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K-Means Clustering-based Kernel Canonical Correlation Analysis for Multimodal Emotion Recognition ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10250-10254页

作者： Luefeng Chen Kuanlin Wang Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada Tokyo Institute of Technology Yokohama 226-8502 Japan

Emotion is an important part of human interaction. Emotional recognition can greatly promote human-centered interaction techniques. On this basis, multimodal feature fusion can effectively improve the emotion recognition rate. However, in the multimodal feature fusion at the feature level, most of the methods do not consider the intrinsic relationship between different modes. Only the fusion of analysis and transformation of the feature matrices of different modes does not make better use of modal differences to improve the recognition rate. This problem led us to propose feature fusion method based on K-Means clustering and kernel canonical correlation analysis (KCCA). Clustering makes the classification of features not classified by mode, but by the degree of influence on emotional labels, thus positively affecting the results of KCCA. The experimental results obtained on the Savee database show that the proposed K-Means based KCCA improves overall classification performance and produces higher recognition rate than that of the state of art methods, such as the Informed Segmentation and Labeling Approach.

关键词： Emotion Recognition K-Means Clustering Kernel Canonical Correlation Analysis Feature Fusion

Author Correction: The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci

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Nature computational science 2023年第7期3卷 658页

作者： Gengjie Jia Yu Li Xue Zhong Kanix Wang Milton Pividori Rabab Alomairy Aniello Esposito Hatem Ltaief Chikashi Terao Masato Akiyama Koichi Matsuda David E Keyes Hae Kyung Im Takashi Gojobori Yoichiro Kamatani Michiaki Kubo Nancy J Cox James Evans Xin Gao Andrey Rzhetsky Shenzhen Branch Guangdong Laboratory of Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China. jiagengjie@***. Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia. Computer Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong People's Republic of China. Department of Medicine and Vanderbilt Genetics Institute Vanderbilt University Medical Center Nashville TN US. Department of Medicine Institute of Genomics and Systems Biology Committee on Genomics Genetics and Systems Biology University of Chicago Chicago IL US. Department of Operations Business Analytics and Information Systems University of Cincinnati Cincinnati OH US. Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA US. Extreme Computing Research Center Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia. HPE HPC/AI EMEA Research Laboratory Bristol UK. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Clinical Research Center Shizuoka General Hospital Shizuoka Japan. Department of Applied Genetics The School of Pharmaceutical Sciences University of Shizuoka Shizuoka Japan. Department of Ophthalmology Graduate School of Medical Sciences Kyushu University Fukuoka Japan. Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan. Biological and Environmenta

RRNet: Relational reasoning network with parallel multi-scale attention for salient object detection in optical remote sensing images

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arXiv 2021年

作者： Cong, Runmin Zhang, Yumo Fang, Leyuan Li, Jun Zhang, Chunjie Zhao, Yao Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Department of Computer Science City University of Hong Kong Hong Kong College of Electrical and Information Engineering Hunan University Changsha410082 China Peng Cheng Laboratory Shenzhen518000 China Guangdong Provincial Key Laboratory of Urbanization and Geosimulation School of Geography and Planning Sun Yat-sen University Guangzhou510275 China Key Laboratory of Information Perception and Systems for Public Security of MIIT Nanjing University of Science and Technology Nanjing210094 China Department of Computer Science City University of Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. Despite some saliency models were proposed to solve the intrinsic problem of optical RSIs (such as complex background and scale-variant objects), the accuracy and completeness are still unsatisfactory. To this end, we propose a relational reasoning network with parallel multi-scale attention for SOD in optical RSIs in this paper. The relational reasoning module that integrates the spatial and the channel dimensions is designed to infer the semantic relationship by utilizing high-level encoder features, thereby promoting the generation of more complete detection results. The parallel multi-scale attention module is proposed to effectively restore the detail information and address the scale variation of salient objects by using the low-level features refined by multi-scale attention. Extensive experiments on two datasets demonstrate that our proposed RRNet outperforms the existing state-of-the-art SOD competitors both qualitatively and quantitatively. https://***/proj_***. © 2021, CC BY-NC-SA.

