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作者： Gui, R.Z. Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai 200092 China

In high frequency radar, we should avoid noise disturbances in the radar's working-frequency segment. Moreover, the sidelobes of strong targets interfere with the detection of weak targets. A new method based on an adaptive selecting working-frequency is proposed. Frequency spectrum monitor is designed for selecting quiet frequency segment for the radar. Frequency spectrum monitor and the receiver of the radar are arranged to work according to special time periods respectively. So the radar can work in the frequency segments with lower noise disturbances. Moreover, there is no correlation between the noise and the useful echo signal, though the correlation between noises over very short time periods is strong, the noise data produced by frequency spectrum monitor can be exploited effectively Adjusting system parameters in real-time by adaptive methods can be utilized to reduce noise disturbances. Algorithm based on the properties of crosscorrelation between noise and target is exploited for suppressing sidelobe disturbances of strong targets. Lastly, the feasibility of the methods is verified by processing actual radar data.

关键词： Data handling

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An efficient webpage classification algorithm based on LSH

An efficient webpage classification algorithm based on LSH

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International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015

作者： Liu, Junjun Sun, Haichun Ding, Zhijun The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China

ISBN: (纸本)9783662462478

With the explosive growth of Internet information, it is more and more important to fetch real-time and related information. And it puts forward higher requirement on the speed of webpage classification which is one of common methods to retrieve and manage information. To get a more efficient classifier, this paper proposes a webpage classification method based on locality sensitive hash function. In which, three innovative modules including building feature dictionary, mapping feature vectors to fingerprints using Localitysensitive hashing, and extending webpage features are contained. The compare results show that the proposed algorithm has better performance in lower time than the naïve bayes one. © Springer-Verlag Berlin Heidelberg 2015.

关键词： Hash functions

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Joint Optimization of Dynamic service Selection and Request Routing in Cloud-Edge Collaborative Environments

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IEEE Internet of Things Journal 2025年

作者： Tang, Bing Xu, Wei Zhang, Li Cao, Buqing Yang, Qing Li, Kuanching Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Guangzhou Maritime University School of Computer Science Guangzhou510725 China Huaihua University School of Computer and Artificial Intelligence Huaihua418000 China Hunan Institute of Engineering School of Information Science and Engineering Xiangtan411104 China Hunan University of Technology School of Computer Zhuzhou412007 China Hunan University of Science and Technology Sanya Research Institute Sanya572024 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China

Optimizing multi-instance service composition and dynamic request routing has become a critical challenge in cloud-edge collaborative systems. Existing solutions struggle with effectively balancing performance, cost, and bandwidth constraints in dynamic and resource-constrained environments. In this work, we address these challenges by proposing the Removed Minimum Cost Flow (RMCF) algorithm, aiming to minimize average response time while considering constraints such as budget and bandwidth, making it well-suited for time-sensitive services in a cloud-edge collaborative service provision system. Simulations were conducted using real-world data from China Telecom’s base stations in Shanghai, and experiments in various scenarios were considered, including time-sensitive services and general application services, with key performance indicators such as time utility, cost-utility, request completion rate, and timeout rate. The experimental results demonstrate that RMCF achieves lower response times, superior performance, and improved cost-effectiveness compared to other baseline algorithms. © 2014 IEEE.

关键词： Edge computing

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Fuzzy H∞ Filtering for Nonlinear Networked systems with Multiple Stochastic Time-Varying Delays, Packet Dropouts and Sensor Saturation 34

Fuzzy H∞ Filtering for Nonlinear Networked Systems with Mul...

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第三十四届中国控制会议

作者： XU Xiaoli YAN Huaicheng ZHANG Hao SHI Hongbo Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education School of information Science and Engineering East China University of Science and Technology Key Laboratory of Embedded System and Service Computing Ministry of Education

ISBN: (纸本)9781467374439

This paper investigates the problem of H∞ filter design for a class of nonlinear networked system based on T-S fuzzy model. Multiple stochastic time-varying delays and some incomplete information are considered simultaneously. Incomplete information includes randomly occurring sensor saturation and packet dropouts. Stochastic time-varying delays are depicted as a sequence of stochastic and independent variables, which take values on 0 and 1. Two sets of Bernoulli distributed white noises are introduced to describe randomly occurring sensor saturation and packet dropouts. system conservatism is reduced due to introduce an approach of piecewise quadratic Lyapunov function. By solving a set of linear matrix inequalities(LMIs), the filter parameters are obtained. Finally, a simulation example is provided to illustrate the effectiveness of the proposed filter design approach.

