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第35届中国控制会议

作者： MA Zhuang FANG Yiming LIU Le KANG Kesong Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Tangshan University National Engineering Research Center for Equipment and Technology of Cold Strip Rolling

ISBN: (纸本)9781509009107

In order to improve the displacement control precision of continuous casting mold, the establishment of precise mathematical model of non-sinusoidal vibration displacement control system is important. The modeling method of BP neural network optimized by genetic algorithm is adopted, which can solve the problem of the mechanism modeling of various approximate treatments. Firstly the system model of BP neural network is constructed according to the measured data received from industrial field. Secondly, BP neural network model of genetic optimization is built. Then simulations are eventually performed, and then by comparing the error between the predicted displacement outputs of BP neural network optimized by genetic algorithm and the actual output displacement of the system, the simulation results show that the system error of the identified model is within 4%, and the identification method of BP neural network optimized by genetic algorithm is superior to the mechanism modeling.

关键词： continuous casting mold servo motor non-sinusoidal vibration BP neural network genetic algorithm

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Flatness pattern recognition via EA-ABC cloud inference network implemented by DSP 35

Flatness pattern recognition via EA-ABC cloud inference netw...

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第35届中国控制会议

作者： Zhang Xiuling Gao Wuyang Lai Yongjin Cheng Yantao Hou Daibiao Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Yanshan University

ISBN: (纸本)9781509009107

The present flatness pattern recognition method based on neural network belongs to software simulation,and it can't be directly applied to engineering ***'s more,the accuracy of present flatness pattern recognition is *** order to improve it,flatness pattern recognition via EA-ABC cloud inference network implemented by digital signal processor(DSP) is presented in this ***,based on T-S cloud inference network,flatness pattern recognition model is designed in *** it is applied to 900 HC reversible cold rolling ***,flatness pattern recognition model optimized by efficient adaptive artificial bee colony(EA-ABC) algorithm is ***,the two results of flatness pattern recognition model,which run onMATLAB and DSP respectively,are compared and *** results confirm that EA- ABC algorithm has such advantages:high optimizing precision,less number of iterations,fast speed of *** pattern recognition model via EA-ABC cloud inference network can identify the defect types of flatness *** the same time,the experimental results verify that the T-S cloud inference network can run on the hardware TI TMS320F2812 in a fast speed and it provide a basis for neural network which is applied to practical engineering.

关键词： T-S cloud inference network flatness pattern recognition DSP hardware implementation EA-ABC algorithm

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Nonlinear control of triple inverted pendulum based on T-S cloud inference network 29

Nonlinear control of triple inverted pendulum based on T-S c...

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第29届中国控制与决策会议

作者： Xiu-ling Zhang Jing-huan Dai Yan-tao Cheng Shuang Hao Jia-huan Li Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Yanshan University

ISBN: (纸本)9781509046584

The triple inverted pendulum is a nonlinear, dynamic and unsteady system. The traditional control methods of triple inverted pendulum have limited control accuracy and slow responding. This kind of pendulum system is difficult to control due to the inherent instability and nonlinear behavior. In this paper, GA-RBF-ARX model is applied to model triple inverted pendulum based on its input/output data. According to the nonlinear identification model, a novel method via T-S cloud inference network controller optimized by genetic algorithm(GA) is proposed. T-S cloud inference network is constituted by T-S fuzzy neural network and the cloud model. Therefore, the rapid of fuzzy logic and the uncertain of cloud model for processing data are both taken into account. What's more, GA possesses global optimization characteristics and good parallel design structure. Compared with the simulation recognition results of T-S fuzzy neural network controller, T-S cloud reference network controller has strong robust and fast calculate speed. So, T-S cloud inference network by GA is an effective method in control.

关键词： T-S cloud inference network GA-RBF-ARX Triple inverted pendulum

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Image retrieval based on multi-concept detector and semantic correlation

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Science China Chemistry 2015年第12期58卷 100-114页

作者： XU HaiJiao HUANG ChangQin PAN Peng ZHAO GanSen XU ChunYan LU YanSheng CHEN Deng WU JiYi School of Computer Science and Technology Huazhong University of Science and Technology Research Center for Information Services and Software Technology South China Normal University E-Service Research Center Zhejiang University Electrical and Computer Engineering National University of Singapore

With the rapid development of future network, there has been an explosive growth in multimedia data such as web images. Hence, an efficient image retrieval engine is necessary. Previous studies concentrate on the single concept image retrieval, which has limited practical usability. In practice, users always employ an Internet image retrieval system with multi-concept queries, but, the related existing approaches are often ineffective because the only combination of single-concept query techniques is adopted. At present semantic concept based multi-concept image retrieval is becoming an urgent issue to be solved. In this paper, a novel Multi-Concept image Retrieval Model(MCRM) based on the multi-concept detector is proposed, which takes a multi-concept as a whole and directly learns each multi-concept from the rearranged multi-concept training set. After the corresponding retrieval algorithm is presented, and the log-likelihood function of predictions is maximized by the gradient descent approach. Besides, semantic correlations among single-concepts and multiconcepts are employed to improve the retrieval performance, in which the semantic correlation probability is estimated with three correlation measures, and the visual evidence is expressed by Bayes theorem, estimated by Support Vector Machine(SVM). Experimental results on Corel and IAPR data sets show that the approach outperforms the state-of-the-arts. Furthermore, the model is beneficial for multi-concept retrieval and difficult retrieval with few relevant images.

