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arXiv 2019年

作者： Liu, Hao Yuan, Hui Liu, Qi Hou, Junhui Liu, Ju School of Information Science and Engineering Shandong University Qingdao266200 China School of Control Science and Engineering Shandong University Jinan250000 China Department of Computer Science City University of Hong Kong Hong Kong999077 Hong Kong

Point cloud based 3D visual re resentation is becoming popular due to its ability to exhibit the real world in a more comprehensive and immersive way. However, under a limited network bandwidth, it is very challenging to communicate this kind of media due to its huge data volume. Therefore, the MPEG have launched the standardization for point cloud compression (PCC), and proposed three model categories, i.e., TMC1, TMC2, and TMC3. Because the 3D geometry compression methods of TMC1 and TMC3 are similar, TMC1 and TMC3 are further merged into a new platform namely TMC13. In this paper, we first introduce some basic technologies that are usually used in 3D point cloud compression, then review the encoder architectures of these test models in detail, and finally analyze their rate distortion performance as well as complexity quantitatively for different cases (i.e., lossless geometry and lossless color, lossless geometry and lossy color, lossy geometry and lossy color) by using 16 benchmark 3D point clouds that are recommended by MPEG. Experimental results demonstrate that the coding efficiency of TMC2 is the best on average (especially for lossy geometry and lossy color compression) for dense point clouds while TMC13 achieves the optimal coding performance for sparse and noisy point clouds with lower time complexity. Copyright © 2019, The Authors. All rights reserved.

关键词： Color

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Autonomous Swimmers Tracking Algorithm Based on Kalman Filter and CamShift

Autonomous Swimmers Tracking Algorithm Based on Kalman Filte...

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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Muhammad Aftab Hayat Goutian Yang Atif Iqbal Adeel Saleem Muhammad Mateen School of Control & Computer Engineering North China Electric power University Beijing China School of Renewable Energy & Clean Energy North China Electric power University Beijing China Department of Electrical Engineering The University of Lahore Lahore Pakistan

ISBN: (数字)9781728146133

ISBN: (纸本)9781728146140

The visual system is the most important sensory organ of human beings. Most of the external information obtained by human beings comes from vision. With the continuous exploration of the natural world by human beings, people always hope to realize the function of human perception through some digital machine and automatically acquire the information of the outside world. The traditional CamShift algorithm needs to manually locate the target during tracking, and it can be used in complex backgrounds such as color interference and occlusion. In this paper, a comfortable and autonomous swimmer tracking algorithm based on Kalman filter with CamShift is proposed. First, we use the Canny edge detection and inter-frame difference method to segment the entire region of the moving swimmer and then use the extracted target region to initialize the CamShift algorithm. The modified search window supports the automatic tracking of the swimmer. When there is similar color interference in the background, or the swimmer is severely impeded, an improved algorithm combined the Kalman filter with the CamShift algorithm for tracking the target accurately. Experimental results confirmed that the enhanced algorithm significantly improves the outcomes and it can still follow the object effectively and steadily under severe occlusion, noisy environment and color interference.

关键词： Target tracking Image color analysis Image edge detection Interference Kalman filters Heuristic algorithms

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Brain Mapping using a Blockchain Approach

Brain Mapping using a Blockchain Approach

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E-Health and Bioengineering Conference (EHB)

作者： Felix-Constantin Adochiei Radu Ciucu Ioana Raluca Adochiei Florin Ciprian Argatu Bogdan Enache Casian Miron George Seritan Faculty of Electrical Engineering University Politehnica of Bucharest Romania Department of Aircraft Integrated Systems and Mechanics Military Technical Academy "Ferdinand I" Bucharest Romania Faculty of Automatic Control and Computer Engineering "Gheorghe Asachi" Technical University of Iasi Romania

In this paper we propose a pipeline for brain mapping using a decentralized database with a blockchain architecture. We use a two-layer model for a distributed imaging data ledger capable of version control and authoring augmented data. Our platform is capable of direct volume rendering inside a browser window using WebGL. The data visualization pipeline allows for real-time multi-intensity transfer functions and global opacity of a given imaging recording. The platform is intended as a collaboration tool for multiple imaging laboratories and institutes and aims to help in organizing augmented data and multiple level-of-detail recordings used on a wide array of devices. We believe our platform will aid in brain mapping efforts for classification, measurement and visualization of cortical structures trough the aid of virtual reality and a scalable backend based on a standard blockchain technology.

