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2017 International Conference on E-Health, EH 2017

作者： Bocaj, Enkeleda Queiroz, Jonas Leitão, Paulo Patrikakis, Charalampos Z Polytechnic Institute of Bragança Bragança5300-253 Portugal Faculty of Automation Engineering Piraeus University of Applied Sciences Thivon 250 Egaleo122 41 Greece Faculty of Engineering University of Porto Rua Dr. Roberto Frias Porto4200-465 Portugal LIACC - Artificial Intelligence and Computer Science Laboratory Rua Campo Alegre 1021 Porto4169-007 Portugal Faculty of Electronics Engineering Piraeus University of Applied Sciences Thivon 250 Egaleo122 41 Greece

ISBN: (纸本)9789898533654

The wearable devices technology has evolved dramatically in many respects over the last decade, e.g., in the accuracy and quality of the data provided, as well as the number of parameters measured. This created new opportunities not only in the fitness domain, but also in the well-being and general health. Indeed, each day, an ever-increasing number of people are interested in monitoring their daily habits and activities, as well as their physiological and psychological condition, aiming to improve their overall quality of life. Such tasks can be realized by the use of predictive tools that can guide and support individuals during their daily living activities. In this regard, wearable devices produce large volume of data, which should be properly analyzed in a fast and personalized manner. Furthermore, in order to provide the individuals with more effective and actionable information, other individuals and health professionals should be involved in the process. Having this in mind, this paper presents a smart health collaborative approach that considers the extensive analysis of the individual's wearable data, as well as a health social network to provide an infrastructure for the individuals to manage their data and easily interact with others individuals and physicians. The experiments focused on the data analysis module and considered data from four elderly individuals, wearing a Fitbit Charge HR. A clustering analysis was performed to identify and characterize patterns in their physiological behaviors and daily activities that were used for the prediction of the individuals' daily conditions and activities.

关键词： Physiology

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A Method for Correcting Illumination Unevenness of Solar Image

A Method for Correcting Illumination Unevenness of Solar Ima...

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2017 IEEE 8th International Conference on Software engineering and Service science

作者： Xiaona Fu Kaifan Ji Yunfei Yang Wenliang Duan Hui Deng Xiaoli Zhang Yunnan Key Laboratory of Computer Technology Application/Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming Yunnan Observatory Chinese Academy of Sciences Key Laboratory of Solar Activity National Astronomical Observatories Chinese Academy of Sciences

Traditional correction methods can not be used to correct effectively the solar images with uneven illumination,such as the bilinear interpolation method. We adopt an improved algorithm that combine the background fitting method and the mask method. The algorithm consists of the following main steps:segmenting the image, selecting the sampling points, interpolating the sampling points, calculating the mask, correcting the image. By comparing four evaluation indicators of the corrected image, including the information entropy of the image, the mean brightness, the mean variance and the peak signal-to-noise ratio（PSNR）, this improved algorithm is proved to be effectively in the solar images with uneven illumination.

关键词： Uneven illumination background fitting mask

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Feature Selection based on Improved Maximal Relevance and Minimal Redundancy

Feature Selection based on Improved Maximal Relevance and Mi...

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2016 IEEE Advanced Information Management,Communicates,Electronic and automation Control Conference(IMCEC 2016)

作者： Huijuan Hao Maoli Wang Yongwei Tang Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan) School of Automation Engineering Qingdao Technological University

ISBN: (纸本)9781509001668

A new method for feature selection based on improved maximal relevance and minimal redundancy(m RMR) is proposed in this *** order to describe the influence of the added features on correlation between candidate features subset and decision,the standard m RMR was improved by introducing the calculation of parameter Sig(a,B,D)*** value of Sig(a,B,D)3is used to determine whether a new feature is added in the candidate features subset,and it can be applied to determine whether the searching for the remaining subset is stopped ahead of *** support vector machine(SVM) as classifier,the results based on UCI datasets show that the new method is effective.

