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2024 International Conference on Actual Problems of Electron Devices engineering, APEDE 2024

作者： Svetlov, Mikhail S. L’vov, Alexey A. Martirosyan, Arman M. Martirosyan, David M. Bagaev, Igor S. Khasanov, Semen T. Laboratory of Control and Automation Problems in Mechanical Engineering Institute of Precision Mechanics and Control of RAS Saratov Russia Institute of Institute of Electronic Engineering and Instrumentation Yuri Gagarin State Technical University of Saratov Saratov Russia Institute of Applied Information Technologies and Communications Yuri Gagarin State Technical University of Saratov Saratov Russia Phystech-School of Applied Mathematics and Computer Science Moscow Institute of Physics and Technology Moscow Russia

ISBN: (纸本)9798350393316

This paper proposes a semi-Markov model of telecommunication network (TCN). The variant of dynamic traffic adaptive control of queuing system as a special case of TCN is considered. The main purpose of control is to minimize the average cost per unit of time to service the incoming flow of information (packets). This takes into account the different bandwidth of the channels, the processing speed of information in the channel and the information capacity of the buffers. The approach to the organization of dynamic control taking into account noise immunity (information reliability) and information security is discussed. © 2024 IEEE.

关键词： Bandwidth

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Undo the codebook bias by linear transformation for visual applications 13

Undo the codebook bias by linear transformation for visual a...

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21st ACM International Conference on Multimedia, MM 2013

作者： Zhang, Chunjie Zhang, Yifan Wang, Shuhui Pang, Junbiao Liang, Chao Huang, Qingming Tian, Qi School of Computer and Control Engineering University of Chinese Academy of Sciences 100049 Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Key Lab of Intell. Info. Process Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China College of Computer Science and Technology Beijing University of Technology 100124 Beijing China School of Computer National Engineering Research Center for Multimedia Software Wuhan University 430072 Wuhan China Department of Computer Sciences University of Texas at San Antonio San Antonio TX 78249 United States

ISBN: (纸本)9781450324045

The bag of visual words model (BoW) and its variants have demonstrate their effectiveness for visual applications and have been widely used by researchers. The BoW model first extracts local features and generates the corresponding codebook, the elements of a codebook are viewed as visual words. The local features within each image are then encoded to get the final histogram representation. However, the codebook is dataset dependent and has to be generated for each image dataset. This costs a lot of computational time and weakens the generalization power of the BoW model. To solve these problems, in this paper, we propose to undo the dataset bias by codebook linear transformation. To represent every points within the local feature space using Euclidean distance, the number of bases should be no less than the space dimensions. Hence, each codebook can be viewed as a linear transformation of these bases. In this way, we can transform the pre-learned codebooks for a new dataset. However, not all of the visual words are equally important for the new dataset, it would be more effective if we can make some selection using sparsity constraints and choose the most discriminative visual words for transformation. We propose an alternative optimization algorithm to jointly search for the optimal linear transformation matrixes and the encoding parameters. Image classification experimental results on several image datasets show the effectiveness of the proposed method. Copyright © 2013 ACM.

关键词： Linear transformations

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Hierarchical Retrieval of High-Resolution Fingerprints Based on Pore Feature

Hierarchical Retrieval of High-Resolution Fingerprints Based...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jing Ma Yuanrong Xu Suyu Dong Wei Wang School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Computer Science and Technology Harbin Institute of Technology Weihai China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Faced with an escalating number of fingerprint images, most existing retrieval approachs suffer from a common problem: diminishing computational efficiency. This paper presents a hierarchical retrieval system tailored for high-resolution fingerprint images that utilizes abundant pore features and robust recognizability to improve retrieval performance. The framework comprises two core components. Firstly, a CNN-based feature extraction network is established, incorporating an attention mechanism to capture pore features in fingerprint images comprehensively. Subsequently, a hierarchical fingerprint retrieval approach is introduced, involving connection graph construction and a hierarchy of jump table structures for efficient retrieval of query pores. Empirical experiments conducted on high-resolution fingerprint image datasets underscore the system’s effectiveness. Compared with other advanced pore-based fingerprint retrieval methods, the proposed method exhibits a notable rise in the hit rate with reduced penetration rates, significantly reducing the retrieval time.

关键词： Accuracy Image recognition Attention mechanisms Image matching Fingerprint recognition Feature extraction Robustness Computational efficiency Bioinformatics

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Fuzzy synthetic condition assessment of wind turbine based on combination weighting and cloud model

Fuzzy synthetic condition assessment of wind turbine based o...

