• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.2
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• pp.11-16
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• 2015

A lot of factor effects on the enterprise's innovation and business performance, for instance CEO and members intention etc.. Niehoff et al. says, The success factors of innovation enterprise's management lead to members of vision, innovation, innovation activities and support for an aggressive attitude of the enterprise members. However, today's products consumers wanted diverse and complex needs. CEO and members of the enterprise has been the diversity effort. The increase cost savings as well as in the profit improve factors that enterprise's participated a education and training, Subgroup activities, process quality, eliminate waste, improve yields, lead time reduction, process capability increasing, ets. This paper is a report of an empirical survey performed to 277 small and medium-sized enterprise in the korea. Cronbach's alpha coefficient is employed to analyze the reliability of the data. The effect analysis of each group is performed by the SEM(structural equation model). We use the SPSS' Amos program to analyze the equation modeling and test the hypotheses of the model.

• Childhood Kidney Diseases
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• v.19 no.2
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• pp.56-64
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• 2015

To revise the clinical guideline for childhood urinary tract infections (UTIs) of the Korean Society of Pediatric Nephrology (2007), the recently updated guidelines and new data were reviewed. The major revisions are as follows. In diagnosis, the criterion for a positive culture of the catheterized or suprapubic aspirated urine is reduced to 50,000 colony forming uits (CFUs)/mL from 100,000 CFU/mL. Diagnosis is more confirmatory if the urinalysis is abnormal. In treating febrile UTI and pyelonephritis, oral antibiotics is considered to be as effective as parenteral antibiotics. In urologic imaging studies, the traditional aggressive approach to find primary vesicoureteral reflux (VUR) and renal scar is shifted to the targeted restrictive approach. A voiding cystourethrography is not routinely recommended and is indicated only in atypical or complex clinical conditions, abnormal ultrasonography and recurrent UTIs. $^{99m}Tc$-DMSA renal scan is valuable in diagnosing pyelonephritis in children with negative culture or normal RBUS. Although it is not routinely recommended, normal scan can safely avoid VCUG. In prevention, a more natural approach is preferred. Antimicrobial prophylaxis is not supported any more even in children with VUR. Topical steroid (2-4 weeks) to non-retractile physiologic phimosis or labial adhesion is a reasonable first-line treatment. Urogenital hygiene is important and must be adequately performed. Breast milk, probiotics and cranberries are dietary factors to prevent UTIs. Voiding dysfunction and constipation should be properly treated and prevented by initiating toilet training at an appropriate age (18-24 months). The follow-up urine test on subsequent unexplained febrile illness is strongly recommended. Changes of this revision is not exclusive and appropriate variation still may be accepted.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.59-64
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• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to chose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless it can learn the optimal policy if the agent can visit every state- action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem. we suggest Adaptive Mediation-based Modular Q-Learning (AMMQL)as an improvement of the existing Modular Q-Learning (MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. This paper introduces the concept of AMMQL and presents details of its application into dynamic positioning of robot soccer agents.

• Korean Journal of Applied Biomechanics
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• v.21 no.2
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• pp.207-213
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• 2011

The purpose of this study was to investigate changes in elastic properties of tendon structure of human ankle dorsiflexor following eccentric exercise. Six male subjects(age: $27.3{\pm}2.0$ years, height: $180.3{\pm}1.4$ cm, weight: $82.6{\pm}5.3$ kg) and three female subjects(age: $26.7{\pm}2.9$ years, height: $170.0{\pm}4.2$ cm, weight: $66.6{\pm}1.4$ kg) performed a single bout eccentric exercise consisting of 120 repetitions of maximum eccentric contractions. Prior to and following the eccentric exercise, isometric ankle dorsiflexion strength along with longitudinal ultrasound image of the tibialis anterior(TA) were collected. Muscle strength decreased about 30% after eccentric exercise. From the muscle strength vs. aponeurosis elongation curve, we obtained an index of stiffness. Stiffness of deep aponeurosis of the TA was assessed and found to be decreased from $87.4{\pm}33.56$ N/mm to $73.1{\pm}23.52$ N/mm. The results of this study suggest that decrease in stiffness of the TA aponeurosis following eccentric exercise might have significant implications to functions of the muscle-tendon complex and the involved joint motion and provide better understanding of eccentric exercise in the fields of training and rehabilitation.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.203-213
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• 2009

Medicine is one of great application fields where Virtual Reality (VR) technologies have been successfully utilized. The VR technologies in medicine bring together an interdisciplinary community of computer scientists and engineers, physicians and surgeon, medical educator and students, military medical specialists, and biomedical futurists. The primary feedback of a VR system has been visual feedback. The complex geometry for graphic objects and utilizing hardware acceleration can be incorporated with in order to produce realistic virtual environments. To enhance human-computer interaction (HCI), in term of immersive experiences perceived by users, haptic, speech, olfactory and other non-traditional interfaces should also be exploited. Among those, hapic feedback has been tightly coupled with visual feedback. The combination of the two sensory feedbacks can give users more immersive, realistic and perceptive VR environments. Haptic feedback has been studied over decades and many haptic based VR systems have been developed. This paper focuses on haptic feedback in term of its medical usages. It presents a survey of haptic feedback techniques with their applications in medicine.

