• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.6A
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• pp.676-684
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• 2008

In digital video broadcasting-terrestrial (DVB-T), which is the European digital terrestrial television standard, the orthogonal frequency division multiplexing (OFDM) has been adopted for signal transmission. The main reasons using OFDM are to increase the robustness against the frequency selective fading and impulse noise, and to use available bandwidth efficiently. However, channel variation within an OFDM symbol destroys orthogonality between subcarriers, resulting in inter-carrier interference (ICI), which increases an error floor in proportional to maximum Doppler spread. This paper provides an ICI analysis in both time and frequency domains while existing literatures analyze the ICI effects mainly in frequency domain and proposes the algorithms that estimate the channel impulse response and channel variation using least square (LS) algorithm which is the most simple channel estimation technique. And we propose adaptive channel estimation algorithm that estimates the velocity of terminals. The simulation results show that proposed algorithm has similar performance with about 1.5% computational complexity of noise and ICI reduction LS algorithm in low speed environments.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.6
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• pp.521-537
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• 2000

Ultrasonic imaging is the most widely used modality among modern imaging device for medical diagnosis and the system performance has been improved dramatically since early 90's due to the rapid advances in DSP performance and VLSI technology that made it possible to employ more sophisticated algorithms. This paper describes "main stream" digital signal processing functions along with the associated implementation considerations in modern medical ultrasound imaging systems. Topics covered include signal processing methods for resolution improvement, ultrasound imaging system architectures, roles and necessity of the applications of DSP and VLSI technology in the development of the medical ultrasound imaging systems, and array signal processing techniques for ultrasound focusing.

• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.56-68
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• 2022

Recently, as the demand for real-time processing increases, studies on a dynamic graph that changes over time has been actively done. There is a connected components processing algorithm as one of the algorithms for analyzing dynamic graphs. GPUs are suitable for large-scale graph calculations due to their high memory bandwidth and computational performance. However, when computing the connected components of a dynamic graph using the GPU, frequent data exchange occurs between the CPU and the GPU during real graph processing due to the limited memory of the GPU. The proposed scheme utilizes the Weighted-Quick-Union algorithm to process large-scale graphs on the GPU. It supports fast connected components computation by applying the size to the connected component label. It computes the connected component by determining the parts to be recalculated and minimizing the data to be transmitted to the GPU. In addition, we propose a processing structure in which the GPU and the CPU execute asynchronously to reduce the data transfer time between GPU and CPU. We show the excellence of the proposed scheme through performance evaluation using real dataset.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.555-564
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• 2022

Recently, cyberattacks are using hacking techniques utilizing intelligent and advanced malicious codes for non-face-to-face environments such as telecommuting, telemedicine, and automatic industrial facilities, and the damage is increasing. Traditional information protection systems, such as anti-virus, are a method of detecting known malicious URLs based on signature patterns, so unknown malicious URLs cannot be detected. In addition, the conventional static analysis-based malicious URL detection method is vulnerable to dynamic loading and cryptographic attacks. This study proposes a technique for efficiently detecting malicious URLs by dynamically learning malicious URL data. In the proposed detection technique, malicious codes are classified using machine learning-based feature selection algorithms, and the accuracy is improved by removing obfuscation elements after preprocessing using Weighted Euclidean Distance(WED). According to the experimental results, the proposed machine learning-based malicious URL detection technique shows an accuracy of 89.17%, which is improved by 2.82% compared to the conventional method.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.478-481
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• 2010

본 논문에서는 경제학, 사회학, 수학 분야에서 수십년 전부터 연구해오던 죄수의 딜레마 게임의 협동진화에 대해 고찰해보고자 한다. 반복적 죄수의 딜레마 게임은 게임이론의 가장 기본적인 이론으로써, 사회적 상호작용, 경제활동, 국제관계 등 다양한 현상들을 모델링 하기 위한 하나의 방법이다. 그 중에 N명이 참가하는 반복적 죄수 딜레마 게임의 전략은 유전 알고리즘(Genetic Algorithms, GAs)을 통해 진화적으로 만들어 낼 수 있으며, 이 경우에 그 결과를 일반적인 내쉬 균형 이 아닌, 모든 개체들이 유전알고리즘을 통해 협동으로 수렴하도록 유도할 수 있다는 사실은 상당히 시사하는 바가 크다. 기존에 주로 연구되어오던 죄수의 딜레마 게임은 협동으로의 수렴과정에서 일반적으로 순위기반선택(Rank-based selection)과 1점 교배기법(1point crossover)을 사용한다. 그러나 순위기반선택은 모든 개체에 순위을 매겨야 하기 때문에, 개체수가 커질수록 성능이 저하되며, 1점 교배기법은 개체 값이 분산되어있을 경우, 최적해(Optimal solution)을 찾기 힘들다는 단점이 있어, 개체수가 많은 경우에 적용하기에는 비효율적이다. 본 논문에서는 토너먼트 선택기법(Tournament selection)과 자기 적응형 교배기법(Self-adaptive crossover)을 적용한 새로운 기법을 제안한다. 또한 기존 기법과 비교 실험을 통해 제안기법이 기존기법에 비해 평균 수렴시간과 수렴 횟수에서 뛰어난 성능을 보이고 있음을 확인하였다.

