• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5A
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• pp.479-486
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• 2008

In this paper, an efficient training symbol design with m-sequence is proposed for the MIMO-OFDM based next generation wireless transmission system which supports gigabits per second data rate. In the traditional blute force method, the preamble design is based on the case by case comparison with the system requirements. This paper discusses a training symbol design methodology for the MIMO-OFDM system based on the m-sequence which has been widely used in the spread spectrum communication areas due to its good correlation characteristics. Also the step-by-step design and performance verification method within the limited search space is discussed. The proposed method targets the design of the training symbol which satisfies system requirements for the packet based MIMO-OFDM wireless communication system including automatic gain control(AGC), timing synchronization, frequency and sampling offset estimation, and MIMO channel estimation.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.15-21
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• 2019

The fourth industrial revolution is noted. It is a smarter factory. At present, research on CNC (Computerized Numeric Controller) is actively underway in the manufacturing field. Domestic CNC equipment, acoustic sensors, vibration sensors, etc. This study can improve efficiency through CNC. Collect various data such as X-axis, Y-axis, Z-axis force, moving speed. Data exploration of the characteristics of the collected data. You can use your data as Random Forest (RF), Extreme Gradient Boost (XGB), and Support Vector Machine (SVM). The result of this study is CNC equipment.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.71-78
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• 2019

This study aims to identify good borrowers within the context of P2P lending. P2P lending is a growing platform that allows individuals to lend and borrow money from each other. Inherent in any loans is credit risk of borrowers and needs to be considered before any lending. Specifically in the context of P2P lending, traditional models fall short and thus this study aimed to rectify this as well as explore the problem of class imbalances seen within credit risk data sets. This study implemented an over-sampling technique known as Synthetic Minority Over-sampling Technique (SMOTE). To test our approach, we implemented five benchmarking classifiers such as support vector machines, logistic regression, k-nearest neighbor, random forest, and deep neural network. The data sample used was retrieved from the publicly available LendingClub dataset. The proposed SMOTE revealed significantly improved results in comparison with the benchmarking classifiers. These results should help actors engaged within P2P lending to make better informed decisions when selecting potential borrowers eliminating the higher risks present in P2P lending.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.4
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• pp.89-108
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• 2010

Recently many mutual authentication protocol for light-weight hash-based for RFID have been proposed. Most of them have assumed that communications between a backend server and reader are secure, and not considered threats for backend server and RFID reader impersonation. In the real world, however, attacks against database or reader are more effective rather than attacks against RFID tag, at least from attacker's perspective. In this paper, we assume that all communications are not secure to attackers except the physical attack, and considering realistic threats for designing a mutual authentication protocol based on hash function. And It supports a mutual authentication and can protect against the replay attack, impersonation attack, location tracking attack, and denial of service attack in the related work. We besides provide a secure and efficient RFID mutual authentication protocol which resists impersonation attacks on all of the entities and alow a backend server to search tag-related information efficiently. We conclude with analyzing the safety and efficiency among latest works.

• Journal of The Korean Association For Science Education
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• v.41 no.3
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• pp.203-220
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• 2021

We explored the performance improvement method of automated scoring for scientific argumentation. We analyzed the pattern of argumentation using automated scoring models. For this purpose, we assessed the level of argumentation for student's scientific discourses in classrooms. The dataset consists of four units of argumentation features and argumentation levels for episodes. We utilized argumentation clusters and n-gram to enhance automated scoring accuracy. We used the three supervised learning algorithms resulting in 33 automatic scoring models. As a result of automated scoring, we got a good scoring accuracy of 77.59% on average and up to 85.37%. In this process, we found that argumentation cluster patterns could enhance automated scoring performance accuracy. Then, we analyzed argumentation patterns using the model of decision tree and random forest. Our results were consistent with the previous research in which justification in coordination with claim and evidence determines scientific argumentation quality. Our research method suggests a novel approach for analyzing the quality of scientific argumentation in classrooms.

