• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1189-1198
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• 2015

For the effects of the global financial crisis cause hedge funds to have a strong influence on financial markets, it is needed to study new approach method to predict hedge fund survival. This paper proposes to organize fuzzy neural network using hedge fund data as input to predict hedge fund survival. The variables of hedge fund data are ambiguous to analyze and have internal uncertainty and these characteristics make it challenging to predict their survival from the past records. The object of this study is to evaluate the predictability of fuzzy neural network which uses grades of membership to predict survival. The results of this study show that proposed system is effective to predict the hedge funds survival and can be a desirable solution which helps investors to support decision-making.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.836-848
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• 2019

This paper aims at providing a critical view over the cybernetics theory especially of first generation on which the artificial intelligence heavily depends nowadays. There has been a commonly accepted thought that the conception of artificial intelligence could not has been possible without being influenced by N. Wiener's cybernetic feedback based information system. Despite the founder of contemporary cybernetics' ethical concerns in order to avoid an increasing entropy phenomena(social violence, economic misery, wars) produced through a negative dynamics of the western modernity regarded as the most advanced form of humanism. In this civilizationally changing atmosphere, the newly born cybernetic technology was thus firmly believed as an antidote to these vices deeply rooted in humanism itself. But cybernetics has been turned out to be a self-organizing, self-controlling mechanical system that entails the possibility of telegraphing human brain (which are transformed into patterns) through the uploading of human brain neurons digitalized by the artificial intelligence embedded into computing technology. On this background emerges posthuman (or posthumanism) movement of which concepts have been theorized mainly by its ardent apostles like N. K. Hayles, Neil Bedington, Laurent Alexandre, Donna J. Haraway. The converging of NBIC Technologies leading to the opening of a much more digitalizing society has served as a catalyst to promote the posthuman representations and different narratives especially in the contemporary visual arts as well as in the study of humanities including philosophy and fictional literature. Once Bruno Latour wrote "Modernity is often defined in terms of humanism, either as a way of saluting the birth of 'man' or as a way of announcing his death. But this habit is itself modern, because it remains asymmetrical. It overlooks the simultaneous birth of 'nonhumaniy' - things, or objects, or beasts, - and the equally strange beginning of a crossed-out God, relegated to the sidelines."4) These highly suggestive ideas enable us to better understand what kind of human beings would emerge following the dazzlingly accelerating advancement of artificial intelligence technology. We wonder whether or not this newly born humankind would become essentially Homo Artificialis as a neuronal man stripping off his biological apparatus. However due to this unprecedented situation humans should deal with enormous challenges involving ethical, metaphysical, existential implications on their life.

• 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.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.219-224
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• 2021

The convergence of various industries has removed the boundaries of software application fields and reduced the restrictions on convergence fields. Software requirements are diversified and they want to reconfigure software requirements in a fast cycle. Since various changes in requirements have to be accepted technically, research on methodologies and standards to increase the efficiency of software productivity and methods for standardizing and producing software are needed. In this study, we studied how the reusability and complexity of the software asset reconfiguration system appeared according to the developer's characteristics and environment to utilize the assets optimized in previous studies. At this time, we measured how the change in complexity according to the usability and asset composition method that appears according to the developer's characteristics appears, but there is a limit to the collected data, so it is necessary to secure the quality of the measured value through continuous data collection. In addition, an intelligent system application plan is needed to supplement the problem of context classification in the use stage of complex assets.

• Smart Media Journal
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• v.12 no.9
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• pp.60-71
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• 2023

A microservice architecture that accommodates the heterogeneity of various development environments and enables flexible maintenance can secure business agility to manage services in line with rapidly changing requirements. Due to the nature of MSA, where communication between microservices within a service is frequent, the boundary security that has been used in the past is not sufficient in terms of security, and a Zerotrust system is required. In addition, as the size of microservices increases, definition of access control policies according to the API format of each service is required, and difficulties in policy management increase, such as unnecessary governance overhead in the process of redistributing services. In this paper, we propose a microservice architecture that centrally manages policies by separating access control decision and enforcement with a general-purpose policy engine called OPA (Open Policy Agent) for collective and flexible policy management in Zerotrust security-applied environments.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.11 no.1
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• pp.110-123
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• 2000

The short-term music therapy was performed for adolescents with conduct disorder admitted to Seoul National Mental Hospital for 3 months from Jun to September, 1998. This case study focused mainly on two female patients who participated regularly in the group music therapy. The music therapy process was divided into three phases；beginning, opening up, and closing. This music therapy session consisted of three parts；hello song as beginning, various musical activities, and sound & movement activity as closing. Free musical improvisation, song discussion, musical monodrama, and sound & movement were the mainly applied techniques. Free improvisation was used to enhance, motivate, identify and contain the adolescents' feelings and ideas. Song discussion was used to convey their thoughts and to support each other. Musical monodrama was used to make them have insights into interpersonal relationships. Sound & movement was used to enhance spontaneity. It made them explore their body and voice as an expressive medium. Throughout three months period of music therapy, patient A's communication skill, socialization, and behavior areas were assessed with improvement. She could use music as a symbolic form and was able to share her feelings about herself and her family. Patient B's self-expression and cognitive areas were assessed with improvement. She became more spontaneous and could verbalize her emotions during the group session. Music as a non-verbal and therefore often a non-threatening medium wherein so much can be expressed provided two female patients an atmosphere where a sense of trust may be regained.

