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检索条件"主题词=Clustering algorithm"

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An Adaptive Extracting Method for Motion-blurred Stars

An Adaptive Extracting Method for Motion-blurred Stars

引用

Chinese Automation Congress (CAC)

作者： Liu, Lin Bao, Qilian Zheng, Xunjiang Sun, Shuodong Shanghai Jiao Tong Univ Dept Instrument Sci & Engn Shanghai Peoples R China Shanghai Aerosp Control Technol Inst Shanghai Peoples R China

ISBN: (纸本)9781728140940

In this paper, a novel method for extracting difficult to determine a suitable global threshold, and a large motion-blurred stars in high dynamic imaging environment amount of noise causes the connected domain analysis to take affected by stray light is proposed. According to the characteristic of the background noise, an adaptive threshold estimation algorithm for each pixel with down sampling method and block weighted least squares fitting is presented. Furthermore, based on the scanning method and DBSCAN clustering, a new algorithm for extracting pixels of each star is also proposed. The experimental results indicate that the proposed method can improve the accuracy and real-time performance of star image processing even affected by inhomogeneous stray light under high dynamic conditions. The centroid error can be reduced to less than 1 pixel even if the star is motion-blurred to more than 30 pixels and the peak gray value is relatively low.

关键词： star sensor threshold algorithm star point extraction clustering algorithm

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Cooperative trajectory planning for mother/child aircrafts in aerial recovery 3

Cooperative trajectory planning for mother/child aircrafts i...

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3rd International Conference on Unmanned Systems (ICUS)

作者： Liu, Yongbei Qi, Naiming Tang, Mengying Yao, Weiran Zhao, Jun Harbin Inst Technol Sch Astronaut Harbin Peoples R China Expace Technol Co Ltd Fourth Acad China Aerosp Sci & Ind Corp Wuhan Peoples R China

ISBN: (纸本)9781728180250

Aerial recovery technology for aircrafts plays a significant role in practical applications, which presents a challenging problem in cooperative trajectory planning. To solve the problem, a hierarchical trajectory planning architecture is proposed in this paper. Firstly, the waypoints of the mother aircraft trajectory are obtained based on the distribution of the child aircrafts by using a hierarchical clustering algorithm. A greedy algorithm is used to obtain the traverse sequence for the waypoints and the mother aircraft trajectory is generated based on the Dubins path. Secondly, a genetic algorithm is employed to optimize the recovery position and recovery time for each child aircraft. Lastly, the trajectory of each child aircraft is generated by using a double-stage trajectory generation method. Simulation results validate the effectiveness of the proposed method for cooperative trajectory planning in aerial recovery.

关键词： mother aircraft aerial recovery trajectory planning clustering algorithm

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A Novel Isotherm Extracting Method for Temperature Sensitive Paint Image 2

A Novel Isotherm Extracting Method for Temperature Sensitive...

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2nd International Conference on Frontiers of Sensors Technologies (ICFST)

作者： Wang, Zhan Du, Ping-an Wang, Shuai Univ Elect Sci & Technol China Sch Mechatron Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9781509048601

Temperature Sensitive Paint (TSP) is a kind of special temperature sensor. TSP test is an essential approach to measure the surface temperature distribution, especially for high temperature, large area aero-engine components. The manual method of TSP test has a weak repeatability because it's heavily rely on the operator's personal experience and proficiency. Based on image processing algorithms, this paper presented an automatic method for TSP test and this method try to extract more information from the TSP image. The algorithms are tested on both standard test piece and combustor images. The result indicated that method in this paper has high automation and efficiency [1-3].

关键词： temperature sensitive pains temperature measurement aero-engine image processing edge detection clustering algorithm

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Unsupervised HEp-2 mitosis recognition in Indirect Immunofluorescence Imaging 37

Unsupervised HEp-2 mitosis recognition in Indirect Immunoflu...

