scholarly journals Analysis of Human-Land Coupled Bearing Capacity of Qiangtang Meadow in Northern Tibet Based on Fuzzy Clustering Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
ShiBao Li
Keyword(s):  
Energy Consumption ◽  
Bearing Capacity ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
High Energy ◽  
Geographic Information ◽  
Superior Performance ◽  
Capacity Analysis ◽  
Northern Tibet ◽  
Fuzzy Clustering Algorithm

Aiming to address the problems of high energy consumption, low efficiency, low correlation between the analyzed and actual results, and poor rationality of research indexes in current methods of analysis of human-land coupled bearing capacity of meadows, a novel method of human-land coupled bearing capacity analysis of Qiangtang meadow in northern Tibet, based on fuzzy clustering algorithm, is proposed. Basic geographic information data in Tibet were acquired, the collected data images were registered by ENVI4.2 software, and the collected data were vectorized by ArcGIS 9.3 software to construct a basic geographic information database in Tibet. Based on the frequency domain processing algorithm, the geographic information image was suppressed by noise and filtered by using a high-pass filter to realize the geographic information data processing in the study area. The human-land coupled bearing capacity analysis of Qiangtang meadow in northern Tibet was evaluated through fuzzy clustering, bearing capacity evaluation, and bearing capacity calculation under the sharing of closure. The experimental results showed that the average running energy consumption of the method was 81 J, and 97% of the analyzed results were consistent with the actual situation. These results indicate that the operation efficiency of the method is high, and the rationality coefficient of the research index is large. The proposed method has superior performance and feasibility.

scholarly journals Vehicle Dimensions Based Passenger Car Classification using Fuzzy and Non-Fuzzy Clustering Methods

2021 ◽  
pp. 036119812110107
Author(s):  
Naghmeh Niroomand ◽  
Christian Bach ◽  
Miriam Elser
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Real Data ◽  
Superior Performance ◽  
Clustering Methods ◽  
Data Set ◽  
Passenger Car ◽  
Continuous Growth ◽  
Taxation Policy ◽  
Fuzzy Clustering Algorithm

There has been globally continuous growth in passenger car sizes and types over the past few decades. To assess the development of vehicular specifications in this context and to evaluate changes in powertrain technologies depending on surrounding frame conditions, such as charging stations and vehicle taxation policy, we need a detailed understanding of the vehicle fleet composition. This paper aims therefore to introduce a novel mathematical approach to segment passenger vehicles based on dimensions features using a means fuzzy clustering algorithm, Fuzzy C-means (FCM), and a non-fuzzy clustering algorithm, K-means (KM). We analyze the performance of the proposed algorithms and compare them with Swiss expert segmentation. Experiments on the real data sets demonstrate that the FCM classifier has better correlation with the expert segmentation than KM. Furthermore, the outputs from FCM with five clusters show that the proposed algorithm has a superior performance for accurate vehicle categorization because of its capacity to recognize and consolidate dimension attributes from the unsupervised data set. Its performance in categorizing vehicles was promising with an average accuracy rate of 79% and an average positive predictive value of 75%.

scholarly journals Solving the Problem of Target k-Coverage in WSNs Using Fuzzy Clustering Algorithm

2021 ◽  
pp. 55-76
Author(s):  
Mohammad Hossein .. ◽  
◽  
◽  
◽  
Zohre .. ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Multimedia Networks ◽  
Wireless Multimedia ◽  
Coverage Problem ◽  
Fuzzy Clustering Algorithm ◽  
Annealing Algorithm ◽  
Simulation Results

The purpose of the present research was to introduce an algorithm to solve the coverage problem in wireless multimedia networks that can be used to optimize energy consumption and network lifetime. In this regard, the problem of target k-coverage in WSNs was solved by dividing the environment into the proportional area and random selection. This can be done using a fuzzy clustering algorithm. It is worth noting that the results of the proposed algorithm were compared with previous methods such as genetic and annealing algorithm. The simulation results and comparison with other algorithms show a 27% superiority of the proposed algorithm. It is hoped that this method can be used in networks with larger dimensions in the future

Transfer of knowledge in chemical equipment reliability

1989 ◽  
Vol 54 (10) ◽  
pp. 2692-2710 ◽  
Author(s):  
František Babinec ◽  
Mirko Dohnal
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Chemical Equipment ◽  
Transfer Of Knowledge ◽  
Equipment Reliability ◽  
Fuzzy Clustering Algorithm

The problem of transformation of data on the reliability of chemical equipment obtained in particular conditions to other equipment in other conditions is treated. A fuzzy clustering algorithm is defined for this problem. The method is illustrated on a case study.

