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arXiv 2019年

作者： Verschueren, Robin Frison, Gianluca Kouzoupis, Dimitris van Duijkeren, Niels Zanelli, Andrea Novoselnik, Branimir Frey, Jonathan Albin, Thivaharan Quirynen, Rien Diehl, Moritz ABB Corporate Research Baden-Dättwil Switzerland IMTEK University of Freiburg Germany Department of Mechanical Engineering KU Leuven Belgium Faculty of Electrical Engineering and Computing University of Zagreb Croatia Institute for Dynamic Systems and Control ETH Zürich Switzerland Mitsubishi Electric Research Labs CambridgeMA United States IMTEK Faculty of Mathematics University of Freiburg Germany

The acados software package is a collection of solvers for fast embedded optimization, intended for fast embedded applications. Its interfaces to higher-level languages make it useful for quickly designing an optimization-based control algorithm by putting together different algorithmic components that can be readily connected and interchanged. However, since the core of acados is written in a low-level language close to the hardware, we do not sacrifice computational efficiency. Thus, we aim to provide both flexibility and efficiency through modularity, without the need to rely on automatic code generation, which facilitates maintainability and extensibility. The main features of acados are: efficient optimal control algorithms targeting embedded devices implemented in C, linear algebra based on the high-performance BLASFEO [25] library, user-friendly interfaces to Matlab and Python, and compatibility with the modeling language of CasADi [5]. acados is published under the BSD 2-Clause open source license. Copyright © 2019, The Authors. All rights reserved.

关键词： Efficiency

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Robot-based image analysis for evaluating rehabilitation after brain surgery

Robot-based image analysis for evaluating rehabilitation aft...

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作者： Kohout, Jan Crha, Jan Trnková, Kateřina Štícha, Karel Mareš, Jan Chovanec, Martin University of Chemistry and Technology in Prague Department of Computing and Control Engineering Technická 1905/5 Praha 6 166 28 Czech Republic Faculty Hospital Královské Vinohrady Department of Otorhinolaryngology Šrobárova 1150/50 Praha 10 100 34 Czech Republic

After certain types of brain surgery, patients are often affected by changes in both their dynamic balance and facial disorder. Because rehabilitation takes several months, it is important that both doctors and patients are able to monitor progress quantitatively. At present, such quantification is subjective and highly dependent on the doctor’s opinion. Thus, we here investigate the use of robot-based image analysis for measuring rehabilitation. To evaluate a patient’s dynamic balance, we developed a mobile robotic platform that uses a stereovision camera (MS Kinect) to capture a video of the subject walking along a hospital corridor. To evaluate a patient’s facial disorders, the same camera is used in a static mode to detect and capture precise facial movements that the subject is asked to perform. From these videos, specific patterns can be extracted for rehabilitation process description. © 2018, Brno University of Technology. All rights reserved.

关键词： Computer vision

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New Grid for Particle Filtering of Multivariable Nonlinear Objects

New Grid for Particle Filtering of Multivariable Nonlinear O...

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Federated Conference on Computer Science and Information Systems

作者： Piotr Kozierski Jacek Michalski Talar Sadalla Wojciech Giernacki Joanna Zietkiewicz Szymon Drgas Faculty of Computing Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan University of Technology Faculty of Electrical Engineering Institute of Control Robotics and Information Engineering Division of Control and Robotics Poznan University of Technology

ISBN: (纸本)9781538623718

In the paper a new grid (potentially linear, nonlinear and even semi-Markovian jump system) was presented. All transition and measurement functions were proposed. Moreover, the transition functions of two types were considered - dependent on one and many different state variables. Also 10 types of measurements were proposed for both nodal and branch cases. Based on the obtained results one can see, which measurement functions are "easy", and which are "hard" for state estimation task.