关键词： multi-concept image retrieval semantic correlation probability estimation concept learning visualevidence

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Person re-identification by features fusion

Person re-identification by features fusion

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2016 IEEE Information technology, Networking, Electronic and Automation Control Conference, ITNEC 2016

作者： Xin, Wan Dongdong, Ge Peng, Li Zhe, Ji National Computer Network Emergency Response Technical Team Coordination Center of China China National Digital Switching System Engineering and Technology Research Center Zhengzhou China

ISBN: (纸本)9781467391924

Person re-identification has an important role in the public security. The methods based on body segmentation have a good performance in distinguishing different people, but it will bring lots of calculation work. To solve this problem, a method combined color distribution fields and Weber Local Descriptor (WLD) is proposed. The method here takes advantage of the color distribution fields to extract the color information;and differential excitation in WLD to extract the texture information in images;and the gradient orientation to extract by orientation in WLD. The experiments show the method in this paper can achieve a good identification results in common data sets. © 2016 IEEE.

关键词： Color

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Human-Cascaded network for Robust Detection of Occluded Pedestrian 3

Human-Cascaded network for Robust Detection of Occluded Pede...

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3rd International Conference on Computing, Networks and Internet of Things, CNIOT 2022

作者： Xu, Zhewei Fu, Xiufeng Feng, Dacheng Li, Wei Liu, Yang Beijing Institute of Computer Technology and Applications Beijing China Engineering Technology Research Center National Digital Switching System Henan Province China

ISBN: (纸本)9781665469104

Occlusion is a key challenge in real-world on-road pedestrian detection task. Due to constrained viewpoint geometry, a pedestrian is very likely to be obstructed by other pedestrians and/or other objects such as cars and bicycles. For Advanced Driver Assistance System (ADAS), heavily occluded pedestrians are as important as reasonable pedestrians because they may burst out from crowds or roadside obstacles. In this paper, a human-cascaded network is proposed for robust detection of heavily occluded pedestrians. Specifically, a sharp-response proposal network (SRPN) is designed to refine the feature responses in a narrow area to handle crowded pedestrian detection, followed by outputting the head and full body proposals for diverse occlusion situations. After RoI-pooling, a visible-guided attention (VGA) module is developed to leverage the head and visible area information. The VGA module also suppresses the feature noise of occluded area to enhance the feature representation learning of the backbone network. Finally, a head-cascade RCNN (HRCNN) network is proposed to predict the pedestrian bounding box from the head proposal. The proposed approach is validated through a widely used pedestrian detection dataset: CityPersons. Experimental results show that our approach achieves promising detection performance (log-average miss rate, MR) improvement of 11.4% on heavy occlusion subset, compared to the baseline detector. © 2022 IEEE.

关键词： Advanced driver assistance systems

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Machine Learning Applications in Internet-of-Drones: Systematic Review, Recent Deployments, and Open Issues

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ACM Computing Surveys 2023年第12期55卷 1-45页

作者： Heidari, Arash Navimipour, Nima Jafari Unal, Mehmet Zhang, Guodao Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul34083 Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Yunlin64002 Taiwan Department of Computer Engineering Nisantasi University Istanbul Turkey Department of Digital Media Technology Hangzhou Dianzi University Hangzhou310018 China

Deep Learning (DL) and Machine Learning (ML) are effectively utilized in various complicated challenges in healthcare, industry, and academia. The Internet of Drones (IoD) has lately cropped up due to high adjustability to a broad range of unpredictable circumstances. In addition, Unmanned Aerial Vehicles (UAVs) could be utilized efficiently in a multitude of scenarios, including rescue missions and search, farming, mission-critical services, surveillance systems, and so on, owing to technical and realistic benefits such as low movement, the capacity to lengthen wireless coverage zones, and the ability to attain places unreachable to human beings. In many studies, IoD and UAV are utilized interchangeably. Besides, drones enhance the efficiency aspects of various network topologies, including delay, throughput, interconnectivity, and dependability. Nonetheless, the deployment of drone systems raises various challenges relating to the inherent unpredictability of the wireless medium, the high mobility degrees, and the battery life that could result in rapid topological changes. In this paper, the IoD is originally explained in terms of potential applications and comparative operational scenarios. Then, we classify ML in the IoD-UAV world according to its applications, including resource management, surveillance and monitoring, object detection, power control, energy management, mobility management, and security management. This research aims to supply the readers with a better understanding of (1) the fundamentals of IoD/UAV, (2) the most recent developments and breakthroughs in this field, (3) the benefits and drawbacks of existing methods, and (4) areas that need further investigation and consideration. The results suggest that the Convolutional Neural Networks (CNN) method is the most often employed ML method in publications. According to research, most papers are on resource and mobility management. Most articles have focused on enhancing only one parameter, with