关键词： Maximal Relevance and Minimal Redundancy feature Selection RMI SVM

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Investigation of intergranular bright points from the New Vacuum Solar Telescope

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Research in Astronomy and Astrophysics 2016年第5期16卷 69-80页

作者： Kai-Fan Ji Jian-Ping Xiong Yong-Yuan Xiang Song Feng Hui Deng Feng Wang Yun-Fei Yang Faculty of Information Engineering and Automation/Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and Technology Key Laboratory of Solar Activity National Astronomical Observatories Chinese Academy of Sciences Key Laboratory of Modern Astronomy and Astrophysics Nanjing University Ministry of Education Yunnan Observatories Chinese Academy of Sciences

Six high-resolution TiO-band image sequences from the New Vacuum Solar Telescope （NVST） are used to investigate the properties of intergranular bright points （igBPs）. We detect the igBPs using a Laplacian and morphological dilation algorithm （LMD） and automatically track them using a three- dimensional segmentation algorithm, and then investigate the morphologic, photometric and dynamic prop- erties of igBPs in terms of equivalent diameter, intensity contrast, lifetime, horizontal velocity, diffusion index, motion range and motion type. The statistical results confirm previous studies based on G-band or TiO-band igBPs from other telescopes. These results illustrate that TiO data from the NVST are stable and reliable, and are suitable for studying igBPs. In addition, our method is feasible for detecting and track- ing igBPs with TiO data from the NVST. With the aid of vector magnetograms obtained from the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, the properties of igBPs are found to be strongly influenced by their embedded magnetic environments. The areal coverage, size and intensity contrast values of igBPs are generally larger in regions with higher magnetic flux. However, the dynamics of igBPs, includ- ing the horizontal velocity, diffusion index, ratio of motion range and index of motion type are generally larger in the regions with lower magnetic flux. This suggests that the absence of strong magnetic fields in the medium makes it possible for the igBPs to look smaller and weaker, diffuse faster, and move faster and further along a straighter path.

关键词： techniques： image processing -- Sun： photosphere -- methods： data analysis -- methods：statistical

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Active Disturbance Rejection Control in Stribeck Friction Dynamic Compensation for High-accuracy Servo Systems 35

Active Disturbance Rejection Control in Stribeck Friction Dy...

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第35届中国控制会议

作者： WANG Lijun JIAO Ruihua LI Qing YIN Yixin ZHENG Qing School of Automation & Electrical Engineering University of Science and Technology Beijing Key Laboratory of Advanced Control of Iron and Steel Process Ministry of Education Department of Electrical and Computer Engineering Gannon University

ISBN: (纸本)9781509009107

The servo systems are always difficult to design with high-accuracy due to nonlinear stick-slip at the low velocities and the uncertainties of friction dynamic model.A linear active disturbance rejection control(ADRC) dynamic compensation solution is presented independent of the friction *** deal with the problem of observing or tracking for unknown discontinuous signals of friction with Stribeck effect,a kind of linear extended state observer(LESO) algorithm is *** a linear control law is proposed to compensate the friction disturbance *** comparison with proportional-integral-derivative(PID) controller,the simulation results show that the proposed method not only has better accuracy of the discontinuous friction signal tracking,but also ensures better dynamic friction compensation at the low velocities.

关键词： Disturbance rejection Extended state observer Servo systems Stribeck friction Discontinuous tracking

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Pose-driven deep convolutional model for person re-identification

arXiv

引用

arXiv 2017年

作者： Su, Chi Li, Jianing Zhang, Shiliang Xing, Junliang Gao, Wen Tian, Qi School of Electronics Engineering and Computer Science Peking University Beijing100871 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249-1604 United States

Feature extraction and matching are two crucial components in person Re-Identification (ReID). The large pose deformations and the complex view variations exhibited by the captured person images significantly increase the difficulty of learning and matching of the features from person images. To overcome these difficulties, in this work we propose a Pose-driven Deep Convolutional (PDC) model to learn improved feature extraction and matching models from end to end. Our deep architecture explicitly leverages the human part cues to alleviate the pose variations and learn robust feature representations from both the global image and different local parts. To match the features from global human body and local body parts, a pose driven feature weighting sub-network is further designed to learn adaptive feature fusions. Extensive experimental analyses and results on three popular datasets demonstrate significant performance improvements of our model over all published state-of-the-art methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Feature extraction

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Micro-Nano Fabrication: Fabrication of Sub-Micrometer-Sized MoS2Thin-Film Transistor by Phase Mode AFM Lithography (Small 49/2018)

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Small 2018年第49期14卷

作者： Lianqing Liu Jialin Shi Meng Li Peng Yu Tie Yang Guangyong Li State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang 110016 China University of Chinese Academy of Science Beijing 100049 China Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA 15213 USA

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Deep ranking: Triplet MatchNet for music metric learning

