• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.05a
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• pp.101-104
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• 2009

This study investigated compensatory mechanisms in the brain during a verbal working memory task among people with Alcohol Use Disorders (AUD). A total of 21 college male students participated in the study: eleven AUD participants and 10 normal controls. Study participants were asked to complete the Korean version of the Wechsler Adult Intelligence Scale-III (K-WAIS-III) prior to the fMRI experiment. Verbal 0-back and 2-back tasks were used to assess brain activities of the participants' verbal working memory. Brain scanning was performed on Siemens SONATA 1.5T Scanner while participants were performing the 0-back and 2-back tasks. Within the AUD group, participants with greater dependency to alcohol (based on DSM-IV criteria) in the past 1 year showed lower mean score on the 'Similarities' of the K-WAIS-III (r=-0.63, p<0.05, N=11). The more participants experienced alcohol withdrawal symptoms in the past 1 year, the lower the score they received on the K-WAIS-III 'Picture Arrangement' (r=-0.69, p<0.05, n=11). The fMRI regression results showed that individuals who present greater degree of alcohol dependency symptoms are likely to show greater brain activation in the bilateral middle frontal gyri (BA 9) during the verbal working memory task. The degree of alcohol withdrawal symptoms were associated with increased brain activation in the left superior and middle frontal gyri (BA8), left precentral gyrus (BA 6), and left inferior parietal lobule (BA 40). The study findings showed that the degree of alcohol abuse/dependence and withdrawal symptoms were associated with decreased cognitive function and increased activations in brain regions particularly important for abstract reasoning (BA 9), central executive (BA 9), or spatial storage (BA 40) during a working memory task. Therefore, these results could support previous studies suggesting that the neural system of people with ADD may adopt a brain compensatory mechanism to maintain normal level of cognitive functions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.4
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• pp.2415-2423
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• 2015

Experiments were conducted on sine wave fin-and-tube heat exchangers having oval tubes under wet condition. Oval tubes having an aspect ratio of 0.6 were made, by deforming 12.7mm round tubes. Twelve samples, having different fin pitch and tube row, were tested. Results showed that, for oval tube samples, the effect of fin pitch on j and f factor was not significant. As for the effect of tube row, the lowest j factor was obtained for one row configuration(81% of two row configuration), which is clear contrast to round tube samples, where the highest j factor was obtained for one row configuration. Possible reasoning is provided considering the flow and heat transfer characteristics of sine wave channel combined with connecting oval tubes. Comparison of $j/f^{1/3}$ with plain fin-and-tube heat exchanger having 15.9mm O.D. round tube reveals that present oval fin-and-tube heat exchanger shows superior thermal performance except for one row configuration. In other words, $j/f^{1/3}$ of the two row oval tube heat exchanger was 1.6~2.5 times larger than those of round tube heat exchanger, 1.4~2.4 times larger for three row configuration and 1.2~2.8 times for four row configuration.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.113-130
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• 2010

During the decades, considerable human interventions to comprehend the web information were increased continually. The successful expansion of the web services made it more complex and required more contributions of the users. Many researchers have tried to improve the comprehension ability of computers in supporting an intelligent web service. One reasonable approach is enriching the information with machine understandable semantics. They applied ontology design, intelligent reasoning and other logical representation schemes to design an infrastructure of the semantic web. For the features, the semantic web is considered as an intelligent access to understanding, transforming, storing, retrieving, and processing the information gathered from heterogeneous, distributed web resources. The goal of this study is firstly to explore the problems that restrict the applications of web services and the basic concepts, languages, and tools of the semantic web. Then we highlight some of the researches, solutions, and projects that have attempted to combine the semantic web and business support, and find out the pros and cons of the approaches. Through the study, we were able to know that the semantic web technology is trying to offer a new and higher level of web service to the online users. The services are overcoming the limitations of traditional web technologies/services. In traditional web services, too much human interventions were needed to seek and interpret the information. The semantic web service, however, is based on machine-understandable semantics and knowledge representation. Therefore, most of information processing activities will be executed by computers. The main elements required to develop a semantic web-based business support are business logics, ontologies, ontology languages, intelligent agents, applications, and etc. In using/managing the infrastructure of the semantic web services, software developers, service consumers, and service providers are the main representatives. Some researchers integrated those technologies, languages, tools, mechanisms, and applications into a semantic web services framework. Therefore, future directions of the semantic web-based business support should be start over from the infrastructure.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.97-111
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• 2009