关键词： Drones

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Studying the enigma of particle dynamics using a new type of regular charged black hole

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Nuclear Physics B 2025年 1017卷

作者： Ditta, Allah Hussain, Ibrar Mushtaq, Farzan Mustafa, G. Atamurotov, Farruh Department of Mathematics School of Science University of Management and Technology Lahore 54000 Pakistan Research Center of Astrophysics and Cosmology Khazar University 41 Mehseti Street Baku AZ1096 Azerbaijan School of Electrical Engineering and Computer Science National University of Sciences and Technology H-12 Islamabad Pakistan Department of Mathematics Shanghai University Newtouch Center for Mathematics of Shanghai University Shanghai 200444 China Department of Physics Zhejiang Normal University Jinhua 321004 China Inha University in Tashkent Ziyolilar 9 Tashkent 100170 Uzbekistan Alfraganus University Yukori Karakamish street 2a Tashkent 100190 Uzbekistan University of Tashkent for Applied Sciences Str. Gavhar 1 Tashkent 100149 Uzbekistan

This study examines the regular charged black hole solution by investigating particle dynamics and various related properties, such as the radius of the horizon, the effective force, the energy, the angular momentum, the ISCO radius, the photon radius, the red-blue shift, and the weak plasma lensing in uniform plasma fields, SIS, and NSIS. Furthermore, we examine the image magnification for the black hole solution. A salient characteristic of this black hole solution is the incorporation of the magnetic charge parameter q, which differentiates it from the Schwarzschild black hole solution. By taking q=0, we compare our results with the Schwarzschild black hole scenario and identify substantial variations. Our research elucidates the impact of the magnetic charge parameter q on multiple physical parameters of the black hole and offers a comprehensive comparison with the Schwarzschild black hole solution. © 2025 The Author(s)

关键词： Effective force Image magnification New charge regular black hole Particle motion Photon radius Plasma lensing Red-blue shift

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Full system simulation and verification framework

Full system simulation and verification framework

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5th International Conference on Information Assurance and Security, IAS 2009

作者： Lin, Jing-Wun Wang, Chen-Chieh Chang, Chin-Yao Chen, Chung-Ho Lee, Kuen-Jong Chu, Yuan-Hua Yeh, Jen-Chieh Hsiao, Ying-Chuan Institute of Computer and Communication Engineering National Cheng Kung University Tainan Taiwan SoC Technology Center Industrial Technology Research Institute Taiwan

ISBN: (纸本)9780769537443

In this paper, we propose a framework to develop high-performance system accelerator hardware and the corresponding software at system-level. This framework is designed by integrating a virtual machine, an electronic system level platform, and an enhanced QEMU-SystemC. The enhancement includes a local master interface for fast memory transfer, and an interrupt handling hardware for software/hardware communication that enables full system simulation. Finally, the PAC DSP core is used as examples to demonstrate the proposed framework for full system simulation. © 2009 IEEE.

关键词： System theory

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The data partition strategy based on hybrid range consistent hash in NoSQL database 13

The data partition strategy based on hybrid range consistent...

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5th International Conference on Internet Multimedia Computing and Service, ICIMCS 2013

作者： Chen, Zhikun Zhang, Lumin Yang, Shuqiang Tan, Shuang He, Li Zhang, Ge Yang, Huiyu School of Computer Science National University of Defense Technology Changsha 410073 China Beijing Aeronautics Engineering Technology Research Center Beijing China

ISBN: (纸本)9781450322522

With the development of Internet technology and Cloud Computing, more and more applications have to be confronted with the challenges of big data. NoSQL Database is fit to the management of big data because of the characteristics of high scalability, high availability and high fault-tolerance. The data partitioning strategy plays an important role in the NoSQL database. The existing data partitioning strategies will cause some problems such as low scalability, hot spot and low performance and so on. In this paper we proposed a new data partitioning strategy - -HRCH, which can partitioning the data in a reasonable way. At last we use some experiments to verify the effectiveness of HRCH. It shows that the HRCH can improve the scalability of the system. It also can avoid the hot spot problem as far as possible. And it also can improve the parallel degree of processing to improve the system's performance in some processing. © 2013 ACM.

关键词： Information management

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