• Journal of the Korean Society of Physical Medicine
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• v.14 no.4
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• pp.55-62
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• 2019

PURPOSE: This study examined the muscle activity while stepping over obstacles with various heights and widths to provide basic data for training and preventing falls. METHODS: Fifteen normal young adults (seven males and eight females) were recruited. The participants walked on a 5m walkway with six obstacles. The heights of obstacles were 0%, 10%, and 40% of the subject's leg length, and the width of the obstacles was 7cm and 14cm. The participants traversed the course twice per obstacle. The muscle activities of the soleus, tibialis anterior (TA), vastus medialis (VM), and vastus lateralis (VL) were measured using surface electromyography. A Mann-Whitney test and Kruskal-Wallis test were used to examine the differences between obstacles. RESULTS: The muscle activities of the VL and the soleus of the stance leg and lead leg after crossing over the obstacles increased with increasing width, and there were significant differences in muscle activities between obstacle width (p<.05) except for the muscle activity of TA of the stance leg after crossing over the obstacles. A significant difference in muscle activities was observed according to the height of the obstacles with 14 cm (p<.05) except for the muscle activity of the VL, soleus of the leading leg, and TA of the stance leg CONCLUSION: The role of the VL and Soleus increased with increasing obstacle width, and the overall muscle activities of the lower extremities increased with increasing obstacle height. These results can be used to suggest a program to prevent falls.

• Journal of the Korean Society for Library and Information Science
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• v.50 no.4
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• pp.55-75
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• 2016

We analyzed the characteristics of the physicians preferred information sources and barriers through in-depth interviews. Information searches for patient treatment were subdivided into deciding patient treatment methods, understanding the latest treatment trends, and preparing presentation materials for conferences. The variables that affected the search behaviors were identified as being background knowledge on the topic, clinical experience, job title, search skills, user training, and familiarity with the library homepage. PubMed was the most preferred choice because of users' familiarity, reliability, and the vastness of information; Google was also used frequently for easy access and fast search result. The accuracy and the recentness of information were the most significant criteria. Easy interface and convenient access were also considered important due to physicians' time constraints. Searching obstacles were divided into difficulty of searching system, unfamiliar term, too vast resources, difficulty to get fulltext articles and complex advanced search features. The results of this study can be utilized as a basis for improving information service of library and curriculum development for physicians.

• The Journal of the Korea Contents Association
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• v.9 no.10
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• pp.13-21
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• 2009

This paper describes research of image enhancement for detection of face area. Typical face recognition algorithms used fixed parameter filtering algorithms to optimize face images for the recognition process. A fixed filtering scheme introduces errors when applied to face images captured in various different environmental conditions. For acquiring face image of good quality from the image including complex background and illumination, we propose a method for image enhancement using the categories based on the image intensity values. When an image is acquired average values of image from sub-window are computed and then compared to training values that were computed during preprocessing. The category is selected and the most suitable image filter method is applied to the image. We used histogram equalization, and gamma correction filters with two different parameters, and then used the most suitable filter among those three. An increase in enrollment of filtered images was observed compared to enrollment rates of the original images.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.321-324
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• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement Beaming is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement loaming is different from supervised teaming in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement loaming algorithms like Q-learning do not require defining or loaming any models of the surrounding environment. Nevertheless it can learn the optimal policy if the agent can visit every state-action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem, we suggest Adaptive Mediation-based Modular Q-Learning(AMMQL) as an improvement of the existing Modular Q-Learning(MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state space effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. This paper introduces the concept of AMMQL and presents details of its application into dynamic positioning of robot soccer agents.

• 한국태양에너지학회:학술대회논문집
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• 2009.04a
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• pp.184-189
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• 2009

In order to improve the operation of energy systems, it is necessary for the urban communities to have reliable optimization routines, both computerized and manual, implemented in their organizations. However, before a production plan for the energy system units can be constructed, a prediction of the energy systems first needs to be determined. So, several methodologies have been proposed for energy demand prediction, but due to uncertainties in urban community, many of them will fail in practice. The main topic of this paper has been the development of a method for energy demand prediction at urban community. Energy demand prediction is important input parameters to plan for the energy planing. This paper presents a energy demand prediction method which estimates heat and electricity for various building categories. The method has been based on artificial neural networks(ANN). The advantage of ANN with respect to the other method is their ability of modeling a multivariable problem given by the complex relationships between the variables. Also, the ANN can extract the relationships among these variables by means of learning with training data. In this paper, the ANN have been applied in oder to correlate weather conditions, calendar data, schedules, etc. Space heating, cooling, hot water and HVAC electricity can be predicted using this method. This method can produce 10% of errors hourly load profile from individual building to urban community.