• The Journal of the Korea Contents Association
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• v.5 no.1
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• pp.1-8
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• 2005

E-learning is the application of e-business technology and services to teaching and learning. It use of new multimedia technologies and Internet to improved the qualify of learning by facilitating access to remote resources and services. In this paper, we show a computer-based test system, which is carefully designed and implemented. The system consists of a contents delivery mechanism, computer-adaptive test algorithm, and review engine. In this papepr, we describe what are points to be considered when design and implementing a computer-based test system. In addition, this paper shows how to control the bias value for computer-adaptive algorithm using real data.

• Journal of Korea Multimedia Society
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• v.24 no.7
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• pp.890-902
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• 2021

Although the object detection accuracy with still images has been significantly improved with the advance of deep learning techniques, the object detection problem with video data remains as a challenging problem due to the real-time requirement and accuracy drop with occlusion. In this research, we propose a method in pig detection for video monitoring environment. First, we determine a motion, from a video data obtained from a tilted-down-view camera, based on the average size of each pig at each location with the training data, and extract key frames based on the motion information. For each key frame, we then apply YOLO, which is known to have a superior trade-off between accuracy and execution speed among many deep learning-based object detectors, in order to get pig's bounding boxes. Finally, we merge the bounding boxes between consecutive key frames in order to reduce false positive and negative cases. Based on the experiment results with a video data set obtained from a pig farm, we confirmed that the pigs could be detected with an accuracy of 97% at a processing speed of 37fps.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.75-78
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• 2017

This paper compares operational and network analysis of centralized and distributed repository for big data solutions in the IoT enabled Smart Grid environment. The comparative analysis clearly depicts that centralize repository consumes less memory consumption while distributed locality-based repository reduce network complexity issues than centralize repository in state-of-the-art Big Data Solution.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.479-484
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• 1998

As technology in a computer hardware and software advances, efficient information retrieval from multimedia database gets highly demanded. Recently, it has been actively exploited to retrieve information based on the stored contents. However, most of the methods emphasize on the points which are far from human intuition or emotion. In order to overcome this shortcoming , this paper attempts to apply interactive genetic algorithm to content-based image retrieval. A preliminary result with subjective test shows the usefulness of this approach.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1147-1148
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• 2008

In this paper, we propose action-learning method based on teaching. By adopting this method, we can handle an exception case which cannot be handled in an Ethology-based Action SElection mechanism. Our proposed method is verified by employing AIBO robot as well as EASE platform.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.4-6
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• 2003

A holistic representation, such as sparse ceding or independent component analysis (ICA), was successfully applied to explain early auditory processing and sound classification. In contrast, Part-based representation is an alternative way of understanding object recognition in brain. In this paper. we employ the non-negative matrix factorization (NMF)［1］which learns parts-based representation for sound classification. Feature extraction methods from spectrogram using NMF are explained. Experimental results show that NMF-based features improve the performance of sound classification over ICA-based features.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.111-115
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• 2023

Knowledge Based Authentication is the most well-known technique for user authentication in a computer security framework. Most frameworks utilize a straightforward PIN (Personal Identification Number) or psssword as an data authenticator. Since password based authenticators typically will be software based, they are inclined to different attacks and weaknesses, from both human and software.Some of the attacks are talked about in this paper.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.1098-1099
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• 2007

In this paper, we propose a public key based security architecture for Wireless Sensor Networks (WSNs). The basic architecture comprises of two schemes; a key handshaking scheme based on simple linear operations for fast computation and an identity based cryptosystem which does not require any certificate authority. Our analysis shows that, the combined scheme ensures a good level of security and is very much suitable for the energy constrained trend of wireless sensor network.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.193-202
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• 2022

The classification of hyperspectral imagery (HSI) is essential in the surface of earth observation. Due to the continuous large number of bands, HSI data provide rich information about the object of study; however, it suffers from the curse of dimensionality. Dimensionality reduction is an essential aspect of Machine learning classification. The algorithms based on feature extraction can overcome the data dimensionality issue, thereby allowing the classifiers to utilize comprehensive models to reduce computational costs. This paper assesses and compares two HSI classification techniques. The first is based on the Joint Spatial-Spectral Stacked Autoencoder (JSSSA) method, the second is based on a shallow Artificial Neural Network (SNN), and the third is used the SVM model. The performance of the JSSSA technique is better than the SNN classification technique based on the overall accuracy and Kappa coefficient values. We observed that the JSSSA based method surpasses the SNN technique with an overall accuracy of 96.13% and Kappa coefficient value of 0.95. SNN also achieved a good accuracy of 92.40% and a Kappa coefficient value of 0.90, and SVM achieved an accuracy of 82.87%. The current study suggests that both JSSSA and SNN based techniques prove to be efficient methods for hyperspectral classification of snow features. This work classified the labeled/ground-truth datasets of snow in multiple classes. The labeled/ground-truth data can be valuable for applying deep neural networks such as CNN, hybrid CNN, RNN for glaciology, and snow-related hazard applications.

• Smart Structures and Systems
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• v.29 no.2
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• pp.375-387
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• 2022

One of the latest teaching strategies is smart classroom teaching. Teaching is carried out with the assistance of smart teaching technologies to improve teacher-student contact, increase students' learning autonomy, and give fresh ideas for the fulfillment of students' deep learning. Computer-based technology has improved students' language learning and significantly motivating them to continue learning while also stimulating their creativity and enthusiasm. However, the difficulties and barriers that many EFL instructors are faced on seeking to integrate information and communication technology (ICT) into their instruction have raised discussions and concerns regarding ICT's real worth in the language classroom. This is a case study that includes observations in the classroom, field notes, interviews, and written materials. In EFL classrooms, both computer-based and non-computer-based activities were recorded and analyzed. The main instrument in this study was a survey questionnaire comprising 43 items, which was used to examine the efficiency of ICT integration in teaching and learning in public schools in Kuala Lumpur. A total of 101 questionnaires were delivered, while each responder being requested to read the statements provided. The total number of respondents for this study was 101 teachers from Kuala Lumpur's public secondary schools. The questionnaire was randomly distributed to respondents with a teaching background. This study indicated the accuracy of utilizing Teaching-Learning-Based Optimization (TLBO) in analyzing the survey results and potential for students to learn English as a foreign language using computers. Also, the usage of foreign language may be improved if real computer-based activities are introduced into the lesson.