Papers by Keyword: Pattern Recognition

Abstract: Quantitative design focuses on drugs between biological activity and structure parameters of quantitative change rule, so as to apply these rules to guide the design and synthesis of new drugs to predict unknown compounds of biological activity, agent theory and inference mechanism of drugs. This paper briefly introduces the concept of quantitative drug design and computer graphics and its typical applications in pattern recognition, quantitative drug design, and introduces a quantitative drug design system based on pattern recognition, finally will point out their application prospects and some problems to be solved. Quantitative drug design is of great significance for the diagnosis of the disease.

Abstract: This paper studies adaptive learning for diagnostic image recognition and expounds that adaptive resonance theory is utilized to achieve ART artificial neural network of self-stability and self-makeup for recognition, which meets the requirement of learning and adaption. In terms of the principle, an algorithm of self-stability and classifier learning is also provided.

Abstract: In this paper, an effective method for Thai face cartoon detection and recognition is used based on haar like feature and eigenface model. The basic idea of this method is to detection and recognition a cartoon Thai from the database based on a cartoon drawn by an artist. This method consists of three steps. We first manually, haar like feature is applied for Thai face cartoon detection. Second, those faces are extracted feature using eigenface. Final, those features are recognized using Euclidean distance. For experimental result, detection rate of 95% and recognition rate of 97%.

Abstract: The wavelet packet decomposition can be used to extract the frequency band containing bearing fault feature, because the fault signal can be decomposed into different frequency bands by using the wavelet packet decomposition, that is to say the optimal wavelet packet decomposition node needs to be found. A method applying the average Euclidean distance to find the optimal wavelet packet decomposition node was presented. First of all, the bearing fault signals were decomposed into three layers wavelet coefficients by which the bearing fault signals were reconstructed. The peak values extracted from the reconstructing signal spectrum constructed a feature space. Then, the minimum average Euclidean distance calculated from the feature space indicated the optimal wavelet packet node. The optimal feature space could be constructed by the feature points extracted from the signals reconstructed by the optimal wavelet packet nodes. Finally, the optimal feature space was used for the K-means clustering. The feature extraction and pattern recognition test of the four kinds of bearing conditions under four kinds of rotation speeds was detailed. The test results show this method, which can extract the bearing fault feature efficiently and make the fault feature space have the lowest within-class scatter, wons a high pattern recognition accuracy.

Abstract: This paper discusses the various texture-based and vector-based approaches to classify coins.These two types of approaches are common for software-based coin sorting systems. Many researchers have applied algorithms known in artificial intelligence research for feature extraction, selection and classification of coin images. However, without a common benchmark, it is difficult to assess the accuracy, robustness and efficiency of the coin sorting systems across the different approaches. It is proposed that the use of a standard benchmark image databank such as the CIS Benchmark will allow a more objective and accurate comparison of the performance of these coin classification approaches.

Abstract: A class of fuzzy neural network design problem H controller. By TS fuzzy theory, a model of nonlinear complex systems. Then, based on Lyapunov-Krasovskii functional and LMI technique, gives the design an H controller. By using the Matlab LMI toolbox, we can get the corresponding feasible solution of linear matrix inequalities. Finally, a numerical simulation examples are given to prove the correctness of the H controller.

Abstract: Based on a number of stratigraphic sedimentary information included in log data, application of the Artificial Neural Network to identify sedimentary microfacies from well logging data can complete the series auto-interpreting. The application can improve the auto-interpreting accuracy and make us get more satisfied results. Ten parameters from the well logging curves are selected for to describing their shape characteristics when the deposition patterns of 8 in gas-bearing formation of Upper Paleozoic group, Ordos basin are studied. Effective parameters were selected on the basis of cores, and based on artificial neural network pattern recognition technique; the sedimentary microfacies of well cross section were auto-interpreted. About 300 wells and the results were interpreted by using the software. The software will be fit for the researchers who have the experiences of geological interpretation and some backgrounds of local geology.

Abstract: Among various pattern recognition methods used for liquid identification, the method based on neural network has the advantages of robustness and fault tolerance, which can study and adapt to the uncertain system. The waveform analysis is exploited for feature extraction of the liquid droplet fingerprint (LDF) in this paper, and the liquid identification is carried out by means of BP and RBF neural network. The experimental results proved that the recognition rate is excellent in both of these two methods. In condition that the training data is limited, RBF network is better than BP network in recognition speed and rate.

Abstract: Thinning is widely used in image processing and pattern recognition, it reduces redundancy of the original image and for easily extracting features. By a lot of experiments, a new improved thinning algorithm is proposed in this paper. A flow process diagram is given too. In the new algorithm, thinking about some details. For example, thing of P1 cant be deleted, thing of how to wipe off burr. The experiments show that these algorithms achieve anticipate results.

Abstract: Gaussian-Hermite moments and their invariants have been widely used in image processing and pattern recognition. The moments are strictly invariant for the continuous function. However, the digital images are discrete. The image function and the moment imvariants may change during image geometric transformation. To address this problem, an analysis with respect to the fluctuation of moment invariants on image geometric transformation is presented. The guidance is provided as well to minimizing the fluctuation of the Gaussian-Hermite moments.