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11th IEEE International Conference on Data Mining Workshops, ICDMW 2011

作者： Feng, Deying Yang, Jie Yang, Cheng Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Ministry of Education Shanghai China

ISBN: (纸本)9780769544090

In this work, we present a novel indexing method, which contains visual phrase quantization, two-dimensional inverted index and Approximate RANSAC (ARANSAC), for mobile image retrieval. First of all, visual phrase quantization is proposed by mapping the SIFT descriptor to two visual words in an order and representing the image by bag of visual phrases. Then, a two-dimensional inverted index is developed, in which the similarity is computed by counting the co-occurrences of visual phrases between query image and database image. Finally, ARANSAC is investigated to re-rank the top-ranked images returned from the above indexing. Compared with the state-of-the-art methods, our proposed method in mobile image retrieval improves the retrieval efficiency while ensuring the retrieval accuracy. © 2011 IEEE.

关键词： Image retrieval

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Classification of Chromosome Karyotype Based on Faster-RCNN with the Segmatation and Enhancement Preprocessing Model 12

Classification of Chromosome Karyotype Based on Faster-RCNN ...

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12th International Congress on Image and Signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

作者： Ding, Wei Chang, Ling Gu, Chaochen Wu, Kaijie Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Department of Automation Shanghai China

ISBN: (纸本)9781728148526

Prenatal screening of chromosomal abnormalities is an important means of ensuring the healthy survival rate of newborns. The complex information and tedious workload of chromosome karyotype image analysis is a major difficulty in medical diagnosis. In this paper, a preprocessing model with object segmentation and feature enhancement is proposed. Combined with the framework of deep learning network, an automatic classification model for karyotype recognition of chromosomes is constructed. The preprocessing model studies the extraction of chromosome karyotype images at the pixel level and the feature enhancement of chromosome karyotype images. The model aims at providing more interpretable information for the deep learning network. In this paper, the algorithm analysis of chromosome karyotype preprocessing is carried out, the classification recognition network is built, and the detection results of the network verify the positive role of the preprocessing model. The model of chromosome karyotype automatic analysis based upon deep learning network may provide accurate reference information for doctors and reduce the workload of repeated diagnoses, which has very high application values. © 2019 IEEE.

关键词： Diagnosis

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3D Localization for Multiple AUVs in Anchor-Free Environments by Exploring the Use of Depth information

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1051-1053页

作者： Yichen Li Wenbin Yu Xinping Guan the Department of Automation Shanghai Jiao Tong UniversityShanghai 200240 the Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai Jiao Tong UniversityShanghai 200240China

Dear Editor,This letter investigates the cooperative localization problem for multiple autonomous underwater vehicles(AUVs)in underwater anchor-free environments,where AUV localization errors grow without bound due to the accumulated errors in inertial measurements(termed accumulated errors hereafter)and the lack of anchors(with known positions).

关键词： AUVs underwater hereafter

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Data Based Fault Diagnosis of Hot Axle for High-Speed Train

Data Based Fault Diagnosis of Hot Axle for High-Speed Train

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2018 Chinese Automation Congress, CAC 2018

作者： Sun, Lanlan Hei, Xinhong Xie, Guo Qian, Fueai Wang, Zhuxin Liu, Ran System Control and Intelligent Information Processing University of Technology Xi'An Shaanxi Key Laboratory of Complex Xi'an China

ISBN: (纸本)9781728113128

The axle temperature is an important performance index for high-speed train axle, and many types of high-precision detection device have been developed. However, because the existing hotbox detection system works by comparing the measured value with fixed and experiential temperature thresholds, without considering the dynamic influence of running environment, the rates of fault and failure alarm are still high. Regarding this problem, this paper is to realize high precision fault diagnosis. Firstly, the characteristics of the temperature rising rates of all axles, the axles on the same side of a carriage, the axles on the same side of a whole train are analyzed. Secondly, the support vector machine (SVM) is employed to establish a data-based fault diagnosis model, and the model is optimized using genetic algorithm. Finally, the proposed method is applied to an actual running data, the analysis results demonstrated the effectiveness and feasibility. © 2018 IEEE.

关键词： Genetic algorithms

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Cost efficient subcategory-aware CNN for object detection 24

Cost efficient subcategory-aware CNN for object detection

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24th IEEE International Conference on Image processing, ICIP 2017

作者： Li, Tingfeng Zhao, Xu Key Laboratory of System Control and Information Processing MOE Department of Automation Shanghai Jiao Tong University China

ISBN: (纸本)9781509021758

In this paper, we propose an accurate and cost efficient deep CNN network for object detection. In contrast to the previous region-based methods like Sub-CNN [1], our detector is almost fully convolutional so that the computation cost can be reduced efficiently. By introducing position-sensitive score maps and exploiting subcategory information, our method is less time consuming while maintaining competitive performance on detecting objects with various scales. In addition, we remove image pyramid used in Sub-CNN to achieve further acceleration. The experimental results show that our approach is 1.3 times faster than Sub-CNN with only 14% number of parameters and archives comparable results on the challenging KITTI dataset. Compared with the state-of-the-art methods for object detection, our approach provides an efficient solution that takes into account both accuracy and speed. © 2017 IEEE.

关键词： Object detection

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Robust Stability of Linear Time-Invariant systems with Hybrid Uncertainties

Robust Stability of Linear Time-Invariant Systems with Hybri...

