• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.608-611
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• 2004

In this paper, we propose a robust face recognition technique based on the principle of eigenfaces. The traditional eigenface recognition (EFR) method works quite well when the input test patterns are cropped fares. However, when confronted with recognizing faces embedded in arbitrary backgrounds, the EFR method fails to discriminate effectively between faces and background patterns, giving rise to many false alarms. In order to improve robustness in the presence of background, we argue in favor of loaming the distribution of background patterns. A background space is constructed from the background patterns and this space together with the face space is used for recognizing faces. The proposed method outperforms the traditional EFR technique and gives very good results even on complicated scenes.

• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.1-7
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• 2020

The demand on the underwater communications is extremely increasing in searching for underwater resources, marine expedition, or environmental researches, yet there are many problems with the wireless communications because of the characteristics of the underwater environments. Especially, with the underwater wireless networks, there happen inevitable delay time and spacial inequality due to the distances between the nodes. To solve these problems, this paper suggests a new solution based on ALOHA-Q. The suggested method use random NAV value. and Environments take reward through communications success or fail. After then, The environments setting NAV value from reward. This model minimizes usage of energy and computing resources under the underwater wireless networks, and learns and setting NAV values through intense learning. The results of the simulations show that NAV values can be environmentally adopted and select best value to the circumstances, so the problems which are unnecessary delay times and spacial inequality can be solved. Result of simulations, NAV time decreasing 17.5% compared with original NAV.

• 대한공업교육학회지
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• v.42 no.1
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• pp.87-105
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• 2017

The purpose of this study was to verify that students' interest in team activities for problem solving, the effect of interest on it, and students' changes in perceptions and their behavioral characteristics in the process of problem solving. The study reviewed documents prepared by students, such as work sheets, descriptive questionnaires, works and their photos, student activity photos and observation journals of teachers. The results of this research are as below. First, a problem solving team activity for making a model car was considered an interesting assignment by more than 90% of male/female students. The fact that female students could be more focused on this assignment than male students was discovered. Interest in the assignment not only had an influence on the points from the start (the blueprint) to the end (model cars completed based upon the designs) of problem solving, but also provided the traction power behind the assignment. Second, the problem solving team activity allowed the students to change their existing recognition (thoughts) while positively taking a lead or indirectly utilizing various learning experiences (including experiences of failure). Third, $2^{nd}$ graders in middle school had a tendency to solve problems in dependently rather than to receive help from others when they encountered problematic situations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.1
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• pp.1-11
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• 2014

In this paper we propose a system for vehicle detection and tracking which has the ability to learn on-line appearance changes of vehicles being tracked. The proposed system uses feature-based tracking method to estimate rapidly and robustly the motion of the newly detected vehicles between consecutive frames. Simultaneously, the system trains an online vehicle detector for the tracked vehicles. If the tracker fails, it is re-initialized by the detection of the online vehicle detector. An improved vehicle appearance model update rule is presented to increase a tracking performance and a speed of the proposed system. Performance of the proposed system is evaluated on the dataset acquired on various driving environment. In particular, the experimental results proved that the performance of the vehicle tracking is significantly improved under bad conditions such as entering a tunnel and passing rain.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.23-34
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• 2004

In this paper we propose a feature point tracking algorithm using optical flow under non-prior taming active feature model (NPT-AFM). The proposed algorithm mainly focuses on analysis non-rigid objects[1], and provides real-time, robust tracking by NPT-AFM. NPT-AFM algorithm can be divided into two steps: (i) localization of an object-of-interest and (ii) prediction and correction of the object position by utilizing the inter-frame information. The localization step was realized by using a modified Shi-Tomasi's feature tracking algoriam[2] after motion-based segmentation. In the prediction-correction step, given feature points are continuously tracked by using optical flow method[3] and if a feature point cannot be properly tracked, temporal and spatial prediction schemes can be employed for that point until it becomes uncovered again. Feature points inside an object are estimated instead of its shape boundary, and are updated an element of the training set for AFH Experimental results, show that the proposed NPT-AFM-based algerian can robustly track non-rigid objects in real-time.

