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.

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.

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.

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.

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.

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.

AStudy ofthe Balanced Area of Index's Fluctuation in Shanghai Stock Exchange

Chinese stock market is a developing one. In the present stages, to control scientifically the expansion speed and avoid drastic fluctuations is an important problem. Through analysis of plenty of data of SSE(Shanghai Stock Exchange) Index and relevant economic quotas, we find that the problem of predicting SSE Index is a typical multi variable, nonlinear one. On the basis of the analysis, we apply the technology of fuzzy pattern recognition, to the optimum pattern division of SSE Index's time alignments from Jan. of 1993 to Dec. of 1997, and get a balanced pattern of the stock index fluctuation. At the same time, by using database technology, we find the optimum expansion speed of Shanghai stock, which can make SSE Index fluctuate steadily within the balanced area. We verified this model with the latest data and found it coincides with the reality perfectly. So it has the practical value and provides the policy makers with a scientific basis in controlling the expansion pace.