scholarly journals Study on Regional Control of Tourism Flow Based on Fuzzy Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Wei Shang ◽  
Guo Chuangle
Keyword(s):  
Clustering Algorithm ◽  
Fuzzy Theory ◽  
Control Effect ◽  
Regional Control ◽  
Regional Tourism ◽  
Scenic Spot ◽  
Automatic Clustering ◽  
Sample Data ◽  
High Control ◽  
Tourist Flow

In order to solve the problems of poor regional control effect and high control difficulty coefficient of a traditional tourism flow, this paper puts forward the research of a regional control of tourism flow based on fuzzy theory. The capacity of regional tourism is determined by analyzing the factors that influence the regional control of the tourism flow. The regional tourism flow is divided into different time series by automatic clustering algorithm, the same sample data is fused, and the Euclidean distance between traffic is obtained. The regional tourism flow prediction model is constructed according to fuzzy theory. On this basis, the real-time capacity of regional scenic spot flow is calculated, and the regional tourist flow control model is constructed to realize the regional tourist capacity control. The experimental results show that the regional control error of tourism flow is always lower than 0.40, and the difficulty coefficient of control is low, which has certain advantages.

Segmentation of SAR Image using Fuzzy C-Means and Filters

2020 ◽  
Vol 8 (1) ◽  
pp. 84-90
Author(s):  
R. Lalchhanhima ◽  
◽  
Debdatta Kandar ◽  
R. Chawngsangpuii ◽  
Vanlalmuansangi Khenglawt ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
State Of The Art ◽  
Speckle Noise ◽  
Synthetic Aperture Radar Image ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Spatial Filters ◽  
Fuzzy C Means ◽  
Automatic Clustering ◽  
Intensity Information

Fuzzy C-Means is an unsupervised clustering algorithm for the automatic clustering of data. Synthetic Aperture Radar Image Segmentation has been a challenging task because of the presence of speckle noise. Therefore the segmentation process can not directly rely on the intensity information alone but must consider several derived features in order to get satisfactory segmentation results. In this paper, it is attempted to use the fuzzy nature of classification for the purpose of unsupervised region segmentation in which FCM is employed. Different features are obtained by filtering of the image by using different spatial filters and are selected for segmentation criteria. The segmentation performance is determined by the accuracy compared with a different state of the art techniques proposed recently.

A population-based automatic clustering algorithm for image segmentation

2021 ◽  
Author(s):  
Seyed Jalaleddin Mousavirad ◽  
Gerald Schaefer ◽  
Mahshid Helali Moghadam ◽  
Mehrdad Saadatmand ◽  
Mahdi Pedram
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Population Based ◽  
Automatic Clustering

Management of Abstract Algebra Concepts Based on Knowledge Structure

2013 ◽  
Vol 284-287 ◽  
pp. 3537-3542
Author(s):  
Chin Chun Chen ◽  
Yuan Horng Lin ◽  
Jeng Ming Yih
Keyword(s):  
Mahalanobis Distance ◽  
Clustering Algorithm ◽  
Fuzzy Theory ◽  
Clustering Algorithms ◽  
Knowledge Structures ◽  
Interpretive Structural Modeling ◽  
Fuzzy C Means ◽  
Integrated Method ◽  
Combined Algorithm ◽  
Fuzzy C Means Algorithm

Knowledge Management of Mathematics Concepts was essential in educational environment. The purpose of this study is to provide an integrated method of fuzzy theory basis for individualized concept structure analysis. This method integrates Fuzzy Logic Model of Perception (FLMP) and Interpretive Structural Modeling (ISM). The combined algorithm could analyze individualized concepts structure based on the comparisons with concept structure of expert. Fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. A Fuzzy C-Means algorithm based on Mahalanobis distance (FCM-M) was proposed to improve those limitations of GG and GK algorithms, but it is not stable enough when some of its covariance matrices are not equal. A new improved Fuzzy C-Means algorithm based on a Normalized Mahalanobis distance (FCM-NM) is proposed. Use the best performance of clustering Algorithm FCM-NM in data analysis and interpretation. Each cluster of data can easily describe features of knowledge structures. Manage the knowledge structures of Mathematics Concepts to construct the model of features in the pattern recognition completely. This procedure will also useful for cognition diagnosis. To sum up, this integrated algorithm could improve the assessment methodology of cognition diagnosis and manage the knowledge structures of Mathematics Concepts easily.