• Journal of Digital Contents Society
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• v.11 no.4
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• pp.417-424
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• 2010

In this paper the real-time face region was detected by suggesting the rectangular feature-based classifier and the robust detection algorithm that satisfied the efficiency of computation and detection performance was suggested. By using the detected face region as a recognition input image, in this paper the face recognition method combined with PCA and the multi-layer network which is one of the intelligent classification was suggested and its performance was evaluated. As a pre-processing algorithm of input face image, this method computes the eigenface through PCA and expresses the training images with it as a fundamental vector. Each image takes the set of weights for the fundamental vector as a feature vector and it reduces the dimension of image at the same time, and then the face recognition is performed by inputting the multi-layer neural network.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.197-202
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• 2009

Active Appearance Models(AAMs)은 얼굴인식, 얼굴추적, 표정인식 뿐만 아니라 눈동자 추적과 같은 분야에도 적용되어 좋은 성능을 보여 주었다. 보통 AAM 을 생성하기 위해서는 얼굴 영상과 얼굴의 특징을 나타내는 점으로 구성된 매쉬로 이루어 지는 트레이닝 셋이 필요하다. AAM fitting algorithm 은 학습한 얼굴과 유사한 얼굴을 Fitting 할 때에는 뛰어난 성능을 보이지만 조명에 의한 그림자 또는 액세서리에 의한 얼굴의 피부 가림과 같이 전체 얼굴이 잘 나타나지 않는 불완전한 영상의 Fitting 은 입력영상과 템플릿 영상간의 오차가 커지기 때문에 실패할 가능성이 매우 높다. 본 논문에서 우리는 AAMs 에서 사용되는 PCA를 Higher-order Singular Value Decomposition(HOSVD)로 대체하여 이 문제를 보완하는 강화된 AAM 을 제안한다. 제안된 AAM 에는 기존에 사용하던 고유벡터와 함께 HOSVD 를 통해 획득할 수 있는 Eigen-Modes 를 추가하여 사용한다. 또한 우리는 Yale Face Database를 이용한 평가를 통해 제안된 AAM 이 기존 AAM 보다 불완전한 영상에 효과적으로 대응하는 것을 보여준다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.37-38
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• 2015

본 논문에서는 XNA Framework 기반의 모바일 게임 History Adventure를 설계하고 구현한다. 이 모바일 게임은 영웅별 3개의 스테이지 구성되어 있으며 각 단계별로 각 나라를 대표를 하는 영웅들과 전쟁의 발발 원인에 대한 퀴즈 풀이를 진행한다. 이때 정답을 선택하면 HP를 공급하고 실패하면 정답을 알려줌으로써 게임과 학습을 함께 할 수 있다. 영웅들이 적을 격파하지 못하고 적선이 통과를 하게 된다면 HP(Hit Point)는 감소하고, 적선을 격퇴하면 난이도가 높은 스테이지로 이동한다. 게임중에 일정 점수를 획득하면 보너스 코인과 HP를 지급한다. 지급받은 코인 및 HP는 현재 스테이지를 수행하고 다음 스테이지로 이동해도 유효하다. 적의 보스를 처리할시 실제 전쟁의 결과를 알려주면서 다음 단계로 이동한다.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.301-307
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• 2023

Deep learning requires building a deep neural network, collecting a large amount of training data, and then training the built neural network for a long time. If training does not proceed properly or overfitting occurs, training will fail. When using deep learning tools that have been developed so far, it takes a lot of time to collect training data and learn. However, due to the rapid advent of the mobile environment and the increase in sensor data, the demand for real-time deep learning technology that can dramatically reduce the time required for neural network learning is rapidly increasing. In this study, a real-time deep learning system was implemented using an Arduino system equipped with a fine dust sensor. In the implemented system, fine dust data is measured every 30 seconds, and when up to 120 are accumulated, learning is performed using the previously accumulated data and the newly accumulated data as a dataset. The neural network for learning was composed of one input layer, one hidden layer, and one output. To evaluate the performance of the implemented system, learning time and root mean square error (RMSE) were measured. As a result of the experiment, the average learning error was 0.04053796, and the average learning time of one epoch was about 3,447 seconds.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.63-68
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• 2020

This paper proposes a face authentication system based on deep learning framework. The proposed system is consisted of face region detection and feature extraction using deep learning algorithm, and performed the face authentication using joint-bayesian matrix learning algorithm. The performance of proposed paper is evaluated by various face database , and the face image of one person consists of 2 images. The face authentication algorithm was performed by measuring similarity by applying 2048 dimension characteristic and combined Bayesian algorithm through Deep Neural network and calculating the same error rate that failed face certification. The result of proposed paper shows that the proposed system using deep learning and joint bayesian algorithms showed the equal error rate of 1.2%, and have a good performance compared to previous approach.