• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.579-586
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• 2003

In this paper, we propose a novel approach to employ emotional features to document retrieval systems. Fine emotional features, such as HAPPY, SAD, ANGRY, FEAR, and DISGUST, have been used to represent Korean document. Users are allowed to use these features for retrieving their documents. Next, retrieved documents are learned by classification methods like cohesion factor, naive Bayesian, and, k-nearest neighbor approaches. In order to combine various approaches, voting method has been used. In addition, k-means clustering has been used for our experimentation. The performance of our approach proved to be better in accuracy than other methods, and be better in short texts rather than large documents.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.27-45
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• 2001

Sequential discovery from time series data has mainly concerned about events or item sets. Recently, the research has stated to applied to the numerical data. An example is sensor information generated by checking a machine state. The numerical data hardly have the same valuers while making patterns. So, it is important to extract suitable number of pattern features, which can be transformed to events or item sets and be applied to sequential pattern mining tasks. The popular methods to extract the patterns are sliding window and clustering. The results of these methods are sensitive to window sine or clustering parameters; that makes users to apply data mining task repeatedly and to interpret the results. This paper suggests the method to retrieve pattern features making numerical data into vector of an angle and a magnitude. The retrieved pattern features using this method make the result easy to understand and sequential patterns finding fast. We define an inclusion relation among pattern features using angles and magnitudes of vectors. Using this relation, we can fad sequential patterns faster than other methods, which use all data by reducing the data size.

• Journal of KIISE:Software and Applications
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• v.36 no.9
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• pp.749-757
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• 2009

A Internet user can easily access to the target information by web searching using some key-words or categories in the present Internet environment. When some meta-data which represent attributes of several data structures well are used, then more accurate result which is matched with the intention of users can be provided. This study proposes a multiple dimensional vector tagging method for the small web user group who interest in maintaining and sharing the bookmark for common interesting topics. The proposed method uses vector tag method for increasing the effect of categorization, management, and retrieval of target information. The vector tag composes with two or more components of the user defined priority. The basic vector space is created time of information and reference value. The calculated vector value shows the usability of information and became the metric of ranking. The ranking accuracy of the proposed method compares with that of a simply link structure, The proposed method shows better results for corresponding the intention of users.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.12 no.1
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• pp.75-82
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• 1999

일반 해양 구조물이나 해저면에 설치되는 해저 관로는 외력에 의한 변형이 발생된다. 구조물 형상이 복잡하거나, 구성 요소의 개수가 많을 경우 응력해석 시 많은 초기값이 필요하고 해석 시간 또는 장 시간 소요된다. 해양 구조물에 작용하는 대표적인 외력은 파도, 조류, 바람이고 이런 외력은 구조물의 사용 기간(operation life)동안 계속적으로 작용하기 때문에 구조물의 변형율은 항상 허용치 안에서 발생되도록 설계되어야 한다. 허용 변형은 탄성범위 내에 존재해야 하며, 비교적 큰 변형을 일으키는 구조물이나 해저파이프라인의 응력해석을 수치적으로 접근하는 방법을 고찰하였다. 평행상태의 하중 벡터값만 직각 좌표계에서 인트린직(intrinsic) 좌료로 변환시킬 때 변형이 발생함으로, 본 논문에서 소개하는 이차 요소(quadratic element)방법을 사용할 경우 수치해석 시 많은 장점이 있다는 것을 보여준다. 본 방법을 도입함으로써 비교적 큰 변형이 발생되는 구조물 해석 시 일반 수치해석 방법과 그 결과는 같으나 해석 시간을 단축시킬 수 있다는 장점이 있다. 응력 해석 시 국부 강도 행열(element stiffness matrix)은 방향과 무관하며 이차요소 방법을 사용하여 각 요소 벡터를 발생시켰다. 해저관로 일점 상승 시 관로에 작용하는 변형과 상승력에 따른 휨 모멘트를 산출하여 일반적으로 사용되는 선형이론과 비교하였다.

