• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.282-296
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• 2010

OWL is an ontology language for the Semantic Web, and suited to modelling the knowledge of a specific domain in the real-world. Ontology also can infer new implicit knowledge from the explicit knowledge. However, the modeled knowledge cannot be complete as the whole of the common-sense of the human cannot be represented totally. Ontology do not concern handling nonmonotonic reasoning to detect incomplete modeling such as the integrity constraints and exceptions. A default rule can handle the exception about a specific class in ontology. Integrity constraint can be clear that restrictions on class define which and how many relationships the instances of that class must hold. In this paper, we propose a practical reasoning system for open and closed-world reasoning that supports a novel hybrid integration of ontology based on open world assumption (OWA) and non-monotonic rule based on closed-world assumption (CWA). The system utilizes a method to solve the problem which occurs when dealing with the incomplete knowledge under the OWA. The method uses the answer set programming (ASP) to find a solution. ASP is a logic-program, which can be seen as the computational embodiment of non-monotonic reasoning, and enables a query based on CWA to knowledge base (KB) of description logic. Our system not only finds practical cases from examples by the Protege, which require non-monotonic reasoning, but also estimates novel reasoning results for the cases based on KB which realizes a transparent integration of rules and ontologies supported by some well-known projects.

• Progress in Medical Physics
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• v.23 no.1
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• pp.1-7
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• 2012

The purpose of this study was to develop the respiratory training system using individual characteristic guiding waveform to reduce the impact of respiratory motion that causes artifact in radiotherapy. In order to evaluate the improvement of respiratory regularity, 5 volunteers were included and their respiratory signals were acquired using the in-house developed belt-type sensor. Respiratory training system needs 10 free breathing cycles of each volunteer to make individual characteristic guiding waveform based on Fourier series and it guides patient's next breathing. For each volunteer, free breathing and guided breathing which uses individual characteristic guiding waveform were performed to acquire the respiratory cycles for 3 min. The root mean square error (RMSE) was computed to analyze improvement of respiratory regularity in period and displacement. It was found that respiratory regularity was improved by using respiratory training system. RMSE of guided breathing decreased up to 40% in displacement and 76% in period compared with free breathing. In conclusion, since the guiding waveform was easy to follow for the volunteers, the respiratory regularity was significantly improved by using in-house developed respiratory training system. So it would be helpful to improve accuracy and efficiency during 4D-RT, 4D-CT.

• Korean Journal of Childcare and Education
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• v.6 no.1
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• pp.47-65
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• 2010

The current study aims to examine differences of mother's child-rearing attitude by background variables of infant's and mothers, the influence of mother's child-rearing attitude, temperament and goodness of fit of infant's on adjustment to childcare center, and relative influence among elements. Subjects for the study were 165 infant's of two year old attending 8 childcare centers in Seoul, Gyeonggi-do and Jeju-do, and their mothers and homeroom teachers at childcare centers. As a result of the study, it was found that there was no difference in background variables such as sex and order among siblings of infant's, childcare experience and average hours at childcare centers a day, and mother's age and job, while there were differences in mother's child-rearing attitude by mother's education, household's monthly income and mother's working hours per week. Also it was observed that mother's restrictive attitude, positive attitude and infant's regular temperament influenced their adjustment to childcare centers, and relatively influential elements on general adjustment to childcare centers were mother's restrictive attitude and positive attitude. That IS, it was found that as mother's restrictive attitude and positive attitude were high, infant's general adjustment to childcare centers became high. These study results show that if infant's learn basic rules and habits from parents at home by having positive relationship with mothers, getting appropriate educative instruction, having proper autonomy and obtaining positive assessment from their mothers, rather than strict restriction or bluff, can adapt themselves to childcare centers with no difficulty.

• Journal of KIISE:Databases
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• v.35 no.6
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• pp.505-517
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• 2008

This paper proposes a rewriting algorithm of OWL-DL ontology query in SPARQL. Currently, to obtain inference results of given SPARQL queries, Web ontology repositories construct inference ontology models and match the SPARQL queries with the models. However, an inference model requires much larger space than its original base model, and reusability of the model is not available for other inferrable SPARQL queries. Therefore, the aforementioned approach is not suitable for large scale SPARQL query processing. To resolve tills issue, this paper proposes a novel SPARQL query rewriting algorithm that can obtain results by rewriting SPARQL queries and accomplishing query operations against the base ontology model. To achieve this goal, we first define OWL-DL inference rules and apply them on rewriting graph pattern in queries. The paper categorizes the inference rules and discusses on how these rules affect the query rewriting. To show the advantages of our proposal, a prototype system based on lena is implemented. For comparative evaluation, we conduct an experiment with a set of test queries and compare of our proposal with the previous approach. The evaluation result showed the proposed algorithm supports an improved performance in efficiency of the inferrable SPARQL query processing without loss of completeness and soundness.

