• Journal of Internet Computing and Services
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• v.10 no.2
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• pp.171-177
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• 2009

Optical packet switching(OPS) is a strong candidate for the next-generation internet, since it has a fine switching granularity at the packet level for providing flexible bandwidth, and provides seamless integration between WDM layer and IP layer. Optical packet switching have been studied in two categories: OPS in synchronous and OPS in asynchronous networks. In this article we are focused on contention resolution of OPS in asynchronous networks. The hybrid buffer have been addressed, to reduce packet loss further as one of the alternative buffer structures for contention resolution of asynchronous and variable length packets, which consists of the FDL buffer and the electronic buffer. The OPS design issue for the limited number of TWCs and internal wavelengths is important in the aspect of switch cost and resource efficiency. Therefore, an hybrid buffer structured optical packet switch and its scheduling algorithm is presented for considering the limited number of TWCs and internal wavelengths, for contention resolution of asynchronous and variable length packets. The proposed algorithm could lead to the packet loss improvement compared to the legacy LAUC-VF algorithm with only the FDL buffer.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.5
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• pp.427-435
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• 2005

With the accumulation of protein and its related data on the Internet, many domain based computational techniques to predict protein interactions have been developed. However, most of the techniques still have many limitations to be used in real fields. They usually suffer from a low accuracy problem in prediction and do not provide any interaction possibility ranking method for multiple protein pairs. In this paper, we reevaluate a domain combination based protein interaction prediction method and develop an interaction possibility ranking method for multiple protein pairs. Probability equations are devised and proposed in the framework of domain combination based protein interaction prediction method. Using the ranking method, one can discern which protein pair is more probable to interact with each other than other protein pairs in multiple protein pairs. In the validation of the ranking method, we revealed that there exist some correlations between the interacting probability and the precision of the prediction in case of the protein pair group having the matching PIP(Primary Interaction Probability) values in the interacting or non interacting PIP distributions.

• Journal of Legislation Research
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• no.53
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• pp.269-310
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• 2017

This article aims at the study on Improvement of legal System which is related to automated vehicles in the era of the 4th industrial revolution. Legal aspects of driving automation have two view points. One is to permit a automated vehicle, the other is to regulate the behavior of driver on the road. Signifying elements of the 4th industrial revolution are IoT, AI, big data, cloud computing etc. Automated vehicles are the imbodiment of those new ICT technologies. The vehicle management act(VMA) rules about vehicle registration and approval of vehicle types. VMA defines a automated vehicle as a vehicle which can be self driven without handling of driver or passenger. Vehicle makers can take temporary driving permission for testing and research the driving automation. Current definition of automated vehicle of VMA is not enough for including all levels of SAE driving automation. In the VMA must be made also a new vehicle safty standard for automated vehicle. In the national assembly is curruntly pending three draft bills about legislation of artificial intelligence. Driving automation and AI technologies must be parallel developed. It is highly expected that more proceeding research of driving automation can be realized as soon as possible.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.469-481
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• 2018

DL (Deep Learning) is getting popular in various fields to implement artificial intelligence that resembles human learning and cognition. DL based on complicate structure of the ANN (Artificial Neural Network) requires computing power and computation cost. Variety of DL models with improved performance have been developed with powerful computer specification. The main purpose of this paper is to detect buildings from aerial images and evaluate performance of Mask R-CNN (Region-based Convolutional Neural Network) developed by FAIR (Facebook AI Research) team recently. Mask R-CNN is a R-CNN that is evaluated to be one of the best ANN models in terms of performance for semantic segmentation with pixel-level accuracy. The performance of the DL models is determined by training ability as well as architecture of the ANN. In this paper, we characteristics of the Mask R-CNN with various types of the images and evaluate possibility of the generalization which is the ultimate goal of the DL. As for future study, it is expected that reliability and generalization of DL will be improved by using a variety of spatial information data for training of the DL models.

• Journal of the Korea Computer Graphics Society
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• v.27 no.5
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• pp.1-11
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• 2021

The purpose of our research is to efficiently deform a 3D models which is composed of a triangular mesh and a skeleton. We designed a novel inverse kinematics (IK) solver that calculates the updated positions of mesh vertices with fewer computing operations. Through our user interface, one or more markers are selected on the surface of the model and their target positions are set, then the system updates the positions of surface vertices to construct a deformed model. The IK solving process for updating vertex positions includes many computations for obtaining transformations of the markers, their affecting joints, and their parent joints. Many of these computations are often redundant. We precompute those redundant terms in advance so that the 3-nested loop computation structure was improved to a 2-nested loop structure, and thus the computation time for a deformation is greatly reduced. This novel IK solver can be adopted for efficient performance in various research fields, such as handling 3D models implemented by LBS method, or object tracking without any markers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1423-1431
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• 2022