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作者： Zheng, Kai Han, Lina Guo, Shuli Wang, Zhenyu Zhang, Xinmiao Dong, Xinghui College of Energy Power and Mechanical Engineering North China Electric Power University ChangPing District Beijing China Department of Cardiovascular Internal Medicine Nanlou Branch of Chinese PLA General Hosptal Beijing China School of Automation Beijing Institute of Technology Beijing100081 China School of Control and Computer Engineering North China Electric Power University ChangPing District Beijing China

This paper presented an improved fuzzy synthetic model based on combination weighting and a cloud model to optimize wind turbine maintenance strategy and improve operational reliability. First, a condition assessment framework was proposed by analyzing the monitored physical quantities of a working wind turbine. Based on the establishment of a state health evaluation index and health status classification of wind turbines, the weight of each index was determined with a combination weighting method while the membership degree of each state grade was determined with a membership cloud model. A comprehensive evaluation of the health status of the wind turbine was carried out using the method of stratified evaluation. The results showed that the proposed method was effective and feasible. The results also showed that the condition assessment that utilized the improved method predicted the change of operating conditions and more closely matched real operating conditions than the traditional fuzzy assessment method. © 2017-IOS Press and the authors. All rights reserved.

关键词： Wind turbines

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Enhancement of Two-Photon Fluorescence Microscopy Through Auxiliary Microspheres for Microscopic Observation 14

Enhancement of Two-Photon Fluorescence Microscopy Through Au...

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14th IEEE International Conference on Cyber Technology in Automation, control, and Intelligent Systems, CYBER 2024

作者： Zhang, Feng Jin, Zijian Jiang, Chaodi Yuan, Shuai Yang, Yongliang Wang, Xiaoduo School of Electrical & Control Engineering Shenyang Jianzhu University Liaoning Province Shenyang110168 China School of Computer Science and Engineering Shenyang Jianzhu University Liaoning Province Shenyang110168 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9798331506056

At present, it is still a great challenge to improve the observation accuracy of two-photon fluorescence microscope. A new method that using auxiliary microspheres as movable components on the sample is proposed to solve this challenge in this study, it can improve high-resolution imaging. In experiments using two-photon fluorescence microscopy, the half-width height (FWHM) of red fluorescent nanoparticles with a diameter of 20 nm was observed to be 296 nm using a primitive microscope system. In this paper by combining barium titanate microspheres with two-photon fluorescence microscopy, the accuracy of the point spread function of the nanoparticles can be improved by a factor of about 1.65, which results in a significant reduction of their half-width height measurements to 179 nm. The research is of great significance to improve the resolution of two-photon fluorescence microscope. This progress is very valuable for studying biological tissues and cell structures, it provides a new impetus for the sustainable development in the microscopic imaging field. © 2024 IEEE.

关键词： Microspheres

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Protein-Folding Inspired Programmable control System for Robot Swarms

Protein-Folding Inspired Programmable Control System for Rob...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Zhang, Feng Gu, Yuping Wang, Zebin Yuan, Shuai Yang, Yongliang School of Electrical and Control Engineering Shenyang Jianzhu University Liaoning Shenyang110168 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Liaoning Shenyang110106 China School of Computer Science and Engineering Shenyang Jianzhu University Liaoning Shenyang110168 China

ISBN: (纸本)9781665481090

In swarm robotics, most of the control algorithms are inspired by the behaviors of animal swarms. The capability gap between robots and animals, however, hinders the reliability of these algorithms. Toward developing reliable algorithms of robot swarms, a promising solution is learning from the collective behaviors of low-intelligent natural swarms, such as cell collectives and molecular assemblies. Here, we proposed a programmable control system inspired by protein folding. Using our homemade RoboFold platform, we demonstrated a protein-folding inspired programmable control method for robot swarms. By programming and designing the sequence of a thread of protein-like robots, the developed system can mimic the formation of hierarchical structures in robot swarms as the three-level of protein structures. We quantitatively characterized the specific features of self-organized morphologies. The experimental results together demonstrated the feasibility of this method. To summarize, this work successfully translated the principles of protein-folding into programming methods to control the morphology of robot swarms. Considering that the protein folding problem is essentially a physicochemical problem, this system is not only beneficial for the control of swarm robots but also has the potential to develop into a tool for studying the protein folding problem. © 2024 IEEE.