引用

37th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Tonti, Simone Di Cataldo, Santa Macii, Enrico Ficarra, Elisa Politecn Torino Dept Comp & Control Engn Cso Duca Abruzzi 24 I-10129 Turin Italy

ISBN: (纸本)9781424492701

Automated HEp-2 mitotic cell recognition in IIF images is an important and yet scarcely explored step in the computer-aided diagnosis of autoimmune disorders. Such step is necessary to assess the goodness of the HEp-2 samples and helps the early diagnosis of the most difficult or ambiguous cases. In this work, we propose a completely unsupervised approach for HEp-2 mitotic cell recognition that overcomes the problem of mitotic/non-mitotic class imbalance due to the limited number of mitotic cells. Our technique automatically selects a limited set of candidate cells from the HEp-2 slide and then applies a clustering algorithm to identify the mitotic ones based on their texture. Finally, a second stage of clustering discriminates between positive and negative mitoses. Experiments on public IIF images demonstrate the performance of our technique compared to previous approaches.

关键词： biomedical optical imaging cellular biophysics fluorescence image texture medical disorders medical image processing HEp-2 slide autoimmune disorders automated HEp-2 mitotic cell recognition clustering algorithm computer-aided diagnosis texture unsupervised HEp-2 mitosis recognition Accuracy clustering algorithms Image analysis Image recognition Image segmentation Imaging Pattern recognition

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Identifying Urban Functional Regions by LDA Topic Model with POI Data 1

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10th CCF Big Data Conference (BigData)

作者： Huang, Yuhao Zhang, Lijun Wang, Haijun Wang, Siqi Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Northwestern Polytech Univ Minist Ind & Informat Technol Key Lab Big Data Storage & Management Xian Peoples R China

ISBN: (数字)9789811983313

ISBN: (纸本)9789811983306;9789811983313

Identifying Urban Functional Regions (UFR) can achieve the rational aggregation of social resource space, realize urban economic and social functions, promote the deployment of urban infrastructure, radiate and drive the development of surrounding regions, so the identification of urban functional regions can promote the efficient development of cities. However, the traditional functional region identification method is mainly based on remote sensing mapping, which relies more on the natural geographical characteristics of the region to describe and identify the region, while the urban functional region is closely related to human activities, and the traditional functional region identification results are not ideal. Social data includes a series of data that reflect people's activities and behaviors, such as trajectory data, social media data, and travel data, thus the analysis of social data can more effectively solve the difficulties of traditional mapping and identification. POI (Point of Interest) data, as a typical type of social data, can be used to identify urban functional regions. We apply the LDA topic model to the POI data, and propose a new urban functional region identification method, which makes full use of the POI data to reflect the activity categories of urban populations to characterize the features of regional functions and achieve a high degree of identification of urban functional regions. Through experimental verification on real data, the experimental results show that the proposed method can more accurately identify urban functions, which proves the method reliable.

关键词： Urban functional regions identification POI LDA topic model clustering algorithm

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A Machine Learning Approach to Estimating Queuing Delay on a Router over a Single-Hop Path

A Machine Learning Approach to Estimating Queuing Delay on a...

引用

IEEE International Conference on Communications (ICC)

作者： Ricker, Travis Salehin, Khondaker Wang, Yi Chen, Alex Oki, Eiji Rojas-Cessa, Roberto Calif State Univ Dominguez Hills Carson CA 90747 USA Kyoto Univ Kyoto Japan NJIT Newark NJ USA

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

Queuing delay is a dynamic network parameter that plays an important role in defining the performance of Internet applications over an end-to-end path. However, measurement of queuing delay is challenging because it requires a large infrastructural support from the path under test. In this paper, we propose an active scheme to measure queuing delay on a router using a probe-gap model. The scheme uses a popular data-clustering algorithm to process its data samples;therefore, its measurement efficacy is not dependent on the issues related to infrastructural access, certain variations (e.g., compression) in the probe gaps, and the number of clusters in the data processing. Here, we present a detailed evaluation of the scheme against the current state-of-the-art on a single-hop path through ns-3 simulation. Our results show that the proposed scheme is robust, consistent, quick, and highly accurate under different traffic conditions.

关键词： Network measurement queuing delay clustering algorithm wired networks ns-3 simulator

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An Alert Aggregation algorithm Based on Iterative Self-Organization

An Alert Aggregation Algorithm Based on Iterative Self-Organ...

