• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.19-27
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• 2020

Recently, rapid and accurate 3D models creation is required in various applications using virtual reality and augmented reality technology. In this paper, we propose an on-site learning based shape deformation method which transforms the clothed 3D human model into the shape of an input point cloud. The proposed algorithm consists of two main parts: one is pre-learning and the other is on-site learning. Each learning consists of encoder, template transformation and decoder network. The proposed network is learned by unsupervised method, which uses the Chamfer distance between the input point cloud form and the template vertices as the loss function. By performing on-site learning on the input point clouds during the inference process, the high accuracy of the inference results can be obtained and presented through experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.344-346
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• 2019

In this paper, we propose a distance estimation method using the convolutional neural network in Ultra-Wideband (UWB) systems. The training data set used to learn the deep learning model using the convolutional neural network is generated by the MATLAB program and utilizes the IEEE 802.15.4a standard. The performance of the proposed distance estimation method is verified by comparing the threshold based distance estimation technique and the performance comparison used in the conventional distance estimation.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.5
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• pp.81-90
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• 2004

This paper presents new testability measure based on learning, which can be useful in the deterministic process of test pattern generation algorithms. This testability measure uses the structural information that are obtained by teaming. The proposed testability measure searches for test pattern that can early detect the conflict in case of the hardest decision problems. On the other hand in case of the easiest decision problem, it searches for test pattern that likely results in the least conflict. The proposed testability measure reduces CPU time to generate test pattern that accomplishes the same fault coverage as that of the distance-based measure.

• International Journal of Costume and Fashion
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• v.9 no.2
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• pp.1-18
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• 2009

Since the 1990s, the domestic fashion industry has been changing rapidly and has become more competitive. Due to these circumstances, the roles of Shop masters were intensified and a training course to acquire a certificate of qualification as a Shop master was in great demand. The 1st Shop master certification exam took place in the year 2001. The purpose of this study was to research the formality of Shop master certificate training courses via e-learning, which is a hot topic in 21st century education, and to provide a development example. First, an analysis was made of the definition and basic characteristics needed of a Shop-master. Next, we noted the problems of former Shop master training facilities and their training process. Thirdly, we did a research on the definition of e-learning and the elements to embody the system. Based on the information obtained through this research, we provided a development example on Shop-master certificate training courses via e-learning that overcame the problems of courses that are currently provided.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.299-307
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• 2021

As the COVID-19 pandemic spread around the world, more and more universities are conducting real-time distance video lectures using ZOOM, Webex, and MS Teams. This study attempts to identify the factors influencing learner satisfaction of real-time distance video lectures. Based on the existing research, it was composed of five elements (system factor, content quality, interaction, self-direction, and learning motivation) as learner satisfaction elements of real-time distance video lectures. As a result of analyzing the structural equation model of 160 effective questionnaires by conducting a survey of college students in the metropolitan and Chungcheong areas, it was found that three factors (interaction, self-direction, and learning motivation) influence learner satisfaction. Real-time distance video lectures are expected to continue to expand in the future. Therefore, universities should continuously increase learner satisfaction through the development and evaluation of real-time distance video lecture satisfaction models.

• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.676-683
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• 2021

Wi-Fi Round-trip timing (RTT) based location estimation technology estimates the distance between the user and the AP based on the transmission and reception time of the signal. This is because reception instability and signal distortion are greater than that of a Received Signal Strength Indicator (RSSI) based fingerprint in an indoor NLOS environment, resulting in a large position error due to multipath fading. To solve this problem, in this paper, we propose an end-to-end based WiFi Trilateration Net (WTN) that combines neural network-based RTT correction and trilateral positioning network, respectively. The proposed WTN is composed of an RNN-based correction network to improve the RTT distance accuracy and a neural network-based trilateral positioning network for real-time positioning implemented in an end-to-end structure. The proposed network improves learning efficiency by changing the trilateral positioning algorithm, which cannot be learned through differentiation due to mathematical operations, to a neural network. In addition, in order to increase the stability of the TOA based RTT, a correction network is applied in the scanning step to collect reliable distance estimation values from each RTT AP.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.4
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• pp.97-106
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• 2007

According to the 2004 Sloan Consortium Report, distance education is the fastest growing sector of higher education. This study suggests a research model, based on an e-Learning success model, the relationship of the e-learner's self-regulated learning strategy, computer self-efficacy, and system quality perception of the e-Learning environment. As a result, perceived usefulness, perceived ease of use, and service quality effect on e-learner's satisfaction. In addition to, self-regulated learning strategy based on computer self-efficacy is also important variable regarding e-learner's satisfaction.

• The Journal of the Korea Contents Association
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• v.5 no.2
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• pp.53-60
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• 2005

e-learning which is beyond time and space restrictions as a new education tool, has been practically used in many fields in order to support instrument or displacement for traditional education. This research shows that the survey on the significance and the effect of e-learning to teachers who have experience in e-learning through the internet compared with effect of tradition education. Furthermore, we investigate variable factors affecting on effect of e-learning, the correlation of factors and coefficient of correlation between factors associated with effects of e-learning based on the results of our survey.

• The KIPS Transactions:PartD
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• v.18D no.5
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• pp.329-338
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• 2011

Terminology recognition system which is a preceding research for text mining, information extraction, information retrieval, semantic web, and question-answering has been intensively studied in limited range of domains, especially in bio-medical domain. We propose a domain independent terminology recognition system based on machine learning method using dictionary, syntactic features, and Web search results, since the previous works revealed limitation on applying their approaches to general domain because their resources were domain specific. We achieved F-score 80.8 and 6.5% improvement after comparing the proposed approach with the related approach, C-value, which has been widely used and is based on local domain frequencies. In the second experiment with various combinations of unithood features, the method combined with NGD(Normalized Google Distance) showed the best performance of 81.8 on F-score. We applied three machine learning methods such as Logistic regression, C4.5, and SVMs, and got the best score from the decision tree method, C4.5.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.118-133
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• 2020

Research interest in the Mobility-as-a-Service (MaaS) concept for enhancing users' mobility experience is increasing. In particular, dynamic pricing techniques based on reinforcement learning have emerged since adjusting prices based on the demand is expected to help mobility services, such as taxi and car-sharing services, to gain more profit. This paper provides a simulation framework that considers more practical factors, such as demand density per location, preferred prices, the distance between users and drivers, and distance to the destination that critically affect the probability of matching between the users and the mobility service providers (e.g., drivers). The aforementioned new practical features are reflected on a data structure referred to as the Matching Matrix. Using an efficient algorithm of computing the probability of matching between the users and drivers and given a set of precisely identified high-demand locations using HDBSCAN, this study developed a better reward function that can gear the reinforcement learning process towards finding more realistic dynamic pricing policies.