Fuzzy cluster analysis of water mass in the western Taiwan Strait in spring 2019

The classification of the springtime water mass has an important influence on the hydrography,regional climate change and fishery in the Taiwan Strait.Based on 58 stations of CTD profiling data collected in the western and southwestern Taiwan Strait during the spring cruise of 2019,we analyze the spatial distributions of temperature(T)and salinity(S)in the investigation area.Then by using the fuzzy cluster method combined with the T-S similarity number,we classify the investigation area into 5 water masses:the Minzhe Coastal Water(MZCW),the Taiwan Strait Mixed Water(TSMW),the South China Sea Surface Water(SCSSW),the South China Sea Subsurface Water(SCSUW)and the Kuroshio Branch Water(KBW).The MZCW appears in the near surface layer along the western coast of Taiwan Strait,showing low-salinity(<32.0)tongues near the Minjiang River Estuary and the Xiamen Bay mouth.The TSMW covers most upper layer of the investigation area.The SCSSW is mainly distributed in the upper layer of the southwestern Taiwan Strait,beneath which is the SCSUW.The KBW is a high temperature(core value of 26.36℃)and high salinity(core value of 34.62)water mass located southeast of the Taiwan Bank and partially in the central Taiwan Strait.

Canopy Spectral Reflectance Characteristics of Rice with Different Cultural Practices and Their Fuzzy Cluster Analysis

The influence of major cultural practices including different nitrogen application rates, population densities, transplanting leaf ages of seedling, and water regimes on rice canopy spectral reflectance was investigated. Results showed that increased nitrogen rates, water regimes and population densities and decreased seedling ages could enhance reflectance at NIR （near infrared） bands and reduce reflectance at visible bands. Using reflectance of green, red and NIR band and ratio index of 810-560 nm could distinguish the different type of rice by fuzzy cluster analysis,

Canopy Spectral Reflectance Characteristics of Rice with Different Cultural Practices and Their Fuzzy Cluster Analysis

The influence of major cultural practices including different nitrogen application rates, population densities, transplanting leaf ages of seedling, and water regimes on rice canopy spectral reflectance was investigated. Results showed that increased nitrogen rates, water regimes and population densities and decreased seedling ages could enhance reflectance at NIR （near infrared） bands and reduce reflectance at visible bands. Using reflectance of green, red and NIR band and ratio index of 810-560 nm could distinguish the different type of rice by fuzzy cluster analysis,

Application of Fuzzy Cluster Analysis Method in Evaluating Relevant Index and Recognizing Risks of Coronary Heart Disease in the Aged

The risk recognition model for preventing and monitoring the Coronary Heart Diseases (CHD) in the aged is proposed, which is based on the testing results of four indexes and includes Low Density Lipoprotein (LDL), Total Cholesterol (TC), Triglyceridemia (TG)and age. Some people who took the health checkup in Shanghai Xinhua Hospital are classified into 3 groups,and each group is associated with prevalence risk of contracting CHD. Then the fuzzy recognition method is applied to evaluate the risk of CHD. The accuracy rate is up to 85%. The model is applicable to not only analysis of risk in medical but also analysis of risk in finance, insurance and some other fields.