关键词： Object detection

Neural-Learning Trajectory Tracking Control of Flexible-Joint Robot Manipulators with Unknown Dynamics

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Neural-Learning Trajectory Tracking Control of Flexible-Join...

2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Shuyang Chen John T. Wen Department of Mechanical Aerospace and Nuclear Engineering Rensselaer Polytechnic Institute 110 8th St Troy NY USA Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA

Fast and precise motion control is important for industrial robots in manufacturing applications. However, some collaborative robots sacrifice precision for safety, particular for high motion speed. The performance degradation is caused by the inability of the joint servo controller to address the uncertain nonlinear dynamics of the robot arm, e.g., due to joint flexibility. We consider two approaches to improve the trajectory tracking performance through feedforward compensation. The first approach uses iterative learning control, with the gradient-based iterative update generated from the robot forward dynamics model. The second approach uses dynamic inversion to directly compensate for the robot forward dynamics. If the forward dynamics is strictly proper or is non-minimum-phase (e.g., due to time delays), its stable inverse would be non-causal. Both approaches require robot dynamical models. This paper presents results of using recurrent neural networks (RNNs) to approximate these dynamical models - forward dynamics in the first case, inverse dynamics (possibly non-causal) in the second case. We use the bi-directional RNN to capture the noncausality. The RNNs are trained based on a collection of commanded trajectories and the actual robot responses. We use a Baxter robot to evaluate the two approaches. The Baxter robot exhibits significant joint flexibility due to the series-elastic joint actuators. Both approaches achieve sizable improvement over the uncompensated robot motion, for both random joint trajectories and Cartesian motion. The inverse dynamics method is particularly attractive as it may be used to more accurately track a user input as in teleoperation.

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A hybrid seven level inverter topology formed by cascading T-type and active neutral point clamped inverter for induction motor drives 44

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A hybrid seven level inverter topology formed by cascading T...

44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018

作者： Yadav, Apurv Kumar Gopakumar, K. Krishna Raj, R. Umanand, L. Bhattacharya, Subhashish Jarzyna, Wojciech Department of Electronic Systems Engineering Indian Institute of Science Bangalore India Department of Electrical and Computer Engineering North Carolina State University North Carolina United States Electrical Engineering and Computer Science Faculty Lublin University of Technology Lublin Poland

ISBN: (纸本)9781509066841

This paper proposes a hybrid 7-level inverter scheme formed by cascading basic inverter cells. The proposed inverter is realized by cascading basic 3-level T-type converter with 5-level active neutral point clamped inverter. The proposed topology uses low voltage semiconductor devices and floating capacitors which are balanced in every PWM switching cycle using pole voltage redundancies for every pole voltage levels. The balancing of capacitors are independent of modulation index and load power factor. The topology forms two stacks at the front-end which uses individual symmetrical reduced DC sources and each stack works only for half the fundamental period. The open loop V/f control is simulated as well as experimentally verified on induction machine using proposed inverter topology. The experimental and simulation results for transient and steady state operations are included in the paper. Further, a brief comparison of proposed topology with conventional 7-level topologies have also been included. © 2018 IEEE.

关键词： Topology

Experimental and numerical investigation of corona discharge induced flow on a flat plate

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arXiv 2019年

作者： Vaddi, Ravi Sankar Guan, Yifei Chen, Zhi Yan Mamishev, Alexander Novosselov, Igor V. Department of Mechanical Engineering University of Washington Seattle United States Department of Electrical and Computer Engineering University of Washington Seattle United States Institute for Nano-Engineering Systems University of Washington Seattle United States

Electrohydrodynamic (EHD) flow induced by planar corona discharge in the wall boundary layer region is investigated experimentally and via a multiphysics computational model. The EHD phenomena has many potential engineering applications, its optimization requires a mechanistic understanding of the ion and flow transport. The corona EHD actuator consisting of two electrodes located in the wall boundary layer creates an EHD driven wall jet. The applied voltage between the electrodes is varied and the resulting effects in the charge density and flow field are measured. Constant current hotwire anemometry is used to measure velocity profile. The airflow near the wall acts a jet and it reaches a maximum of 1.7 m/s with an energy conversion efficiency of ∼2%. The velocity decreases sharply in the normal direction. Multiphysics numerical model couples ion transport equation and the Navier Stokes equations to solve for the spatiotemporal distribution of electric field, charge density and flow field. The numerical results match experimental data shedding new insights into mass, charge and momentum transport phenomena. The EHD driven flow can be applied to flow control strategies and design of novel particle collectors. Copyright © 2019, The Authors. All rights reserved.