关键词： Measuring Function Particle filters grids Grid State variable Multivariable nonlinearity

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Research and Implementation of Breast Cancer Intelligent Recognition Algorithm Based on Deep Convolutional Neural Network

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Journal of Physics: Conference Series 2020年第1期1634卷

作者： Taixing Chen Lidan Wang Yunfei Li Shukai Duan College of Electronic and Information Engineering Southwest University Chongqing 400715 China Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Chongqing 400715 China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715 China College of Artificial Intelligence Southwest University Chongqing 400715 China

Breast cancer is one of the most dangerous cancers with a particularly high mortality rate for women. In the face of thousands of female pathological pictures, how to use computer related control algorithms to quickly and efficiently intelligently idently female breast cancer images has become an urgent problem in the world. With the development of computer vision and deep convolutional neural networks, this paper applies the convolutional neural network (CNN) algorithm to solve the classification and recognition computer tasks of histological images for breast cancer, aiming to learn from tens of thousands of histological images features for breast cancer, and to assist doctors in diagnosing patients' disease was found by computer control algorithm. There are many well-known neural network computer algorithms, such as AlexNet, Inception-Net, ResNet. Image recognition for breast is a binary classification problem, and it is not appropriate to use a ResNet network with a large amount of parameters. Therefore, this paper borrowed Alexnet algorithm and redesigned the algorithm, then used it for breast cancer experiments. We compared with the capsule algorithm used by Anupama authors. Our experiment improves the accuracy rate by 2%, and reduces the amount of neural network parameters, which greatly speeds up the training time.

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Ensemble of online sequential extreme learning machine based on cross-validation

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Journal of Physics: Conference Series 2020年第3期1550卷

作者： Liyun Wei Lidan Wang Yunfei Li Shukai Duan College of Electronic and Information Engineering Southwest University Chongqing 400715 China Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Chongqing 400715 China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715 China College of Artificial Intelligence Southwest University Chongqing 400715 China

The online sequential extreme learning machine (OS-ELM) was proposed as an evolution of the Extreme learning machine (ELM) in 2006. The learning algorithm can learn data by fixing or changing the size of the chunk (a block of data). Compared with other online algorithms, it has faster training speed, and the generalization performance is better. The proposed ensemble of neural networks further demonstrates that the use of the neural network sets with multiple consensus schemes has a greater improvement over the stability of a single network. The basic idea of cross-validation is to group the original data sets, part of which is to train as the training set and the other part of which is to verify as the test set, so the algorithm can reduce the over-fitting problem of the data to a certain extent, and extract more valid information in the limited data set. In this paper, a new ensemble of online sequential extreme learning machine based on cross-validation (ENOS-ELM) is proposed. Through application of cross-validation and integration to the training stage, the accuracy of the algorithm in classification is further improved.

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Car Plate Detection Based on Yolov3

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Journal of Physics: Conference Series 2020年第1期1544卷

作者： Siyuan Shen Lidan Wang Shukai Duan Xin He College of Electronic and Information Engineering Southwest University Chongqing 400715 China Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Chongqing 400715 China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715 China College of Artificial Intelligence Southwest University Chongqing 400715 China

An intelligent car plate detection method can make the travel more convenient and efficient. However, traditional methods are reasonably effective under the specific circumstances or strong assumptions only, and there are few databases for car plate detection. Therefore, a novel real-time car plate detection method based on improved Yolov3 has been proposed. In order to select the more precise number of candidate anchor boxed and aspect ratio dimensions, the K-Means algorithm is utilized. To solve the short of the available car plate database, a car plate database which has 6668 pictures has been established. As shown in the experimental results, the method which is proposed by this paper is better than original Yolov3. Thanks to the car plate database, the proposed method obtained better results even in the situation of inclination, too bright or too dark, different weather and so on.

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Rain Removal from images based on Computer Vision and conditional Generative Adversarial Networks

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Journal of Physics: Conference Series 2020年第1期1570卷

作者： Yunfei Li Lidan Wang Taixing Chen Ziyuan Wang Shukai Duan College of Electronic and Information Engineering Southwest University Chongqing 400715 China Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Chongqing 400715 China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715 China ChongqingBrain Science Collaborative Innovation Center Chongqing 400715 China College of Artificial Intelligence Southwest University Chongqing 400715 China

In view of the quick development of modern computer technology, more pictures of natural scenes have been collected and analyzed for application in military fields, scientific research, and computer vision fields related to object detection. However, the sharpness of these pictures collected will be affected by rain and snow, which will reduce the visibility of things in the pictures. This will not only affect the viewing, but also affect scientific research fields such as autonomous driving and video surveillance. Therefore, the research on image rain removing is very practical. An improved algorithm model named pixel-GAN is proposed in this article, which based on conditional Generative Adversarial Networks (cGAN). This new method regards the problem of removing rain from specific images as the style conversion problem. What we improved is that a VGG-16 module is added to the original network to extract perceptual loss as a new part of the loss function. We show the excellent rain-removing ability of the improved algorithm and also illustrate the reason why we add the perceptual loss. Compared with other existing methods, there is no need to set a prior model, the training speed is faster and the proposed model finally generates higher quality pictures.

