检索结果-内蒙古大学图书馆

Fuzzy c-means based coincidental link filtering in support of inferring social networks from spatiotemporal data streams

引用

SOFT COMPUTING 2018年第21期22卷 7015-7025页

作者： Zhang, Pu Shen, Qiang Aberystwyth Univ Dept Comp Sci Inst Math Phys & Comp Sci Aberystwyth SY23 3DB Dyfed Wales

Social network modelling offers an important computational mechanism for analysis of complex system behaviour. Social networks can be established by linking individuals based on observations of certain activities like physical proximity, by exploiting spatiotemporal data streams that have been acquired. A potentially powerful approach to building such networks is to computationally imitate the real-world foraging process of great tits, where the data streams required consist of the times and locations each tit of a certain population has appeared at. The method works by clustering individuals within the population into groups representing different gathering events with respect to the time and location. It links up the individuals appearing in the same events and subsequently, filters out those links that are set up coincidentally. However, the original filtering technique faces significant challenges when considering issues such as time and space complexity and non-unique parameters that are required for removing coincidental links. This paper presents an improved approach by the use of the popular fuzzy c-means method to reinforce the clustering of coincidental links in an emerging social network derived from spatiotemporal data. In particular, all links are organised into two groups: strong links or weak links, prior to the running of the filtering process. The efficacy of the modified version is demonstrated via systematic experimental comparisons against the performance of the original method.

关键词： Social networks Coincidental links clustering algorithm Fuzzy c-means Spatiotemporal data

来源：评论

学校读者我要写书评

暂无评论

Improved method for deinterleaving radar signals and estimating PRI values

引用

IET RADAR SONAR AND NAVIGATION 2018年第5期12卷 506-514页

作者： Liu, Yanchao Zhang, Qunying Univ Chinese Acad Sci 19 A Yuquan Rd Beijing 1000492 Peoples R China Chinese Acad Sci Inst Elect 19 North 4th Ring Rd West Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Electromagnet Radiat & Sensing Technol Lab 10 Beijing 100190 Peoples R China

In the electronic countermeasure, the electronic support measure (ESM) receiver intercepts the interleaved radar pulse streams, which radiates from surrounding radar emitters. At present, deinterleaving algorithm based on time of arrival which is called pulse repetition interval (PRI) deinterleaving occupies an important part of the ESM. The sequential difference histogram (SDIF) is a well-known and effective PRI deinterleaving algorithm, but it has several disadvantages. When the PRI jitter and pulse missing exist in the pulse streams, the SDIF algorithm cannot estimate the real PRI value reliably, and then cannot separate the radar pulse trains corresponding to the real PRI value from the interleaved pulse streams. In order to overcome these disadvantages, an improved SDIF algorithm using clustering algorithm and PRI transform is proposed. Simulation results have proved that this improved algorithm has higher reliability and accuracy in the presence of PRI jitter and pulse missing.

关键词： radar signal processing electronic countermeasures jitter military radar deinterleaving radar signals PRI value estimation electronic countermeasure electronic support measure ESM receiver interleaved radar pulse streams surrounding radar emitters time-of-arrival sequential difference histogram effective PRI deinterleaving algorithm PRI jitter pulse missing pulse streams SDIF algorithm radar pulse trains interleaved pulse streams PRI transform clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Towards Obtaining Upper Bound on Sensitivity Computation Process for Cluster Validity Measures

引用

FUNDAMENTA INFORMATICAE 2018年第4期163卷 351-374页

作者： Mishra, Sumit Mondal, Samrat Saha, Sriparna Indian Inst Technol Patna Dept Comp Sci & Engn Patna 801103 Bihar India Indian Inst Informat Technol Guwahati Dept Comp Sci & Engn Gauhati 781015 Assam India