• The Transactions of the Korea Information Processing Society
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• v.2 no.4
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• pp.611-618
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• 1995

In the high information societies, the requirement of encryption security is increasing so as to protect information from the threat of attacks by illegal changes of data, illegal leakage of data, disorder of data sequences and the unauthorized sender and an unauthorized receiver etc. In this paper, multivariable knapsack crytosystem is proposed for security of computer communication. This system is securer and simpler than the conventional knapsack cryptosystems. And, proposed cryptosystem composed what represented each element of superincreasing vector with multivar able polynomial after transforming it of ciphervector. For the deciphering of ciphertext, the plaintext is determined by using the integers of secret and the superincreasing vector of secret key. Thus, the stability of this cryptosystem is based on the difficulty of obtaining the root that ciphervector becomes the superincreasing vector, in substituting the integers of secret for ciphervector to represent with the miltivariable polynomial. The propriety of proposed multivariable knapsack cryptosystem was proved through computer simulation.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.4
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• pp.189-194
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• 2006

In this paper, design of MUSIC algorithm, which is one of high resolution DOA (direction of arrival) estimation techniques was studied. Generally the complex-valued correlation matrix of MUSIC algorithm is transformed to unitary matrix or matrix expansion for the real hardware implementation. Using the orthogonality between the noise subspace eigenvectors and the steering vectors corresponding to signal component, we estimate DOA with the real-valued computation between steering vectors and noise subspace eigenvectors. The DOA algorithm was designed with VHDL models with considerations of 2 elements and 1 incident wave and its simulation results are derived.

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.209-216
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• 2009

The accurate estimation of software development cost is important to a successful development in software engineering. This paper presents a software cost estimation method using a support vector machine. Support vector machine is one of the efficient techniques for classification, and it is the classification method of input data based on Maximum-Margin Hyperplane. But SVM has the problem of the selection of optimal parameters, because it is dependent on user's parameters. This paper selects optimized SVM parameters using advanced method, and estimates software development cost. The proposed approach outperform some recent results reported in the literature.

• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.729-736
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• 2009

The accurate estimation of software development cost is important to a successful development in software engineering. Until recent days, the model using regression analysis based on statistical algorithm and machine learning method have been used. However, this paper estimates the software cost using support vector regression, a sort of machine learning technique. Also, it finds the best set of optimized parameters applying genetic algorithm. The proposed GA-SVR model outperform some recent results reported in the literature.

• Convergence Security Journal
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• v.6 no.4
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• pp.83-90
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• 2006

In this paper, we propose a new design method for a robust vector quantizer that is independent of the statistical characteristics of input images in the wavelet transformed image coding. The conventional vector quantizers have failed to get quality coding results because of the different statistical properties between the image to be quantized and the training sequence for a codebook of the vector quantizer. Therefore, in order to solve this problem, we used a pseudo image as a training sequence to generate a codebook of the vector quantizer; the pseudo image is created by adding correlation coefficient and edge components to uniformly distributed random numbers. We will clearly define the problem of the conventional vector quantizers, which use real images as a training sequence to generate a codebook used, by comparing the conventional methods with the proposed through computer simulation. Also, we will show the proposed vector quantizer yields better coding results.

• Communications of Mathematical Education
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• v.37 no.3
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• pp.443-465
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• 2023

This study examines the content of vectors and matrices in Artificial Intelligence Mathematics textbooks (AIMTs) from the 2015 revised mathematics curriculum. We analyzed the implementation of foundational mathematical concepts, specifically definitions and related sub-concepts of vectors and matrices, in these textbooks, given their importance for understanding AI. The findings reveal significant variations in the presentation of vector-related concepts, definitions, sub-concepts, and levels of contextual information and descriptions such as vector size, distance between vectors, and mathematical interpretation. While there are few discrepancies in the presentation of fundamental matrix concepts, differences emerge in the subtypes of matrices used and the matrix operations applied in image data processing across textbooks. There is also variation in how textbooks emphasize the interconnectedness of mathematics for explaining vector-related concepts versus the textbooks place more emphasis on AI-related knowledge than on mathematical concepts and principles. The implications for future curriculum development and textbook design are discussed, providing insights into improving AI mathematics education.