• Sleep Medicine and Psychophysiology
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• v.10 no.2
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• pp.93-99
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• 2003

Objectives：Sleep loss and excessive daytime sleepiness may have serious consequences, including traffic and industrial accidents, decreased productivity, learning disabilities and interpersonal problems. Yet despite these adverse effects, there are few epidemiological studies on sleep loss and daytime sleepiness in the general population of Korea. This study investigates the number of people who suffer from sleep insufficiency, how much recovery sleep occurs on weekends, and the relationship between the amount of recovery sleep and daytime sleepiness. Methods：A total 164 volunteers, aged 20 and over, were recruited by advertisement. The subjects were workers and college students living in Seoul, Korea. Subjects were excluded if they were aged over 60；if they had medical, neurological, psychiatric or sleep disorders that could cause insomnia or daytime sleepiness；if they were not following a regular sleep schedule；if they traveled abroad during the study；or if they did not leave home to work or were shift workers. They were interviewed and given a sleep log to complete on each of 14 consecutive mornings. They also completed the Epworth Sleepiness Scale (ESS) at noontime on the last day of the second week. All statistical data were analyzed by t-test, $X^2$-test or ANOVA, using SPSS/PC+. Results：The results showed that the subjects woke up at 6：50 (${\pm}1$：16) on weekdays, 7：09 (${\pm}1$：29) on Saturdays, and 8：12 (${\pm}1$：39) on Sundays and holidays. They took more frequent and longer naps on Sundays than on weekdays and Saturdays. The mean sleep duration was 6h 35 min. on week nights, with a mean increase of about 1h on weekends. Only 9.1% of the subjects spent more than 8h in bed on week nights, with 67% spending less than 7h, and 49.4% reported recovery sleep of more than 1h on Sundays. The subjects who reported recovery sleep of more than 2h on Sundays, showed significantly more excessive daytime sleepiness than those who reported less than 30 min (F=2.62, p<.05). Conclusions：These findings suggest that sleep insufficiency and excessive daytime sleepiness are relatively common in Korea, and that the people who get insufficient sleep on weekdays try to compensate for sleep loss with oversleeping and daytime napping on Sundays and holidays. It appeared that daily sleep insufficiency had a cumulative effect and increased daytime sleepiness.

• Clinical and Experimental Pediatrics
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• v.46 no.11
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• pp.1067-1072
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• 2003

Purpose : Non-accidental intracranial hemorrhage in children is not low in incidence and results in high mortality and serious sequelae. So, the authors have researched the distribution of sex and age, causes, symptoms and signs, hemorrhagic types, mortality rate and sequelae of the patients hospitalized with non-accidental intracranial hemorrhage at Hallym University Sacred Heart Hospital. Methods : The medical records of twenty patients, aged 15 or younger, and excluding neonatal patients, were analyzed retrospectively. The patients in this study were admitted with non-accidental intracranial hemorrhage from January 1999 to June 2002. Results : Of the twenty cases, the ratio of male to female was 1 : 0.8. The patients aged one or less and between 11 and 15 were discovered to be the most frequent cases. Shaken baby syndrome and arteriovenous malformation were found to be the most frequent causes. Seizure was most frequently found to be a symptom and a sign. Hemorrhagic type was classified into subdural hemorrhage eight, intracerebral hemorrhage five. There were three mortal cases. Twelve surviving patients, excluding five not-followed ones, were reclassified into six cases of complete recovery and six of sequalae. Conclusion : Non-accidental intracranial hemorrhage in children is not low in incidence, with a high mortality rate and a high incidence of serious sequelae after survival. Consequently, early diagnosis and appropriate treatment are required. In addition, appropriate rehabilitation after treatment is needed because the high survival rate due to advanced medical treatment results in an increasing number of neurologic sequelae.

