Assessing seawater quality with a variable fuzzy recognition model

With the rapid development of the marine economy industry, human exploitation of marine resources is increasing, which is contributing to the growing trend of eutrophication and frequent occurrence of red tide. Accordingly, investigations of seawater quality have attracted a great deal of attention. This study was conducted to construct a seawater environmental quality assessment model based on the variable fuzzy recognition model. The uncertainty and ambiguity of the seawater quality assessment were then considered, combining the monitoring values of evaluation indicators with the standard values of seawater quality. Laizhou Bay was subsequently selected for a case study. In this study, the correct variable model for different parameters was obtained according to the linear and nonlinear features of evaluation objects. Application of the variable fuzzy recognition model for Laizhou Bay, water quality evaluation and comparison with performance obtained using other approaches revealed that the generated model is more reliable than traditional methods, can more reasonably determine the water quality of various samples, and is more suitable for evaluation of a multi-index, multi-level, nonlinear marine environment system; accordingly, the generated model will be an effective tool for seawater quality evaluation.

Fuzzy pattern recognition model of geological sweetspot for coalbed methane development

From the perspective of geological zone selection for coalbed methane(CBM) development, the evaluation parameters(covering geological conditions and production conditions) of geological sweetspot for CBM development are determined, and the evaluation index system of geological sweetspot for CBM development is established. On this basis, the fuzzy pattern recognition(FPR) model of geological sweetspot for CBM development is built. The model is applied to evaluate four units of No.3 Coal Seam in the Fanzhuang Block, southern Qinshui Basin, China. The evaluation results are consistent with the actual development effect and the existing research results, which verifies the rationality and reliability of the FPR model. The research shows that the proposed FPR model of geological sweetspot for CBM development does not involve parameter weighting which leads to uncertainties in the results of the conventional models such as analytic hierarchy process and multi-level fuzzy synthesis judgment, and features a simple computation without the construction of multi-level judgment matrix. The FPR model provides reliable results to support the efficient development of CBM.

Fuzzy Jamming Pattern Recognition Based on Statistic Parameters of Signal’s PSD

In order to recognize the jamming pattern in anti-jamming, a novel fuzzy jamming recognition method based on statistic parameters of received signal’s power spectral density (PSD) is proposed. It exploits PSD’s shape factor and skewness of received signal as classified characters of jamming pattern. After the mean center and variance of each jamming pattern are calculated by using some jamming samples, an exponential fuzzy membership function is used to calculate the membership value of the recognized sample. Finally, the jamming pattern of received signal is recognized by the maximum membership principle. The simulation results show that the proposed algorithm can recognize common eight jamming patterns accurately.

Research on the Recognition Method of Electric Energy Meter Lead Title based on the Fuzzy Image Processing

This article puts forward an automatic recognition algorithm of electric energy meter lead seals： firstly, the image will be histogram equalization, smoothing, binaryzation pretreatment, then according to the image characteristics of text changes, the system can quickly and accurately segment image from complex background, finally the system extract different dimension and the feature of English and Arabia using digital projection transform coefficient method and to identify the corresponding number by BP neural network, solves the problem of automatic recognition of electric energy meter lead sealing.

Water quality assessment for Ulansuhai Lake using fuzzy clustering and pattern recognition

Water quality assessment of lakes is important to determine functional zones of water use.Considering the fuzziness during the partitioning process for lake water quality in an arid area,a multiplex model of fuzzy clustering with pattern recognition was developed by integrating transitive closure method,ISODATA algorithm in fuzzy clustering and fuzzy pattern recognition.The model was applied to partition the Ulansuhai Lake,a typical shallow lake in arid climate zone in the west part of Inner Mongolia,China and grade the condition of water quality divisions.The results showed that the partition well matched the real conditions of the lake,and the method has been proved accurate in the application.

Fuzzy pattern recognition method for assessing groundwater vulnerability to pollution in the Zhangji area

Based on the widely used DRASTIC method, a fuzzy pattern recognition and optimization method was proposed and applied to the fissured-karstic aquifer of Zhangji area for assessing groundwater vulnerability to pollution. The result is compared with DRASTIC method. It is shown that by taking the fuzziness into consideration, the fuzzy pattern recognition and optimization method reflects more efficiently the fuzzy nature of the groundwater vulnerability to pollution and is more applicable in reality.

