• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.304-310
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• 2010

Chronic pain and herniated disk is a common disease that 80% of adults are experienced. There diseases rates of caused by the physical shock, such as the traffic accident, and the accidental fall is about 10%. And the most of these diseases is caused by having habitual incorrect position. People know that incorrect position would cause to accumulate continuous stress, but it is not easy to correct position. Because it does not recognize incorrect position repeated habitual consequently. This system collects data of user position after sensors that could measure position attach on use and presumes correct position used by position presumption algorithms. Its system purpose is continuing incorrect position could be aware to user and lead to change to correct position to prevent habituation of incorrect position. If habitual of correct position continues through accurate measurement and repeating cognitive learning, it would help for children and chronic patience.

• 한국경영정보학회:학술대회논문집
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• 2007.11a
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• pp.123-128
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• 2007

It has been argued that Enterprise systems (ES) implementations are overshadowed by a high failure rate despite their promised benefits. One of the commonly cited reasons for ES implementation failures in the context of mandatory use is end-user's unwillingness or sabotage to adopt or use systems. Considering that the appropriate management of expectations may play an important role in making positive behavior toward newly implemented systems, this study examines the effect of outcome expectations on the system use activity in the mandatory use context of information systems from the Social Cognitive Theory perspective. Structural equation model analysis using LISREL 8.7 provides significant support for the proposed relationships. The empirical results suggest that outcome expectations and user satisfaction have positive effects on system use activity conceptualized by immersion, reinvention, and learning. Theoretical and practical implications of the study shed some light on how to improve system use activity in the mandatory use context of information systems.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.175-187
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• 2011

Since insects play important roles in existence of plants and other animals in the natural environment, they are considered as necessary biological resources from the perspectives of those biodiversity conservation and national utilization strategy. For the conservation and utilization of insect species, an observational learning environment is needed for non-experts such as citizens and students to take interest in insects in the natural ecosystem. The insect identification is a main factor for the observational learning. A current time-consuming search method by insect classification is inefficient because it needs much time for the non-experts who lack insect knowledge to identify insect species. To solve this problem, we proposed an smart phone-based insect identification inference system that helps the non-experts identify insect species from observational characteristics in the natural environment. This system is based on the similarity between the observational information by an observer and the biological insect characteristics. For this system, we classified the observational characteristics of insects into 27 elements according to order, family, and species, and proposed similarity indexes to search similar insects. In addition, we developed an insect identification inference prototype system to show this study's viability and performed comparison experimentation between our system and a general insect classification search method. As the results, we showed that our system is more effective in identifying insect species and it can be more efficient in search time.

• The Journal of Korean Association of Computer Education
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• v.16 no.5
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• pp.59-68
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• 2013

The purpose of this study is to analyze the satisfaction and effective variables in online learning for low-income students. An Web-survey was conducted to measure its satisfaction and effectiveness. 285 students participated in the study. T-test and ANOVA were used to analyze the data. The result of satisfaction was fairly positive. In addition, there were differences in effectiveness. In specific, the each group of female, elementary school, higher level of achievement, and higher level of self-regulated learning group students showed higher satisfaction than the others. Suggestions and future research are discussed.

• The Journal of Information Technology and Database
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• v.2 no.2
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• pp.71-85
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• 1995

Many research results conducted by neural network researchers have claimed that the classification accuracy of neural networks is superior to, or at least equal to that of conventional methods. However, in series of neural network classifications, it was found that the classification accuracy strongly depends on the characteristics of training data set. Even though there are many research reports that the classification accuracy of neural networks can be different, depending on the composition and architecture of the networks, training algorithm, and test data set, very few research addressed the problem of classification accuracy when the basic assumption of data monotonicity is violated, In this research, development project of automated credit evaluation system is described. The finding was that arrangement of training data is critical to successful implementation of neural training to maintain monotonicity of the data set, for enhancing classification accuracy of neural networks.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.122-127
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• 2001

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.315-318
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• 2018

대부분의 대학생들은 재학기간 중에 학교에서 운영되는 교육프로그램, 자원봉사, 어학연수, 동아리활동 등 다양한 경험을 하게 된다. 최근에는 학교에서 운영하는 교육프로그램이 더욱 다양해지고 많은 학생들이 참여하고 있다. 하지만 학교에서는 학생들이 어떤 활동을 했는지 효율적으로 관리하고 참여 학생의 적극적인 참여율 유도가 필요가 있지만, 이를 효율적으로 관리하는 것은 어려운 일이다. 본 연구는 학교에서 시행중인 다양한 프로그램들에 대하여 학생들이 참여함에 따라 학생들의 참여현황을 조사하여 관리하고 참여율과 학과별 통계를 Dashboard로 열람할 수 있는 프로그램을 개발하여 운영하였다. 교육 참여 관리 시스템을 활용함으로써 담당자의 업무 효율을 기대할 수 있고 체계적인 학생들의 교육 프로그램을 효율적으로 모니터링하고 관리할 것으로 기대된다.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.29.1-29
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• 2001

Neuro-fuzzy systems are multi-layered connectionist networks that realize the elements and functions of traditional fuzzy logic control/decision systems. A trained neuro-fuzzy system is isomorphic to a fuzzy logic system, and fuzzy IF-THEN rule knowledge can be explicitly extracted from the network. This talk presents a brief introduction to self-adaptive neuro-fuzzy systems and addresses some recent research results and applications. Most of the existing neuro-fuzzy systems exhibit several major drawbacks that lead to performance degradation. These drawbacks are the curse of dimensionality (i.e., fuzzy rule explosion), inability to re-structure their internal nodes in a changing environment, and their lack of ability to extract knowledge from a given set of training data. This talk focuses on our investigation of network architectures, self-adaptation algorithms, and efficient learning algorithms that will enable existing neuro-fuzzy systems to self-adapt themselves in an unstructured and uncertain environment.

• Journal of Integrative Natural Science
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• v.7 no.1
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• pp.62-66
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• 2014

This study aims to implement the emergency monitoring robot system which predicts the current state of the patients without visiting the medical institutions by measuring the basic health status of the user's blood pressure, heartbeat, and basic health status of body temperature in the disaster emergency situation based on the Smart Grid. By arranging a large number of sensor(blood pressure, heartbeat, body temperature sensor) and measuring the bio signs, so the attached wireless XBee sensor can be stored in DB of robot, and it aims to draw the current state of the patients by analysis of stored bio data. Among 300 data obtained from the sensor, 1st data to 100th data were used for learning, and from 101st data to 300th data were used for assessment. 12 results were different among the total 300 assessment data, so it shows about 96% accuracy.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.7-13
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• 2024

User preferences and ratings may be anticipated by recommendation systems, which are widely used in social networking, online shopping, healthcare, and even energy efficiency. Constructing trustworthy recommender systems for various applications, requires the analysis and mining of vast quantities of user data, including demographics. This study focuses on holding elections with vague voter and candidate preferences. Collaborative user ratings are used by filtering algorithms to provide suggestions. To avoid information overload, consumers are directed towards items that they are more likely to prefer based on the profile data used by recommender systems. Better interactions between governments, residents, and businesses may result from studies on recommender systems that facilitate the use of e-government services. To broaden people's access to the democratic process, the concept of "e-democracy" applies new media technologies. This study provides a framework for an electronic voting advisory system that uses machine learning.