Context-aware computing, as a core of smart space development, has been widely regarded as useful in realizing individual service provision. However, most of context-aware services so fat are in its early stage to be dispatched for actual usage in the real world, caused mainly by user's privacy concerns. Moreover, since legacy context-aware services have focused on acquiring in an automatic manner the extra-personal context such as location, weather and objects near by, the services are very limited in terms of quality and variety if the service should identify intra-personal context such as attitudes and privacy concern, which are in fact very useful to select the relevant and timely services to a user. Hence, the purpose of this paper is to propose a novel methodology to infer the user's privacy concern as intra-personal context in an intelligent manner. The proposed methodology includes a variety of stimuli from outside the person and then performs model-based reasoning with social theory models from model base to predict the user's level of privacy concern semi-automatically. To show the feasibility of the proposed methodology, a survey has been performed to examine the performance of the proposed methodology.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.127-132
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• 2006

K-NN (k-Nearest Neighbors), which is a well-known instance-based learning algorithm, simply stores entire training patterns in memory, and uses a distance function to classify a test pattern. K-NN is proven to show satisfactory performance, but it is notorious formemory usage and lengthy computation. Various studies have been found in the literature in order to minimize memory usage and computation time, and NGE (Nested Generalized Exemplar) theory is one of them. In this paper, we propose RPA (Recursive Partition Averaging) and IRPA (Incremental RPA) which is an incremental version of RPA. RPA partitions the entire pattern space recursively, and generates representatives from each partition. Also, due to the fact that RPA is prone to produce excessive number of partitions as the number of features in a pattern increases, we present IRPA which reduces the number of representative patterns by processing the training set in an incremental manner. Our proposed methods have been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory.

• Journal of Korean Medicine Rehabilitation
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• v.29 no.3
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• pp.75-85
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• 2019

Objectives This study was perfomed to assess the adherance to CARE (CAse REport) guideline of case reports in the Journal of Korean Medicine Rehabilitation Methods We searched the case reports published in the Journal of Korean Medicine Rehabilitation from January 2016 to April 2019 in the database of oriental medicine advanced searching integrated system (OASIS). Then we evaluated the quality of the searched case reports based on the CARE guideline. Results Totally 31 papers were selected after the screening the case reports by the inclusion and exclusion criteria. The report rate of the sub-items of the CARE guideline was 78.26% at the maximum, 60.87% at the maximum, and 70.97% on the average. The following items were reported only in less than 50% of them; 'Timeline', 'Diagnostic challenges', 'Diagnostic reasoning including other diagnoses considered' 'Prognostic characteristics', 'Follow-up and Outcomes', 'Patient Perspective', 'Informed Consent' Conclusions This study is expected to contribute to the overall improvement of the level of case reports in the Journal of Korean Medicine Rehabilitation.

• Smart Media Journal
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• v.12 no.1
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• pp.9-16
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• 2023

Recently, a convolutional neural network (CNN) based system is being developed to overcome the limitations of human resources in the apple quality classification of farmhouse. However, since convolutional neural networks receive only images of the same size, preprocessing such as sampling may be required, and in the case of oversampling, information loss of the original image such as image quality degradation and blurring occurs. In this paper, in order to minimize the above problem, to generate a image patch based graph of an original image and propose a random walk-based positional encoding method to apply the graph transformer model. The above method continuously learns the position embedding information of patches which don't have a positional information based on the random walk algorithm, and finds the optimal graph structure by aggregating useful node information through the self-attention technique of graph transformer model. Therefore, it is robust and shows good performance even in a new graph structure of random node order and an arbitrary graph structure according to the location of an object in an image. As a result, when experimented with 5 apple quality datasets, the learning accuracy was higher than other GNN models by a minimum of 1.3% to a maximum of 4.7%, and the number of parameters was 3.59M, which was about 15% less than the 23.52M of the ResNet18 model. Therefore, it shows fast reasoning speed according to the reduction of the amount of computation and proves the effect.