• Education of Primary School Mathematics
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• v.27 no.1
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• pp.57-74
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• 2024

This paper analyzed the programming education specialized lessons presented in two types of elementary school mathematics textbooks according to the revised Japanese curriculum in 2017. First, this paper presented in detail how each activity is connected to Korean mathematics areas, what elements of mathematics can be learned through programming education, how each activity is structured, and how the actual programming according to the textbook activities is structured. In Japanese textbooks, geometry and measurement areas were presented the most among Korean mathematics content areas, and mathematical elements such as sequences, rules, and algorithms were most implemented for learning. Digital learning tools that make up actual programming present more elements than those presented in the textbooks and are presented in great detail so that students can do actual programming. Lastly, in blocks, motion, control, and calculation blocks were used a lot. Based on these research results, this study provides implications when conducting programming-related education in Korea.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• The KIPS Transactions:PartD
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• v.17D no.4
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• pp.253-258
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• 2010

Traditional frequent pattern mining considers equal profit/weight value of every item. Weighted Frequent Pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery by considering different weights for different items. Existing algorithms in this area are based on fixed weight. But in our real world scenarios the price/weight/importance of a pattern may vary frequently due to some unavoidable situations. Tracking these dynamic changes is very necessary in different application area such as retail market basket data analysis and web click stream management. In this paper, we propose a novel concept of dynamic weight and an algorithm DWFPM (dynamic weighted frequent pattern mining). Our algorithm can handle the situation where price/weight of a pattern may vary dynamically. It scans the database exactly once and also eligible for real time data processing. To our knowledge, this is the first research work to mine weighted frequent patterns using dynamic weights. Extensive performance analyses show that our algorithm is very efficient and scalable for WFP mining using dynamic weights.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1392-1400
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• 2011

In most digital imaging applications, high-resolution images or videos are usually desired for later image processing and analysis. The image signal obtained from general imaging system occurs image degradation during the process of image acquirement caused by the optics, physical constraints and the atmosphere effects. Super-resolution reconstruction, one of the solution to address this problem, is image reconstruction technique that produces a high-resolution image from several low-resolution frames in video sequences. In this paper, we propose an improved super-resolution method using Projection onto Convex Sets (POCS) method based on Shift & Add (S&A). The image using conventional algorithms is sensitive to noise. To solve this problem, we propose a fusion algorithm of S&A and POCS. Also we solve the problem using BLPF (Butterworth Low-pass Filter) in frequency domain as optical blur. Our method is robust to noise and has sharpness enhancement ability. Experimental results show that the proposed super-resolution method has better resolution enhancement performance than other super-resolution methods.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.345-350
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• 2018

This study is an algorithm for detecting vein pattern features important for finger vein recognition. The feature detection algorithm is important because it greatly affects recognition results in pattern recognition. The recognition rate is degraded because the reference is changed according to the finger position change. In addition, the image obtained by irradiating the finger with infrared light is difficult to separate the image background and the blood vessel pattern, and the detection time is increased because the image preprocessing process is performed. For this purpose, the presented algorithm can be performed without image preprocessing, and the detection time can be reduced. SWDA (Down Slope Trace Waveform) algorithm is applied to the finger vein images to detect the fingertip position and vein pattern. Because of the low infrared transmittance, relatively dark vein images can be detected with minimal detection error. In addition, the fingertip position can be used as a reference in the classification stage to compensate the decrease in the recognition rate. If we apply algorithms proposed to various recognition fields such as palm and wrist, it is expected that it will contribute to improvement of biometric feature detection accuracy and reduction of recognition performance time.