The Fuzzy Cluster Analysis in Identification of Key Temperatures in Machine Tool

The thermal-induced error is a very important sour ce of machining errors of machine tools. To compensate the thermal-induced machin ing errors, a relationship model between the thermal field and deformations was needed. The relationship can be deduced by virtual of FEM (Finite Element Method ), ANN (Artificial Neural Network) or MRA (Multiple Regression Analysis). MR A is on the basis of a total understanding of the temperature distribution of th e machine tool. Although the more the temperatures measured are, the more accura te the MRA is, too more temperatures will hinder the analysis calculation. So it is necessary to identify the key temperatures of the machine tool. The selectio n of key temperatures decides the efficiency and precision of MRA. Because of th e complexities and multi-input and multi-output structure of the relationships , the exact quantitative portions as well as the unclear portions must be taken into consideration together to improve the identification of key temperatures. I n this paper, a fuzzy cluster analysis was used to select the key temperatures. The substance of identifying the key temperatures is to group all temperatures b y their relativity, and then to select a temperature from each group as the repr esentation. A fuzzy cluster analysis can uncover the relationships between t he thermal field and deformations more truly and thoroughly. A fuzzy cluster ana lysis is the cluster analysis based on fuzzy sets. Given U={u i|i=0,...,N}, in which u i is the temperature measured, a fuzzy matrix R can be obta ined. The transfer close package t(R) can be deduced from R. A fuzzy clu ster of U then conducts on the basis of t(R). Based on the fuzzy cluster analysis discussed above, this paper identified the k ey temperatures of a horizontal machining center. The number of the temperatures measured was reduced to 4 from 32, and then the multiple regression relationshi p models between the 4 temperatures and the thermal deformations of the spindle were drawn. The remnant errors between the regression models and measured deform ations reached a satisfying low level. At the same time, the decreasing of tempe rature variable number improved the efficiency of measure and analysis greatly.

FUZZY CLUSTER ANALYSIS OF TURBULENT SCALES

Turbulent motion could be regarded as the superposition of fluctuations with different scales. It's of great theoretical and practical importance to determine the classification of turbulent scales quantitatively to the better description of vortex motions with different scales, and to the research of the interaction among different sclaes of vortex and the construction of better turbulent models. The mathematical method, which carries out the classification on a certain requirement, is called cluster analysis. In this paper, fuzzy cluster analysis method is used to study the classification of turbulent scales quantitatively in smooth and rough wall boundary conditions. Furthermore, the properties and interactions among all kinds of flow structures are also studied. The results are helpful to gain some insight into the properties and interactions of all kinds of turbulent scales in wall turbulent shear flow.

Comparison of cations recorded in Antarctica and the Qing-hai-Tibetan Plateau ice core by using fuzzy cluster analysis

A new approach of glacier classification is suggested on the basis of fuzzy cluster analysis of cations in ice cores. Cations in an ice core act as a synthetic index to refelect both the local and the global climate. Fuzzy cluster analysis of long time series data of cations in ice cores from five representative glacial ice cores (from south to north) has been used to create a similarity scale matrix R among these glaciers. Accordingly, any change in R represents a change in environment and climate. This type of analysis can determine the relativity of samples (glaciers) according to a cluster level ( λ ). Fuzzy cluster analysis of cations in ice cores collected from Antarctica and the Qinghai Tibetan Plateau indicates drastic difference between glaciers of these two regions.

AN ANALYSIS OF THE APPLICABILITY OF FUZZY CLUSTERING IN ESTABLISHING AN INDEX FOR THE EVALUATION OF METEOROLOGICAL SERVICE SATISFACTION

An evaluation index is a prerequisite for the scientific evaluation of a public meteorological service.This paper aims to explore a technical method for determining and screening evaluation indicators.Based on public satisfaction survey data obtained in Wafangdian,China in 2010,this study investigates the suitability of fuzzy clustering analysis method in establishing an evaluation index.Through quantitative analysis of multilayer fuzzy clustering of various evaluation indicators,correlation analysis indicates that if the results of clustering were identical for two evaluation indicators in the same sub-evaluation layer,then one indicator could be removed,or the two indicators merged.For evaluation indicators in different sub-evaluation layers,although clustering reveals attribute correlations,these indicators may not be substituted for one another.Analysis of the applicability of the fuzzy clustering method shows that it plays a certain role in the establishment and correction of an evaluation index.

New judging model of fuzzy cluster optimal dividing based on rough sets theory

To investigate the judging problem of optimal dividing matrix among several fuzzy dividing matrices in fuzzy dividing space, correspondingly, which is determined by the various choices of cluster samples in the totality sample space, two algorithms are proposed on the basis of the data analysis method in rough sets theory： information system discrete algorithm （algorithm 1） and samples representatives judging algorithm （algorithm 2）. On the principle of the farthest distance, algorithm 1 transforms continuous data into discrete form which could be transacted by rough sets theory. Taking the approximate precision as a criterion, algorithm 2 chooses the sample space with a good representative. Hence, the clustering sample set in inducing and computing optimal dividing matrix can be achieved. Several theorems are proposed to provide strict theoretic foundations for the execution of the algorithm model. An applied example based on the new algorithm model is given, whose result verifies the feasibility of this new algorithm model.