• Korean Journal of Culture and Social Issue
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• v.14 no.1
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• pp.47-75
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• 2008

Research on moral judgment has been dominated by rational factor or moral emotion, in which moral judgment is thought to be caused by moral reasoning or moral intuitions. This research demonstrates that offender' anger can influence moral judgment. The study examined the role of offender's anger (control group versus anger group) on moral domain ( life, interpersonal ethic, traffic regulation: 6 case) : 2(groups) ×6(cases) mixed design. Participants were asked to judge the offender, case, sympathy and one's anger about the him or her who offended against the law or convention. Participants who perceived the offender's anger tended to assess questionnaire generous. In interpersonal ethic domain, participants have not been affected by anger. There was not significantly differences between two groups in interpersonal ethic domain. The results of experiment confirmed hypotheses that influence of anger varies with the moral domains affected a value system of culture. These findings indicate functional emotion for moral judgment and suggest people may be decide how much emotion is considered on moral judgment performance according to moral domains.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.23-33
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• 2016

The current smart lighting is shaped to offer the lighting environment suitable for current context, after identifying user's action and location through a sensor. The sensor-based context awareness technology just considers a single user, and the studies to interpret many users' various context occurrences and conflicts lack. In existing studies, a fuzzy theory and algorithm including ReBa have been used as the methodology to solve context conflict. The fuzzy theory and algorithm including ReBa just avoid an opportunity of context conflict that may occur by providing services by each area, after the spaces where users are located are classified into many areas. Therefore, they actually cannot be regarded as customized service type that can offer personal preference-based context conflict. This paper proposes a priority-based LED lighting control system interpreting multiple context conflicts, which decides services, based on the granted priority according to context type, when service conflict is faced with, due to simultaneous occurrence of various contexts to many users. This study classifies the residential environment into such five areas as living room, 'bed room, study room, kitchen and bath room, and the contexts that may occur within each area are defined as 20 contexts such as exercising, doing makeup, reading, dining and entering, targeting several users. The proposed system defines various contexts of users using an ontology-based model and gives service of user oriented lighting environment through rule based on standard and context reasoning engine. To solve the issue of various context conflicts among users in the same space and at the same time point, the context in which user concentration is required is set in the highest priority. Also, visual comfort is offered as the best alternative priority in the case of the same priority. In this manner, they are utilized as the criteria for service selection upon conflict occurrence.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.121-145
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• 2010

University timetabling depending on the educational environments of universities is an NP-hard problem that the amount of computation required to find solutions increases exponentially with the problem size. For many years, there have been lots of studies on university timetabling from the necessity of automatic timetable generation for students' convenience and effective lesson, and for the effective allocation of subjects, lecturers, and classrooms. Timetables are classified into a course timetable and an examination timetable. This study focuses on the former. In general, a course timetable for liberal arts is scheduled by the office of academic affairs and a course timetable for major subjects is scheduled by each department of a university. We found several problems from the analysis of current course timetabling in departments. First, it is time-consuming and inefficient for each department to do the routine and repetitive timetabling work manually. Second, many classes are concentrated into several time slots in a timetable. This tendency decreases the effectiveness of students' classes. Third, several major subjects might overlap some required subjects in liberal arts at the same time slots in the timetable. In this case, it is required that students should choose only one from the overlapped subjects. Fourth, many subjects are lectured by same lecturers every year and most of lecturers prefer the same time slots for the subjects compared with last year. This means that it will be helpful if departments reuse the previous timetables. To solve such problems and support the effective course timetabling in each department, this study proposes a university timetabling support system based on two phases. In the first phase, each department generates a timetable template from the most similar timetable case, which is based on case-based reasoning. In the second phase, the department schedules a timetable with the help of interactive user interface under the timetabling criteria, which is based on rule-based approach. This study provides the illustrations of Hanshin University. We classified timetabling criteria into intrinsic and extrinsic criteria. In intrinsic criteria, there are three criteria related to lecturer, class, and classroom which are all hard constraints. In extrinsic criteria, there are four criteria related to 'the numbers of lesson hours' by the lecturer, 'prohibition of lecture allocation to specific day-hours' for committee members, 'the number of subjects in the same day-hour,' and 'the use of common classrooms.' In 'the numbers of lesson hours' by the lecturer, there are three kinds of criteria : 'minimum number of lesson hours per week,' 'maximum number of lesson hours per week,' 'maximum number of lesson hours per day.' Extrinsic criteria are also all hard constraints except for 'minimum number of lesson hours per week' considered as a soft constraint. In addition, we proposed two indices for measuring similarities between subjects of current semester and subjects of the previous timetables, and for evaluating distribution degrees of a scheduled timetable. Similarity is measured by comparison of two attributes-subject name and its lecturer-between current semester and a previous semester. The index of distribution degree, based on information entropy, indicates a distribution of subjects in the timetable. To show this study's viability, we implemented a prototype system and performed experiments with the real data of Hanshin University. Average similarity from the most similar cases of all departments was estimated as 41.72%. It means that a timetable template generated from the most similar case will be helpful. Through sensitivity analysis, the result shows that distribution degree will increase if we set 'the number of subjects in the same day-hour' to more than 90%.