• Journal of Korea Multimedia Society
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• v.12 no.8
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• pp.1155-1163
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• 2009

This paper proposes an automatic facial expression generation system of vector graphic character using gaussian process model. Proposed method extracts the main feature vectors from twenty-six facial data of character redefined based on Russell's internal emotion state. Also by using new gaussian process model, SGPLVM, we find low-dimensional feature data from extracted high-dimensional feature vectors, and learn probability distribution function (PDF). All parameters of PDF are estimated by maximization the likelihood of learned expression data, and these are used to select wanted facial expressions on two-dimensional space in real time. As a result of simulation, we confirm that proposed facial expression generation tool is working in the small facial expression datasets and can generate various facial expressions without prior knowledge about relation between facial expression and emotion.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.8
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• pp.1239-1249
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• 1990

In this paper, using the neural network design techniques, an adaptive Mahalanobis distance classifier(AMDC) is designed. This classifier has three layers: input layer, internal layer and output layer. The connection from input layer to internal layer is fully connected, and that from internal to output layer has partial connection that might be thought as an Oring. If two ormore clusters of patterns of one class are laid apart in the feature space, the network adaptively generate the internal nodes, whhch are corresponding to the subclusters of that class. The number of the output nodes in just same as the number of the classes to classify, on the other hand, the number of the internal nodes is defined by the number of the subclusters, and can be optimized by itself. Using the method of making the subclasses, the different patterns that are of the same class can easily be distinguished from other classes. If additional training is needed after the completion of the traning, the AMDC does not have to repeat the trainging that has already done. To test the performance of the AMDC, the experiments of classifying 500 Hangeuls were done. In experiment, 20 print font sets of Hangeul characters(10,000 cahracters) were used for training, and with 3 sets(1,500 characters), the AMDC was tested for various initial variance \ulcornerand threshold \ulcorner and compared with other statistical or neural classifiers.

• Tuberculosis and Respiratory Diseases
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• v.67 no.4
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• pp.364-368
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• 2009

Bronchiolitis interstitial pneumonitis (BIP), an unclassified and newly described interstitial pneumonia, has a combined feature of prominent bronchiolitis, interstitial inflammation, and fibrosis. It is distinct from bronchiolitis obliterans or bronchiolitis obliterans organizing pneumonia (BOOP). BIP has a better prognosis than common cases of interstitial pneumonia. However, BIP has a poorer prognosis than BOOP. BIP's response to corticosteroids is not as successful as BOOP's response to this treatment. We encountered the case of a 31-year-old woman with BIP with an initial presentation of dyspnea and a cough that had lasted for 3 months. The patient's chest CT scan demonstrated patchy ground glass opacities and multiple ill-defined centrilobular nodules in both lungs, suggesting military tuberculosis or nontuberculous mycobacterial infection. A video-assisted thoracoscopic lung biopsy resulted in the diagnosis of BIP. Clinical symptoms, pulmonary lesions, and pulmonary function tests were improved after oral glucocorticoid therapy.

• The Journal of Internal Korean Medicine
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• v.25 no.3
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• pp.590-595
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• 2004

Conversion Disorder is a disorder whose predominant feature is a loss or alteration in physical functioning that suggests a physical disorder but that is actually a direct expression of a psychological conflict or need. The Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) guidelines for Conversion Disorder include these definitions: A psychosocial stressor produces a psychological conflict that is believed to help initiate or exacerbate the illness The symptoms are not under conscious control, etc. While functional disabilities are common with conversion disorders, physical and laboratory abnormalities are absent or minor in comparison with the patient's subjective complaints. Symptoms of Conversion Disorder are similar to those of stroke. But the mechanism of Conversion Disorder is similar as that of Stagnation Syndrome of Ki (氣鬱證) in Oriental medicine. Gaegyeolseogyeong-tang has been used to treat women who suffer from Conversion Disorder induced by the Stagnation Syndrome of Ki (氣鬱證). After application of the Gaegyeolseogyeong-tang for 7 days, symptoms and signs improved dramatically.

• Tuberculosis and Respiratory Diseases
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• v.63 no.4
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• pp.382-386
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• 2007

Paraneoplastic limbic encephalitis is a rare disorder that is characterized by personality changes, irritability, depression, seizures, memory loss and dementia, and is commonly associated with small cell lung cancer. The cause is unknown but it is believed to be an autoimmune disorder that develops secondary to a carcinomatous process. We report a patient with the clinical feature consistent with limbic encephalitis. A 64-year-old women developed disorientation, memory loss and general weakness. She was diagnosed with NSCLC (adenocarcinoma) with a brain metastasis 1 year earlier and was treated with radiation and chemotherapy. Although the lung mass and brain metastatic lesions had improved, the brain T2-weighted MRI showed high signal intensity in the right temporal region. This lesion consisted of with limbic encephalitis and was negative to the other viral and immune markers. The patient's symptoms did not improve after steroid treatment. Our case demonstrated that a NSCLC (adenocarcinoma) also can be associated with paraneoplastic limbic encephalitis.

