• Journal of Korea Multimedia Society
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• v.12 no.8
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• pp.1155-1163
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• 2009

This paper proposes an automatic facial expression generation system of vector graphic character using gaussian process model. Proposed method extracts the main feature vectors from twenty-six facial data of character redefined based on Russell's internal emotion state. Also by using new gaussian process model, SGPLVM, we find low-dimensional feature data from extracted high-dimensional feature vectors, and learn probability distribution function (PDF). All parameters of PDF are estimated by maximization the likelihood of learned expression data, and these are used to select wanted facial expressions on two-dimensional space in real time. As a result of simulation, we confirm that proposed facial expression generation tool is working in the small facial expression datasets and can generate various facial expressions without prior knowledge about relation between facial expression and emotion.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1193-1201
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• 2008

In this paper, we propose a automated techniques for generating the gait animation of humanoid character model required in game applications. The proposed method can generate motion data of various styles with intuitive manipulation, and techniques for editing motion data are also proposed. In addition, we introduce an automated tool for gait animation of character model developed with the proposed method to verify the effectiveness of our method. The proposed method tan be successfully employed in motion authoring tools. In this paper, only the gait animation was considered. However, the proposed method can be applied to various motions and can be utilized as a motion engine that automatically generates proper motion according to the state of the character. The motion engine can express various behavior of the character without requiring motion data base which is usually employed in 3D online games.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.197-206
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• 2012

A exisiting recommedation system using association rules has the problem, such as delay of processing speed from a cause of frequent scanning a large data, scalability and accuracy as well. In this paper, using a Implicit method which is not used user's profile for rating, we propose the personalized recommendation system which is a new method using the FP-tree mining based on RFM. It is necessary for us to keep the analysis of RFM method and FP-tree mining to be able to reflect attributes of customers and items based on the whole customers' data and purchased data in order to find the items with high purchasability. The proposed makes frequent items and creates association rule by using the FP-tree mining based on RFM without occurrence of candidate set. We can recommend the items with efficiency, are used to generate the recommendable item according to the basic threshold for association rules with support, confidence and lift. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.12
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• pp.1641-1647
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• 2018

In this paper, we describe a portable potentiostat which is capable of cyclic voltammetry(CV) and amperometry for electrochemical dissolved oxygen sensor. In addition, this portable potentiostat can also transmit the measured data wirelessly to android devices such as smart phone, tablet, etc. through Bluetooth. The potentiostat system consists of three parts; a voltage generator circuit which is controlled by Arduino nano and 12-bit DAC(digital to analog converter) to generate necessary electric potential for operating the electrochemical sensor, an oxidation/reduction current measurement circuit, and a Bluetooth module to transmit data wirelessly to an android device. Once measurements are carried out with the android application, the measured data is transmitted to the android device via Bluetooth and displayed using the android app. in real time. In this paper, we report the measured reduction current with a fabricated dissolved oxygen sensor in both saturated-oxygen state and zero-oxygen states. The results of the developed portable potentiostat system are in good agreement with those of the commercial portable potentiostat (${\mu}stat200$, Dropsens inc.). The measured peak reduction currents using the developed potentiostat and the commercial ${\mu}stat200$ potentiostat were $-0.755{\mu}A$ and $-0.724{\mu}A$, respectively. The reduction currents measured at zero-oxygen state were $-0.005{\mu}A$ and $-0.004{\mu}A$. The discrepancy between those two systems seems very small, which implies successful development of a portable and wireless potentionstat.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.99-109
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• 2019

Data Envelopment Analysis (DEA), a non-parametric productivity analysis tool, has become an accepted approach for assessing efficiency in a wide range of fields. Despite of its extensive applications, some features of DEA remain bothersome. For example DEA is good at estimating "relative" efficiency of a DMU(Decision Making Unit), it only tells us how well we are doing compared with our peers but not compared with a "theoretical maximum." Thus, in order to measure efficiency of a new DMU, we have to develop entirely new DEA with the data of previously used DMUs. Also we cannot predict the efficiency level of the new DMU without another DEA analysis. We aim to show that DEA can be used to evaluate the efficiency of ports and suggest the methodology which overcomes the limitation of DEA through hybrid analysis utilizing DEA along with C5.0. We can generate classification rules C5.0 in order to classify any new Port without perturbing previously existing evaluation structures by proposed methodology.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.19-26
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• 2019

In this paper, the classification performance of learning algorithms is compared for TAB digit recognition. The TAB digits that are segmented from TAB musical notes contain TAB lines and musical symbols. The labeling method and non-linear filter are designed and applied to extract fret digits only. The shift operation of the 4 directions is applied to generate more data. The selected models are Bayesian classifier, support vector machine, prototype based learning, multi-layer perceptron, and convolutional neural network. The result shows that the mean accuracy of the Bayesian classifier is about 85.0% while that of the others reaches more than 99.0%. In addition, the convolutional neural network outperforms the others in terms of generalization and the step of the data preprocessing.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.3
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• pp.23-29
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• 2019

Conventional crack detection methods have a problem of consuming a lot of labor, time and cost. To solve these problems, an automatic detection system is needed to detect cracks in images obtained by using vehicles or UAVs(unmanned aerial vehicles). In this paper, we have studied road crack detection with unmanned aerial photographs. Aerial images are generated through preprocessing and labeling to generate morphological information data sets of cracks. The generated data set was applied to the mask R-CNN model to obtain a new model in which various crack information was learned. Experimental results show that the cracks in the proposed aerial image were detected with an accuracy of 73.5% and some of them were predicted in a certain type of crack region.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1239-1249
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• 2018

Optical remote sensing sensors provide visually more familiar images than radar images. However, it is difficult to discriminate sea ice types in optical images using spectral information based machine learning algorithms. This study addresses two topics. First, we propose a semantic segmentation which is a part of the state-of-the-art deep learning algorithms to identify ice types by learning hierarchical and spatial features of sea ice. Second, we propose a new approach by combining of semi-supervised and active learning to obtain accurate and meaningful labels from unlabeled or unseen images to improve the performance of supervised classification for multiple images. Therefore, we successfully added new labels from unlabeled data to automatically update the semantic segmentation model. This should be noted that an operational system to generate ice type products from optical remote sensing data may be possible in the near future.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.8
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• pp.331-342
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• 2019

This paper proposes a method to generate test cases and test scripts from software requirements written in a controlled natural language, which helps develop reliable embedded software. In the proposed method, natural language requirements are written in a controlled language, the requirements are parsed and then inputs, outputs and operator data are extracted from the requirements. Test cases are generated from the extracted data following test case generation strategies such as decision coverage, condition coverage or modified condition/decision coverage. And then the test scripts, physical inputs of the test cases are generated with help of the test command dictionary. With the proposed method, it becomes possible to directly check whether software properly satisfies the requirements. Effectiveness of the proposed method is verified empirically with an requirement set.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.49-63
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• 2019

Crowdedness management at subway platforms is essential to improve services, including the prevention of train delays and ensuring passenger safety. Establishing effective crowdedness mitigation measures for platforms requires accurate estimation of the congestion level. There are temporal and spatial constraints since crowdedness on subway platforms is assessed at certain locations every 1-2 years by hand counting. However, smart cards generate real-time big data 24 hours a day and could be used in estimating congestion. This study proposes a model based on data from transit cards to estimate crowdedness dynamically. Crowdedness was defined as demand, which can be translated into passengers dynamically moving along a subway network. The trajectory of an individual passenger can be identified through this model. Passenger flow that concentrates or disperses at a platform is also calculated every minute. Lastly, the platform congestion level is estimated based on effective waiting areas for each platform structure.