• Sleep Medicine and Psychophysiology
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• v.12 no.2
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• pp.122-132
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• 2005

Objectives: The objective of this study is to assess cognitive functions and their relationship with sleep symptoms in young narcoleptic patients. Methods: Eighteen young narcolepsy patients and 18 normal controls (age: 17-35 years old) were recruited. All narcolepsy patients had HLA $DQB_1$ *0602 allele and cataplexy. Several important areas of cognition were assessed by a battery of neuropsychological tests consisting of 13 tests: executive functions (e.g. cognitive set shifting, inhibition, and selective attention) through Wisconsin card sorting test, Trail Making A/B, Stroop test, Ruff test, Digit Symbol, Controlled Oral Word Association and Boston Naming Test; alertness and sustained attention through paced auditory serial addition test; verbal/nonverbal short-term memory and working memory through Digit Span and Spatial Span; visuospatial memory through Rey-Osterrieth complex figure test; verbal learning and memory through California verbal learning test; and fine motor activity through grooved pegboard test. Sleep symptoms in narcolepsy patients were assessed with Epworth sleepiness scale, Ullanlinna narcolepsy scale, multiple sleep latency test, and nocturnal polysomnography. Relationship between cognitive functions and sleep symptoms in narcolepsy patients was also explored. Results: Compared with normal controls, narcolepsy patients showed poor performance in paced auditory serial addition (2.0 s and 2.4 s), digit symbol tests, and spatial span (forward)(t=3.86, p<0.01; t=-2.47, p=0.02; t=-3.95, p<0.01; t=-2.22, p=0.03, respectively). There were no significant between-group differences in other neuropsychological tests. In addition, results of neuropsychological test in narcolepsy patients were not correlated with Epworth sleepiness scale score, Ullanlinna narcolepsy scale score and sleep variables in multiple sleep latency test or nocturnal polysomnography. Conclusion: The current findings suggest that young narcolepsy patients have impaired attention. In addition, impairment of attention in narcolepsy might not be solely due to sleep symptoms such as excessive daytime sleepiness.

• Korean Journal of Psychosomatic Medicine
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• v.11 no.2
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• pp.97-117
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• 2003

Objective: This study is to examine the validity and reliability of Computerized Neurocognitive Function Test among normal children in elementary school. Methods: K-ABC, K-PIC, and Computerized Neurocognitive Function Test were performed to the 120 body of normal children(10 of each male and female) from June, 2002 to January, 2003. Those children had over the average of intelligence and passed the rule out criteria. To verify test-retest reliability for those 30 children who were randomly selected, Computerized Neurocognitive Function Test was carried out again 4 weeks later. Results: As a results of correlation analysis for validity test, four of continues performance tests matched with those on adults. In the memory tests, results presented the same as previous research with a difference between forward test and backward test in short-term memory. In higher cognitive function tests, tests were consist of those with different purpose respectively. After performing factor analysis on 43 variables out of 12 tests, 10 factors were raised and the total percent of variance was 75.5%. The reasons were such as: 'sustained attention, information processing speed, vigilance, verbal learning, allocation of attention and concept formation, flexibility, concept formation, visual learning, short-term memory, and selective attention' in order. In correlation with K-ABC to prepare explanatory criteria, selectively significant correlation(p<.0.5-001) was found in subscale of K-ABC. In the test-retest reliability test, the results reflecting practice effect were found and prominent especially in higher cognitive function tests. However, split-half reliability(r=0.548-0.7726, p<.05) and internal consistency(0.628-0.878, p<.05) of each examined group were significantly high. Conclusion: The performance of Computerized Neurocognitive Function Test in normal children represented differ developmental character than that in adult. And basal information for preparing the explanatory criteria could be acquired by searching for the relation with standardized intelligence test which contains neuropsycological background.

• Sleep Medicine and Psychophysiology
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• v.8 no.2
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• pp.129-137
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• 2001

Objectives: The purpose of this study is to investigate the effects of 38-hour sleep deprivation on fine motor performance. The Motor Performance Series (MPS) in the Vienna Test System (computerized neurocognitive function tests) was used in this study. Methods: Twenty four subjects participated in this study. Subjects had no past history of psychiatric disorders and physical illness. Subjects had normal sleep-waking cycle without current sleep disturbances and were all right-handed (Annett's Hand Preference Questionnaire: above +9 points). To minimize the learning effects, familiarization with the Vienna Test System was performed one day before the study. Subjects were to get up at 6:00 in the morning after getting enough sleep according to his or her usual sleep-wake cycle. After awakening, subjects remained awake for 38 hours under continuous surveillance. During two consecutive study days, the subjects tested MPS at 7 AM and 7 PM each day, which means the MPS was done four times in total. During the experiment, anything that could affect the subjects' sleep such as coffee, tea, alcohol, a nap, tiring sports, and all medications were prohibited. Results: In MPS, the fine motor functions of both hands decreased after 38 hours of sleep deprivation. The decrement in motor performance was prominent in the dominant right hand. In the right hand, the total number of tapping was reduced (p<.005), and the number of misses (p<.05) and the length of misses (p<.05) of line tracking, the total length of inserting a short pin (p<.01), the total length of inserting a long pin (p<.05), and the number of misses in aiming (p<.05) increased. Such performance decrement was distinct in the morning sessions. Conclusions: These results suggest that fine motor performance decrement during sleep deprivation is predominant in the right hand, which exerts maximal motor function. The finding of decrement in motor function in tapping during sleep deprivation also suggested that the time required for exhaustion of muscles is shortened during sleep deprivation. More deterioration of motor performance was shown in the morning, which could be explained as circadian rhythm effects.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.14 no.1
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• pp.12-25
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• 2003

