Life of modern people becomes more convenient and rich in material side but worse in mental side due to life stress. This results in bloom of some diseases such as insomnia. Listening to musiccould be one way to make...
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ISBN:
(纸本)9781614994404;9781614994398
Life of modern people becomes more convenient and rich in material side but worse in mental side due to life stress. This results in bloom of some diseases such as insomnia. Listening to musiccould be one way to make people feel smooth. Some previous literature had advocated the efficiency of music therapy, however, only a few previous studies discussed and connected personal cognition (subjective indicators) with music features (objective indicators). Therefore, the aim of the study is to investigate what kind of musiccharacteristics can spiritually relax people and obtain the therapeutic music from above results. Firstly, this study collected 25 different styles of music as samples. These songs were classified with fuzzy c-means clustering algorithm. According to our experimental result, music with mild amplitude, slow speed, and positive feelings can enable soothing in mind. The findings would also fit in with physiological signals (Heart Rate Variability) to ensure the consistency in psychology and physiology. This finding can provide suggestions on selection of therapeutic music. In addition, musicians can compose appropriate therapeutic music for patients of different mental illness.
In this study, real-time design and implementation of an energy-efficient cluster-based protocol for wireless sensor networks (WSNs) are presented. The formation of suitable clusters is of prime importance to balance ...
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In this study, real-time design and implementation of an energy-efficient cluster-based protocol for wireless sensor networks (WSNs) are presented. The formation of suitable clusters is of prime importance to balance energy usage by sensor nodes within each cluster of the cluster-based WSNs. This leads to energy savings for the sensor nodes resulting in longer network lifetime. To obtain this objective, the fuzzyc-means (FcM) clusteringalgorithm is incorporated in the protocol. The protocol is realised on a hardware test-bed with the support of the embedded operating system, TinyOS. Experimental results obtained from a scale-down laboratory based test-bed with up to 50 sensor nodes are provided to illustrate the efficacy of the WSNs using the proposed protocol and compared with the well-known cluster-based protocols such as Low Energy Adaptive clustering Hierarchy. It has been shown that the FcM protocol is able to achieve better organisation of the network and thus can extend its lifetime under varying operating conditions and with different number of nodes.
The results of traditional traffic status analysis are mostly single values, whose accuracy can't be determined;fuzzyc-meansclustering (FcM) algorithm based on fuzzy theory can calculate the clusteringcenter of...
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ISBN:
(纸本)9781479947522
The results of traditional traffic status analysis are mostly single values, whose accuracy can't be determined;fuzzyc-meansclustering (FcM) algorithm based on fuzzy theory can calculate the clusteringcenter of plenty data quickly and easily;linguistic dynamic systems could describe the dynamic rules of complex systems in the language level. In this paper, membership functions are decided by FcM;result of a specific time period taken as one example is obtained;it's discussed that linguistic dynamic analysis of traffic status in different period within a day by the same method.
In this study, the concepts of competitive agglomeration clusteringalgorithm is incorporated into fuzzyc-means (FcM) clusteringalgorithm for symbolic interval-values data. In the proposed approach, called as IFcMwU...
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ISBN:
(纸本)9784907764296
In this study, the concepts of competitive agglomeration clusteringalgorithm is incorporated into fuzzyc-means (FcM) clusteringalgorithm for symbolic interval-values data. In the proposed approach, called as IFcMwUNcclusteringalgorithm, the problems of the unknown clusters number and the initialization of prototypes in the FcM clusteringalgorithm for symbolic interval-values data are overcome and discussed. Due to the competitive agglomeration clusteringalgorithm possess the advantages of the hierarchical clusteringalgorithm and the partitional clusteringalgorithm, IFcMwUNcclusteringalgorithmcan be fast converges in a few iterations regardless of the initial number of clusters. Moreover, it is also converges to the same optimal partition regardless of its initialization. Experiments results show the merits and usefulness of IFcMwUNcclusteringalgorithm for the symbolic interval-values data.
The results of traditional traffic status analysis are mostly single values,whose accuracy can’t be determined;fuzzyc-meansclustering(FcM)algorithm based on fuzzy theory can calculate the clusteringcenter of plent...
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The results of traditional traffic status analysis are mostly single values,whose accuracy can’t be determined;fuzzyc-meansclustering(FcM)algorithm based on fuzzy theory can calculate the clusteringcenter of plenty data quickly and easily;linguistic dynamic systems could describe the dynamic rules of complex systems in the language *** this paper,membership functions are decided by FcM;result of a specific time period taken as one example is obtained;it’s discussed that linguistic dynamic analysis of traffic status in different period within a day by the same method.
In observational studies, unbalanced observed covariates between treatment groups often cause biased inferences on the estimation of treatment effects. Recently, generalized propensity score (GPS) has been proposed to...
