• YAKHAK HOEJI
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• v.53 no.6
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• pp.368-376
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• 2009

DUR (Drug Utilization Review) originally referred to the evaluation of drug usage details: however DUR refers to the system used to support the services of prescribing and dispensing through linking from Health Insurance Review and Assessment (HIRA) Service in Korea. HIRA is going to begin the DUR enforcement for extending to nationwide coverage after pilot test. Objectives: The aims for this study were to evaluate and clarify the current opinions of the pharmacists for the recognition and acceptance rates before nationwide coverage concerning DUR system. Methods: A 16-question-questionnaire was developed and pilot tested. For 40 days of survey by both on-line and fax paper, it was carried out on 80 pharmacists working at community pharmacy in Goyang-si, Gyeonggi-do. Results: Most of answers were broadly positive and interested in begining the DUR system and kept in mind that the goal of DUR is safety guarantee for people. On the other hand, most of answerers worry that delay of patient waiting time and inharmonious communication with doctors in DUR processing can be a major obstacle to begin the DUR system. Conclusion: To solve several problems, the most important things are to make good reciprocal relationships between doctors and pharmacists, investigate intervention tool to shorten patient waiting time, and activate educational program of inspecting items for the pharmacists.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.45-50
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• 2023

As the ownership of pets has steadily increased in recent years, the need for an effective pet management system has grown. In this study, we propose a pet management system with a deep learning-based emotion recognition SNS. The system detects emotions through pet facial expressions using a convolutional neural network (CNN) and shares them with a user community through SNS. Through SNS, pet owners can connect with other users, share their experiences, and receive support and advice for pet management. Additionally, the system provides comprehensive pet management, including tracking pet health and vaccination and reservation reminders. Furthermore, we added a function to manage and share pet walking records so that pet owners can share their walking experiences with other users. This study demonstrates the potential of utilizing AI technology to improve pet management systems and enhance the well-being of pets and their owners.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1791-1798
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• 2016

Recently, research about ESS(Emergency Support System) has been actively carried out to provide a variety of medical services using smart devices and wearable devices. Smart healthcare provides a personalized health care service using various types of bio-signal measuring sensors and smart devices. For the smart healthcare using a smart device, it is need to research about personal health monitoring using a smart wearable devices, and also need to research on service methods for first aid measures after an emergency. In this paper, we proposed about group management based emergency support system, that is monitoring about personal bio signal using smart devices and wearable devices to protect patient's life. The system notices to the medical volunteers based on the position information when an emergency situation. In addition, we have designed and implemented an emergency support system providing the information of the patient on the display when transmitting a picture of a patient using a smart device to the server.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.4
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• pp.268-276
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• 2015

A ship's sailing route or plan is determined by the master as the decision maker of the vessel, and depends on the characteristics of the navigational environment and the conditions of the ship. The trajectory, which appears as a result of the ship's navigation, is monitored and stored by a Vessel Traffic Service center, and is used for an analysis of the ship's navigational pattern and risk assessment within a particular area. However, such an analysis is performed in the same manner, despite the different navigational environments between coastal areas and the harbor limits. The navigational environment within the harbor limits changes rapidly owing to construction of the port facilities, dredging operations, and so on. In this study, a support vector machine was used for processing and modeling the trajectory data. A K-fold cross-validation and a grid search were used for selecting the optimal parameters. A complicated traffic route similar to the circumstances of the harbor limits was constructed for a validation of the model. A group of vessels was composed, each vessel of which was given various speed and course changes along a specified route. As a result of the machine learning, the optimal route and voyage data model were obtained. Finally, the model was presented to Vessel Traffic Service operators to detect any anomalous vessel behaviors. Using the proposed data modeling method, we intend to support the decision-making of Vessel Traffic Service operators in terms of navigational patterns and their characteristics.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.259-266
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• 2022

The purpose of this study is to explore the effect of K-university professors' perception of the university on job satisfaction. To do this, correlation analysis and multiple regression analysis were applied on job satisfaction and university perception (curriculum, lecture, research support, research system, undergraduate(major), organizational communication, personnel system, welfare system) data of 81 professors at K-University. As results, each factor showed a statistically significant correlation, and it was found that the perception of lectures, the perception of research support, the recognition of the research system, and the perception of personnel system had a significant positive effect on job satisfaction. Based on the results of this study, policy measures to help increase job satisfaction were suggested.

