of Cognitive Sciences Institute for Studies in Theoretical Physics and Mathematics Tehran Iran Image Analysis Lab. Radiology Dept. Henry Ford Health System Detroit MI United States

ISBN: (纸本)0889865183

Because of poor signal-to-noise ratio (SNR) of the fMRI time series and confounding effects, the results of fMRI analysis are often unsatisfactory. Existence of significant noise and artifacts in fMRI time-series as well as their unknown structure, complicates the problem of activation detection in the time domain. This makes the fMRI noise suppression a challenging problem. Based on some assumptions, different parametric denoising methods such as wavelet based denoising methods have been introduced in the literature. But these assumptions may not necessarily hold for the fMRI data. To remedy this problem, using randomization analysis, we propose a novel wavelet-based denoising method for fMRI analysis. The proposed denoising method is employed to build a feature space for fMRI cluster analysis and its efficiency is shown using simulated and experimental datasets.

关键词： Noise abatement

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Statistical inference in fuzzy cluster analysis of functional MRI

Statistical inference in fuzzy cluster analysis of functiona...

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Seventh IASTED International Conference on Signal and Image Processing, SIP 2005

作者： Jahanian, Hesamoddin Soltanian-Zadeh, Hamid Hossein-Zadeh, Gholam Ali Control and Intelligent Processing Center of Excellence Elec. and Comp. Eng. Dept. University of Tehran Tehran 14399 Iran School of Cognitive Sciences Institute for Studies in Theoretical Physics and Mathematics Tehran Iran Image Analysis Lab. Radiology Dept. Henry Ford Health System Detroit MI United States

ISBN: (纸本)0889865183

Fuzzy cluster analysis (FCA) of functional magnetic resonance images, suffers from some drawbacks such as a priori definition of number of clusters and unidentified statistical significance of results. Here, we introduce a method to control the rate of false positive detection in FCA which gives a meaningful statistical significance to the results. Using this method, we also derive the optimal number of clusters. In this study by measuring the rate of false alarm detection while analyzing 6 experimental datasets, we evaluate the introduced method for making statistical inference.

关键词： Fuzzy control

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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on intelligent Transportation systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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control method for redundant manipulators with multi-constraints 7

Control method for redundant manipulators with multi-constra...

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7th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2016

作者： Lu, Beibei Guo, Shuli Han, Lina Key Lab. of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Beijing100081 China Mathematical Institute University of Oxford Andrew Wiles Building Radcliffe Observatory Quarter Woodstock Road OxfordOX2 6GG United Kingdom Department of Cardiovascular Internal Medicine Nanlou Branch Chinese PLA General Hospital Beijing100853 China

ISBN: (纸本)9784990534349

When redundant manipulators work in a complex environment, many constraints need to be met. Gradient projection method (GPM) is a typical way to fulfil constraints that does not affect the main task. However it's difficult to fulfil all the restrictions simultaneously. In this paper, the Jacobian matrix for constraints is proposed and an extended Jacobian matrix form equivalent to the GPM is obtained. Then the multi-objective compliance control scheme is applied to the extended Jacobian matrix form to ensure that the main task and multi-constraints are fulfilled simultaneously. At last we test this scheme in a sixdegree-of-freedom (6-DOF) manipulator and the simulation results confirm our conclusions. © 2016, Fuji Technology Press. All rights reserved.

关键词： Redundant manipulators

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On end-to-end network clock offset compensation algorithm and its implementation

On end-to-end network clock offset compensation algorithm an...

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26th Chinese control Conference, CCC 2007

作者： Yunyi, Jia Bugong, Xu Shihua, Wang Buchun, Liu Lab. of Real-Time Control Through Internet and Fieldbuses College of Automation Science and Engineering South China Univ.of Technology Guangzhou 510640 China Lab. of Pattern Recognition and Intelligent System College of Information Engineering Beijing Univ. of Posts and Telecommunications Beijing 100876 China

ISBN: (纸本)7900719229

This paper addresses the problem of clock offset in end-to-end network time synchronization. Firstly, an algorithm for measuring the end-to-end clock offset is proposed. Secondly, the traditional linear-compensation method is analyzed and an algorithm to obtain the compensation line using least-squared algorithm is proposed. Finally, a new method of dynamic non-linear predicting compensation and its realization algorithm are proposed. Through experiments, it shows that the method of dynamic non-linear predicting compensation could compensate clock offset better than linear-compensation method. The superiority, validity and feasibility of the new method are proved.

关键词： Nonlinear systems

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Research on Lightweight Small Object Detection Method with Fusion Attention Mechanism

Research on Lightweight Small Object Detection Method with F...