Group Decision Making Based Fuzzy Pattern Recognition Model for Lectotype Optimization of Offshore Platforms 1

This paper develops a fuzzy pattern recognition model for group decision making to solve the problem of lectotype optimization of offshore platforms. The lack of data and the inexact or incomplete information for criteria are the main cause of uncertainty in the evaluation process, therefore it is necessary to integrate the judgments from different decision makers with different experience, knowledge and preference. This paper first uses a complementary principle based pairwise comparison method to obtain the subjective weight of the criteria from each decision maker. A fuzzy pattern recognition model is then developed to integrate the judgments from all the decision makers and the information from the criteria, under the supervision of the subjective weights. Finally a case study is given to show the efficiency and robustness of the proposed model.

QUALITY ASSESSMENT OF GINSENG BY CHEMICAL FUZZY PATTERN RECOGNITION

A new method of assessing ginseng and screening its bioactive constituents by fuzzy pattern recognition has been proposed.

Investigation and Application of Automatic Fingerprint Identification Based on Fuzzy Pattern Recognition

Fingerprint image is a typical non-restraint image that has some uncertainty, which makes it difficult to perform identification using classical approach. Therefore, fuzzy pattern recognition is applied to match individual query by searching the entire template database. The fuzzy maximum subordinate principle is used to solve shift matching. Through experimenting and analyzing, the approximate principle fuzzy method is employed by selecting fuzzy characteristics and determining the similarity function to achieve the further accuracy. Theoretical and experimental results show this approach is effective and reasonable.

Structural Stress Identification Using Fuzzy Pattern Recognition and Information Fusion Technique

In order to ensure the service security of space structures under wind load, the stress identification method based on the combination of fuzzy pattern recognition and information fusion technique is proposed, in which the measurements of limited strain sensors arranged on the structure are used. Firstly, the structure is divided into several regions according to the similarity and the most unfavorable region is selected to be the key region for stress identification, while the different numbers of the strain sensors are located on the key region and the normal regions; secondly, the different stress distributions of the key region are obtained based on the measurements of the strain sensors located on the key region and the normal regions separately, in which the fuzzy pattern recognition is used to identify the different stress distributions; thirdly, the stress distributions obtained by the measurements of sensors in normal regions are selected to calculate the synthesized stress distribution of the key region by D-S evidence theory; fourthly, the weighted fusion algorithm is used to assign the different fusion coefficients to the selected stress distributions obtained by the measurements of the normal regions and the key region, while the synthesized stress distribution of the key region can be obtained. Numerical study on a lattice shell model is carried out to validate the reliability of the proposed stress identification method. The simulated results indicate that the method can improve identification accuracy and be effective by different noise disturbing.

Air Target Fuzzy Pattern Recognition Threat-Judgment Model

Threat-judgment is a complicated fuzzy inference problem. Up to now no relevant unified theory and measur-ing standard have been developed. It is very difficult to establish a threat-judgment model with high reliability in the airdefense system for the naval warships. Air target threat level judgment is an important component in naval warship com-bat command decision-making systems. According to the threat level judgment of air targets during the air defense of sin-gle naval warship, a fuzzy pattern recognition model for judging the threat from air targets is established. Then an algo-rithm for identifying the parameters in the model is presented. The model has an adaptive feature and can dynamicallyupdate its parameters according to the state change of the attacking targets and the environment. The method presentedhere can be used for the air defense system threat judgment in the naval warships.

Fabric Wrinkle Grade Assessment Based on Fuzzy Pattern Recognition

The basic principle of fuzzy pattern recognition is brief introduced firstly in this paper, which mainly includes fuzzy rules and fuzzy inference system. Then, the algorithm procedure of fuzzy pattern recognition is proposed. Finally, the application of Mamdani fuzzy model is introduced to evaluate fabric wrinkle grade in detail, and used the correlation coefficient between subject and object evaluation to verify the reliability of fuzzy pattern recognition. It shows the method of fuzzy pattern recognition needs not a large number of testing data and the accuracy of evaluation is up to 97.38%.

Fuzzy Pattern Recognition in Atlas and Images of the Unevenness of Carbide in Tool Steel

Fuzzy pattern recognition has been employed to identify some atlas and images of the unevenness of carbide in tool steel. Three models have been constructed. These models were based on fuzzy mathematics theory, as well as fuzzy pattern recognition method. Distribution rule of the unevenness of eutectic carbide in ledeburite steel is proposed in these models respectively.