关键词： H ∞ filtering,multiple time-varying delays,randomly occurring sensor saturation,packet dropouts,piecewise quadratic Lyapunov function

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A effective clustering indexing based on EMD for Web image retrieval

A effective clustering indexing based on EMD for Web image r...

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2008 the 1st IEEE International Conference on Ubi-Media computing and Workshops, U-Media2008

作者： Tao, Wenbing Luo, Feng Service Computing Technology and System Laboratory Ministry of Education Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781424418657

In this paper, we develop an effective clustering indexing scheme based on Earth Movers' Distance (EMD) for Web image retrieval. By the proposed clustering method, the collected web images can be automatically classified into some image subclasses with the number of the elements less than a given threshold. Then the resulted clustering structure is hierarchical tree, which extremely facilitate to quickly locate the similar image subclass nodes. Additionally, a triple tree structure is presented to effectively represent hierarchical clustering tree, and a query scheme based on approximative EMD measure is developed to achieve fast image retrieval based on the sample image. The experimental results also demonstrate the excellent performance of the proposed method. © 2008 IEEE.

关键词： Indexing (of information)

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Multiscale modeling for classification of SAR imagery using hybrid EM algorithm and genetic algorithm

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Progress in Natural Science:Materials International 2009年第8期19卷 1033-1036页

作者： Xianbin Wen a,b, Hua Zhang a,b, Jianguang Zhang a,b, Xu Jiao a,b, Lei Wang a,b a Tianjin key laboratory of Intelligence computing and Novel Software technology, Tianjin University of technology, Tianjin 300191, China b key laboratory of Computer Vision and system (Tianjin University of technology), Ministry of Education, Tianjin 300191, China [a]Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300191 China [b]Key Laboratory of Computer Vision and System (Tianjin University of Technology) Ministry of Education Tianjin 300191 China

A novel method that hybridizes genetic algorithm (GA) and expectation maximization (EM) algorithm for the classification of synthetic aperture radar (SAR) imagery is proposed by the finite Gaussian mixtures model (GMM) and multiscale autoregressive (MAR) model. This algorithm is capable of improving the global optimality and consistency of the classification performance. The experiments on the SAR images show that the proposed algorithm outperforms the standard EM method significantly in classification accuracy.

关键词： SAR imagery Classification Multiscale Hybrid EM algorithm Genetic algorithm

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Integrating image clustering and memory indexing for large scale content-based image retrieval

Integrating image clustering and memory indexing for large s...

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MIPPR 2009 - Remote Sensing and GIS Data Processing and Other Applications: 6th International Symposium on Multispectral Image Processing and Pattern Recognition

作者： Tao, Wenbing Jin, Hai Luo, Feng Wu, Kun Service Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9780819478092

Content-Based Image Retrieval (CBIR) is an important research topic of information retrieval, involved in computer graphics, image processing, data mining and pattern recognizing. To make content-based image retrieval suitable large-scale image database, we develop an effective dynamic hierarchical clustering index scheme. Although this system uses a hierarchical clustering technology, with the increasing in the number of cluster centers, it is slow to find the centers, and it becomes a system performance bottleneck. In this paper, content features of image memory indexing is built. This method effectively improves the retrieval speed without loss of the precision. Moreover, the clustering model was improved, integrating the content features and textual features of image, which greatly improve the accuracy of the clustering, thus significantly improves the system precision. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Data mining

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DPNet: Dynamic Pooling Network for Accurate and Efficient Size-Aware Tiny Object Detection

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IEEE Internet of Things Journal 2025年

