• The Korean Journal of Franchise Management
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• v.5 no.1
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• pp.49-64
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• 2014

The purpose of this work is to analyze successful business strategy of Daekyo Noonnoppi. Daekyo Noonnoppi, a franchise company of educational service, activated education business by establishing new way of providing education opportunity: self-directed learning center. They introduced not only the concept of learning center but also sustainable business strategies, which leads to remarkable success in the education business field. Daekyo Noonnoppi deployed three managerial concepts for study achievement: goal management, study management, and environment management. This Franchise company has three advantages of its success: Goal, Study and environment management: First, the goal management helps students to develop self-directed attitudes by making(appropriate) atmosphere which is able to build study goal and plan. In addition, this company provides information to their students to searches ways of study through the test reflecting their tendency. Furthermore, this company offers a variety of events for motivating study. Second, study management is helpful for students to develop holistic fundamental knowledge through its textbooks of this company and provides solutions and time management for study through 1 on 1 study advice. Third, environment management is used to making atmosphere to develop self-directed learning way for its students and provides spaces for students equipped with multimedia systems and cyber learning infrastructures.

• 한국정보교육학회:학술대회논문집
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• 2004.01a
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• pp.195-206
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• 2004

본 연구는 음성 인식 기술을 적용해서 시범적으로 영어 학습 제품을 제작하고 이것을 현장에 적용한 후 설문과 관찰을 통해 결과를 해석하는 Pilot Test로 계획되었다. 본 연구의 목적은 음성 인식 영어 학습 제품 사용 후, 학생들의 학습태도 변화를 확인하고, 실험에 참가한 회원, 학부모, 교사의 만족도를 조사하고, 영어 교육학 전공자를 통해서 음성인식 영어 학습 제품의 완성도를 조사 분석하는 것을 목적으로 한다. 본 연구를 통해 도출된 data 들은 음성 인식 기술의 제품 적용 가능성을 검증해 주고, 향후 사업화 추진을 위한 근거 자료나 기초 자료로서의 역할을 할 것으로 기대한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.191-194
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• 2009

포털에서 제공하는 지도 서비스와 같은 지도의 축척이 변화될 수 있는 환경에서 센싱 정보의 등고선식 표출을 하기 위해서는 기존의 이미지 표출 방식이 갖는 방대한 데이터량이 문제가 된다. 본 연구에서는 센싱 정보의 지역적 집중도와 센싱 데이터의 수에 따른 효율적인 정보처리를 위하여 센싱 데이터들을 그룹핑하여 등고선 형태의 이미지를 생성하였고, 이를 서버에서 제공하는 형태를 통한 웹 포털 지도 서비스와 연계 방법을 제시하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.135-136
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• 2009

공기질 정보에 대한 등고선식 표현법은 분포에 대한 시각적인 이해도를 높이는 좋은 방법중의 하나이다. 그러나 기초데이터로부터 등고선식 이미지를 생성하는데 시간이 오래 걸리기 때문에 실시간적인 환경 정보의 제공에 활용하기 어렵다. 본 연구는 등고선식 표현을 동적으로 생성하여 실시간 환경정보의 제공에 활용할 수 있도록 그룹상수를 이용한 기초데이터의 수를 줄이는 방법을 적용한 표출 도구를 설계 및 구현하였다.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.459-469
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• 2012

This article proposes a couple of parallel implementations of the self-organizing network for normal mixtures. In principle, self-organizing networks should be able to be implemented in a parallel computing environment without issue. However, the network for normal mixtures has inherent problem in being operated parallel in pure sense due to estimating conditional expectations of the mixing proportion in each iteration. This article shows the result of the parallel implementations of the network using Java. According to the results, both of the implementations achieved a faster execution without any performance degradation.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.1
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• pp.22-30
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• 2023

When tracking small UAVs and drone targets in cloud clutter environments, MWIR sensors are often unable to track targets continuously. To overcome this problem, the SWIR sensor is mounted on the same gimbal. Target tracking uses sensor information fusion or selectively applies information from each sensor. In this case, parallax correction using the target distance is often used. However, it is difficult to apply the existing method to small UAVs and drone targets because the laser rangefinder's beam divergence angle is small, making it difficult to measure the distance. We propose a tracking method which needs not parallax correction of sensors. In the method, images from MWIR and SWIR sensors are captured simultaneously and a tracking error for gimbal driving is chosen by effectiveness measure. In order to prove the method, tracking performance was demonstrated for UAVs and drone targets in the real sky background using MWIR and SWIR image sensors.