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Privacy of real-time pricing in smart grid

arXiv

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arXiv 2019年

作者： GhoddousiBoroujeni, Mahrokh Fay, Dominik Dimitrakakis, Christos Kamgarpour, Maryam Department of Electrical Engineering Sharif University of Technology Iran EECS school KTH Royal Institute of Technology Sweden Computer Science and Engineering faculty Chalmers University of Technology Sweden Automatic Control Laboratory D-ITET ETH Zurich Switzerland

Installing smart meters to publish real-time electricity rates has been controversial while it might lead to privacy concerns. Dispatched rates include fine-grained data on aggregate electricity consumption in a zone and could potentially be used to infer a household's pattern of energy use or its occupancy. In this paper, we propose Blowfish privacy to protect the occupancy state of the houses connected to a smart grid. First, we introduce a Markov model of the relationship between electricity rate and electricity consumption. Next, we develop an algorithm that perturbs electricity rates before publishing them to ensure users' privacy. Last, the proposed algorithm is tested on data inspired by household occupancy models and its performance is compared to an alternative solution. Copyright © 2019, The Authors. All rights reserved.

关键词： Electric power utilization

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Optimized Design of an Onboard Resonant Self-Heater for Automotive Lithium-Ion Batteries at Cold Climates

Optimized Design of an Onboard Resonant Self-Heater for Auto...

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IEEE Energy Conversion Congress and Exposition

作者： Chong Zhu Yunlong Shang Fei Lu Hua Zhang School of Mechanical Engineering Shanghai Jiao Tong University Shanghai P. R. China School of Control Science and Engineering Shandong University Jinan P. R. China Department of Electrical and Computer Engineering Drexel University Philadelphia PA USA

The automotive lithium-ion batteries suffer severe capacity and power degradation at subzero temperatures, leading to serious "range anxiety" of the electric vehicles (EVs). Therefore, the onboard battery preheating equipment is essential for EVs at cold climates. In this study, an interleaved resonant onboard battery self-heater is developed for internally preheating the automotive batteries without external power supplies, thereby providing great flexibility for EVs at different parking areas. By properly adjusting the switching frequency, the self-heater can achieve the zero-current-switching (ZCS) to improve the energy consumption and eliminate the voltage spikes during the turn-off. Meanwhile, a detailed guideline for optimizing the resonant tank parameters is presented so that the efficiency of the self-heater can be further developed by reducing the circulating current. The experimental validation on 18650 cells demonstrates the proposed heater can preheat the battery from -20℃ to 0℃ within only 3.5 minutes, and only consumes 5% of the cell energy.

关键词： connected and automated hybrid electric vehicles (CAHEVs) battery thermal management model predictive control energy saving

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Profiling Adaptive Immunity: A Quantitative Framework for Immune Repertoire Dynamics and Clinical Diagnostics

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Fundamental Research 2025年

作者： Yexing Chen Haiwen Ni Yongjie Li Jin Ma Chen Huang Sixian Yang Xiangfei Xie Haitao Lv Min Li Peng Cao State Key Laboratory of Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture Nanjing University of Chinese Medicine Nanjing 210000 China Jiangsu Provincial Medical Innovation Center Affiliated Hospital of Integrated Traditional Chinese and Western Medicine Nanjing University of Chinese Medicine Nanjing 210000 China Department of Hematology Affiliated Hospital of Nanjing University of Chinese Medicine The First Clinical College of Nanjing University of Chinese Medicine Nanjing 210000 China Children’s Hospital of Soochow University Suzhou 215000 China Key Laboratory of Quantum Information University of Science and Technology of China Hefei 230000 China Department of Computer Science College of Engineering and Applied Science University of Cincinnati Cincinnati 45221 USA Department of Oncology Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine Nanjing 210000 China

While immune repertoire(IR) dynamics encode critical disease signatures, existing models fail to capture the non-equilibrium processes that governing clonal selection and somatic hypermutation (SHM). We develop a biophysical framework that mathematically reconstructs IR evolution through energy landscape optimization, where 1) clonal emergence probabilities map to metastable states, 2) repertoire transitions obey non-equilibrium dynamics, and 3) inter-IR distances quantify the distribution transformation costs via optimal transport theory. This model enables macroscopic immune state detection from 10k cells by resolving critical fluctuations in sparse sampling regimes. Experimental validation across murine/human cohorts demonstrates precise unsupervised stratification of immune stages and disease states without prior clinical annotations. By bridging stochastic SHM kinetics with deterministic repertoire shifts, our approach establishes quantitative metrics for tracking immunological trajectories and pathological progression. This paradigm shift from static pattern recognition to dynamic process decoding empowers sensitive immunomonitoring and personalized therapeutic design through ultra-low-input clinical sampling.

关键词： Deductive reasoning Immune repertoire analysis Repertoire shift quantification Early disease screening Systemic immunity inference Kawasaki disease Colorectal cancer

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Triple Generalized-Inverse Neural Network for Diagnosis of Flat Foot

Triple Generalized-Inverse Neural Network for Diagnosis of F...