关键词： Robot programming

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A data aggregation scheme based on secret sharing with error tolerance in smart grid

arXiv

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arXiv 2018年

作者： Guan, Zhitao Si, Guanlin Du, Xiaojiang Liu, Peng School of Control and Computer Engineering North China Electric Power University China Department of Computer and Information Science Temple University PhiladelphiaPA United States School of Computer Hangzhou Dianzi University Hangzhou China

As the next generation of power system, smart grid provides people with great convenience in efficiency and quality. It supports the two-way communication and extremely improves the efficiency of utilization of energy resource. In order to dispatch accurately and support the dynamic price, a lot of smart meters are installed at user's house to collect the real-time data. However, all these collected data are related to user privacy. In this paper, we propose a data aggregation scheme based on secret sharing with error tolerance in smart grid, which ensures that the control center gets the integrated data without revealing users' privacy. Meanwhile, we also consider the differential privacy and error tolerance during the data aggregation. At last, we analyze the security of our scheme and carry out experiments to validate the results. Copyright © 2018, The Authors. All rights reserved.

关键词： Energy resources

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Scene Hierarchy: A Missing Piece in Scene Classification 14

Scene Hierarchy: A Missing Piece in Scene Classification

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14th IEEE International Conference on control and Automation, ICCA 2018

作者： Yusong, Leng Xiaotian, Lin Ramesh, Bharath Cheng, Xiang Heng, Lee Tong NUS Graduate School for Integrative Sciences and Engineering National University of Singapore 21 Lower Kent Ridge Rd Singapore Singapore School of Astronautics Control Science and Engineering Harbin Institute of Technology 92 West Dazhi Street Harbin China Temasek Laboratories National University of Singapore 21 Lower Kent Ridge Rd Singapore Singapore Faculty of Engineering Electrical and Computer Engineering National University of Singapore 21 Lower Kent Ridge Rd Singapore Singapore

ISBN: (纸本)9781538660898

With the ultimate aim of semantic understanding of visual scenes, categorizing them into meaningful groups has been a central task in computer vision for more than two decades. From classical handcrafted features to recent self-learnt features, substantial progress has been made to improve its performance. Failed to be noticed by most solutions proposed so far, a hierarchical structure existing in scene categories, termed as scene hierarchy, is discovered in this paper. We argue that such hierarchy could reveal the latent relations among different visual scenes and further advance the state of the art of scene classification. In particular, we present a data-driven approach to identify the scene hierarchy and demonstrate its effectiveness in assisting scene classification. © 2018 IEEE.

关键词： Semantics

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Filter-Based Fault Diagnosis of Heat Exchangers 6th

Filter-Based Fault Diagnosis of Heat Exchangers

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6th International Conference on Life System Modeling and Simulation, LSMS 2020, and 6th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2020

作者： Zhang, Jianhua Li, Hongrui Fan, Penghao Hou, Guolian Ren, Mifeng State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China College of Information Engineering Taiyuan University of Technology Taiyuan030024 China

ISBN: (纸本)9789813363779

In this paper, a filter is proposed to diagnose fouling and sensor faults of heat exchangers. First, a typical heat exchanger system model is established, a filter based on the survival information potential is then designed for non-Gaussian stochastic systems. Consequently, the state estimation generated by the filter is used for fault detection, and the support vector machine is then employed to identify fouling and fault location of heat exchangers. The simulation results show that the fault diagnosis of heat exchangers can be effectively. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Granger-Causality Mining in Atmospheric Visibility Based on Deep Learning 9th

Granger-Causality Mining in Atmospheric Visibility Based on ...

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9th International Conference on Frontier Computing, FC 2019

作者： Liu, Bo He, Xi Li, Jianqiang Qu, Guangzhi Lang, Jianlei Gu, Rentao Faculty of Information Technology School of Software Engineering Beijing University of Technology Beijing100124 China Computer Science and Engineering Department Oakland University RochesterMI48309 United States Key Laboratory of Beijing on Regional Air Pollution Control College of Environmental and Energy Engineering Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789811532498

Causal relationship mining of multi-dimensional meteorological time series data can reveal the potential connection between visibility and other influencing factors, and it is meaningful in terms of environmental management, air pollution chasing, and haze control. However, because causality analysis based on statistical methods or traditional machine learning techniques cannot represent the complex relationship between visibility and its influencing factors, as an extension of traditional Granger-causality analysis, we propose a causality mining method based on the seq2seq-LSTM deep learning model. The method can profoundly reveal the hidden relationship between different features and visibility and the regular pattern of bad weather, which can provide theoretical support for air pollution control. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Time series

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