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Pattern Recognition Methods (pattern + recognition_methods)
Selected AbstractsTHE DATING OF ANCIENT CHINESE CELADON BY INAA AND PATTERN RECOGNITION METHODS,ARCHAEOMETRY, Issue 4 2009GUOXI XIE In 2005, sherds of a special type of ancient Longquan celadon ware were excavated at Maojiawan, in the city of Beijing, China. Although archaeologists agree that these sherds were fired in the period between the Yuan and Ming Dynasties, their specific date is unclear. In order to solve this problem, five other groups of ancient Longquan celadon sherds of known date were selected as reference samples. The elemental body composition of all the sherds was determined by Instrumental Neutron Activation Analysis (INAA). Using the same principles as in provenance research, pattern recognition methods were used to build classification functions to specify the date of the unknown sherds. After analysing the experimental data by discriminant analysis, three classification functions were built. All the unknown sherds were classified as Ming Dynasty. This prediction is well in accordance with the fact that these sherds are similar to other Longquan Ming celadon, and so they should be fired in the same dynasty. This also verified the judgement of the Beijing Institute of Cultural Relics. [source] Structural Health Monitoring via Measured Ritz Vectors Utilizing Artificial Neural NetworksCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2006Heung-Fai Lam Unlike most other pattern recognition methods, an artificial neural network (ANN) technique is employed as a tool for systematically identifying the damage pattern corresponding to an observed feature. An important aspect of using an ANN is its design but this is usually skipped in the literature on ANN-based SHM. The design of an ANN has significant effects on both the training and performance of the ANN. As the multi-layer perceptron ANN model is adopted in this work, ANN design refers to the selection of the number of hidden layers and the number of neurons in each hidden layer. A design method based on a Bayesian probabilistic approach for model selection is proposed. The combination of the pattern recognition method and the Bayesian ANN design method forms a practical SHM methodology. A truss model is employed to demonstrate the proposed methodology. [source] Pattern recognition in capillary electrophoresis data using dynamic programming in the wavelet domainELECTROPHORESIS, Issue 13 2008Gerardo A. Ceballos Abstract A novel approach for CE data analysis based on pattern recognition techniques in the wavelet domain is presented. Low-resolution, denoised electropherograms are obtained by applying several preprocessing algorithms including denoising, baseline correction, and detection of the region of interest in the wavelet domain. The resultant signals are mapped into character sequences using first derivative information and multilevel peak height quantization. Next, a local alignment algorithm is applied on the coded sequences for peak pattern recognition. We also propose 2-D and 3-D representations of the found patterns for fast visual evaluation of the variability of chemical substances concentration in the analyzed samples. The proposed approach is tested on the analysis of intracerebral microdialysate data obtained by CE and LIF detection, achieving a correct detection rate of about 85% with a processing time of less than 0.3,s per 25,000-point electropherogram. Using a local alignment algorithm on low-resolution denoised electropherograms might have a great impact on high-throughput CE since the proposed methodology will substitute automatic fast pattern recognition analysis for slow, human based time-consuming visual pattern recognition methods. [source] Data processing in metabolic fingerprinting by CE-UV: Application to urine samples from autistic childrenELECTROPHORESIS, Issue 6 2007Ana C. Soria Abstract Metabolic fingerprinting of biofluids such as urine can be used to detect and analyse differences between individuals. However, before pattern recognition methods can be utilised for classification, preprocessing techniques for the denoising, baseline removal, normalisation and alignment of electropherograms must be applied. Here a MEKC method using diode array detection has been used for high-resolution separation of both charged and neutral metabolites. Novel and generic algorithms have been developed for use prior to multivariate data analysis. Alignment is achieved by combining the use of reference peaks with a method that uses information from multiple wavelengths to align electropherograms to a reference signal. This metabolic fingerprinting approach by MEKC has been applied for the first time to urine samples from autistic and control children in a nontargeted and unbiased search for markers for autism. Although no biomarkers for autism could be determined using MEKC data here, the general approach presented could also be applied to the processing of other data collected by CE with UV,Vis detection. [source] Applying pattern recognition methods plus quantum and physico-chemical molecular descriptors to analyze the anabolic activity of structurally diverse steroidsJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 3 2008Yoanna Marķa Alvarez-Ginarte Abstract The great cost associated with the development of new anabolic,androgenic steroid (AASs) makes necessary the development of computational methods that shorten the drug discovery pipeline. Toward this end, quantum, and physicochemical molecular descriptors, plus linear discriminant analysis (LDA) were used to analyze the anabolic/androgenic activity of structurally diverse steroids and to discover novel AASs, as well as also to give a structural interpretation of their anabolic,androgenic ratio (AAR). The obtained models are able to correctly classify 91.67% (86.27%) of the AASs in the training (test) sets, respectively. The results of predictions on the 10% full-out cross-validation test also evidence the robustness of the obtained model. Moreover, these classification functions are applied to an "in house" library of chemicals, to find novel AASs. Two new AASs are synthesized and tested for in vivo activity. Although both AASs are less active than some commercially AASs, this result leaves a door open to a virtual variational study of the structure of the two compounds, to improve their biological activity. The LDA-assisted QSAR models presented here, could significantly reduce the number of synthesized and tested AASs, as well as could increase the chance of finding new chemical entities with higher AAR. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2008 [source] THE DATING OF ANCIENT CHINESE CELADON BY INAA AND PATTERN RECOGNITION METHODS,ARCHAEOMETRY, Issue 4 2009GUOXI XIE In 2005, sherds of a special type of ancient Longquan celadon ware were excavated at Maojiawan, in the city of Beijing, China. Although archaeologists agree that these sherds were fired in the period between the Yuan and Ming Dynasties, their specific date is unclear. In order to solve this problem, five other groups of ancient Longquan celadon sherds of known date were selected as reference samples. The elemental body composition of all the sherds was determined by Instrumental Neutron Activation Analysis (INAA). Using the same principles as in provenance research, pattern recognition methods were used to build classification functions to specify the date of the unknown sherds. After analysing the experimental data by discriminant analysis, three classification functions were built. All the unknown sherds were classified as Ming Dynasty. This prediction is well in accordance with the fact that these sherds are similar to other Longquan Ming celadon, and so they should be fired in the same dynasty. This also verified the judgement of the Beijing Institute of Cultural Relics. [source] Growth Behavior in Plant Cell Cultures Based on Emissions Detected by a Multisensor ArrayBIOTECHNOLOGY PROGRESS, Issue 4 2004Palle Komaraiah The use of a multisensor array based on chemical gas sensors to monitor plant cell cultures is described. The multisensor array, also referred to as an electronic nose, consisted of 19 different metal oxide semiconductor sensors and one carbon dioxide sensor. The device was used to continuously monitor the off-gas from two plant cell suspension cultures, Morinda citrifolia and Nicotiana tabacum, cultivated under batch conditions. By analyzing the multiarray responses using two pattern recognition methods, principal component analysis and artificial neural networks, it was possible to monitor the course of the cultivations and, in turn, to predict (1) the biomass concentration in both systems and (2) the formation of the secondary metabolite, antraquinone, by M. citrifolia. The results identify the multisensor array method as a potentially useful analytical tool for monitoring plant process variables that are otherwise difficult to analyze on-line. [source] | ||||||||||