• Journal of Biosystems Engineering
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• v.28 no.3
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• pp.253-260
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• 2003

In this study, the feasibility of magnetic resonance techniques for nondestructive internal quality evaluation of Korean red ginseng was examined. Relaxation time constants were measured using various grades of red ginsengs. Solid state magnetic resonance imaging technique was applied to image dried red ginsengs which have low moisture contents (about 13%). A 7 tesla magnetic resonance imaging system operating at a proton resonant frequency of 300 ㎒ was used for acquiring MR images of dried Korean red ginseng. The comparison test of cross cut digital images and magnetic resonance images of heaven grade, good grade with cavity inside, and good grade with white part inside red ginseng suggested the feasibility of the internal quality evaluation of Korean red ginsengs using MRI techniques. A good grade red ginseng included abnormal tissues such as cavities or white parts inside was observed by the signal intensity of MR image based on magnetic resonance properties of proton nucleus. Analysis on an one dimensional profile of acquired MR image of Korean red ginseng showed easy discrimination of normal and abnormal tissues. MR techniques suggested ways to detect internal defects of red ginsengs effectively.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.965-976
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• 2020

We present in this work a segmentation method of E. coli bacterial images generated via phase contrast microscopy using a deep learning based hybrid feature generation. Unlike conventional machine learning methods that use the hand-crafted features, we adopt the denoising autoencoder in order to generate a precise and accurate representation of the pixels. We first construct a hybrid vector that combines original image, difference of Gaussians and image gradients. The created hybrid features are then given to a deep autoencoder that learns the pixels' internal dependencies and the cells' shape and boundary information. The latent representations learned by the autoencoder are used as the inputs of a softmax classification layer and the direct outputs from the classifier represent the coarse segmentation mask. Finally, the classifier's outputs are used as prior information for a graph partitioning based fine segmentation. We demonstrate that the proposed hybrid vector representation manages to preserve the global shape and boundary information of the cells, allowing to retrieve the majority of the cellular patterns without the need of any post-processing.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.670-675
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• 2015

Medical imaging is one of the most powerful tools for gaining information about internal organs and tissues. It is a challenging task to develop sophisticated image analysis methods in order to improve the accuracy of diagnosis. The objective of this paper is to develop a Computer Aided Diagnostics (CAD) scheme for Brain Tumour detection from Magnetic Resonance Image (MRI) using active contour models and to investigate with several approaches for improving CAD performances. The problem in clinical medicine is the automatic detection of brain Tumours with maximum accuracy and in less time. This work involves the following steps: i) Segmentation performed by Fuzzy Clustering with Level Set Method (FCMLSM) and performance is compared with snake models based on Balloon force and Gradient Vector Force (GVF), Distance Regularized Level Set Method (DRLSE). ii) Feature extraction done by Shape and Texture based features. iii) Brain Tumour detection performed by various tree classifiers. Based on investigation FCMLSM is well suited segmentation method and Random Forest is the most optimum classifier for this problem. This method gives accuracy of 97% and with minimum classification error. The time taken to detect Tumour is approximately 2 mins for an examination (30 slices).

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.14 no.1
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• pp.84-92
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• 2001

In this papers, we proposed the new method in order to diagnosis aging state of transformers. For wavelet transform, Daubechies filter is used, we can obtain wavelet coefficients which is used to extract feature of statistical parameters (maximum value, average value, dispersion skewness, kurtosis) about each acoustic emission signal. Also, these coefficients are used to identify normal and fault signal of internal partial discharge in transformer. As improved method for classification use neural network. Extracted statistical parameters are input into an back-propagation neural network. The number of neurons of hidden layer are obtained through Result of Cross-Validation. The network, after training, can decide whether the test signal is early aging state, alst aging state or normal state. In quantity analysis, capability of proposed method is superior to compared that of classical method.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.913-916
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• 1998

In this paper, to provide the robustness of character recognition, we propose a recognition method using the dilated boundary curve feature which has the invariance characteristics for the shift, scale, and rotation changes of character pattern. And its some characteristics and effectieness are evaluated through the experiments for both the english alphabets and the numeral digits. The feature vector is represented by the fourier descriptor for a boundary curve of the dilated character pattern which is generated by the circular mask dilation method, and is used for a nearest neighbort classifier(NNC) or a nearest neighbor mean classifier(NNMC). These the processing time and the recognition rate, and take also the robustness of recognition for both some internal noise and partial corruption of an image pattern.