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Chinese Automation Congress (CAC)

作者： Haowen Dai Bingyou Liu Guoyang Wan Jingjing Qi Luanfei Wan School of Electrical Engineering Anhui Polytechnic University Wuhu China The Key Lab. of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Anhui Polytechnic University Wuhu China Anhui Dar Intelligent Control System Co. Ltd. Wuhu China Wuhu Institute of Technology Wuhu China

In the task of small object detection, due to the relatively small size of the objects, complex situations such as occlusion and overlap may arise in high-density scenes. These factors can significantly increase the issues of missed detections and false detections in object detection tasks. To address the aforementioned issues, this paper proposes a lightweight small object detection model, YOLOv5s-MGC. Firstly, we introduce an improved Mobilenetv3 network to replace the backbone network of YOLOv5s, aiming to improve the network's capability for effective feature extraction. Secondly, we incorporate the GSConv module into the feature fusion network to reduce model parameters and strengthen the network's ability in fusing feature information. Finally, we have integrated Convolutional Block Attention Module (CBAM) at the forefront of the detection heads, achieving the fusion of fine-grained features. We assessed our approach on the openly accessible dataset ‘SHWD’ and contrasted the results with the baseline YOLOv5s and various SOTA object detection models. Precisely, our method attains an accuracy of 91.1%, exceeding the baseline module by 1.6%. Furthermore, the module size is reduced to 1.97MB and it achieves a 52.4 FPS. In comparison to other prevailing algorithms, YOLOv5s-MGC enables efficient and accurate small object detection.

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Single channel speech separation with a frame-based pitch range estimation method in modulation frequency

2010 5th International Symposium on Telecommunications, IST ...

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2010 5th International Symposium on Telecommunications, IST 2010 2010年 609-613页

作者： Mahmoodzadeh, A. Abutalebi, H.R. Soltanian-Zadeh, H. Sheikhzadeh, H. Speech Proc. Research Lab. ECE Dept. Yazd University Iran Control and Intelligent Processing Center of Excellence University of Tehran Iran EE Dept. Amirkabir University of Technology Tehran Iran Idiap Research Institute Martigny Switzerland Image Analysis Lab. Henry Ford Health System Detroit United States

ISBN: (纸本)9781424481835

Computational Auditory Scene Analysis (CASA) has attracted a lot of interest in segregating speech from monaural mixtures. In this paper, we propose a new method for single channel speech separation with frame-based pitch range estimation in modulation frequency domain. This range is estimated in each frame of modulation spectrum of speech by analyzing onsets and offsets. In the proposed method, target speaker is separated from interfering speaker by filtering the mixture signal with a mask extracted from the modulation spectrogram of mixture signal. systematic evaluation shows an acceptable level of separation comparing with classic methods. © 2010 IEEE.

关键词： Frequency estimation

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NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM Image Processing

NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

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2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

作者： Hu, Rui Cai, Jiaming Zheng, Wangjie Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Jiao Tong University Department of Bioinformatics and Biostatistics Shanghai200240 China

ISBN: (纸本)9781728162157

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-lab.ls and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-lab.ls are updated iteratively to enhance the reliability of lab.ls. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools. © 2020 IEEE.

关键词： Image reconstruction

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Structure Design and Analysis of Reconfigurable Fixture Robot Based on the Auto-body Panels

Structure Design and Analysis of Reconfigurable Fixture Robo...

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IEEE International Conference on Mechatronics and Automation

作者： Jilong Li Jun Liu Xiaofeng Wang Weimin Ge School of Mechanical Engineering Tianjin University of Technology Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System Xiqing District Tianjin China Tianjin Key Lab. of the Design & Intell. Control Adv. Mechatronical Syst. Tianjin China

The fixture is an important equipment, and is widely used in the auto-body panels to be assembled and welded. However, most fixtures, which installed on the production lines, are special to make the production cost higher and the production efficiency lower. Therefore, the reconfigurability of the fixture becomes a key factor that effects the production efficiency of automobiles. To handle the above problems, the new reconfigurable fixture robot is proposed in this paper, which can meet the handing, assembling and welding requirements of different automobile's sheet parts on the automobile production lines. First, the three-dimensional model and the kinematic equations of the proposed reconfigurable fixture robot are established. Then, the working space of the reconfigurable fixture robot is also obtained. Finally, the modal analysis of the reconfigurable fixture robot is performed to determine the cause of resonances. Reconfigurable fixture robots are capable of gripping the auto-body panels with the different radii of curvature, which can also meet the work scopes on the production lines.

关键词： Fixtures Manipulators Kinematics Modal analysis Clamps

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