Deep ranking: Triplet MatchNet for music metric learning

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Rui Lu Kailun Wu Zhiyao Duan Changshui Zhang Department of Automation Tsinghua University State Key Lab of Intelligent Technologies and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Beijing China Department of Electrical and Computer Engineering University of Rochester NY USA

ISBN: (纸本)9781509041183

Metric learning for music is an important problem for many music information retrieval (MIR) applications such as music generation, analysis, retrieval, classification and recommendation. Traditional music metrics are mostly defined on linear transformations of handcrafted audio features, and may be improper in many situations given the large variety of music styles and instrumentations. In this paper, we propose a deep neural network named Triplet MatchNet to learn metrics directly from raw audio signals of triplets of music excerpts with human-annotated relative similarity in a supervised fashion. It has the advantage of learning highly nonlinear feature representations and metrics in this end-to-end architecture. Experiments on a widely used music similarity measure dataset show that our method significantly outperforms three state-of-the-art music metric learning methods. Experiments also show that the learned features better preserve the partial orders of the relative similarity than handcrafted features.

关键词： Metric learning music similarity deep learning convolutional neural networks

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The ultra-high-energy event KM3-230213A within the global neutrino landscape

arXiv

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arXiv 2025年

作者： Adriani, O. Aiello, S. Albert, A. Alhebsi, A.R. Alshamsi, M. Garre, S. Alves Ambrosone, A. Ameli, F. Andre, M. Aphecetche, L. Ardid, M. Ardid, S. Argüelles, C. Aublin, J. Badaracco, F. Bailly-Salins, L. Bardačová, Z. Baret, B. Bariego-Quintana, A. Becherini, Y. Bendahman, M. Gualandi, F. Benfenati Benhassi, M. Bennani, M. Benoit, D.M. Berbee, E. Berti, E. Bertin, V. Betti, P. Biagi, S. Boettcher, M. Bonanno, D. Bottai, S. Bouasla, A.B. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Bozza, R.M. Brânzaş, H. Bretaudeau, F. Breuhaus, M. Bruijn, R. Brunner, J. Bruno, R. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Capone, A. Carenini, F. Carretero, V. Cartraud, T. Castaldi, P. Cecchini, V. Celli, S. Cerisy, L. Chabab, M. Chen, A. Cherubini, S. Chiarusi, T. Circella, M. Clark, R. Cocimano, R. Coelho, J.A.B. Coleiro, A. Condorelli, A. Coniglione, R. Coyle, P. Creusot, A. Cuttone, G. Dallier, R. De Benedittis, A. De Wasseige, G. Decoene, V. Deguire, P. Del Rosso, I. Di Mauro, L.S. Di Palma, I. Díaz, A.F. Diego-Tortosa, D. Distefano, C. Domi, A. Donzaud, C. Dornic, D. Drakopoulou, E. Drouhin, D. Ducoin, J.-G. Duverne, P. Dvornický, R. Eberl, T. Eckerová, E. Eddymaoui, A. van Eeden, T. Eff, M. van Eijk, D. El Bojaddaini, I. El Hedri, S. El Mentawi, S. Ellajosyula, V. Enzenhöfer, A. Ferrara, G. Filipović, M.D. Filippini, F. Franciotti, D. Fusco, L.A. Gal, T. Méndez, J. García Soto, A. Garcia Oliver, C. Gatius Geißelbrecht, N. Genton, E. Ghaddari, H. Gialanella, L. Gibson, B.K. Giorgio, E. Goos, I. Goswami, P. Gozzini, S.R. Gracia, R. Guidi, C. Guillon, B. Gutiérrez, M. Haack, C. van Haren, H. Heijboer, A. Hennig, L. Hernández-Rey, J.J. Idrissi, A. Ibnsalih, W. Idrissi Illuminati, G. Janik, O. Joly, D. de Jong, M. de Jong, P. Jung, B.J. Kalaczyński, P. Kamp, N. Keegans, J. Kikvadze, V. Kistauri, G. Kopper, C. Kouchner, A. Kovalev, Y.Y. Krupa, L. Kueviakoe, V. Kulikovskiy, V. Kvatadze, R. Labalme, M. Lahmann, R. INFN Sezione di Firenze via Sansone 1 Sesto Fiorentino50019 Italy Università di Firenze Dipartimento di Fisica e Astronomia via Sansone 1 Sesto Fiorentino50019 Italy Via Santa Sofia 64 Catania95123 Italy Université de Strasbourg CNRS IPHC UMR 7178 StrasbourgF-67000 France Université de Haute Alsace rue des Frères Lumière Mulhouse68093 Cedex France Khalifa University of Science and Technology Department of Physics PO Box 127788 Abu Dhabi United Arab Emirates Aix Marseille Univ CNRS IN2P3 CPPM Marseille France c/Catedrático José Beltrán 2 Valencia Paterna46980 Spain Università di Napoli "Federico II" Dip. Scienze Fisiche "E. Pancini" Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy INFN Sezione di Roma Piazzale Aldo Moro 2 Roma00185 Italy Universitat Politècnica de Catalunya Laboratori d’Aplicacions Bioacústiques Centre Tecnològic de Vilanova i la Geltrú Avda. Rambla Exposició s/n Vilanova i la Geltrú08800 Spain Subatech IMT Atlantique IN2P3-CNRS Nantes Université 4 rue Alfred Kastler - La Chantrerie BP Nantes20722 44307 France Universitat Politècnica de València Instituto de Investigación para la Gestión Integrada de las Zonas Costeras C/ Paranimf 1 Gandia46730 Spain Harvard University Department of Physics Laboratory for Particle Physics and Cosmology Lyman Laboratory 17 Oxford St. CambridgeMA02138 United States Université Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France INFN Sezione di Genova Via Dodecaneso 33 Genova16146 Italy Università di Genova Via Dodecaneso 33 Genova16146 Italy LPC CAEN Normandie Univ ENSICAEN UNICAEN CNRS IN2P3 6 boulevard Maréchal Juin Caen14050 France Comenius University in Bratislava Department of Nuclear Physics and Biophysics Mlynska dolina F1 Bratislava842 48 Slovakia Czech Technical University in Prague Institute of Experimen