Objectives：As increasing number of new antidepressants have been being introduced in clinical practice, pharmacological understanding has been broadened. These changes mandate new information and theories to be incorporated into the treatment process of children with depressive disorders. In light of newly coming knowledge, this review intended to recapitulate the characteristics of new antidepressants and to consider the pivotal issues to develope guidelines for the treatment of depression in childhood and adolescence. Methods：Searching the Pub-Med online database for the articles with the key words of 'new', 'antidepressants' and 'children' ninety-seven headings of review articles were obtained. The author selected the articles of pertinent subjects in terms of either treatment guideline or psychopharmacology of new antidepressants. When required, articles about the clinical effectiveness of individual antidepressants were separatedly searched. In addition, the safety information of new antidepressants was acquired by browsing the official sites of the United States Food and Drugs Administration and Department of Health and Human Services. Results：1) For the clinical course, treatment phase, and treatment outcome, the reviews or treatment guidelines adopted the information from adult treatment guidelines. 2) Systematic and critical reviews unambiguously concluded that selective serotonin reuptake inhibitors(SSRIs) excelled tricyclic antidepressants( TCAs) for both efficacy and side effect profiles, and were recommend for the first-line choice for the treatment of children with depressive disorders. 3) New antidepressants generally lacked treatment experiences and randomized controlled clinical trials. 4) SSRIs and other new antidepressants, when used together, might result in pharmacokinetic and/or pharmacodynamic drug-to-drug interaction. 5) The difference of the clinical effectiveness of antidepressants between children and adults should be addressed from developmental aspects, which required further evidence. Conclusion：Treatment guidelines for the pharmacological treatment of childhood and adolescence depression could be constructed on the basis of clinical trial findings and practical experiences. Treatment guidelines are to best serve as the frame of reference for a clinician to make reasonable decisions for a particular therapeutic situation. In order to fulfill this role, guidelines should be updated as soon as new research data become available.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.119-138
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• 2017

Recently, SNS has become an important channel for marketing as well as personal communication. However, cybercrime has also evolved with the development of information and communication technology, and illegal advertising is distributed to SNS in large quantity. As a result, personal information is lost and even monetary damages occur more frequently. In this study, we propose a method to analyze which sentences and documents, which have been sent to the SNS, are related to financial fraud. First of all, as a conceptual framework, we developed a matrix of conceptual characteristics of cybercriminality on SNS and emergency management. We also suggested emergency management process which consists of Pre-Cybercriminality (e.g. risk identification) and Post-Cybercriminality steps. Among those we focused on risk identification in this paper. The main process consists of data collection, preprocessing and analysis. First, we selected two words 'daechul(loan)' and 'sachae(private loan)' as seed words and collected data with this word from SNS such as twitter. The collected data are given to the two researchers to decide whether they are related to the cybercriminality, particularly financial fraud, or not. Then we selected some of them as keywords if the vocabularies are related to the nominals and symbols. With the selected keywords, we searched and collected data from web materials such as twitter, news, blog, and more than 820,000 articles collected. The collected articles were refined through preprocessing and made into learning data. The preprocessing process is divided into performing morphological analysis step, removing stop words step, and selecting valid part-of-speech step. In the morphological analysis step, a complex sentence is transformed into some morpheme units to enable mechanical analysis. In the removing stop words step, non-lexical elements such as numbers, punctuation marks, and double spaces are removed from the text. In the step of selecting valid part-of-speech, only two kinds of nouns and symbols are considered. Since nouns could refer to things, the intent of message is expressed better than the other part-of-speech. Moreover, the more illegal the text is, the more frequently symbols are used. The selected data is given 'legal' or 'illegal'. To make the selected data as learning data through the preprocessing process, it is necessary to classify whether each data is legitimate or not. The processed data is then converted into Corpus type and Document-Term Matrix. Finally, the two types of 'legal' and 'illegal' files were mixed and randomly divided into learning data set and test data set. In this study, we set the learning data as 70% and the test data as 30%. SVM was used as the discrimination algorithm. Since SVM requires gamma and cost values as the main parameters, we set gamma as 0.5 and cost as 10, based on the optimal value function. The cost is set higher than general cases. To show the feasibility of the idea proposed in this paper, we compared the proposed method with MLE (Maximum Likelihood Estimation), Term Frequency, and Collective Intelligence method. Overall accuracy and was used as the metric. As a result, the overall accuracy of the proposed method was 92.41% of illegal loan advertisement and 77.75% of illegal visit sales, which is apparently superior to that of the Term Frequency, MLE, etc. Hence, the result suggests that the proposed method is valid and usable practically. In this paper, we propose a framework for crisis management caused by abnormalities of unstructured data sources such as SNS. We hope this study will contribute to the academia by identifying what to consider when applying the SVM-like discrimination algorithm to text analysis. Moreover, the study will also contribute to the practitioners in the field of brand management and opinion mining.