• Journal of the Korean Society of Radiology
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• v.16 no.1
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• pp.25-34
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• 2022

Brain computed tomography (CT) is useful for brain lesion diagnosis, such as brain hemorrhage, due to non-invasive methodology, 3-dimensional image provision, low radiation dose. However, there has been numerous misdiagnosis owing to a lack of radiologist and heavy workload. Recently, object detection technologies based on artificial intelligence have been developed in order to overcome the limitations of traditional diagnosis. In this study, the applicability of a deep learning-based YOLOv5s model was evaluated for brain hemorrhage detection using brain CT images. Also, the effect of hyperparameters in the trained YOLOv5s model was analyzed. The YOLOv5s model consisted of backbone, neck and output modules. The trained model was able to detect a region of brain hemorrhage and provide the information of the region. The YOLOv5s model was trained with various activation functions, optimizer functions, loss functions and epochs, and the performance of the trained model was evaluated in terms of brain hemorrhage detection accuracy and training time. The results showed that the trained YOLOv5s model is able to provide a bounding box for a region of brain hemorrhage and the accuracy of the corresponding box. The performance of the YOLOv5s model was improved by using the mish activation function, the stochastic gradient descent (SGD) optimizer function and the completed intersection over union (CIoU) loss function. Also, the accuracy and training time of the YOLOv5s model increased with the number of epochs. Therefore, the YOLOv5s model is suitable for brain hemorrhage detection using brain CT images, and the performance of the model can be maximized by using appropriate hyperparameters.

• 한국벤처창업학회:학술대회논문집
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• 2017.04a
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• pp.62-62
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• 2017

최근 인공지능 시대 및 제 4차 산업 혁명 시대의 도래로 제품 간, 제품과 서비스 간, 서비스 간 산업이 다양한 형태로 진화 중이며, 이종기술 산업 간의 융 복합화로 새로운 제3의 신산업이 등장하고 있다. 또한 소위 말하는 O2O(Online to Offline)는 이미 운송, 금융(핀테크), 자동차(카테크), 숙박, 음식, 의료(헬스케어테크) 등 많은 산업 분야에 진출, 기존 전통 산업과 충돌, 인허가에 어려움을 겪고 있으며 이러한 O2O는 IOT(사물인터넷)로 급속도로 가속화 되고 있다. 이렇듯 4차 산업 혁명에 따른 신산업의 탄생은 산업의 경계를 붕괴하고 있음에도 산업별로 구분된 제도, 규제, 지원정책 등은 여전히 신산업 창업의 장애가 되고 있다. 이에, 본 연구에서는 국내 벤처특별법, 창업지원법, 1인 창조기업법 내 투자 지원 업종과 미국을 비롯한 해외 창업 관련법 내 창업 지원 제한 업종과의 비교 분석, 몇 가지 중요한 시사점을 도출했다. 먼저, 우리나라가 선진국에 비해 지나치게 투자 제한 업종이 많음을 확인했다. 또한 선진국은 미풍양속을 해치는 업종, 투기적 사업 등 사회통념상 문제가 되는 사행성 업종을 제외하고는 대다수의 업종이 정책 지원 대상이다. 업종과 더불어 투자 행위로 투자 심사를 해 신산업 투자에 매우 탄력적으로 운영할 수 있게 설계되어 있다. 심지어 미국의 경우, 적극적 창업 투자를 위해 업종 및 투자 행위에 대한 심의를 중소기업청이 직접 수행한다. 특히, 우리나라의 중소기업창업투자회사제도는 미국의 중소기업투자회사(SBIC) 제도와 유사한 반면 투자 제한 업종 뿐 아니라 제도 운영 특히, 창업투자회사에 대한 관리 측면에서 매우 큰 차이가 있음을 보여주고 있다. 우리나라 중기청은 간접관리를 하는 반면, 미국은 중기청 내에서 직접 관리를 하고 있다. 따라서 본 연구에서는 우리나라 창업 벤처 투자 제한을 미국을 비롯한 기타 외국의 사례에서와 같이 사회통념상 불가피한 업종과 더불어 투자 행위로 정의하길 제안한다.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.4
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• pp.308-315
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• 2009

Because the attenuation of AE signal in composite materials is relatively higher than that of metallic materials, it is required to develop a damage assessment technique less affected by the attenuation property of composite materials in order to use AE sensing as a damage detection method. In the signal processing procedure, it is profitable to use the leading wave that arrives first because the leading wave is less influenced by the boundary conditions. Using wavelet transform, we investigated the frequency characteristics of impact induced AE signals focused on the leading wave in advance and chose the key factors to discriminate the damaged condition quantitatively. In this research, we established a damage assessment technique using the sharing percentage of the wavelet detail components of AE signal, and conducted a low-velocity impact test on composite laminates to confirm the feasibility of the proposed signal processing method.