• Industry Promotion Research
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• v.7 no.1
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• pp.1-8
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• 2022

We investigated various types of machine learning methods that can be applied to censored data. Exploratory data analysis reveals the distribution of each feature, relationships among features. Next, classification problem has been set up where the dependent variable is death_event while the rest of the features are independent variables. After applying various machine learning methods to the data, it has been found that just like many other reports from the artificial intelligence arena random forest performs better than logistic regression. But recently well performed artificial neural network and gradient boost do not perform as expected due to the lack of data. Finally Kaplan-Meier and Cox proportional hazard model have been employed to explore the relationship of the dependent variable (t_i, δ_i) with the independent variables. Also random forest which is used in machine learning has been applied to the survival analysis with censored data.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.823-832
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• 2020

The analgesic effects of music in people with glasses on perceived pain through cold-pressor task (CPT) is assessed based on three-sequence, three-period, crossover trial with three treatment conditions(music-listening, news-listening, and no-sound) to each subject. Fifty subjects are divided into three sequence groups by randomization, and CPTs under the pre-assigned treatment conditions at each period are performed. Pain responses after each CPT, subjects' pain tolerance (PT) in time scale and pain intensity (PI) and pain unpleasantness (PU) in visual analog scale (VAS) are measured. After classifying the group by whether or not to wear glasses, which is the phenotype of the myopia gene, pain responses are compared by F-tests and Tukey's multiple comparisons. CPT pain responses in group with glasses during the music intervention are significantly different from responses during the news intervention and the control conditions, respectively. This study investigates the pain responses of music intervention in the group wearing glasses, which can be seen as a phenotype of the nearsighted gene, and this result would play a role in explaining the biopsychosocial model of the pain mechanism.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.04a
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• pp.265-274
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• 1993

정보화 사회에서 대량으로 생산된 데이타 코드들은 일관된 설계 원칙없이 필요할 때마다 만들어 사용함으로써 정보의 중복 저장 및 정보교환에 있어서의 변환 작업등으로 인한 경비의 소요가 상당한 실정이다. 이러한 문제점에 대한 해결책으로 본 논문에서는 데이타코드 설계자가 일관성있게 데이타코드를 생성할 수 있도록 도와주는 데이타 코드 생성 지원 전문가 시스템의 설계에 관하여 연구하였다. 불완전 영역 설계를 위한 지식 획득과 표현에 적합한 전문가 시스템 쉘인 GUESS(Guideline Underlying Expert system Shell)를 설계하였다. GUESS는 전문가 시스템을 설계 지원 도구로 사용하는 사용자에게 기존에 작성된 적절한 설계 용례를 선택의 기준으로 제공하며, 유연성 있는 작업 지침들을 규칙으로 포함하고 있다. GUESS는 Prolog언어를 기반으로 한 추론기관과 설계지침을 포함하는 정적지식, 외부 데이타베이스를 연결한 동적 정보, 설계 세부방법을 담고 있는 부가도구들로 구성된다. GUESS/DCG는 데이타 코드 생성을 지원하기 위하여 데이타 코드의 유형과 선택기준 및 설계원리를 정적지식으로 가지며, 이를 경험적으로 탐색하는 추론 기관 및 사용자인 데이타 코드 설계자와 적절한 대화식 접근을 가능하게 하는 설명부분과 대화 인터페이스를 GUESS를 바탕으로 구현한 것이다. 특히 동적 정보의 적절한 이용과 데이타 코드의 통합된 저장, 일관성 있는 운영을 보장하기 위하여 개발중인 데이타 코드 관리시스템과의 인터페이스 부분을 추가하여 기존에 운영되고 있는 데이타 코드의 참고와 호환성, 확장성을 유지하였다. 이 시스템은 데이타 코드 관리시스템에 일관된 생성 수단을 제공하는것 외에도, 각 기관에서 대량으로 작성되는 데이타 코드를 유지, 보수하는 작업에도 큰 기여를 할 것이다.지의 선택작업이 행해지는 경우에 촛점을 맞추었다. 그리하여 다작업장의 휴리스틱에 의거한 작업순서 결정을 위해 우선 BB의 상한을 구하는 연구를 행했다. 이를 위해 우선 단일작업장에서 야기될 수 있는 모든 상황을 고려한 최적 작업순서 결정규칙을 연구했으며, 이의 증명을 위해 이 규칙에 의거했을 때의 보완작업량이 최소가 된다는 것을 밝혔다. 보완작업 계산의 효율성을 제고하기 위해 과부하(violation)개념을 도입하였으며, 작업유형이 증가된 상황에서도 과부하 개념이 보완작업량을 충분히 반영할 수 있음을 밝혔다. 본 연구에서 제시한 최적 작업순서 규칙에 의거했을 때 야기될 수 있는 여러가지 경우의 과부하를 모두 계산했다. 앞에서 개발된 단일작업량의 최적 작업순서 결정규칙을 이용하여 다작업장의 문제를 실험했다. 이 문제는 규모가 매우 크므로 Branch & Bound를 이용하였으며, 각 가지에서 과부하량이 최적인 경우만을 고려하는 휴리스틱을 택하여 실험자료를 이용하여 여러 회 반복실험을 행했다. 그리고 본 연구의 성과를 측정하기 위해 휴리스틱 기법시 소요되는 평균 CPU time 범위에서, 랜덤 작업순서에 따른 작업할당을 반복실험하여 이중 가장 좋은 해와 비교했다. 그러나 앞으로 다작업장 문제를 다룰 때, 각 작업장 작업순서들의 상관관계를 고려하여 보다 개선된 해를 구하기 위한 연구가 요구된다. 또한, 준비작업비용을 발생시키는 작업장의 작업순서결정에 대해서도 연구를 행하여, 보완작업비용과 준비비용을 고려한 GMMAL 작업순서문제를 해결하기 위한 연구가 수행되어야 할 것이다.로 이루어 져야 할 것이다.태를 보다 효율적으로 증진시킬 수 있는 대안이 마련되어져야 한다고 사료된다.$\ulcorner$순응$\lrcorner$의 범위를 벗어나지 않는다. 그렇기 때문에도

