• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.11 no.2
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• pp.209-220
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• 2000

The purpose of this study is to find out the characteristics of depressive episode about major depression and bipolar disorder in child and adolescent. The subjects of this study were 34 major depression patients and 17 bipolar disorder patients hospitalized at child and adolescent psychiatry in OO university children's hospital from 1st March 1993 to 31st October 1999. The method of this study is to review socio-demographic characteristics, diagnostic classification, chief problems and symptoms at admission, frequency of symptoms, maternal pregnancy problem history, childhood developmental history, coexisting psychiatric disorders, family psychopathology and family history and therapeutic response through their chart. 1) The ratio of male was higher than that of female in major depressive disorder while they are similar in manic episode, bipolar disorder. 2) Average onset age of bipolar disorder was 14 years 1 month and it was 12 years 8 months in the case of major depression As a result, average onset age of major depression is lower than that of bipolar disorder. 3) The patients complained of vegetative symptoms than somatic symptoms in both bipolar disorder and depressive disorder. Also, the cases of major depression developed more suicide idea symptom while the case of bipolar disorder developed more aggressive symptoms. In the respect of psychotic symptoms, delusion was more frequently shown in major depression, but halucination was more often shown in bipolar disorder. 4) Anxiety disorder coexisted most frequently in two groups. And there coexisted symptoms such as somartoform disorder, mental retardation and personality disorder in both cases. 5) The influence of family loading was remarkable in both cases. Above all, the development of major depression had to do with child abuse history and inappropriate care of family. It is apparent that there are distinctive differences between major depression and bipolar disorder in child and adolescent through the study, just as in adult cases. Therefore the differences of clinical characteristics between two disorders is founded in coexisting disorders and clinical symptoms including onset age, somatic symptoms and vegetative symptoms.

• Journal of Popular Narrative
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• v.25 no.4
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• pp.191-221
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• 2019

This study aims to analyze the post-humanistic imagination that has emerged as a major academic thesis in Korean literature, especially novels. In particular, this paper focuses on Cho Ha-hyung's two novels Chimera's Morning(2004) and A Prefabricated Bodhi Tree(2008), published in the early 2000s, for intensive analysis. Post-humanism can be seen as an extension of post-modernism that tried to overcome the limitations of modernity and seek to establish a new world view. In particular, this thought pays attention to the comprehensive understanding of how the rapid development of science and technology, which has developed since the 20th century, has changed the view of humanity and human-centered civilization itself. At the concrete level, it is developing in the direction of constructing a new subject idea by reflecting and dismantling Western-, reason-, and male-centered power mechanisms that are the core of modern civilization. Cho attempts to discover and re-illuminate the surrounding figures, non-humans, and objects that were not noticed in the classic works written in the past. This ideological flow reflects the fact that the concept of human beings, which had been dominated by the humanities in recent years, has been completely changed, and the natural science and technology perspective is applied to the discourse field in various ways. From the point of view of post-humanism, objects that have not been classified as humans and objects that were considered inferior to humans should be included in human or comparable levels. These questions generate interdisciplinary research tasks by involving the large categories of philosophy, such as ontology, epistemology and empirical fields, as well as calling for the participation of the entire literature, science and social sciences. Against the backdrop of a disaster-hit world, Chimera's Morning and A Prefabricated Bodhi Tree depict human beings as variants transformed by bio-technology, and creatures made out of the artificial intelligence built by computer simulations. Post-humanistic ideas in Cho's novels provide a reflective opportunity to comprehensively reconsider the world's shape and human identity reproduced in the text, and to re-explore boundary lines and hierarchy order that distinguish between human and non-human.

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.17-31
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• 2006

In this paper, passport recognition and face verification methods which can automatically recognize passport codes and discriminate forgery passports to improve efficiency and systematic control of immigration management are proposed. Adjusting the slant is very important for recognition of characters and face verification since slanted passport images can bring various unwanted effects to the recognition of individual codes and faces. Therefore, after smearing the passport image, the longest extracted string of characters is selected. The angle adjustment can be conducted by using the slant of the straight and horizontal line that connects the center of thickness between left and right parts of the string. Extracting passport codes is done by Sobel operator, horizontal smearing, and 8-neighborhood contour tracking algorithm. The string of codes can be transformed into binary format by applying repeating binary method to the area of the extracted passport code strings. The string codes are restored by applying CDM mask to the binary string area and individual codes are extracted by 8-neighborhood contour tracking algerian. The proposed RBF network is applied to the middle layer of RBF network by using the fuzzy logic connection operator and proposing the enhanced fuzzy ART algorithm that dynamically controls the vigilance parameter. The face is authenticated by measuring the similarity between the feature vector of the facial image from the passport and feature vector of the facial image from the database that is constructed with PCA algorithm. After several tests using a forged passport and the passport with slanted images, the proposed method was proven to be effective in recognizing passport codes and verifying facial images.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.8 no.2
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• pp.266-272
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• 1997