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International Workshop on Information and Electronics Engineering (IWIEE) / International Conference on Information, Computing and Telecommunications (ICICT)

作者： Man, Dapeng Yang, Wu Wang, Wei Xuan, Shichang Harbin Engn Univ Informat Secur Res Ctr Harbin 150001 Peoples R China

Considering the problem that intrusion detection systems always produced duplicated alarm information, in this paper we propose an iterative self-organization clustering algorithm. It begins with calculating average value of classes as the new clustering center on the basis of random selection, merging and dividing dynamically, then finish the clustering procedure through the iteration finally. Experimental results with DARPA1999 testing data set show that the clustering method is more excellent than traditional clustering methods in both aggregation rate and error aggregation rate. Besides, it reduces duplicated alarm effectively and provides assistance to further related work. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Harbin University of Science and Technology

关键词： network security intrusion detection system alert aggregation iterative self-organization clustering algorithm

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Eliminating Cold-Start Problem of Music Recommendation through SOM Based Sampling

引用

International Conference on Mechatronics and Control Engineering (ICMCE 2012)

作者： Liu, Ning-Han Chiang, Cheng-Yu Hsu, Hsiang-Ming Natl Pingtung Univ Sci & Technol Dept Management Informat Syst Pingtung Taiwan

ISBN: (纸本)9783037855959

Recommendation systems have a prevalent cold-start problem. The problem is occurred might due to new users or new items (music) are added into the system. In this paper, the meaning of the cold-start is narrowed to that the systems do not understand the new user's preferences. Therefore, systems can not recommend the music to users. Although many recommendation systems have a solution to reduce the cold-start, e.g., general systems utilize random to select songs. The systems random select some music works to user so that systems will know the user's preferences after they rated the music works. However, systems may cost much time to collect the necessary information when the new user is interesting in some special types of music. Therefore, if systems select various type of music initially, the user's preferences will be extracted more quickly. That is the cold-start problem can be reduced when the types of initial recommended music are various. In our approach, we utilize SUM to select some music from clusters. According to experiment, SUM selects type of music more average than k-means and random selection. Therefore, SOM can improve the cold-start problem and increase the precision of recommendation results.

关键词： SOM music recommendation clustering algorithm cold-start problem

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A Cluster Head Allocation algorithm in Wireless Sensor Networks

A Cluster Head Allocation Algorithm in Wireless Sensor Netwo...

引用

International Conference on Computer Systems, Electronics and Control (ICCSEC)

作者： Ma, Fuchen Sun, Wanting Sun, Yi Beijing Univ Posts & Telecommun Sch Software Engn Beijing 100000 Peoples R China

ISBN: (数字)9781538635735

ISBN: (纸本)9781538635735

During cluster head selection in wireless sensor networks, the main concern lies in the minimization of energy consumption in the process of network information dissemination. Aiming at the problem of how to balance the energy consumption of each node in wireless sensor networks with multiple sink nodes, this paper proposes a dynamic cluster head selection algorithm and a new node energy consumption model. An energy consumption model is established by establishing the method of balancing the node energy consumption and introducing the correct transfer probability model. Through analysis, the algorithm can effectively reduce the energy consumption of the whole network and prolong the life cycle of the network. The approach can be used as a new way to replace LEACH.

关键词： Wireless sensor networks clustering algorithm energy model clustering multi-sink nodes

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Research on automatic fingerprint classification based on support vector machine

Research on automatic fingerprint classification based on su...

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6th World Congress on Intelligent Control and Automation

作者： Guo, Lei Wu, Youxi Wu, Qing Yan, Weili Shen, Xueqin Hebei Univ Technol Province Min Joint Key Lab Electromagnet Field & Tianjin Peoples R China Hebei Univ Technol Sch Comp Sci & Software Tianjin Peoples R China

ISBN: (纸本)1424403316

Automatic Finger classification is an important part of Fingerprint Automatic Identification System (FAIS). Its function is to provide a search system for large size database. Accurate classification can reduce searching time and expediate matching speed. Support Vector Machine (SVM) is a new learning technique based on Statistical Learning Theory (SLT). SVM was originally developed for two-class classification. It was extended to solve multi-class classification problem. A hierarchical SVM with clustering algorithm based on stepwise decomposition was established to intellectively classify 5 classes of fingerprints. The design principle was proposed and the classification algorithm was implemented. SVM not only has more solid theoretical foundation, it also has greater generalization ability as our experiment demonstrates. The experimental results show that: SVM is effective and surpasses other classical classification techniques.

关键词： fingerprint classification support vector machine clustering algorithm multi-class classification problem

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