• Journal of the Korea Computer Graphics Society
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• v.22 no.4
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• pp.21-30
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• 2016

Risk management system should be able to support a decision making within a short time to analyze stream data in real time. Many analytical systems consist of CPU computation and disk based database. However, it is more problematic when existing system analyzes stream data in real time. Stream data has various production periods from 1ms to 1 hour, 1day. One sensor generates small data but tens of thousands sensors generate huge amount of data. If hundreds of thousands sensors generate 1GB data per second, CPU based system cannot analyze the data in real time. For this reason, it requires fast processing speed and scalability for analyze stream data. In this paper, we present a fast visualization technique that consists of hybrid database and GPU computation. In order to evaluate our technique, we demonstrate a visual analytics system that analyzes pipeline leak using sensor and tweet data.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.2
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• pp.97-106
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• 2019

Today, many smart sensor's measurement instruments are used to check the safety situation of various medium and large bridge structures that should be maintained in the construction facilities, but most of them use the method of measuring and confirming the displacement behavior of the bridge at regular intervals. In order to continuously check the safety situation, various measuring instruments are used, but most of them are not able to measure and measure the displacement and behavior of main construction structures at regular intervals. In this study, GNSS and environment smart sensors and drone's image data are transmitted to wireless network so that risk of many bridge's structures can be detected beforehand. As a result, by diagnosing the fine displacement of the bridge in real time and its condition, reinforcement, repair and disaster prevention measures for the structural parts of the bridges, which are expected to be dangerous, and various disasters and accidents can be prevented, and disaster can be prevented could suggest a new alternative.

• Journal of the Korea Computer Graphics Society
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• v.25 no.1
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• pp.23-32
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• 2019

Cybersickness is a symptom of dizziness that occurs while experiencing Virtual Reality (VR) technology and it is presumed to occur mainly by crosstalk between the sensory and cognitive systems. However, since the sensory and cognitive systems cannot be measured objectively, it is difficult to measure cybersickness. Therefore, methodologies for measuring cybersickness have been studied in various ways. Traditional studies have collected answers to questionnaires or analyzed EEG data using machine learning algorithms. However, the system relying on the questionnaires lacks objectivity, and it is difficult to obtain highly accurate measurements with the machine learning algorithms. In this work, we apply Deep Neural Network (DNN) deep learning algorithm for objective cybersickness measurement from EEG data. We also propose a data preprocessing for learning and network structures allowing us to achieve high performance when learning EEG data with the deep learning algorithms. Our approach provides cybersickness measurement with an accuracy up to 98.88%. Besides, we analyze video characteristics where cybersickness occurs by examining the video segments causing cybersickness in the experiments. We discover that cybersickness happens even in unusually persistent changes in the darkness such as the light in a room keeps switching on and off.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.1
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• pp.10-21
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• 2023

For various target tracking applications, it is well known that the Kalman filter is the optimal estimator(in the minimum mean-square sense) to predict and estimate the state(position and/or velocity) of linear dynamical systems driven by Gaussian stochastic noise. In the case of nonlinear systems, Extended Kalman filter(EKF) and/or Unscented Kalman filter(UKF) are widely used, which can be viewed as approximations of the(linear) Kalman filter in the sense of the conditional expectation. However, to implement EKF and UKF, the exact dynamical model information and the statistical information of noise are still required. In this paper, we propose the recurrent neural-network based Kalman filter, where its Kalman gain is obtained via the proposed GRU-LSTM based neural-network framework that does not need the precise model information as well as the noise covariance information. By the proposed neural-network based Kalman filter, the state estimation performance is enhanced in terms of the tracking error, which is verified through various linear and nonlinear tracking problems with incomplete model and statistical covariance information.