We examine the clinical efficacies of fluoxetine in treating the children with selective mutism. In an 8-week open-label clinical study, 17 children with selective mutism are received 20-60mg/day of fluoxetine. Our results reveal that 13 subjects(76%) of 17 subjects improve statistically in within subjects comparison of pre- and post-treatment changes in the scores of Clinical Global Impression scale for mutism, Children’s Depression Inventory scale, and Revised Children’s Manifest Anxiety Scale. These data suggest that selective serotonergic antidepressants may be effective in treating selective mutism in children and adolescents.

• Clinical and Experimental Pediatrics
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• v.52 no.3
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• pp.289-294
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• 2009

Purpose : To assess the usefulness of magnetic resonance imaging (MRI), karyotyping, brainstem auditory evoked potential (BAEP), electroencephalogram (EEG), tandem mass screening test, and newborn metabolic screening test in children with language delay for diagnosing underlying diseases. Methods : From January 2000 to June 2007, a retrospective chart review was performed for 122 children with language delay who visited the Child Neurology Clinic at Yeungnam University Hospital and who underwent neuropsychologic tests and other diagnostic evaluations for underlying diseases. They were grouped into phenomenological diagnostic categories, and test results were analyzed according to the underlying diseases. Results : Of 122 patients, 47 (38.5%) had mental retardation, 40 (32.8%) had developmental language disorders, 23 (18.9 %) had borderline IQ, and 12 (9.8%) had autism spectrum disorder. In 26 (21.3%) cases, the causes or relevant clinical findings to explain language delay were found. Eight (10.4%) of 77 MRIs, 6 (8.0%) of 75 EEGs, and 4 (5%) of 80 BAEPs showed abnormal results. Results directly attributed to diagnosing underlying diseases were 2 hearing defects in BAEPs and 1 bilateral perisylvian cortical dysplasia in MRIs. No abnormal results were found in karyotyping, tandem mass screening tests, and new-born screening tests. Conclusion : Commonly used tests to diagnose the cause of language delay are not very effective and should only be used selectively, according to patient characteristics. However, despite the low diagnostic yields from these tests, because many patients show abnormal results, these tests are useful when conducted in complete evaluation.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.482-488
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• 2014

This paper aims to analyze user's emotion automatically by analyzing Twitter, a representative social network service (SNS). In order to create sentiment analysis models by using machine learning techniques, sentiment labels that represent positive/negative emotions are required. However it is very expensive to obtain sentiment labels of tweets. So, in this paper, we propose a sentiment analysis model by using self-training technique in order to utilize "data without sentiment labels" as well as "data with sentiment labels". Self-training technique is that labels of "data without sentiment labels" is determined by utilizing "data with sentiment labels", and then updates models using together with "data with sentiment labels" and newly labeled data. This technique improves the sentiment analysis performance gradually. However, it has a problem that misclassifications of unlabeled data in an early stage affect the model updating through the whole learning process because labels of unlabeled data never changes once those are determined. Thus, labels of "data without sentiment labels" needs to be carefully determined. In this paper, in order to get high performance using self-training technique, we propose 3 policies for updating "data with sentiment labels" and conduct a comparative analysis. The first policy is to select data of which confidence is higher than a given threshold among newly labeled data. The second policy is to choose the same number of the positive and negative data in the newly labeled data in order to avoid the imbalanced class learning problem. The third policy is to choose newly labeled data less than a given maximum number in order to avoid the updates of large amount of data at a time for gradual model updates. Experiments are conducted using Stanford data set and the data set is classified into positive and negative. As a result, the learned model has a high performance than the learned models by using "data with sentiment labels" only and the self-training with a regular model update policy.