作者： Gong, Luqi Chen, Haotian Chen, Yikun Yao, Tianliang Li, Chao Zhao, Shuai Han, Guangjie Beijing University of Posts and Telecommunications School of Computer Science Beijing China Zhejiang Lab Research Center for Space Computing System Hangzhou China Southwest Jiaotong University SWJTU-LEEDS Joint School Chengdu China LTD Guangdong Zhiyun City construction Technology Co Zhuhai China Tongji University College of Electronic and Information Engineering Department of Control Science and Engineering Shanghai China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

In unmanned aerial systems, especially in complex environments, accurately detecting tiny objects is crucial. Resizing images is a common strategy to improve detection accuracy, particularly for small objects. However, simply enlarging images significantly increases computational costs and the number of negative samples, severely degrading detection performance and limiting its applicability. This paper proposes a Dynamic Pooling Network (DPNet) for tiny object detection to mitigate these issues. DPNet employs a flexible down-sampling strategy by introducing a factor (df) to relax the fixed down-sampling process of the feature map to an adjustable one. Furthermore, we design a lightweight predictor to predict df for each input image, which will be used to decrease the resolution of feature map in backbone. Thus, we achieve input-aware down-sampling. We design an Adaptive Normalization Module (ANM) to make a unified detector well compatible with different dfs. At the same time, we also design a guidance loss to supervise the predictor’s training. DPNet realizes the dynamic allocation of computing resources to trade off detection accuracy and efficiency through this. Experiments on the TinyCOCO and TinyPerson datasets show that our DPNet can save over 35% and 25% GFLOPs, respectively, while maintaining comparable detection *** code will be made publicly available. © 2014 IEEE.

关键词： Object detection

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A comparative study of methodologies for designing IDSSs

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 1997年第2期103卷 277-295页

作者： Blair, A Debenham, J Edwards, J Intelligent Decision Support System Laboratory Key Centre for Advanced Computing Studies School of Computing Sciences University of Technology P.O. Box 123 Broadway N.S.W. 2007 Australia

In this paper, we describe a comprehensive study conducted to understand the methodologies which are being used to design Intelligent Decision Support systems (IDSSs) and to identify the key methodological problems and benefits with using these methodologies. This comprehensive study consists of two parts. The first part is two surveys which together identify the design methodologies being used by a group of IDSS developers and how acceptable they believe their methodologies were for designing their IDSSs. The second part is a comparison of six major formal IDSS design methodologies recently published in the literature and which are not yet known to many developers. This paper is presented to assist IDSS developers in understanding what support can be gained from using existing design methodologies and hence choose the correct one for their project. Furthermore, the paper may be used by IDSS developers to compare the way that they work with the approach proposed by other developers. (C) 1997 Elsevier Science B.V.

关键词： decision support systems knowledge based systems intelligent decision support systems

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Multi-Perspective Data Fusion Framework Based on Hierarchical BERT: Provide Visual Predictions of Business Processes

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Computers, Materials & Continua 2024年第1期78卷 1227-1252页

作者： Yongwang Yuan Xiangwei Liu Ke Lu School of Mathematics and Big Data Anhui University of Science and TechnologyHuainan232001China School of Economics and Management Anhui University of Science and TechnologyHuainan232001China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji UniversityShanghai201804China

Predictive Business Process Monitoring(PBPM)is a significant research area in Business Process Management(BPM)aimed at accurately forecasting future behavioral *** present,deep learning methods are widely cited in PBPM research,but no method has been effective in fusing data information into the control flow for multi-perspective process ***,this paper proposes a process prediction method based on the hierarchical BERT and multi-perspective data ***,the first layer BERT network learns the correlations between different category attribute ***,the attribute data is integrated into a weighted event-level feature vector and input into the second layer BERT network to learn the impact and priority relationship of each event on future predicted ***,the multi-head attention mechanism within the framework is visualized for analysis,helping to understand the decision-making logic of the framework and providing visual ***,experimental results show that the predictive accuracy of the framework surpasses the current state-of-the-art research methods and significantly enhances the predictive performance of BPM.

关键词： Business process prediction monitoring deep learning attention mechanism BERT multi-perspective

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