• Natural Product Sciences
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• v.24 no.3
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• pp.164-170
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• 2018

Rapid geographical classification of Bupleuri Radix is important in quality control. In this study, near infrared spectroscopy (NIRS) combined with supervised pattern recognition was attempted to classify Bupleuri Radix according to geographical origins. Three supervised pattern recognitions methods, partial least square discriminant analysis (PLS-DA), quadratic discriminant analysis (QDA) and radial basis function support vector machine (RBF-SVM), were performed to establish the classification models. The QDA and RBF-SVM models were performed based on principal component analysis (PCA). The number of principal components (PCs) was optimized by cross-validation in the model. The results showed that the performance of the QDA model is the optimum among the three models. The optimized QDA model was obtained when 7 PCs were used; the classification rates of the QDA model in the training and test sets are 97.8% and 95.2% respectively. The overall results showed that NIRS combined with supervised pattern recognition could be applied to classify Bupleuri Radix according to geographical origin.

• Journal of Sensor Science and Technology
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• v.27 no.2
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• pp.118-125
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• 2018

An evaluation system for an electronic-nose concept using three types of metal oxide gas sensors that react similarly to the human olfactory cells was constructed for the quantitative and qualitative evaluation of aroma fragrances. Four types of aroma fragrances (lavender, orange, jasmine, and Roman chamomile), which are commonly used in aromatherapy, were evaluated. All the gas sensors reacted remarkably to the aroma fragrances and the good correlation of r=0.58-0.88 with the aromatic odor intensities by olfaction was confirmed. From the results of the analysis of an electronic-nose concept for classifying the characteristics of aroma oil fragrances, aroma oils could be classified using the fragrance characteristics and oil extraction methods with the cumulative variability contribution rate of 95.65% (F1: 69.65%, F2: 26.03%) by principal component analysis. In the pattern recognition based on the artificial neural network, the four aroma fragrances were 100% recognized through the training data of 56 cases (70%) out of 80 cases, and the pattern recognition rate was 57.1%-71.4% through the validation and testing data of 24 cases (30%). The pattern recognition success rate through all confusion matrices was 82.1%, indicating that the classification of aroma oil fragrances using the three types of gas sensors was successful.

• Journal of Acupuncture Research
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• v.35 no.2
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• pp.47-51
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• 2018

Acupuncture is a complex intervention that manifests varied theories, treatment methods, diagnostic methods and diagnostic patterns. Traditionally based systems of acupuncture (TBSAs) often have their own diagnostic approaches and patterns. Despite the wide variety that can be found amongst TBSAs, is it possible that they share a common background in clinical observation and practice? Research has shown that multiple physiological pathways and mechanisms can be triggered by different acupuncture techniques and methods. It is highly likely that clinicians will have observed some of the effects of these responses and used those observations as feedback to help construct the patterns of diagnosis and their associated treatments. This review briefly examines this possibility. Pattern identification will have developed out of a complex interaction of factors that include; theories current at the time of their development, historical theories, personal choices and beliefs, training, practice methods, clinical observations and the natural feedback that comes from observing how things change once the treatment is applied. Researchers investigating TBSAs and pattern identification need to be more explicit about the systems they have investigated in order to understand the biological basis of pattern identification and their treatments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.126-131
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• 2009

This study was carried out for the reliability of PD(Partial Discharge) pattern recognition. For the pattern recognition, the database for PD was established by use of self-designed insulation defects which occur and were mostly critical in GIS(Gas Insulated Switchgear). The acquired database was analyzed to distinguish patterns by means of PRPD(Phase Resolved Partial Discharge) method and stored to the form with to unite the average amplitude of PD pulse and the number of PD pulse as the input data of neural network. In order to prove the performance of genetic algorithm combined with neural network, the neural networks with trial-and-error method and the neural network with genetic algorithm were trained by same training data and compared to the results of their pattern recognition rate. As a result, the recognition success rate of defects was 93.2% and the neural network train process by use of trial-and-error method was very time consuming. The recognition success rate of defects, on the other hand, was 100% by applying the genetic algorithm at neural network and it took a relatively short time to find the best solution of parameters for optimization. Especially, it could be possible that the scrupulous parameters were obtained by genetic algorithm.

