• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.697-700
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• 2020

고위험의 재난 상황에서 사람이 상황을 판단하고, 요구조자를 탐색하며, 구조하는 것은 추가 피해를 발생시킬 수 있다. 따라서 재난 상황에서도 이동과 접근이 용이한 무인항공에 관한 연구와 개발이 활발히 이루어지고 있다. 재난 상황에서 신속하게 대처하기 위해서는 선제적 상황인지 기술이 필요하다. 이에 본 논문은 구조 및 대피를 위해 사람, 자동차, 자전거 등의 객체를 인식하고 중복 인식을 피하기 위해 추적하는 딥러닝 기반 다중 객체 추적 시스템을 제안한다. 2019 인공지능 R&D 그랜드 챌린지 상황인지 부문에서의 대회 결과로 실험 성능을 증명한다.

• Journal of the Korea Society of Computer and Information
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• v.16 no.5
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• pp.33-39
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• 2011

In modern society, in business decisions of our customers are continually increasing in importance, and owing to the development of information and communication technology effectively on a computer to measure the preferences of key customer techniques are being studied. However, this preference reflects significantly on personal ideas, and therefore, it is difficult to commercialize a measure calculated according to the ambiguous results. In this paper, by using biometric information that has been measure; we have configured the multi-sensitivity models based on customer preferences to evaluate the proposed system. This system consists of multiple biometric information of multi-dimensional vector model for learning through the use of structured emotional to apply the same criteria to evaluate customer preferences. In addition, by studying the specific subject-specific emotion model, it is shown to improve accuracy with further experiments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.7
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• pp.4498-4510
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• 2015

The purpose of this study is to investigate effective teaching media for infants' teaching-learning process by comparing the effects of multimedia fairytale activities and picture fairytale listening activities on infants' prosocial actions. Following the purpose of this study, we studied any effect to infants' prosocial actions from multimedia fairytale activities by dividing in two groups. This study was conducted with 5-year-old infants (total 40; 20 experimental group & 20 comparison group) in D daycare center in Daejeon. Verifying data tool were time series corresponding t-test, independent t-test and multi-variant analyses. As a result, multimedia fairytale listening activities are more effective for infants' prosocial actions. It has meaningful difference in the interpersonal relationship skills and early childhood education adapt which is sub-area of infant's prosocial actions. So, for various and high-quality early childhood education, using multimedia teaching media is more effective than existing teaching media.

• Journal of Digital Contents Society
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• v.11 no.3
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• pp.307-314
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• 2010

The demand for cyber education in Korea is constantly increasing and the need for research on online lecture on contents design that increases the learning effect is rising. In this research, the online lecture contents about the technical information type provided by Korea Cyber University was understood and we researched about the most preferred lecture type and the most effective lecture type in learning among the 1,173 students in Korea Cyber University who participated in this online survey. Also, we analyzed if the students' preference for the lecture type depended on their experience on that lecture type and we studied the students' claims postulated on the interface design of the lecture contents. The most preferred lecture type among students was e-Stream+flash and they answered that multi-media type lectures were the most effective lectures in learning. The majority of the students preferred lecture contents that they have experienced before and preferred the menu on the left side of the page in interface design. Not only the completeness, but the applications in design in lecture contents are also an important factor in online lectures. As the demand for cyber education in Korea is increasing, content design that can increase the academic performance should be further researched.

• Journal of The Korean Association of Information Education
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• v.16 no.3
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• pp.373-382
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• 2012

Of computing skills in the field of multi-media, particularly augmented reality technology contents may provide realistic learning experiences with 3D pictures through the learners' manipulation activities. However, the marker systems in the existing studies were not well developed as to maintain the students' interest and concentration. In this study, we have designed the first lesson ("Earth and Moon") of 5th graders' science with augmented reality combined system so that we could deal with manipulation activities of the relationship between augmented objects, From the experimental study, using combined augmented reality contents made a significant difference in their learning achievement and motivation. Thus augmented reality combined system can be utilized for a variety of topics to maintain students' learning motivation.

• Journal of Broadcast Engineering
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• v.19 no.2
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• pp.166-173
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• 2014

Recently, human action recognition have been developed for various broadcasting and video process. Since a video can consist of various scenes, keypoint approaches have been more attracted than template based methods for real application. Keypoint approahces tried to find regions having motion in video, and made 3-dimensional patches. Then, descriptors using histograms were computed from the patches, and a classifier based on machine learning method was applied to detect actions in video. However, a single classifier was difficult to handle various human actions. In order to improve this problem, approaches using multi classifiers were used to detect and to recognize objects. Thus, we propose a new human action recognition using decision-level fusion with support vector machine and sparse representation. The proposed method extracted descriptors based on keypoint approach from a video, and acquired results from each classifier for human action recognition. Then, we applied weights which were acquired by training stage to fuse each results from two classifiers. The experiment results in this paper show better result than a previous fusion method.

• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.21-30
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• 2024

As the self-driving car market continues to grow, the need for charging infrastructure is growing. However, in the case of a wireless charging system, stability issues are being raised because it requires a large amount of power compared with conventional wired charging. SAE J2954 is a standard for building autonomous vehicle wireless charging infrastructure, and the standard defines a communication method between a vehicle and a power transmission system. SAE J2954 recommends using physical media such as Wi-Fi, Bluetooth, and UWB as a wireless charging communication method for autonomous vehicles to enable communication between the vehicle and the charging pad. In particular, UWB is a suitable solution for indoor and outdoor charging environments because it exhibits robust communication capabilities in indoor environments and is not sensitive to interference. In this standard, the process for building a wireless power transmission system is divided into several stages from the start to the completion of charging. In this study, UWB technology is used as a means of fine alignment, a process in the wireless power transmission system. To determine the applicability to an actual autonomous vehicle wireless power transmission system, experiments were conducted based on distance, and the distance information was collected from UWB. To improve the accuracy of the distance data obtained from UWB, we propose a Single Model and Multi Model that apply machine learning and deep learning techniques to the collected data through a three-step preprocessing process.

• Journal of Korean Home Economics Education Association
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• v.11 no.2
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• pp.13-26
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• 1999

The goals of this research are for producing and optimizing the CD-ROM, effective and practical Teaching-Learning method. It consists of Wearing Korean Traditional Costume for the First Grade of Middle School Students in Home Economics Teaching. This research’s summarization is following. First, the multi-media material. CD-ROM making use of Powerpoint. Wearing Korean Traditional Costume, is produced to help the students learn the difficult contents in terms of video and audio. Second, it is introduced the model of Open Education for increasing the efficiency of class. Third, it is developed to proceed the class with the CD-ROM and small group study of place activity. Fourth, it helps students concentrate on the class with proper sound effect whenever the slide films are changed. And it helps to link the web sites related to Korean Traditional Costume. Finally, another kinds of suggestions are following. The effective verification of this software that is tested and applied at the field for a given period will be necessary. And, it is necessary to upgrade for the CD-ROM and the supplementary teaching materials in Korean Traditional Costume education.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2005.11a
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• pp.199-202
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• 2005

TV OSD(On Screen Display) 메뉴 자동검증 시스템에서 다국적 언어의 문자 인식은 표준패턴의 구조적 분석이 쉽지 않을 뿐만 아니라 학습패턴 집합의 규모와 특징의 수가 증가함으로 인하여 특징추출 및 인식 과정에서 방대한 계산량이 요구된다. 이에 본 연구에서는 학습 데이터에 포함되는 다량의 특징 집합으로부터 인식에 필요한 효과적인 특징을 선별함으로써 패턴 분류기의 효율성을 개선하기 위한 방법론을 고찰한다. 이를 위하여 수정된 형태의 Adaboost 기법을 제안하고 이를 적용한 실험 결과로부터 그 유용성을 고찰한다. 제안된 알고리즘은 초기의 특징 집합을 취약한 성능을 갖는 다수의 분류기(classifier)로서 고려하며, 이로부터 반복학습을 통하여 개선된 분류기를 점진적으로 선별해 나가게 된다. 학습의 원리는 주어진 학습패턴 집합에 기초하여 일종의 교사학습(supervised learning) 방식으로 이루어진다. 각 패턴에 할당된 가중치 값은 각 단계에서 산출되는 분류결과에 따라 적응적으로 수정되어 반복학습이 진행됨에 따라 점차 보완적 성능을 갖는 분류기를 선택할 수 있게 한다. 즉, 주어진 각 학습패턴에 대하여 초기에 균등한 가중치가 부여되며, 반복학습의 각 단계에서 적용되는 분류기의 출력을 분석하여 오분류된 패턴의 가중치 분포를 증가시켜 나간다. 본 연구에서는 실제 응용으로서 OSD 메뉴검증 시스템을 대상으로 제안된 이론을 적용하고 그 타당성을 평가한다.

• Advances in nano research
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• v.12 no.5
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• pp.489-499
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• 2022

Nanofluids have recently triggered a substantial scientific interest as cooling media. However, their stability is challenging for successful engagement in industrial applications. Different factors, including temperature, nanoparticles and base fluids characteristics, pH, ultrasonic power and frequency, agitation time, and surfactant type and concentration, determine the nanofluid stability regime. Indeed, it is often too complicated and even impossible to accurately find the conditions resulting in a stabilized nanofluid. Furthermore, there are no empirical, semi-empirical, and even intelligent scenarios for anticipating the stability of nanofluids. Therefore, this study introduces a straightforward and reliable intelligent classifier for discriminating among the stability regimes of alumina-water nanofluids based on the Zeta potential margins. In this regard, various intelligent classifiers (i.e., deep learning and multilayer perceptron neural network, decision tree, GoogleNet, and multi-output least squares support vector regression) have been designed, and their classification accuracy was compared. This comparison approved that the multilayer perceptron neural network (MLPNN) with the SoftMax activation function trained by the Bayesian regularization algorithm is the best classifier for the considered task. This intelligent classifier accurately detects the stability regimes of more than 90% of 345 different nanofluid samples. The overall classification accuracy and misclassification percent of 90.1% and 9.9% have been achieved by this model. This research is the first try toward anticipting the stability of water-alumin nanofluids from some easily measured independent variables.