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第三十八届中国控制会议

作者： Liangming Chen Long Jin Yuhe Li Mei Liu Bolin Liao Chenfu Yi Zhongbo Sun School of Information Science and Engineering Lanzhou University Guangdong Provincial Engineering Technology Research Center for Sports Assistive Device Guangzhou Sport University College of Information Science and Engineering Jishou University College of Mathematics and Computer Science Gannan Normal University Department of Control Engineering Changchun University of Technology

Traditional gradient-based neural networks may fall into local minima, and because these networks involve with lengthy iteration process, they are not fast enough. By the generalized-inverse method, the least squares solution with the smallest2-norm can be obtained. For traditional neural networks based on generalized-inverse method, the weights from the hidden layer to the output layer and the biases are generated by a random approach;the weights from the hidden layer to the output layer are calculated with the aid of generalized-inverse method. In triple generalized-inverse neural network(TGINN) proposed in this paper, all the weight matrixes from one layer to another are calculated with the aid of generalized-inverse method, as well as for biases of neurons in the hidden layer. Comparison experiments between TGINN and other models are carried out, and the experimental results illustrate the superiority of the proposed TGINN.

关键词： Neural network computer-aided diagnosis Generalized-inverse method Flat foot

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A Unifying Approach to Maximal Permissiveness in Modular control of Discrete-Event Systems

A Unifying Approach to Maximal Permissiveness in Modular Con...

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IEEE Annual Conference on Decision and control

作者： Jan Komenda Feng Lin Jan H. van Schuppen Institute of Mathematics Academy of Sciences of the Czech Republic Brno Czech Republic Department of Electrical and Computer Engineering Wayne State University Detroit MI USA Van Schuppen Control Research Amsterdam The Netherlands

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

In this paper, a unifying approach to maximal permissiveness in modular control of discrete-event systems is proposed. It is based on three important concepts of modular closed-loops: monotonicity, distributivity, and exchangeability. Monotonicity of various closed-loops satisfying a given property considered in this paper holds whenever the underlying property is preserved under language unions. Distributivity holds if the inverse projections of local plants satisfy the given property with respect to each other. Among new results, sufficient conditions are proposed for distributed computation of supremal relatively observable sublanguages.

关键词： Generators Observability Supervisory control controllability Discrete-event systems Gold Observers

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Analysis of Harmonics Detection and their Filtration by using Active Band Pass Filter for Domestic Appliances

Analysis of Harmonics Detection and their Filtration by usin...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Ehtisham Lodhi Zeeshan Lodhi Zhu Fenghua Gang Xiong Feiyue Wang Waheed ur Rehman Tariku Sinshaw The SKL for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering COMSATS University Islamabad Abbottabad Campus Pakistan Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan Guangdong China Beijing Engineering Research Center of Intellectual System and Technology Chinese Academy of Sciences Beijing China College of Mechanical Engineering and Applied Electronics Technologies Beijing University of Technology Beijing China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

In modern era, one of the rising power quality (PQ) concern is harmonics distortion (HD). The HD is usually produced by increase in the utilization of nonlinear user loads at domestic level. It not only spoils the PQ but also have harmful effects on electrical appliances. So, this paper presents a new approach to mitigate the HD in home appliances rapidly by utilizing the active band pass filter (ABPF). The fast Fourier transform (FFT) technique is employed for the detection of harmonics in appliances. The band pass filter dramatically affects the response time of active filter. The suggested control methodology is capable to eliminate harmonics from distorted input voltage waveform and provides a pure sinusoidal waveform. The prototype of this model is also implemented. Experimental results along with simulation show that the proposed model is quite effective in eliminating HD despite load variations and also useful to compensate the reactive power in electronic home appliances. Hence, this technique is cost effective for domestic users with high quality performance in all aspects.

关键词： Power harmonic filters Harmonic analysis Band-pass filters Harmonic distortion Home appliances

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Strong Consistency of Variable Selection for Stationary Linear Stochastic Systems

Strong Consistency of Variable Selection for Stationary Line...

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第三十八届中国控制会议

作者： ZHAO Wenxiao YIN G.George BAI Er-Wei Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Department of Mathematics Wayne State University Department of Electrical and Computer Engineering University of Iowa

In this paper, we consider the variable selection for linear stochastic systems. A modified LASSO-type estimator is introduced. Then based on the classical persistent excitation(PE) for systems identification, the strong consistency of the estimates is established, i.e., the zero elements in the unknown parameter vector being correctly identified and estimates for the nonzero elements in the unknown parameter vector converging to the true values with probability one. Compared with the existing results on similar topics, the strong consistency of estimates is established while in existing literature only the convergence in probability was obtained. For this, new theoretical analysis method is adopted in this paper.

关键词： Variable Selection Linear Stochastic System Strong Consistency

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