• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.106-112
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• 2015

In this paper, we propose an on-line signature recognition algorithm using fingertip point in the air from the depth image acquired by Kinect. We use ten statistical features for each X, Y, Z axis to react to changes in Shifting and Scaling of the signature trajectories in three-dimensional space. Artificial Neural Network is a machine learning algorithm used as a tool to solve the complex classification problem in pattern recognition. We implement the proposed algorithm to actual on-line signature recognition system. In experiment, we verify the proposed method is successful to classify 4 different on-line signatures.

• Journal of KIISE:Software and Applications
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• v.27 no.1
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• pp.24-32
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• 2000

BCI(Brain-Computer Interface) is studied to control the machines with brain. In this study, an EEG(Electroencephalography) signal classification model is proposed. The model gets EEG pattern from each subject's brain and extracts characteristic features. The model discriminates the EEG patterns by using those extracted characteristic features of each subject. The proposed method classifies each pair of the given tasks and combines the results to give the final result. Four tasks such as rest, movement, mental-arithmetic calculation and point-fixing were used in the experiment. Over 90% of the trials, the model yielded successful results. The model exploits characteristic features of the subjects and the weight table that was produced after training. The analysis results of the model such as its high success rates and short processing time show that it can be used in a real-time brain-computer interface system.

• The Transactions of the Korea Information Processing Society
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• v.5 no.8
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• pp.2061-2074
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• 1998

In pattern recognition and image analysis upplications, a graph is a useful tool for complex obect representation and recognition. However it takes much time to pair proper nodes between the prototype graph and an input data graph. Futhermore it is difficult to decide whether the two graphs in a class are the same hecause real images are degradd in general by noise and other distortions. In this paper we propose a matching algorithm using a matrix. The matrix is suiable for simple and easily understood representation and enables the ordering and matching process to be convenient due to its predefined matrix manipulation. The nodes which constitute a gaph are ordered in the matrix by their geometrical positions and this makes it possible to save much comparison time for finding proper node pairs. for the classification, we defined a distance measure thatreflects the symbo's structural aspect that is the sum of the mode distance and the relation distance; the fornet is from the parameters describing the node shapes, the latter from the relations with othes node in the matrix. We also introduced a subdivision operation to compensate node merging which is mainly due t the prepreocessing error. The proposed method is applied to the recognition of musteal symbols and the result is given. The result shows that almost all, except heavily degraded symbols are recognized, and the recognition rate is approximately 95 percent.

• The Transactions of the Korea Information Processing Society
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• v.4 no.11
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• pp.2769-2779
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• 1997

This Paper Proposes an efficient method for improving the training performance of the neural networks using a hybrid of conjugate gradient backpropagation algorithm and dynamic tunneling backpropagation algorithm The conjugate gradient backpropagation algorithm, which is the fast gradient algorithm, is applied for high speed optimization. The dynamic tunneling backpropagation algorithm, which is the deterministic method with tunneling phenomenon, is applied for global optimization. Conversing to the local minima by using the conjugate gradient backpropagation algorithm, the new initial point for escaping the local minima is estimated by dynamic tunneling backpropagation algorithm. The proposed method has been applied to the parity check and the pattern classification. The simulation results show that the performance of proposed method is superior to those of gradient descent backpropagtion algorithm and a hybrid of gradient descent and dynamic tunneling backpropagation algorithm, and the new algorithm converges more often to the global minima than gradient descent backpropagation algorithm.

• The Plant Pathology Journal
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• v.32 no.5
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• pp.431-440
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• 2016

In 2004, bacterial spot-causing xanthomonads (BSX) were reclassified into 4 species-Xanthomonas euvesicatoria, X. vesicatoria, X. perforans, and X. gardneri. Bacterial spot disease on pepper plant in Korea is known to be caused by both X. axonopodis pv. vesicatoria and X. vesicatoria. Here, we reidentified the pathogen causing bacterial spots on pepper plant based on the new classification. Accordingly, 72 pathogenic isolates were obtained from the lesions on pepper plants at 42 different locations. All isolates were negative for pectolytic activity. Five isolates were positive for amylolytic activity. All of the Korean pepper isolates had a 32 kDa-protein unique to X. euvesicatoria and had the same band pattern of the rpoB gene as that of X. euvesicatoria and X. perforans as indicated by PCR-restriction fragment length polymorphism analysis. A phylogenetic tree of 16S rDNA sequences showed that all of the Korean pepper plant isolates fit into the same group as did all the reference strains of X. euvesicatoria and X. perforans. A phylogenetic tree of the nucleotide sequences of 3 housekeeping genes-gapA, gyrB, and lepA showed that all of the Korean pepper plant isolates fit into the same group as did all of the references strains of X. euvesicatoria. Based on the phenotypic and genotypic characteristics, we identified the pathogen as X. euvesicatoria. Neither X. vesicatoria, the known pathogen of pepper bacterial spot, nor X. perforans, the known pathogen of tomato plant, was isolated. Thus, we suggest that the pathogen causing bacterial spot disease of pepper plants in Korea is X. euvesicatoria.