• Journal of the Korea Society of Computer and Information
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• v.17 no.4
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• pp.139-145
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• 2012

Generally, Quicksort selects the pivot from leftmost, rightmost, middle, or random location in the array. This paper suggests Quicksort using middle range pivot $P_0$ and continually divides into 2. This method searches the minimum value $L$ and maximum value $H$ in the length n of list $A$. Then compute the initial pivot key $P_0=(H+L)/2$ and swaps $a[i]{\geq}P_0$,$a[j]<P_0$ until $i$=$j$ or $i$>$j$. After the swap, the length of list $A_0$ separates in two lists $a[1]{\leq}A_1{\leq}a[j]$ and $a[i]{\leq}A_2{\leq}a[n]$ and the pivot values are selected by $P_1=P_0/2$, $P_2=P_0+P_1$. This process repeated until the length of partial list is two. At the length of list is two and $a$[1]>$a$[2], swaps as $a[1]{\leftrightarrow}a[2]$. This method is simpler pivot key process than Quicksort and improved the worst-case computational complexity $O(n^2)$ to $O(n{\log}n)$.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.211-218
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• 2015

This paper introduces a 'divide-and-conquer' algorithm to the travelling salesman problem (TSP). Top 10n are selected beforehand from a pool of n(n-1) data which are sorted in the ascending order of each vertex's distance. The proposed algorithm then firstly selects partial paths that are interconnected with the shortest distance $r_1=d\{v_i,v_j\}$ of each vertex $v_i$ and assigns them as individual regions. For $r_2$, it connects all inter-vertex edges within the region and inter-region edges are connected in accordance with the connection rule. Finally for $r_3$, it connects only inter-region edges until one whole Hamiltonian cycle is constructed. When tested on TSP-1(n=26) and TSP-2(n=42) of real cities and on a randomly constructed TSP-3(n=50) of the Euclidean plane, the algorithm has obtained optimal solutions for the first two and an improved one from that of Valenzuela and Jones for the third. In contrast to the brute-force search algorithm which runs in n!, the proposed algorithm runs at most 10n times, with the time complexity of $O(n^2)$.