关键词： Electric corona

Radar-only ego-motion estimation in difficult settings via graph matching

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arXiv 2019年

作者： Cen, Sarah H. Newman, Paul Department of Electrical Engineering and Computer Science Massachussetts Institute of Technology Cambridge02139 United States Oxford Robotics Institute University of Oxford OxfordOX1 3PJ United Kingdom

Radar detects stable, long-range objects under variable weather and lighting conditions, making it a reliable and versatile sensor well suited for ego-motion estimation. In this work, we propose a radar-only odometry pipeline that is highly robust to radar artifacts (e.g., speckle noise and false positives) and requires only one input parameter. We demonstrate its ability to adapt across diverse settings, from urban UK to off-road Iceland, achieving a scan matching accuracy of approximately 5.20 cm and 0.0929 deg when using GPS as ground truth (compared to visual odometry’s 5.77 cm and 0.1032 deg). We present algorithms for keypoint extraction and data association, framing the latter as a graph matching optimization problem, and provide an in-depth system analysis. Copyright © 2019, The Authors. All rights reserved.

关键词： Motion estimation

Erratum to "Automated parameter selection for accelerated MRI reconstruction via low-rank modeling of local k-space neighborhoods" [Z Med. Phys. 33 (2023) 203-219]

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Zeitschrift fur medizinische Physik 2024年第1期35卷 119-119页

作者： Efe Ilicak Emine Ulku Saritas Tolga Çukur Computer Assisted Clinical Medicine Mannheim Institute for Intelligent Systems in Medicine Medical Faculty Mannheim Heidelberg University Mannheim Germany National Magnetic Resonance Research Center (UMRAM) Bilkent University Ankara Turkey. Electronic address: efe.ilicak@medma.uni-heidelberg.de. National Magnetic Resonance Research Center (UMRAM) Bilkent University Ankara Turkey Department of Electrical and Electronics Engineering Bilkent University Ankara Turkey Neuroscience Program Bilkent University Ankara Turkey.

Wireless power transfer with information asymmetry: A public goods perspective

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arXiv 2019年

作者： Zhang, Meng Huang, Jianwei Zhang, Rui Department of Information Engineering Chinese University of Hong Kong School of Science and Engineering Chinese University of Hong Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society Department of Information Engineering Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

Wireless power transfer (WPT) technology enables a cost-effective and sustainable energy supply in wireless networks. However, the broadcast nature of wireless signals makes them non-excludable public goods, which leads to potential free-riders among energy receivers. In this study we formulate the wireless power provision problem as a public goods provision problem, aiming to maximize the social welfare of a system of an energy transmitter (ET) and all the energy users (EUs), while considering their private information and self-interested behaviors. We propose a two-phase all-or-none scheme involving a low-complexity Power And Taxation (PAT) mechanism, which ensures voluntary participation, truthfulness, budget balance, and social optimality at every Nash equilibrium (NE). We propose a distributed PAT (D-PAT) algorithm to reach an NE, and prove its convergence by connecting the structure of NEs and that of the optimal solution to a related optimization problem. We further extend the analysis to a multi-channel system, which brings a further challenge due to the non-strict concavity of the agents' payoffs. We propose a Multi-Channel PAT (M-PAT) mechanism and a distributed M-PAT (D-MPAT) algorithm to address the challenge. Simulation results show that, our design is most beneficial when there are more EUs with more homogeneous channel gains. Copyright © 2019, The Authors. All rights reserved.

关键词： Game theory