Improved Fuzzy FCM-LI Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 670-673
Author(s):  
Li Bo Hou
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Feature Analysis ◽  
Cluster Center ◽  
High Dimensional ◽  
Fuzzy C Means ◽  
Sample Data ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Np Hard Problem

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.

K-Means Clustering Algorithm Based on Prim Improvement

2014 ◽  
Vol 644-650 ◽  
pp. 2063-2066
Author(s):  
He Wei Zhang ◽  
Lei Sun ◽  
Hong Zhang
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Contrastive Analysis ◽  
Greedy Strategy ◽  
Classical Algorithm ◽  
Sample Data ◽  
Clustering Center ◽  
Clustering Effect ◽  
Implementation Steps

K - means algorithm is the classical algorithm to solve the problem of clustering in the area of data mining, when the sample data meets certain conditions, the results of clustering is better. But the algorithm is sensitive to the initial clustering center and clustering results will change as the differences of initial clustering center its number. Aimed at this shortage, this paper proposes a new algorithm based on prim algorithm to select the initial clustering center, details the basic idea of the algorithm and improves the specific methods and implementation steps, finally uses a test for the contrastive analysis. Results show that the improved K - means clustering algorithm needs not to specify the initial clustering center in advance, and it is not sensitive to abnormal value, and at the same time the use of greedy strategy makes the clustering effect more optimal than usual algorithms.

scholarly journals Screening of insecticides against the mulberry fruit gall midge Cotarina sp.

2019 ◽  
Vol 131 ◽  
pp. 01115
Author(s):  
Jiequn Ren ◽  
Li Chen ◽  
Minghai Zhang ◽  
Zhinian Li ◽  
Yi Yang ◽  
...  
Keyword(s):  
Field Experiment ◽  
Pest Management ◽  
Pesticide Residues ◽  
Gall Midge ◽  
High Efficacy ◽  
Control Effect ◽  
Concentration Gradients ◽  
Low Toxicity ◽  
High Control ◽  
Chinese Standard

In recent years, a kind of mulberry pests which were known as the mulberry gall midge Cotarina sp. in different mulberry planting areas occurred popularly in China. This study aims to screening high efficacy and low toxicity insecticides for controlling Cotarina sp.. The current study can help understand integrated pest management (IPM) of Cotarina sp. by scientific and reasonable insecticide use. Field experiment was carried out to investigate eight insecticides, treated with 3 concentration gradients. The result shows that Imidacloprid, Thiamethoxam, Cyromazine, Bifenthrin and Cypermethrin·Profenofos had high control effect on the mulberry gall midge.Their pesticide residues were all less than Chinese Standard GB2763-2016, which means that they were feasible to control this pest. This paper shows that, to control Cotarina sp. in fruit mulberry production, 10% Imidacloprid WP with 2000~3000 dilution and 25% Thiamethoxam WDG with 1500~2500 dilution and 80% Cyromazine WDG with 1500~2000 dilution are the best. 2.5% Bifenthrin EW with 1000~1500 dilution and 440g/l Cypermethrin·Profenofos EC with 1000~2000 dilution should be used by selection. However, Bifenthrin or Cypermethrin·Profenofos cannot be used in mulberry field for both fruits and leaves, so as to avoid causing silkworm poisoning.

scholarly journals Wideband Channel Modeling for mm-Wave inside Trains for 5G-Related Applications

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Juan Moreno García-Loygorri ◽  
César Briso-Rodríguez ◽  
Israel Arnedo ◽  
César Calvo ◽  
Miguel A. G. Laso ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Path Loss ◽  
Channel Modeling ◽  
Wireless Channel ◽  
Full Description ◽  
Model Parameters ◽  
Time Of Arrival ◽  
Traffic Demand ◽  
Automatic Clustering ◽  
Wideband Channel

Passenger trains and especially metro trains have been identified as one of the key scenarios for 5G deployments. The wireless channel inside a train car is reported in the frequency range between 26.5 GHz and 40 GHz. These bands have received a lot of interest for high-density scenarios with a high-traffic demand, two of the most relevant aspects of a 5G network. In this paper we provide a full description of the wideband channel estimating Power-Delay Profiles (PDP), Saleh-Valenzuela model parameters, time-of-arrival (TOA) ranging, and path-loss results. Moreover, the performance of an automatic clustering algorithm is evaluated. The results show a remarkable degree of coherence and general conclusions are obtained.

Sign in / Sign up

Export Citation Format

Share Document