Cluster validity indices are proposed in the literature to measure the goodness of a clustering result. The validity measure provides a value which shows how good or bad the obtained clustering result is, as compared to the actual clustering result. However, the validity measures are not arbitrarily generated. A validity measure should satisfy some of the important properties. However, there are cases when in-spite of satisfying these properties, a validity measure is not able to differentiate the two clustering results correctly. In this regard, sensitivity as a property of validity measure is introduced to capture the differences between the two clustering results. However, sensitivity computation is a computationally expensive task as it requires to explore all the possible combinations of clustering results which are very large in number and these are growing exponentially. So, it is required to compute the sensitivity efficiently. As the possible combinations of clustering results grow exponentially, so it is required to first obtain an upper bound on this possible number of combinations which will be sufficient to compute the value of the sensitivity. In this paper, we obtain an upper bound on the number of possible combinations of clustering results. For this purpose, a generic approach which is suitable for various validity measures and a specific approach which is applicable for two validity measures are proposed. It is also shown that this upper bound is sufficient to compute the sensitivity of various validity measures. This upper bound is very less as compared to the total number of possible combinations of clustering results.

关键词： clustering algorithm sensitivity cluster validity measure

来源：评论

学校读者我要写书评

暂无评论

Inclusion measures of probabilistic linguistic term sets and their application in classifying cities in the Economic Zone of Chengdu Plain

引用

APPLIED SOFT COMPUTING 2019年 82卷

作者： Tang, Ming Long, Yilu Liao, Huchang Xu, Zeshui Sichuan Univ Business Sch Chengdu 610064 Sichuan Peoples R China

The probabilistic linguistic term set is a powerful tool to express and characterize people's cognitive complex information and thus has obtained a great development in the last several years. To better use the probabilistic linguistic term sets in decision making, information measures such as the distance measure, similarity measure, entropy measure and correlation measure should be defined. However, as an important kind of information measure, the inclusion measure has not been defined by scholars. This study aims to propose the inclusion measure for probabilistic linguistic term sets. Formulas to calculate the inclusion degrees are put forward Then, we introduce the normalized axiomatic definitions of the distance, similarity and entropy measures of probabilistic linguistic term sets to construct a unified framework of information measures for probabilistic linguistic term sets. Based on these definitions, we present the relationships and transformation functions among the distance, similarity, entropy and inclusion measures. We believe that more formulas to calculate the distance, similarity, inclusion degree and entropy can be induced based on these transformation functions. Finally, we put forward an orthogonal clustering algorithm based on the inclusion measure and use it in classifying cities in the Economic Zone of Chengdu Plain, China. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Probabilistic linguistic term set Information measure Relationships clustering algorithm Classifying cities

来源：评论

学校读者我要写书评

暂无评论

Cluster-based emergency message dissemination strategy for VANET using V2V communication

引用

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2019年第5期32卷

作者： Benkerdagh, Saliha Duvallet, Claude Univ Abd El Hamid Ibn Badis UMAB Comp Sci Dept Mostaganem Algeria Normandie Univ Comp Sci Dept UNIHAVRE UNIROUENINSA RouenLITIS F-76600 Le Havre France

Data dissemination in vehicular ad hoc network (VANET) is emerging as a critical area of research. One of the challenges posed by this domain is the reliability of connection, which depends on many parameters, such as the bandwidth consumption, transmission delay, and data quality of service (QoS). Dissemination of emergency messages is very critical since the network topology is changing frequently and rapidly, which leads to data loss. So, it is necessary to develop new protocols and enhance dissemination schemes in VANET to avoid more emergencies and hazards cases. In this regard, we have proposed a new strategy, which consists of data handling before dissemination process as the first step of our scheme. In this phase, the original message is optimized in order to reduce the number of exchanged packets. The second part of this proposition consists of constructing fast and stable clusters to improve the message delivery time and to procure efficient bandwidth consumption. This approach is based on a Fitness function, which takes into account different parameters such as the transmission period, the connectivity degree, the relative velocity, and the link lifetime validity. Since exchanging data in VANET is an important process, routing phase is proposed to perform data exchange among clusters. It is based on a rapid and real-time heuristic (real-time adaptive A* [RTAA*]). To evaluate the reliability of the proposed approach, an urban scenario is used with different simulation parameters. The simulation results show that our proposed approach presents a better stability and efficiency performance compared with the discussed approaches. The proposed approach improves the performance of cluster duration (5% - 25%), delivery rate (2% - 8%), and the overhead (5% - 35%) on average compared with the discussed approaches.