• Journal of KIISE
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• v.44 no.8
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• pp.813-821
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• 2017

Entity Linking find the meaning of an entity mention, which indicate the entity using different expressions, in a user's query by linking the entity mention and the entity in the knowledge base. This task has four challenges, including the difficult knowledge base construction problem, multiple presentation of the entity mention, ambiguity of entity linking, and NIL entity recognition. In this paper, we first construct the entity name dictionary based on Wikipedia to build a knowledge base and solve the multiple presentation problem. We then propose various methods for NIL entity recognition and solve the ambiguity of entity linking by training the support vector machine based on several features, including the similarity of the context, semantic relevance, clue word score, named entity type similarity of the mansion, entity name matching score, and object popularity score. We sequentially use the proposed two methods based on the constructed knowledge base, to obtain the good performance in the entity linking. In the result of the experiment, our system achieved 83.66% and 90.81% F1 score, which is the performance of the NIL entity recognition to solve the ambiguity of the entity linking.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.509-515
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• 2005

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved it more powerful than traditional artificial neural networks (ANNs)(Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al, 2005; Kim, 2003). The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is cost-sensitive. Therefore, it is necessary to convert the output of the classifier into well-calibrated posterior probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create probabilities (Platt, 1999; Drish, 2001). This study applies a method to estimate the probability of outputs of SVM to bankruptcy prediction and then suggests credit scoring methods using the estimated probability for bank's loan decision making.

• Smart Structures and Systems
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• v.14 no.2
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• pp.145-158
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• 2014

The objective of this study is to improve the survivability and reliability of the FBG sensor network in the structural health monitoring (SHM) system. Therefore, a model reconstruction soft computing recognition algorithm based on support vector regression (SVR) is proposed to achieve the high reliability of the FBG sensor network, and the grid search algorithm is used to optimize the parameters of SVR model. Furthermore, in order to demonstrate the effectiveness of the proposed model reconstruction algorithm, a SHM system based on an eight-point fiber Bragg grating (FBG) sensor network is designed to monitor the foreign-object low velocity impact of a CFRP composite plate. Simultaneously, some sensors data are neglected to simulate different kinds of FBG sensor network failure modes, the predicting results are compared with non-reconstruction for the same failure mode. The comparative results indicate that the performance of the model reconstruction recognition algorithm based on SVR has more excellence than that of non-reconstruction, and the model reconstruction algorithm almost keeps the consistent predicting accuracy when no sensor, one sensor and two sensors are invalid in the FBG sensor network, thus the reliability is improved when there are FBG sensors are invalid in the structural health monitoring system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.1
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• pp.45-51
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• 2012

In this paper, it's a system which recognize the user's emotions after obtaining the biological informations(pulse sensor, blood pressure sensor, blood sugar sensor etc.) about user's bio informations through wireless sensors in accordance of previously collected informations about user's stress index and classification the Colors & Music. This system collects the inputs, saves in the database and finally, classifies emotions according to the stress quotient by using multiple SVM(Support Vector Machine) algorithm. The experiment of multiple SVM algorithm was conducted by using 2,000 data sets. The experiment has approximately 87.7% accuracy.

• Journal of Family Resource Management and Policy Review
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• v.17 no.3
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• pp.1-17
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• 2013

This study focused on adolescents who are studying in middle schools of the Gyeongnam region, aims to provide methods for improving school adjustment among adolescents through the enhancement of family strength. It will examine the effect of family strength on adolescents' school adjustment verify the mediating effect of social support in the influential relationships of school adjustment. The summarizations, obtained in this study are as follows: First, an analysis of the results of the effect of family strength on social support demonstrated that family strength had a positive effect on social support. That is, as family strength was higher, social support increased. Second, an analysis of the results of the effect of family strength on school adjustment show that family strength would have a direct effect on school adjustment, which is positive. Where family strength was higher, school adjustment of adolescents was also higher. Third, the results of the effect of social support on school adjustment when controlling family strength demonstrate that social support would have a positive effect on school adjustment, however, family strength did not predict school adjustment. Therefore, the complete mediating effect of social support in the relationship between family strength and school adjustment was identified. In conclusion, it was identified that family strength had an indirect effect on school adjustment, but not a direct effect. Therefore, it is indicated that indirect intervention through the social support system as well as direct intervention for the improvement of adolescents' school adjustment is required. In addition, it was confirmed that family strength and social support would be more important variables than control variables, which reflect the characteristics of adolescents and family in terms of school adjustment. Therefore, the recognition that the responsibility in adjusting to school is the common role of families, schools, and community going beyond the individual responsibility of adolescents is needed.