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In observational studies, unbalanced observed covariates between treatment groups often cause biased inferences on the estimation of treatment effects. Recently, generalized propensity score (GPS) has been proposed to overcome this problem;however, a practical technique to apply the GPS is lacking. This study demonstrates how clusteringalgorithms can be used to group similar subjects based on transformed GPS. We compare four popular clusteringalgorithms: k-meansclustering (KMc), model-based clustering, fuzzyc-meansclustering and partitioning around medoids based on the following three criteria: average dissimilarity between subjects within clusters, average Dunn index and average silhouette width under four various covariate scenarios. Simulation studies show that the KMcalgorithm has overall better performance compared with the other three clusteringalgorithms. Therefore, we recommend using the KMcalgorithm to group similar subjects based on the transformed GPS.
clustering analysis provides significant contributions to healthcare or medical service. However, relying only on one set of clusters obtained from employing a clusteringalgorithm, such as fuzzyc-meansalgorithm (Fc...
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ISBN:
(纸本)9781479906529
clustering analysis provides significant contributions to healthcare or medical service. However, relying only on one set of clusters obtained from employing a clusteringalgorithm, such as fuzzyc-meansalgorithm (FcM), with an arbitrary initialization may be not robust and accurate in data clustering. The cluster ensemble, the concept of combining multiple clusters produced by a cluster algorithm with several different initializations, can improve the robustness problem. When the outliers were taken into the ensemble may lead the final cluster ensemble to inaccurate results. Thus, outliers should be removed before merging different clusters. In this paper, an adapted FcM algorithm is proposed to detect and remove the outliers. The cluster ensemble framework will employ this adapted FcM algorithm to generate multiple sets of clusters by giving different initialization parameters. Then, a pairwise approach is used to combine those outlier-free clusters. The experimental results verify that the final clusters obtained from the proposed cluster ensemble framework are more robust.
In order to achieve the rapid and mass diagnosis of fish diseases, it is proposed to set up a new and efficient model which closely connects rough set and fuzzyc-means(FcM) clusteringalgorithm. First, the rough set ...
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ISBN:
(纸本)9780769549231;9781467348935
In order to achieve the rapid and mass diagnosis of fish diseases, it is proposed to set up a new and efficient model which closely connects rough set and fuzzyc-means(FcM) clusteringalgorithm. First, the rough set was used for access to knowledge, that is, the typical cases of fish diseases were regarded as sample room for the formation of the decision-making table of the "symptoms - disease";next, based on rough set of simplified method of knowledge, redundant properties and samples were removed;then, the fine performance of FcM clusteringalgorithm was used to analyze clustering;and finally fish diseases classification rules were formed. The model integrated the strong extracting capabilities of rough set and the excellent classifying ability of FcM clusteringalgorithm, and proved experimentally to be efficient in classification and rapid in fish diseases diagnosis.
clustering analysis provides significant contributions to healthcare or medical service. However, relying only on one set of clusters obtained from employing a clusteringalgorithm, such as fuzzyc-meansalgorithm (Fc...
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ISBN:
(纸本)9781479906505
clustering analysis provides significant contributions to healthcare or medical service. However, relying only on one set of clusters obtained from employing a clusteringalgorithm, such as fuzzyc-meansalgorithm (FcM), with an arbitrary initialization may be not robust and accurate in data clustering. The cluster ensemble, the concept of combining multiple clusters produced by a cluster algorithm with several different initializations, can improve the robustness problem. When the outliers were taken into the ensemble may lead the final cluster ensemble to inaccurate results. Thus, outliers should be removed before merging different clusters. In this paper, an adapted FcM algorithm is proposed to detect and remove the outliers. The cluster ensemble framework will employ this adapted FcM algorithm to generate multiple sets of clusters by giving different initialization parameters. Then, a pairwise approach is used to combine those outlier-free clusters. The experimental results verify that the final clusters obtained from the proposed cluster ensemble framework are more robust.
currently, unmanned aerial vehicles (UAVs) are applied to routine inspection tasks of electric distribution networks. As an important information source, machine vision attracts much attention in the area of the UAV&#...
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ISBN:
(纸本)9780473204853;9781467316439
currently, unmanned aerial vehicles (UAVs) are applied to routine inspection tasks of electric distribution networks. As an important information source, machine vision attracts much attention in the area of the UAV's autonomous control. To this end, real-time algorithms are studied in this paper to detect the power lines in the UAV video images. First, video images are converted into binary images through an adaptive thresholding approach. Then, Hough Transform is used to detect line candidates in the binary images. Finally, a fuzzyc-means (FcM) clusteringalgorithm is used to discriminate the power lines from the detected line candidates. The properties of power lines are used to remove the spurious lines, and the length and slope of the detected lines are used as features to establish the clustering data set. Experimental results show that the algorithms proposed are effective and able to tolerate noises from complicated terrain background and various illuminations.
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