• Journal of Physiology & Pathology in Korean Medicine
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• v.17 no.4
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• pp.845-851
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• 2003

The objective is to establish the standard of criteria for differential diagnosis of signs and symptoms. This study selected signs and symptoms related to heart which stands for Fire(火) as a kind of five phase(五行). Eleven experts was asked to evaluate the adequateness of criteria which was developed by Korea Institute of Oriental Medicine(KIOM) and to suggest the amendment of them. To implement the study, we used the questionnaire which asks about the diagnosis criteria for an insufficiency of the heart-qi(心氣虛證), deficiency of the heart blood(心血氣證), deficiency of the heart-yin(心陰虛證), insufficiency of the heart-yang(心陽虛證), exuberant fire due to hyperactivity of the heart(心火亢盛證), stagnation of the heart blood(心血瘀阻證), heart disturbed by phlegm-fire(痰火擾心證), attack of the heart by retainedfluid(水氣凌心證). Every criteria consists of primary symptoms, secondary symptoms, tongue findings. and pulse findings. In perspectives of the classification of patterns for signs and symptoms and criteria for diagnosis, the result shows that the previous standard doesn't have so many problem. So many of experts were agree with the criteria which was suggested but the trend is that they use, in their actual practice, less than the criteria. Additionally, they pointed that every element in a criterion should have the different weight value, criteria for the overlapped pattern should be added, and, in future, criteria which are based on clinical investigation should be established.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1372-1381
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• 2017

In this paper, a texture feature extraction method using local energy and local correlation of Gabor transformed images is proposed and applied to an image retrieval system. The Gabor wavelet is known to be similar to the response of the human visual system. The outputs of the Gabor transformation are robust to variants of object size and illumination. Due to such advantages, it has been actively studied in various fields such as image retrieval, classification, analysis, etc. In this paper, in order to fully exploit the superior aspects of Gabor wavelet, local energy and local correlation features are extracted from Gabor transformed images and then applied to an image retrieval system. Some experiments are conducted to compare the performance of the proposed method with those of the conventional Gabor method and the popular rotation-invariant uniform local binary pattern (RULBP) method in terms of precision vs recall. The Mahalanobis distance is used to measure the similarity between a query image and a database (DB) image. Experimental results for Corel DB and VisTex DB show that the proposed method is superior to the conventional Gabor method. The proposed method also yields precision and recall 6.58% and 3.66% higher on average in Corel DB, respectively, and 4.87% and 3.37% higher on average in VisTex DB, respectively, than the popular RULBP method.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.297-305
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• 2008

Recently, kernel methods have attracted great interests in the areas of pattern classification, function approximation, and anomaly detection. The role of the kernel is particularly important in the methods such as SVM(support vector machine) and KPCA(kernel principal component analysis), for it can generalize the conventional linear machines to be capable of efficiently handling nonlinearities. This paper considers the problem of combining radar and rain gauge observations utilizing the regression approach based on the kernel-based gaussian process and support vector learning. The data-assimilation results of the considered methods are reported for the radar and rain gauge observations collected over the region covering parts of Gangwon, Kyungbuk, and Chungbuk provinces of Korea, along with performance comparison.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.7
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• pp.1387-1393
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• 2007

Even if the linearly separable patterns can be classified by the conventional single layer perceptron, the non-linear problems such as XOR can not be classified by it. A fuzzy single layer perceptron can solve the conventional XOR problems by applying fuzzy membership functions. However, in the fuzzy single layer perception, there are a couple disadvantages which are a decision boundary is sometimes vibrating and a convergence may be extremely lowered according to the scopes of the initial values and learning rates. In this paper, for these reasons, we proposed an enhanced fuzzy single layer perceptron algorithm that can prevent from vibration the decision boundary by introducing a bias term and can also reduce the learn time by applying the modified delta rule which include the learning rates and the momentum concept and applying the new linear activation function. Consequently, the simulation results of the XOR and pattern classification problems presented that the proposed method provided the shorter learning time and better convergence than the conventional fuzzy single layer perceptron.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.45-55
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• 2015

In this paper, we consider curriculum mining as an application of process mining in the domain of education. The basic objective of the curriculum mining is to construct a registration pattern model by using logs of registration data. However, subject registration patterns of students are very unstructured and complicated, called a spaghetti model, because it has a lot of different cases and high diversity of behaviors. In general, it is typically difficult to develop and analyze registration patterns. In the literature, there was an effort to handle this issue by using clustering based on the features of students and behaviors. However, it is not easy to obtain them in general since they are private and qualitative. Therefore, in this paper, we propose a new framework of curriculum mining applying K-means clustering based on subject attributes to solve the problems caused by unstructured process model obtained. Specifically, we divide subject's attribute data into two parts : categorical and numerical data. Categorical attribute has subject name, class classification, and research field, while numerical attribute has ABEEK goal and semester information. In case of categorical attribute, we suggest a method to quantify them by using binarization. The number of clusters used for K-means clustering, we applied Elbow method using R-squared value representing the variance ratio that can be explained by the number of clusters. The performance of the suggested method was verified by using a log of student registration data from an 'A university' in terms of the simplicity and fitness, which are the typical performance measure of obtained process model in process mining.