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Robust correlation filter that combined temporal regularization with spatial weight L1 constraint

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Journal of Physics: Conference Series 2020年第4期1550卷

作者： Xin He Shukai Duan Lidan Wang College of Electronic and Information Engineering Southwest University Chongqing 400715 China College of Artificial Intelligence Southwest University Chongqing 400715 China Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Chongqing 400715 China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715 China

The parameter optimization of the Object tracking algorithms has achieved excellent performance based on parameter regularization. Adaptive spatial regularization correlation filter can obtain the optimal weight coefficient for a specific target. Inspired by this, we propose a filter combined temporal regularity with spatial L1 regularization (LTASRCF). The time regularizer is used to remember the data of the previous frame to update the current target position. It performs well in the process of occlusion, deformation and motion blur. Meanwhile, L1 regularization of spatial weights reduces the computational complexity of the coefficients, and improves the robustness of the spatial weight matrix. We have conducted comparative experiments on the OTB-2015 dataset, and the proposed LTASRCF model ranks first among the five popular algorithms, which outperforms by 9.2% and 18.9% than KCF, respectively, and reaches real-time performance.

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Performance evaluation of on-chip wavelength conversion based on InP/In1-xGaxAs y P1-ysemiconductor waveguide platforms

arXiv

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arXiv 2020年

作者： Wen, Jin Li, Kang Gong, Yongkang Hughes, Ben Campbell, Michael A. Lazzaini, Mattia Duan, Lina Ma, Chengju Fan, Wei Jia, Zhenan Fu, Haiwei Copner, Nigel School of Science Xi An Shiyou University Xian710065 China Wireless and Optoelectronics Research Innovation Centre Faculty of Computing Engineering and Science University of South Wales CF37 JDL United Kingdom School of Physics AndAstronomy Cardiff University CardiffCF243AA United Kingdom National Physical Laboratory Hampton Road Teddington MiddlesexTWJIOLW United Kingdom National Physical Laboratory Hampton Road Teddington MiddlesexTWJIOLW United Kingdom Shaanxi Key Laboratory of Measurement Control Technology for Oil and Gas Wells Xian710065 China

We propose and design the high confinement InP/Ini-xGaxAsyPi-y semiconductor waveguides and report the results of effective wavelength conversion based on this platform. Efficient confinement and mode field area fluctuation at different wavelength is analyzed to achieve the high nonlinear coefficient. The numerical results show that nearly zero phase-mismatch condition can be satisfied through dispersion tailoring of InP/Ini-GaAsPi- waveguides, and the wavelength conversion ranging over 40 nm with the maximum conversion efficiency -26.3 dB isachieved for fixing pump power 100 mW . Meanwhile, the influences of the doping parameter y and pumping wavelength on the bandwidth and conversion efficiency are also discussed and optimized. It is indicated the excellent optical properties of the InP/In 1-x Ga x As y P 1-y waveguides and pave the way towards direct integration telecom band devices on stand semiconductor platforms. Copyright © 2020, The Authors. All rights reserved.

关键词： Semiconducting indium phosphide

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Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification

Learning Structured Twin-Incoherent Twin-Projective Latent D...

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IEEE International Conference on Data Mining (ICDM)

作者： Zhao Zhang Yulin Sun Zheng Zhang Yang Wang Guangcan Liu Meng Wang School of Computer Science and Technology Soochow University Suzhou China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China School of Information and Control Nanjing University of Information Science and Technology Nanjing China

In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective Latent Flexible DPL (TP-DPL) is proposed, which minimizes the twin-incoherence constrained flexibly-relaxed reconstruction error to avoid the possible over-fitting issue and produce accurate reconstruction. In this setting, TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner. Therefore, TP-DPL can unify the procedures of salient feature representation and classification. The twin-incoherence constraint on coefficients and features can explicitly ensure high intra-class compactness and inter-class separation over them. TP-DPL also integrates the adaptive weighting to preserve local neighborhood of both coefficients and salient features within each class explicitly. For efficiency, TP-DPL selects the Frobenius-norm and abandons the costly l0/l1-norm for group sparse representation. Another byproduct is that TP-DPL can directly apply the class-specific twin-projective reconstruction residual to compute the label of data. Extensive results on public databases show that TP-DPL can deliver the state-of-the-art performance.

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