Volatility is one of the variables that the Black-Scholes model requires for option pricing. It is an unknown variable at the present time, however, since the option price can be observed in the market, implied volatility can be derived from the price of an option at any given point in time and can represent the market's expectation of future volatility. Although volatility in the Black-Scholes model is constant, when calculating implied volatility, it is common to observe a volatility smile which shows that the implied volatility is different depending on the strike prices. We implement supervised learning to target implied volatility by adding V-KOSPI to ease volatility smile. We examine the estimation performance of KOSPI200 index options' implied volatility using various Machine Learning algorithms such as Linear Regression, Tree, Support Vector Machine, KNN and Deep Neural Network. The training accuracy was the highest(99.9%) in Decision Tree model and test accuracy was the highest(96.9%) in Random Forest model.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.107-114
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• 2022

In this paper, availabilities of student-evaluations of team activities in the computer science basic classes were analysed. For the purpose, correlation analysis was conducted to investigate the relationships among peer-evaluation, self-evaluation, and academic achievement, and it was found that there was a statistically significant positive correlation among them. Moreover, the gap between peer-evaluation scores and self-evaluation scores was analyzed. When a one-sample t-test was performed, it was found that the gap was very significant. However, the size of the gap was not different between the two classes. That is, regardless of grade level, the students' self-evaluation scores tended to be on average higher than the evaluation scores received from peers. Finally, when analyzing the relationship between the gap in peer-evaluation and self-evaluation scores and academic achievement, there was no significant correlation between the gap in scores and academic achievement. In other words, there was no difference in the tendency of evaluation for students with high or low academic achievement. The results of the analysis shows the availability of student-evaluations of team activities in the evaluation of team-based instruction. The high correlation between self-evaluation and peer-evaluation indicates the objectivity of student-evaluation. Although it is clear that the self-evaluation score is higher on average than the score received from peers, it is more useful in terms of objectivity because it does not vary according to grade, subject, or academic achievement.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.1
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• pp.88-95
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• 2002

In recent years, the security of hardware and software systems is one of the most essential factor of our safe network community. As elliptic Curve Cryptosystems proposed by N. Koblitz and V. Miller independently in 1985, require fewer bits for the same security as the existing cryptosystems, for example RSA, there is a net reduction in cost size, and time. In this thesis, we propose an efficient hardware architecture of underlying field arithmetic processor for Elliptic Curve Cryptosystems, and a very useful method for implementing the architecture, especially multiplicative inverse operator over GF$GF (2^m)$ onto FPGA and futhermore VLSI, where the method is based on optimized unit operation components. We optimize the arithmetic processor for speed so that it has a resonable number of gates to implement. The proposed architecture could be applied to any finite field $F_{2m}$. According to the simulation result, though the number of gates are increased by a factor of 8.8, the multiplication speed We optimize the arithmetic processor for speed so that it has a resonable number of gates to implement. The proposed architecture could be applied to any finite field $F_{2m}$. According to the simulation result, though the number of gates are increased by a factor of 8.8, the multiplication speed and inversion speed has been improved 150 times, 480 times respectively compared with the thesis presented by Sarwono Sutikno et al. [7]. The designed underlying arithmetic processor can be also applied for implementing other crypto-processor and various finite field applications.

• Journal of Korea Water Resources Association
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• v.43 no.1
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• pp.81-94
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• 2010

Input variable selection is one of the various techniques for improving the performance of artificial neural network. In this study, mutual information is applied for input variable selection technique instead of correlation coefficient that is widely used. Among 152 variables of RDAPS (Regional Data Assimilation and Prediction System) output results, input variables for artificial neural network are chosen by computing mutual information between rainfall records and RDAPS' variables. At first the rainfall forecast variable of RDAPS result, namely APCP, is included as input variable and the other input variables are selected according to the rank of mutual information and correlation coefficient. The input variables using mutual information are usually those variables about wind velocity such as D300, U925, etc. Several statistical error estimates show that the result from mutual information is generally more accurate than those from the previous research and correlation coefficient. In addition, the artificial neural network using input variables computed by mutual information can effectively reduce the relative errors corresponding to the high rainfall events.

• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.379-386
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• 2004

Video segmentation is a fundamental task in video indexing and it includes two kinds of shot change detections such as the abrupt transition and the gradual transition. The abrupt shot boundaries are detected by computing the image-based distance between adjacent frames and comparing this distance with a pre-determined threshold value. However, the gradual shot boundaries are difficult to detect with this approach. To overcome this difficulty, we propose the method that detects gradual transition in the MPEG compressed video using the HMM (Hidden Markov Model). We take two different HMMs such as a discrete HMM and a continuous HMM with a Gaussian mixture model. As image features for HMM's observations, we use two distinct features such as the difference of histogram of DC images between two adjacent frames and the difference of each individual macroblock's deviations at the corresponding macroblock's between two adjacent frames, where deviation means an arithmetic difference of each macroblock's DC value from the mean of DC values in the given frame. Furthermore, we obtain the DC sequences of P and B frame by the first order approximation for a fast and effective computation. Experiment results show that we obtain the best detection and classification performance of gradual transitions when a continuous HMM with one Gaussian model is taken and two image features are used together.