• Korean Journal of Heritage: History & Science
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• v.43 no.3
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• pp.144-179
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• 2010

Investigation into iron manufacture relics has been active since 1970s, especially accelerated in 1990s across the country. Consideration of the importance of production site relics has lately attracted attention to iron manufacture relics. Methodological studies of the investigation into iron manufacture relics, however, were less made compared with those of the investigation into tomb, dwelling, or swampy place relics. It is because the process of iron manufacture is too complicated to understand and also requires professional knowledge of metal engineering. With the recognition of these problems this research is to form an opinion about how to excavate, to rearrange and classify, and to examine iron manufacture relics, based upon the understanding of the nature of iron, iron production process, and metal engineering features of related relics like slag, iron lumps and so on. This research classifies iron manufacture relics into seven types according to the production process; mining, smelting, refining, tempering, melting, steelmaking, and the others. Then it arranges methods to survey in each stage of field study, trial digging, and excavation. It also explains how to classify and examine excavated relics, what field of natural science to be used to know the features of relics, and what efforts have been made to reconstruct a furnace and what their problems were, making the best use of examples, drawings, and photos. It comes to the conclusion, in spite of the lack of in-depth discussion on application and development of various investigation methods, that iron manufacture relics can be classified according to the production process, that natural sciences should be applied to get comprehensive understanding of relics as well as archeological knowledge, and that efforts to reconstruct a furnace should be continued from the aspect of experimental archeology.

• Journal of Science and Technology Studies
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• v.19 no.2
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• pp.169-206
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• 2019

This article seeks to explore new relationships and ethics of human and technology by analyzing a cultural imaginary produced by artificial intelligence. Drawing on theoretical reflections of the Feminist Scientific and Technological Studies which understand science and technology as the matter of care(Puig de la Bellacas, 2011), this paper focuses on the fact that artificial intelligence and robots materialize cultural imaginary such as autonomy. This autonomy, defined as the capacity to adapt to a new environment through self-learning, is accepted as a way to conceptualize an authentic human or an ideal subject. However, this article argues that artificial intelligence is mediated by and dependent on invisible human labor and complex material devices, suggesting that such autonomy is close to fiction. The recent growth of the so-called 'assistant technology' shows that it is differentially visualizing the care work of both machines and humans. Technology and its cultural imaginary hide the care work of human workers and actively visualize the one of the machine. And they make autonomy and agency ideal humanness, leaving disabled bodies and dependency as unworthy. Artificial intelligence and its cultural imaginary negate the value of disabled bodies while idealizing abled-bodies, and result in eliminating the real relationship between man and technology as mutually dependent beings. In conclusion, the author argues that the technology we need is not the one to exclude the non-typical bodies and care work of others, but the one to include them as they are. This technology responsibly empathizes marginalized beings and encourages solidarity between fragile beings. Inspired by an art performance of artist Sue Austin, the author finally comes up with and suggests 'artificial intelligence in wheelchair' as an alternative figuration for the currently dominant 'autonomous artificial intelligence'.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.