关键词： clustering algorithm data dissemination Fitness function quality of service routing transmission delay

来源：评论

学校读者我要写书评

暂无评论

Water-efficient sensing method for soil profiling in the paddy field

引用

International Journal of Agricultural and Biological Engineering 2018年第4期11卷 207-216页

作者： Zhenran Gao Jun Ni Yan Zhu Qi Jiang Weixing Cao National Engineering and Technology Center for Information Agriculture Jiangsu Key Laboratory for Information AgricultureJiangsu Collaborative Innovation Center for Modern Crop ProductionJiangsu Collaborative Innovation Center for the Technology and Application of Internet of ThingsNanjing Agricultural UniversityNanjing 210095China

The comprehensive and reliable perception of moisture in a soil profile is essential to *** establish an efficient method for sensing soil moisture,field trials were conducted to analyze the spatial and temporal variations of moisture in a paddy soil profile by using coefficients of *** results showed that soil layers at shallow depths undergo more extensive changes in the coefficients of *** perception is most sensitive within a depth range of 0-60 cm in the vertical soil profile of a paddy *** using the clustering algorithm of Euclidean distance,the paddy soil profile was divided into three categories based on soil *** first category includes depths ranging from 10-20 cm,the second is from 30-40 cm,and the third is from 50-100 *** analysis indicated that the most sensitive depths for moisture sensing in a paddy soil profile were 20 cm,30 cm,and 50 cm,whereas the most sensitive depths for moisture sensing by time stability analysis were 20 cm,40 cm,and 60 *** on the multiple regression of sensitive depths,the results of quantitative inversion indicated that the time stability analysis results were 0.962 when the(y,y)Coryy of path analysis was 0.980,and the time stability analysis was 0.61 when the root mean square error(RMSE)of path analysis was *** relative error range between the measured and predicted values of path analysis was less than that of time stability *** findings suggest that it is feasible to effectively sense the moisture of the entire vertical soil profile based on the sensitive *** present study has also determined that path analysis is superior to time stability analysis.

关键词： paddy field profile soil moisture perception clustering algorithm path analysis

来源：评论

学校读者我要写书评

暂无评论

Based on the fuzzy clustering algorithm of the classification of the cluster CDN users design

Based on the fuzzy clustering algorithm of the classificatio...

引用

2nd International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)

作者： Liu, Xiaoguo Jing, Tian Jilin Agr Univ Changchun Peoples R China Changchun Vocat Inst Technol Informat Branch Changchun Peoples R China

ISBN: (纸本)9780769549354

the research to improve the performance of network problems, in order to improve the CDN network of user division to solve problems, and put forward a kind of fuzzy clustering algorithm to differentiate the user cluster load balance algorithm, using this algorithm CDN user cluster, can detect the results from the client, update maintenance global load balance of content routing table, the user request redirect to more precise edge servers, improve service percentage. Make the content of the users need timely release, alleviate network congestion problem, enhance the user access to the network response speed.

关键词： global load balanceing elient-side probe user clustering data mining clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

An Energy-efficient clustering algorithm for Underwater Acoustic Sensor Networks

An Energy-efficient Clustering Algorithm for Underwater Acou...

