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Adaptive sparse recovery based on difference algorithm

Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided design and Computer Graphics 2015年第6期27卷 1047-1052页

作者： Zhang, Fengzhen Zhao, Ruizhen Cen, Yigang Hu, Shaohai Zhang, Yongdong Institute of Information Science Beijing Jiaotong University Beijing100044 China Key Laboratory of Advanced Information Science and Network Technology of Beijing Beijing100044 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

To improve the disadvantages that iterative reconstruction algorithms of compressed sensing need priori knowledge of the sparsity of original signal or iterative threshold, an adaptive sparse recovery based on difference algorithm is proposed. When the sparsity of original signal is unknown, the proposed algorithm takes advantage of unbalance of correlation coefficient between the measurement matrix and residual. With those properties, the proposed algorithm can select the support set of the original signal, and approach the sparsity of the original signal. Simulation results show that the proposed algorithm obtains better recovery results under the same conditions. Especially in the lower sampling rate, the advantage is more obvious. ©, 2015, Institute of computing Technology. All right reserved.

关键词： Recovery

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Watermarking embedding algorithm for archive image based on image normalization and principal component analysis

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Journal of Computational and Theoretical Nanoscience 2014年第9期11卷 1947-1953页

作者： Wang, Ling Zhou, Changjun Archives of Dalian University Dalian 116622 China Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian 116622 China

In this paper, an algorithm that embed watermarking into archive image based on image normalization and PCA is proposed, which is based on image normalization and invariant centroid theory. For the purpose of enhancing the invisibility and resistance to geometric attack of watermarking, we first encrypt the watermarking with chaotic method before information embedding, and then eliminate the effect of geometric change to archive image by utilizing image normalization. Finally, PCA was applied to embed the watermarking in subspace. Corresponding, we perform inverse transform to the archive image which is attacked by geometric transformation before extracting the watermarking. Thus, we can prove the truth of the attacked archive image by the extracted watermarking which is recognizable. Copyright © 2014 American Scientific Publishers All rights reserved.

关键词： Principal component analysis

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Time-varying stiffness analysis of a rotor system with a straight–fronted slant breathing crack

Time-varying stiffness analysis of a rotor system with a str...

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2014 International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SMTA 2014

作者： Han, Minglei Zhang, Jianxin Zhang, Qiang Wei, Xiaopeng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian university Dalian China

ISBN: (纸本)9781845649821

This paper calculates the finite element stiffness of a rotating shaft with a straight-fronted slant crack based on neutral axis method. In a steady rotation cycle, a 45-degree straight-fronted slant breathing crack is compared with a transverse one in different depths in the area of the crack breathing mechanism and time-varying characteristics of the shaft stiffness. The comparison between a slant crack and a transverse crack shows that the opening and closing mechanism is substantially the same, while the shaft stiffness differences are significant;as the crack inclination angle decreased, the stiffness of the shaft element tends to harmonic vibration. © 2014 WIT Press.

关键词： Stiffness

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Tooth Surface design for Variable Transmission Ratio Bevel Gearing

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Applied Mathematics 2015年第10期6卷 1685-1695页

作者： Nan Yang Dawei Zhang Yanling Tian School of Mechanical Engineering Tianjin University Tianjin China Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System School of Mechanical Engineering Tianjin University of Technology Tianjin China

A novel CAD method for tooth profile based on the gearing feature has been proposed in this paper, and this method could be applied to the variable transmission ratio and three-dimension situation. The two pitch curves of the two gears can be generated due to the given transmission ratio. The tooth profile curves are formed in each coordinate system of its pitch curve by the gearing process. Finally, the tooth profile could be extracted and translated into a real dimension by CAD method. It is provided a simply thought for tooth profile design to avoid so much complicated mathematical reasoning.

关键词： Computer-Aided design Computational Geometry Gear design Gear Geometry

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The Validity Analysis of Regression: Combining Uniform Experiment design with Nonlinear Regression

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Applied Mathematics 2015年第6期6卷 996-1008页

The data topology structure of uniform experiment design (UD) is too complex to be reasonable regressed. In this paper, the principle and method of distinguish the training data and testing data were described to make a reasonable regression when uniform experiment design combined with support vector regression (SVR). Two equivalent ways which were the smallest enclosing hypersphere perceptron (SEH) and the enclosing simplex perceptron (ES) were provided to discover the topology relationship of the process parameter datum. To give an application, a series of experiments about laser cladding layer quality were conducted by UD to get the relationship of load, velocity and wearing capacity. Results showed that only the testing datum recommended by the two perceptrons got a good forecasting by SVR. Therefore, the two perceptrons could guide experiments with process parameter data of complex topology structure. Further, the application could be extended over a much wider field of experiments.

关键词： the enclosing simplex support vector machine uniform experiment design the smallest enclosing hyper sphere

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Sentence Alignment of Historical Classics based on Mode Prediction and Term Translation Pairs 3

Sentence Alignment of Historical Classics based on Mode Pred...