Fuzzy Pattern Recognition System for Detection of Alga Distribution

To realize the on-line measurement and make analysis on the density of algae and their cluster distribution, the fluorescent detection and fuzzy pattern recognition techniques are used. The principle of fluorescent fiber-optic detection is given as well as the method of fuzzy feature extraction using a class of neural network.

An Adaptive Simulation Algorithm Based on Fuzzy Stiffness Recognition and Its Application to Power Station Simulation

The adaptive simulation algorithm (ASA) based on stiffness recognition is an effective and applicable simulation method. In this paper, a principle of the said method is briefly introduced and more importance is stressed in studying the value of its application by realizing it in MMS.

Assessment of Structural Damage Condition Based on Fuzzy Pattern Recognition

This paper presents a new method of damage condition assessment that allows accommodating other types of uncertainties due to ambiguity, vagueness, and fuzziness that are statistically nondescribable. In this method, healthy observations are used to construct a fury set representing sound performance characteristics. Additionally, the bounds on the similarities among the structural damage states are prescribed by using the state similarity matrix. Thus, an optimal group fuzzy sets representing damage states such as little, moderate, and severe damage can be inferred as an inverse problem from healthy observations only. The optimal group of damage fuzzy sets is used to classify a set of observations at any unknown state of damage using the principles of fitzzy pattern recognition based on an approximate principle . This method can be embedded into the system of Structural Health Monitoring （SHM） to give advice about structural maintenance and life predictio comes from Reference [ 9 ] for damage pattern recognition is presented n. Finally, a case and discussed. The study, which compared result illustrates our method is more effective and general, so it is very practical in engineering.

Research on Recognition Method of Handwritten Numerals Segmentation based on B-P Neural Network

We propose a binarization method based pigment in the ZIP code of 24 bmp image simulation and digital identification by CCD sensors, were extracted the grid binary, image of zip code box and message of the two characters binary image： analyze the image processing, which includes code frame edge detection and separation of the image binarization, denoising smoothing, tilt correction, the extraction code number, position, normalization processing, digital image thinning, character recognition feature extraction. Through testing, the recognition rate of this method can be over 90%. The recognition time of characters for character is less than 1.3 second, which means the method is of more effective recognition ability and can better satisfy the real system requirements.

Research on Recognition Method of Handwritten Numerals Segmentation based on B-P Neural Network

We propose a binarization method based pigment in the ZIP code of 24 bmp image simulation and digital identification by CCD sensors, were extracted the grid binary image of zip code box and message of the two characters binary image; analyze the image processing, which includes code frame edge detection and separation of the image binarization, denoising smoothing, tilt correction, the extraction code number, position, normalization processing, digital image thinning, character recognition feature extraction. Through testing, the recognition rate of this method can be over 90%. The recognition time of characters for character is less than 1.3 second, which means the method is of more effective recognition ability and can better satisfy the real system requirements.

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.

A Prototype for Diagnosis of Psoriasis in Traditional Chinese Medicine

Psoriasis is a chronic,non-communicable,painful,disfiguring and disabling disease for which there is no cure,with great negative impact on patients’quality of life(QoL).Diagnosis and treatment with traditional Chinese medical technique based on syndrome differentiation has been used in practice for a long time and proven effective,though,up to now,there are only a few available studies about the use of semantic technologies and the knowledge systems that use Traditional Chinese Medicine(TCM)-syndrome differentiation for information retrieval and automated reasoning.In this paper we use semantic techniques based on ontologies to develop a prototypical system for the diagnosis of Psoriasis.For this purpose,a domain ontology is developed for syndrome differentiation of psoriasis vulgaris(PV).This ontology is founded on an adapted version of the general formal ontology(GFO),with the evidence-based clinical practice guideline of TCM for psoriasis vulgaris(Guideline 2013)as the primary data sources.The implemented prototype,called ONTOPV,contains this domain ontology and is aimed at a decision support system for diagnosis and treatment of PV.This system uses a case-database for Case Based Reasoning(CBR),combined with fuzzy pattern recognition.Experimental results show that the ONTOPV realizes the basic functionalities of data collection,querying,browsing and navigation,and supports rule-based knowledge reasoning,and integrates fuzzy pattern recognition.It can provide users with clinical decision support for TCM syndrome differentiation in diagnosis of psoriasis.