Segmentation of corpus callosum based on tensor fuzzy clustering algorithm

2021 ◽  
pp. 1-14
Author(s):  
Yujia Qu ◽  
Yuanjun Wang
Keyword(s):  
Corpus Callosum ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Diffusion Tensor ◽  
Region Of Interest ◽  
Midsagittal Plane ◽  
Fuzzy Clustering Algorithm ◽  
Human Connectome Project ◽  
Clustering Center ◽  
Fixed Region

BACKGROUND: The corpus callosum in the midsagittal plane plays a crucial role in the early diagnosis of diseases. When the anisotropy of the diffusion tensor in the midsagittal plane is calculated, the anisotropy of corpus callosum is close to that of the fornix, which leads to blurred boundary of the segmentation region. OBJECTIVE: To apply a fuzzy clustering algorithm combined with new spatial information to achieve accurate segmentation of the corpus callosum in the midsagittal plane in diffusion tensor images. METHODS: In this algorithm, a fixed region of interest is selected from the midsagittal plane, and the anisotropic filtering algorithm based on tensor is implemented by replacing the gradient direction of the structural tensor with an eigenvector, thus filtering the diffusion tensor of region of interest. Then, the iterative clustering center based on K-means clustering is used as the initial clustering center of tensor fuzzy clustering algorithm. Taking filtered diffusion tensor as input data and different metrics as similarity measures, the neighborhood diffusion tensor pixel calculation method of Log Euclidean framework is introduced in the membership function calculation, and tensor fuzzy clustering algorithm is proposed. In this study, MGH35 data from the Human Connectome Project (HCP) are tested and the variance, accuracy and specificity of the experimental results are discussed. RESULTS: Segmentation results of three groups of subjects in MGH35 data are reported. The average segmentation accuracy is 97.34%, and the average specificity is 98.43%. CONCLUSIONS: When segmenting the corpus callosum of diffusion tensor imaging, our method cannot only effective denoise images, but also achieve high accuracy and specificity.

AN EXTENDED FUZZY CLUSTERING ALGORITHM AND ITS APPLICATION

1995 ◽  
Vol 05 (02) ◽  
pp. 239-259
Author(s):  
SU HWAN KIM ◽  
SEON WOOK KIM ◽  
TAE WON RHEE
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Main Memory ◽  
Color Image Segmentation ◽  
Occurrence Rate ◽  
Secondary Memory ◽  
Worst Case ◽  
Memory Space ◽  
Fuzzy Clustering Algorithm

For data analyses, it is very important to combine data with similar attribute values into a categorically homogeneous subset, called a cluster, and this technique is called clustering. Generally crisp clustering algorithms are weak in noise, because each datum should be assigned to exactly one cluster. In order to solve the problem, a fuzzy c-means, a fuzzy maximum likelihood estimation, and an optimal fuzzy clustering algorithms in the fuzzy set theory have been proposed. They, however, require a lot of processing time because of exhaustive iteration with an amount of data and their memberships. Especially large memory space results in the degradation of performance in real-time processing applications, because it takes too much time to swap between the main memory and the secondary memory. To overcome these limitations, an extended fuzzy clustering algorithm based on an unsupervised optimal fuzzy clustering algorithm is proposed in this paper. This algorithm assigns a weight factor to each distinct datum considering its occurrence rate. Also, the proposed extended fuzzy clustering algorithm considers the degree of importances of each attribute, which determines the characteristics of the data. The worst case is that the whole data has an uniformly normal distribution, which means the importance of all attributes are the same. The proposed extended fuzzy clustering algorithm has better performance than the unsupervised optimal fuzzy clustering algorithm in terms of memory space and execution time in most cases. For simulation the proposed algorithm is applied to color image segmentation. Also automatic target detection and multipeak detection are considered as applications. These schemes can be applied to any other fuzzy clustering algorithms.

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