On February 13th, 2023, the KM3NeT/ARCA telescope detected a neutrino candidate with an estimated energy in the hundreds of PeVs. In this article, the observation of this ultra-high-energy neutrino is discussed in light of null observations above tens of PeV from the IceCube and Pierre Auger observatories. Performing a joint fit of all experiments under the assumption of an isotropic E−2 flux, the best-fit single-flavour flux normalisation is E2Φ1fν+ν¯ = 7.5 × 10−10 GeVcm−2s−1sr−1 in the 90% energy range of the KM3NeT event. Furthermore, the ultra-high-energy data are then fit together with the IceCube measurements at lower energies, either with a single power law or with a broken power law, allowing for the presence of a new component in the spectrum. The joint fit including non-observations by other experiments in the ultra-high-energy region shows a slight preference for a break in the PeV regime if the "High-Energy Starting Events" sample is included, and no such preference for the other two IceCube samples investigated. A stronger preference for a break appears if only the KM3NeT data is considered in the ultra-high-energy region, though the flux resulting from such a fit would be inconsistent with null observations from IceCube and Pierre Auger. In all cases, the observed tension between KM3NeT and other datasets is of the order of 2.5σ − 3σ, and increased statistics are required to resolve this apparent tension and better characterise the neutrino landscape at ultra-high energies. Copyright © 2025, The Authors. All rights reserved.

关键词： Neutrons

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Clothing Co-Segmentation Based on HOG Features and E-SVM Classifier 6

Clothing Co-Segmentation Based on HOG Features and E-SVM Cla...

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6th International Conference on Digital Home, ICDH 2016

作者： Zhang, Jianhong Liu, Li Huang, Dongyan Fu, Xiaodong Huang, Qingsong Yunnan Provincial Key Laboratory of Computer Technology Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

ISBN: (纸本)9781509044009

To improve the accuracy of clothing segmentation, a novel co-segmentation method is proposed in this paper based on HOG(Histogram of Oriented Gradients) features and E-SVM(Exemplar Support Vector Machine) classifier. The cosegmentation method uses an auxiliary dataset and is implemented with three steps: superpixel grouping, E-SVM classifier training, and segmentation propagation. Firstly, image input by user is segmented in superpixel, such that the images in auxiliary dataset can be divided into multi-regions. Secondly, some regions are selected to a positive segmentation and HOG information is used to train E-SVM classifiers. Finally, the cloth is segmented into user image with E-SVM classifiers and segmentation propagation. Experimental results show that the proposed method can segment clothing images with high accuracy. © 2016 IEEE.

关键词： Superpixels

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