引用

International Conference on Control Engineering and Communication Technology (ICCECT)

作者： Li, Xia Wang, Yongqian Zhou, Jingjin Southeast Univ Sch Informat Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9781467344999;9780769548814

Energy limited has an important effect on underwater acoustic sensor network (UASN). It has been a key and primary issue to develop an effective algorithm for reducing energy consumption of UASN. In this paper, a new clustering algorithm based on low-energy adaptive clustering hierarchy (LEACH) protocol is proposed. The algorithm combines the factor of the cluster head's position, meanwhile, it involves not only the energy consumption between member nodes and cluster heads but also the energy consumption between cluster heads and base station when the member nodes choose which cluster to join in. Simulation results show that the new algorithm can balance the size of the cluster and the node's energy consumption, and reduce the total energy consumption of the network effectively.

关键词： underwater acoustic sensor network energy consumption clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Design, Optimization and Improvement of FBG Flexible Sensor for Slope Displacement Profiles Measurement

引用

SENSORS 2019年第17期19卷 3750页

作者： Tian, Changbin Wang, Zhengfang Sui, Qingmei Wang, Jing Dong, Yanan Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China

The accurate measurement of slope displacement profiles using a fiber Bragg grating flexible sensor is limited due to the influence of accumulative measurement errors. The measurement errors vary with the deformation forms of the sensor, which dramatically affects the measurement accuracy of the slope displacement profiles. To tackle the limitations and improve the measurement precision of displacement profiles, a segmental correction method based on strain increments clustering was proposed. A K-means clustering algorithm was used to automatically identify the deformation segments of a flexible sensor with different bending shapes. Then, the particle swarm optimization method was adopted to determine the correction coefficients corresponding to different deformation segments. Both finite element simulations and experiments were performed to validate the superiority of the proposed method. The experimental results indicated that the mean absolute errors (MAEs) percentages of the reconstructed displacements using the proposed method for six different bending shapes were 1.87%, 5.28%, 6.98%, 7.62%, 4.16% and 8.31%, respectively, which had improved the accuracy by 26.83%, 18.94%, 29.49%, 26.35%, 7.39%, and 19.65%, respectively. Therefore, it was confirmed that the proposed correction method was competent for effectively mitigating the measurement errors and improving the measurement accuracy of slope displacement profiles, and it presented a vital significance and application promotion value.

关键词： FBG displacement profiles measurement segmental correction method strain increments clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Dimension Reduction Using clustering algorithm and Rough Set Theory

Dimension Reduction Using Clustering Algorithm and Rough Set...

引用

3rd International Conference on Swarm, Evolutionary and Memetic Computing (SEMCCO)

作者： Sengupta, Shampa Das, Asit Kumar MCKV Inst Engn Dept Informat Technol Howrah 711204 W Bengal India Bengal Engn & Sci Univ Dept Comp Sci & Tech Sibpur 711103 W Bengal India

ISBN: (纸本)9783642353796;9783642353802

In real world, datasets have large number of attributes but few are important to describe them properly. The paper proposes a novel dimension reduction algorithm for real valued dataset using the concept of Rough Set Theory and clustering algorithm to generate the reduct. Here, projection of dataset based on two conditional attributes C-i and C-j is taken and K-means clustering algorithm is applied on it with K = number of distinct values of decision attribute D of the dataset to obtain K clusters. Also the dataset is clustered into K-groups using Indiscernibility relation applied on the decision attribute D. Then the connecting factor k of combined conditional attributes (C-i C-j) with respect to D is calculated using two cluster sets and attribute connecting set ACS = {(CiCj ->(K) is an element of D) for all C-i, C-j is an element of C, Conditional attribute set, and D (Decision attribute)} is formed. Each element (CiCj ->(k) D). ACS implies that Ci and Cj connecting together partition the objects that yields ( k* 100) % similar partitions as made on D. Now an undirected weighted graph with weights as the connecting factor k is constructed using attribute connecting set ACS. Finally based on the weight associated with edges, the important attributes, called reduct are generated. Experimental result shows the efficiency of the proposed method.

关键词： Dimension Reduction clustering algorithm Rough Set Theory Attribute Similarity Indiscernibility Relation Undirected weighted graph

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：