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2023 International Conference on Automation, control and Electronics Engineering, CACEE 2023

作者： Li, Yangxuan Lu, Junguo Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Department of Automation Shanghai China

ISBN: (纸本)9798350309201

This paper presents a study on the robust stability analysis of linear time-invariant systems with parameter uncertainties and norm-bounded uncertainties. By utilizing the structured singular value, necessary and sufficient conditions for robust stability are derived. Based on the stability condition, the stability margin of the uncertain system is obtained from the skewed structured singular value. Additionally, numerical simulation results are provided to validate the effectiveness of the proposed methods. © 2023 IEEE.

关键词： Linear time-invariant system

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Energy Efficient Predictive control for Vapor Compression Refrigeration Cycle systems

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IEEE/CAA Journal of Automatica Sinica 2018年第5期5卷 953-960页

作者： Xiaohong Yin Shaoyuan Li the School of Automation and Electronic Engineering Qingdao University of Science and TechnologyQingdao 266042China the Department of Automation Shanghai Jiao Tong Universityand the Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai 200240China

Abstract--Vapor compression refrigeration cycle （VCC） system is a high dimensional coupling thermodynamic system for which the controller design is a great challenge. In this paper, a model predictive control based energy efficient control strategy which aims at maximizing the system efficiency is proposed. Firstly, according to the mass and energy conservation law, an analysis on the nonlinear relationship between superheat and cooling load is carried out, which can produce the maximal effect on the system performance. Then a model predictive control （MPC） based controller is developed for tracking the calculated setting curve of superheat degree and pressure difference based on model identified from data which can be obtained from an experimental rig. The proposed control strategy maximizes the coefficient of performance （COP） which depends on operating conditions, in the meantime, it meets the changing demands of cooling capacity. The effectiveness of the control performance is validated on the experimental rig. Index Terms--Cooling load, model predictive control （MPC）, superheat, vapor compression refrigeration cycle （VCC）.

关键词： Cooling load model predictive control(MPC) superheat vapor compression refrigeration cycle(VCC)

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Optimal Power Allocation for Cooperative Relaying system in Smart Grid 35

Optimal Power Allocation for Cooperative Relaying System in ...

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第35届中国控制会议

作者： MA Kai LIU Xuemei YANG Jie LIU Zhixin WANG Congshan School of Electrical Engineering Yanshan University Key Laboratory of System Control and Information Processing Ministry of EducationShanghai Jiao Tong University

ISBN: (纸本)9781509009107

In this study,cooperative relaying is proposed to reduce the packets loss rate in the communications between the utility company and the *** establish a cost model based on the statistical analysis with the regulation errors of a direct load control method in smart ***,we model the average cost of generation-side regulation using the combinational cost of the generating *** problem of minimizing the average cost to the utility company is convex,and we use the Fibonacci search method to obtain the globally optimal power allocation for the *** results demonstrate that the proposed algorithm can converge to the globally optimal relay power under the amplify-and-forward relaying strategy,and the average cost to the utility company can be reduced significantly.

关键词： Smart Grid Cooperative Relaying Fibonacci Search Method Automatic Generation control(AGC) Power Allocation

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Distributed Economic MPC for Synergetic Regulation of the Voltage of an Island DC Micro-Grid

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 734-745页

作者： Yi Zheng Yanye Wang Xun Meng Shaoyuan Li Hao Chen IEEE the Department of Automation Shanghai Jiao Tong UniversityShanghai 200240 the Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Haixi Institutes Chinese Academy of SciencesJinjiang 362216China

In this paper,distributed model predictive control(DMPC) for island DC micro-grids(MG) with wind/photovoltaic(PV)/battery power is proposed,which coordinates all distributed generations(DG) to stabilize the bus voltage together with the insurance of having computational efficiency under a real-time *** on the feedback of the bus voltage,the deviation of the current is dispatched to each DG according to cost over the prediction ***,to avoid the excessive fluctuation of the battery power,both the discharge-charge switching times and costs are considered in the model predictive control(MPC) optimization problems.A Lyapunov constraint with a time-varying steady-state is designed in each local MPC to guarantee the stabilization of the entire *** voltage stabilization of the MG is achieved by this strategy with the cooperation of *** numeric results of applying the proposed method to a MG of the Shanghai Power Supply Company shows the effectiveness of the distributed economic MPC.

关键词： Distributed model predictive control(DMPC) Lyapunovbased model predictive control micro-grid(MG) voltage control

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Adaptive Autonomous Navigation Method for MEO and HEO Satellites Based on Weak Constellation Signal 35

Adaptive Autonomous Navigation Method for MEO and HEO Satell...

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第35届中国控制会议

作者： YANG Wen-bo LI Shao-yuan Shanghai Polytechnic University Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education

ISBN: (纸本)9781509009107

An adaptive autonomous navigation method for medium earth orbit（MEO） and high earth orbit（HEO） satellites is proposed by using four constellation systems to overcome shortcomings of low visibility,liability and usability of single mode or dual-mode positioning ***,based on the analysis of the visibility and compatibility of constellation satellites,satellites selection is realized by utilizing weighted geometric dilution of precision（WGDOP） ***,considering the fact that there exists a non-synchronization phenomenon in the constellation systems,the synchronization error of each constellation system is estimated in real time as state variables of the navigation ***,measurement noise variance matrix of the designed filter is adjusted adaptively in allusion to the problem of weakness of the received satellites signal and the strong uncertainty of the measurement noise of the ***,simulation results are provided,which illustrate that compared with extended Kalman filter（EKF）,the navigation accuracy can be enhanced by using the proposed adaptive filtering algorithm when there exists strong uncertainty in the measurement noise.

关键词： Autonomous navigation Visibility Geometric dilution of precision Synchronization

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