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3rd CIPS-SIGHAN Joint Conference on Chinese Language Processing, CLP 2014

作者： Che, Chao Zheng, Xiaojun Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian116622 China

Parallel corpora are essential resources for the construction of bilingual term dictionary of historical classics. To obtain large-scale parallel corpora, this paper proposes a sentence alignment method based on mode prediction and term translation pairs. On one hand, the method rebuilds the sentence alignment process according to characteristics of the translation of historical classics, and adds mode prediction into the sentence alignment. On the other hand, due to the lack of bilingual ancient Chinese dictionary, the method exploits the term translation pairs extracted from manually aligned sentence pairs to perform alignment. The method first predicts the alignment mode probability according to the character number, punctuation number and some characters of Chinese sentence, then performs sentence alignment using length alignment probability, term alignment probability and mode probability. Besides, the method selects anchor sentence pairs based on sentence length and predicted mode to prevent the spread of alignment errors. The experiment on "Shi Ji" demonstrates that mode prediction and term translation pair both enhance the performance of sentence alignment obviously. © 2014 CLP 2014 - 3rd CIPS-SIGHAN Joint Conference on Chinese Language Processing. All rights reserved.

关键词： Alignment

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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Noisy image super-resolution reconstruction based on sparse representation

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2015年第4期52卷 943-951页

作者： Dou, Nuo Zhao, Ruizhen Cen, Yigang Hu, Shaohai Zhang, Yongdong Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Institute of Information Science Beijing Jiaotong University Beijing100044 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Denoising and super-resolution reconstruction are performed separately in traditional methods for noisy image super-resolution reconstruction, while in the noisy image super-resolution reconstruction method based on sparse representation and dictionary learning the two processes are compounded together. Since an image patch can be well represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary, two dictionaries are trained respectively from noisy low-and clean high-resolution image patches by enforcing the similarity of two sparse representations with respect to their own dictionary. Given a noisy low-resolution image, sparse representations of low-resolution patches via trained low-dictionary are computed, then the high-resolution image can be reconstructed from high-resolution patches with the help of the related low-resolution sparse representations and trained high-dictionary, after global optimization a clean high-resolution is obtained to accomplish the goal of image super-resolution and denosing simultaneously. The experiments show that zooming low-resolution image to a middle-resolution using locally adaptive zooming algorithm for extracting features can get a better reconstructed image than bicubic interpolation algorithm. By setting the parameter λ, we can obtain the best performance both in super-resolution and denoising with absolute advantages in image quality and visual effect, which demonstrates the validity and robustness of our algorithm. ©, 2015, Science Press. All right reserved.

关键词： Image denoising

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Identification of mild cognitive impairment using extreme learning machines model 11th

Identification of mild cognitive impairment using extreme le...

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11th International Conference on intelligent computing, ICIC 2015

作者： Zhang, Wen Shen, Hao Ji, Zhiwei Meng, Guanmin Wang, Bing School of Electronics and Information Engineering Tongji University Shanghai201804 China The Advanced Research Institute of Intelligent Sensing Network Tongji University Shanghai201804 China The Key Laboratory of Embedded System and Service Computing Tongji University Shanghai201804 China Department of Clinical Laboratory Tongde Hospital of Zhejiang Province 234th Gucui Road Hangzhou310012 China

ISBN: (纸本)9783319221854

Alzheimer’s disease (AD) is very common disorder among the older people. Predicting mild cognitive impairment (MCI), an intermediate stage between normal cognition and dementia, in individuals with some symptoms of cognitive decline may have great influence on treatment choice and disease progression. In this work, a novel prediction model based on extreme learning machines algorithm was proposed to identify mild cognitive impairment using the information of some biomarkers, i.e., Position Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and the cerebrospinal fluid (CSF). Firstly, each subject will be represented by three biomarkers using a vector with 201 dimensions after some essential pre-processing. Then, a prediction model based on extreme learning machine algorithm was trained to catch the difference between the patients and healthy persons. Finally, the model had been employed to identify identifying MCI participants from the normal people. Based on a dataset, including 99 MCI patients and 52 health controls, our proposed method achieved 70.86 % prediction accuracy with 67.68 % sensitivity at the precision of 76.92 %. Extensive experiments are performed to compare our method with state-of-the-art techniques, i.e., support vector machine (SVM) and SVM with fusion kernels. Experimental results demonstrate that proposed extreme learning machine is a powerful tool for predicting MCI with excellent performance and less time. © Springer International Publishing Switzerland 2015.

关键词： Support vector machines

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Dynamic analysis of a 3-DOF 3-PUU parallel manipulator based on the principle of virtual work

Dynamic analysis of a 3-DOF 3-PUU parallel manipulator based...

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IEEE/ASME (AIM) International Conference on advanced intelligent Mechatronics

作者： Song Lu Yangmin Li Department of Electromechanical Engineering University of Macau Macao Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin China

ISBN: (纸本)9781467391085

This paper presents a straightforward and systematical methodology for conducting the inverse dynamic analysis of a 3-prismatic-universal-universal (PUU) parallel kinematic manipulator (PKM) through employing the principle of virtual work. The proposed 3-PUU PKM possesses three translational degree-of-freedom (DOF). The position, velocity and acceleration of joints are solved via the inverse kinematic analysis, the link Jacobian matrices which map the velocity of the moving platform into the velocity and angular velocity of struts are derived in the fixed coordinate system. Based on the d'Alembert's form of the principle of virtual work, the applied generalized forces of the whole mechanical system are divided into the applied forces of moving platform, struts, sliders, and motor-coupling-leadscrew, respectively. The joint actuated torques are obtained through eliminating the virtual displacement in the dynamic equations. Furthermore, the inertial term, centrifugal and Coriolis term, and gravitational term of the total joint torque are determined. Simulation for a conical spiral trajectory demonstrates that the total torque is dominated by the inertial term.

关键词： Manipulator dynamics Joints Acceleration Dynamics Kinematics Mathematical model

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