• Journal of Korea Multimedia Society
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• v.6 no.6
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• pp.1062-1070
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• 2003

In this paper, we observe one of the aquatic insect, fish(Medaka)'s behavior which reacts to giving toxic chemicals until lethal conditions using automatic tracking sl$.$stem. For the result, we define the Pattern A is a normal movement of fish and Pattern B is after giving the chemicals. In order to detect the movement of fish automatically, these patterns are selected for the training data of the artificial neural networks. The average recognition rates of the pattern B are remarkably increased after inputs of toxic chemical(diazinon) while the Pattern A is decreased distinctively. This study demonstrates that artificial neural networks are useful method for detecting presence of toxicoid in environment as for an alternative of in-situ behavioral monitoring tool.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.2
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• pp.1-6
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• 1999

We propose a new algorithm for pattern classification by extracting feature vectors based on Gibbs distributions which are well suited for representing the characteristic of an images. The extracted feature vectors are comprised of 2-D moments which are invariant under translation rotation, and scale of the image less sensitive to noise. This implementation contains two puts: feature extraction and pattern classification First of all, we extract feature vector which consists of an improved 2-D moments on the basis of estimated Gibbs distribution Next, in the classification phase the minimization of the discrimination cost function for a specific pattern determines the corresponding template pattern. In order to evaluate the performance of the proposed scheme, classification experiments with training document sets of characters have been carried out on SUN ULTRA 10 Workstation Experiment results reveal that the proposed scheme had high classification rate over 98%.

• Journal of Fashion Business
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• v.18 no.5
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• pp.144-158
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• 2014

National Competency Standards(NCS) is to systemize the competency that is necessary for performing duties in industrial fields and to utilize synthetically duty, vocational education training, and qualification at the national level. The purpose of this study is to analyze the process of NCS development and verification, which systemizes competency units and competency unit elements through the analysis of duty in pattern field. It is to cultivate competent people to be able to promote the development of pattern industry ultimately through being a complementary to educational circles and industry then to give them practical knowledge that is demanded in the field. Furthermore, it is to be utilized as a fundamental data for reforming the system of duty competency evaluation so as to manage personnel career systematically and to improve their competency. Focus Group Interview(FGI) was adapted as the method of this study, which was proceeded 3 times, and validity of the drawn result is verified through expert questionnaire survey. Research result, which is competency units, is 10 as follows; Fit trend analysis, Analysis of sample garment Specification sheet, Pattern making for sample garment, Pattern making for manufacturing garment, Creation of sewing specification, Instruction of manufacturing technique, Sample garment Inspection for quality control, Grading, Calculation of the required material quantities, Quality control.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.526-533
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• 2007

In this paper, we study to the pattern of job satisfaction using four theories of transactional analysis-egogram, life positions, strokes, time structuring-for organizational members. The tool of pattern analysis is used fuzzy TAM network which Is especially effective for pattern analysis. The input data of fuzzy TAM network ate values of four theories in transactional analysis, the output data is the classes which is divided by two groups from score of job satisfaction. From the result of this study, the correct rates of training data and checking data are 85-100% and 60%, respectively.

• The Journal of Korea Robotics Society
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• v.14 no.3
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• pp.228-235
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• 2019

This paper presents a multiple DoFs (degrees-of-freedom) prosthetic forearm and sEMG (surface electromyogram) pattern recognition and motion intent classification of forearm amputee. The developed prosthetic forearm has 9 DoFs hand and single-DoF wrist, and the socket is designed considering wearability. In addition, the pattern recognition based on sEMG is proposed for prosthetic control. Several experiments were conducted to substantiate the performance of the prosthetic forearm. First, the developed prosthetic forearm could perform various motions required for activity of daily living of forearm amputee. It was able to control according to shape and size of the object. Additionally, the amputee was able to perform 'tying up shoe' using the prosthetic forearm. Secondly, pattern recognition and classification experiments using the sEMG signals were performed to find out whether it could classify the motions according to the user's intents. For this purpose, sEMG signals were applied to the multilayer perceptron (MLP) for training and testing. As a result, overall classification accuracy arrived at 99.6% for all participants, and all the postures showed more than 97% accuracy.

• PNF and Movement
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• v.19 no.2
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• pp.173-182
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• 2021

Purpose: The purpose of this study was to compare the effects of underwater and ground-based diagonal pattern exercises on the balance confidence and respiratory functions of chronic stroke patients. Methods: Thirty chronic stroke patients were assigned randomly to an experimental (n = 15) or control (n = 15) group. The experimental group performed an underwater diagonal pattern exercise. The control group performed a ground-based diagonal pattern exercise. Training was conducted once a day for 30 minutes, five days per week for six weeks. Balance confidence was measured using the Activities-Specific Balance Confidence Scale-Korea version. Respiratory function was measured using a spirometer. Results: In a comparison within groups, the experimental and control groups showed significant differences in balance confidence after the experiment (p < 0.05). In a comparison between the two groups, the experimental group showed a more significant difference in balance confidence than the control group (p < 0.05). In a comparison within groups, the experimental group showed a significant difference in respiratory functions after the experiment (p < 0.05). In a comparison between the two groups, the experimental group showed a more significant difference in respiratory functions than the control group (p < 0.05). Conclusion: Based on these results, underwater diagonal pattern exercises effectively improved the balance confidence and respiratory functions of chronic stroke patients.