• Archives of design research
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• v.19 no.4 s.66
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• pp.187-204
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• 2006

The brand personality of online products and services is know as its e-brand personality. Although, in the competitive conditions of online markets, e-brand personality is agreed to be an important factor, few studies have suggested how to establish e-brand personality through the visual design of web sites. This study identifies and verifies causal relationships between the visual attributes of web pages and e-brand personalities. The first identifies four major dimensions of e-brand personality on diverse web sites. The second uses 52 experimental home pages to identify key visual attributes associated with those four personality dimensions. The third is a confirmatory study with 16 experimental web sites that verifies causal relationships between visual attributes and e-brand personalities. The results show that two visual attributes, 'simplicity' and 'cohesion,' help to establish a 'bold' personality. Three attributes, 'contrast,' 'density,' and 'regularity,' affect whether a site has an 'analytical' personality. 'Contrast,' 'cohesion,' 'density,' and 'regularity' all influence whether a web site is perceived to have a 'friendly' personality. 'Regularity' and 'balance' were expected to affect the 'sophisticated' personality dimension, but no such impact was observed. The paper concludes with a discussion of implications, limitations, and future research directions.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.389-390
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• 2002

It is important that acquire information about if customer has some habit in electronic commerce application of internet base that led in recommendation service for customer in dynamic web contents supply. Collaborative filtering that has been used as a standard approach to Web personalization can not get rapidly user's preference change due to static user profiles and has shortcomings such as reliance on user ratings, lack of scalability, and poor performance in the high-dimensional data. In order to overcome this drawbacks, Web usage mining has been prevalent. Web usage mining is a technique that discovers patterns from We usage data logged to server. Specially. a technique that discovers Web usage patterns and clusters patterns is used. However, the discovery of patterns using Afriori algorithm creates many useless patterns. In this paper, the enhanced method for the construction of dynamic user profiles using validated Web usage patterns is proposed. First, to discover patterns Apriori is used and in order to create clusters for user profiles, ARHP algorithm is chosen. Before creating clusters using discovered patterns, validation that removes useless patterns by Dempster-Shafer theory is performed. And user profiles are created dynamically based on current user sessions for Web personalization.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.3 s.303
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• pp.17-28
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• 2005

Recently intelligent characters in computer games have been an important element more and more because they continually stimulate gamers' interests. As a typical method for implementing such intelligent characters, neural networks have been used for training action patterns of opponent's characters and game rules. However, some of the game rules can be abruptly changed and action properties of garners in on-line game environments are quite different according to gamers. In this paper, we address how a neural network adapts to those environmental changes. Our adaptation solution includes two components: an individual adaptation mechanism and a group adaptation mechanism. With the individual adaptation algorithm, an intelligent character steadily checks its game score, assesses the environmental change with taking into consideration of the lastly earned scores, and initiates a new learning process when a change is detected. In multi-user games, including massively multiple on-line games, intelligent characters confront diverse opponents that have various action patterns and strategies depending on the gamers controlling the opponents. The group adaptation algorithm controls the birth of intelligent characters to conserve an equilibrium state of a game world by using a genetic algorithm. To show the performance of the proposed schemes, we implement a simple fighting action game and experiment on it with changing game rules and opponent characters' action patterns. The experimental results show that the proposed algorithms are able to make intelligent characters adapt themselves to the change.

• Journal of IKEEE
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• v.7 no.2 s.13
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• pp.288-298
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• 2003

We consider a window-controlled selective-repeat ARQ scheme for error control between two adjacent nodes lying on a communication path. In this scheme, each packet to be transmitted is endowed with a serial number in a cyclic and sequential fashion. In turn, the transmitting node is not allowed to transmit a packet belonging to a window before every packet in the previous window is positively acknowledged. Such postponement of packet transmission incurs a degradation in throughput and delay performance. In this paper, aiming at improving packet delay performance, we employs a supplement scheme in which a serial number is duplicated within a frame. Classifying duplication rules into fixed, random and adaptive categories, we present candidate rules in each category and evaluate the packet delay performance induced by each duplication rule. From numerical examples, we observe that duplicating serial numbers, especially ADR-T2 effectively reduces mean packet delay for the forward channel characterized by a low packet error rate. We also reveal that such delay enhancement is achieved by a high probability of hitting local optimal window size.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.182-191
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• 2019

The purpose of this study was to identify correlation among social support, recovery resilience, and self-care behavior among the elderly with hypertension, as well as to clarify factors that affect self-care behavior. This was a descriptive study conducted with 183 hypertensive seniors over age 65 from three different senior welfare centers in C region. Data of this study were collected from Aug 20-31, 2018. T-test, ANOVA, Pearson's coefficient, and stepwise multiple regression were used for analysis. As a result, the mean score of social support was $3.79{\pm}0.78$ out of 5, recovery resilience was $4.10{\pm}0.71$ out of 5, and self-care behavior was $3.93{\pm}0.51$ out of 5. Self-care behavior had a statistically significant positive correlation with social support(r=.204, p<.001) and recovery resilience(r=.405, p<.001). Factors influencing Self-care behavior were recovery resilience(${\beta}=.36$, p<.001) and regularity of exercise(${\beta}=.17$, p=.019). These factors explained 18.9% of self-care behavior(F=21.02, p<.001). The study results indicate that recovery resilience and regularity of exercise are critical factors affecting self-care behavior among the elderly with of hypertension. Therefore, to promote self-care behavior among the hypertensive seniors, regular exercise must be advised and the development and evaluation of nursing interventions that can improve recovery resilience may be necessary.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.21-29
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• 2023

In this paper, we propose an AI-based system for automatically classifying industry and occupation codes in the population census. The accurate classification of industry and occupation codes is crucial for informing policy decisions, allocating resources, and conducting research. However, this task has traditionally been performed by human coders, which is time-consuming, resource-intensive, and prone to errors. Our system represents a significant improvement over the existing rule-based system used by the statistics agency, which relies on user-entered data for code classification. In this paper, we trained and evaluated several models, and developed an ensemble model that achieved an 86.76% match accuracy in industry and 81.84% in occupation, outperforming the best individual model. Additionally, we propose process improvement work based on the classification probability results of the model. Our proposed method utilizes an ensemble model that combines transfer learning techniques with pre-trained models. In this paper, we demonstrate the potential for AI-based systems to improve the accuracy and efficiency of population census data classification. By automating this process with AI, we can achieve more accurate and consistent results while reducing the workload on agency staff.