Kernel method-based fuzzy clustering algorithm

The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.

Discrete Variable Structural Optimization based on Multidirectional Fuzzy Genetic Algorithm

Round method is the common method for discrete variable optimization in optimal design of complex mechanical structures;however,it has some disadvantages such as poor precision,simple model and lacking of working conditions＇ description,etc.To solve these problems,a new model is constructed by defining parameterized fuzzy entropy,and the rationality of parameterized fuzzy entropy is verified.And a new multidirectional searching algorithm is further put forward,which takes information of actual working conditions into consideration and has a powerful local searching capability.Then this new algorithm is combined with the GA by the fuzzy clustering algorithm（FCA）.With the application of FCA,the optimal solution can be effectively filtered so as to retain the diversity and the elite of the optimal solution,and avoid the structural re-analysis phenomenon between the two algorithms.The structure design of a high pressure bypass-valve body is used as an example to make a structural optimization by the proposed HGA and finite element method（FEM）,respectively.The comparison result shows that the improved HGA fully considers the characteristic of discrete variable and information of working conditions,and is more suitable to the optimal problems with complex working conditions.Meanwhile,the research provides a new approach for discrete variable structure optimization problems.

A method of automatic image segmentation for gas bubbles in water

This paper presents an algorithm of automatic bubble image segmentation using the improved ant colony optimization methodology. The ant colony optimization method is a metaheuristic algorithm, and has been applied in many fields. To reveal the versatility and appropriateness of automatic bubble image segmentation, the fuzzy clustering analysis method is employed in ant colony optimization algorithm. Compared with the well-known image feature extraction operators such as SUSAN and Canny, the proposed method can comparatively suitable to extract the gas bubbles image edge features. The experimental results show that the proposed method is effective and reliable, and can achieve satisfactory image edge extraction effect.

A quantification model of Traditional Chinese Medicine syndromes in children with idiopathic precocious puberty and early puberty

OBJECTIVE:To establish a quantification model of Traditional Chinese Medicine(TCM)syndromes by sampling patients undergoing idiopathic precocious puberty(IPP)and early puberty.METHODS:A questionnaire for classifying and quantifying TCM syndromes was designed and administered.All the results were analyzed;the relationship between 3 types of syndrome and 47symptoms were summated.Meanwhile,the frequency distribution of each symptom or sign was aggregated.Fuzzy mathematics was used to develop a quantification model ofTCM syndromes.RESULTS:We found that precocious puberty had 3types of syndrome,including hyperactivity of fire due to Yin deficiency(Syndrome I),depressed liver Qi transforming into fire(Syndrome II),and end retention of damp heat(Syndrome III).In the IPP group,Syndrome I was the most common principal syndrome(100%).Forty-six patients(43.81%)werediagnosed with Syndrome I accompanied by Syndrome II and 11(10.48%)were diagnosed with Syndrome I accompanied by Syndrome III.In the early puberty group,Syndrome I was also the main syndrome(98.39%).The degrees of most symptoms were mild to moderate.Reddened tongue was the most common tongue manifestation(62.86%prevalence)in the IPP group.The most common pulse manifestations were slippery pulse,thread pulse,and taut pulse.The Asymptotic Normalization Coefficient(ANC)method was used to quantify the TCM syndromes in 167 cases.Diagnostic accuracy rate reached 91%,comparable to expert diagnosis.CONCLUSION:We find that there are 3 types of syndrome in the IPP group and in the early puberty group.Syndrome I(hyperactivity of fire due to Yin deficiency)is the main syndrome in the two groups.ANC may be an appropriate for quantification model ofTCM syndromes.