• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2017년도 춘계학술발표대회
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• pp.64-67
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• 2017

Applying concurrent structure and parallel processing are a common issue for these day's programs. In this research, Dynamic Programming is used to compare the parallel performance of Go language and Intel C++ Thread Building Blocks. The experiment was performed on 4 core machine and its result contains execution time under Simultaneous Multi-Threading environment. Static Optimal Binary Search Tree was used as an example. From the result, the speed-up of Go was higher than the number of cores, and that of TBB was close to it. TBB performed better in general, but for larger scale, Go was partially faster than the other.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2003년도 추계학술대회 및 정기총회
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• pp.93-96
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• 2003

The generalized knapsack problem, or gknap is the combinatorial optimization problem of optimizing a nonnegative linear functional over the integral hull of the intersection of a polynomially separable 0 - 1 polytope and a knapsack constraint. Among many potential applications, the knapsack, the restricted shortest path, and the restricted spanning tree problem are such examples. We establish some necessary and sufficient conditions for a gknap to admit a fully polynomial approximation scheme, or FPTAS, To do so, we recapture the scaling and approximate binary search techniques in the framework of gknap. This also enables us to find a condition that a gknap does not have an FP-TAS. This condition is more general than the strong NP-hardness.

• 한국경영과학회지
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• 제34권3호
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• pp.29-41
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• 2009

The purpose of this paper is to develop a new classification method for predicting consumer choice based on genetic algorithm, and to validate Its prediction power over existing methods. To serve this purpose, we propose a hybrid model, and discuss Its methodological characteristics in comparison with other existing classification methods. Also, we conduct a series of experiments employing survey data of consumer choices of MP3 players to assess the prediction power of the model. The results show that the suggested model in this paper is statistically superior to the existing methods such as logistic regression model, artificial neural network model and decision tree model in terms of prediction accuracy. The model is also shown to have an advantage of providing several strategic information of practical use for consumer choice.

• Journal of Electrical Engineering and Technology
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• 제3권3호
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• pp.430-435
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• 2008

In the paper, an algorithm called a Constraint algorithm is proposed to solve the fan-in problem occurred in ADC encoding circuits. The Flash ADC architecture uses a double-base number system (DBNS). The DBNS has known to represent the multi-dimensional logarithmic number system (MDLNS) used for implementing the multiplier accumulator architecture of FIR filter in digital signal processing (DSP) applications. The authors use the DBNS with the base 2 and 3 to represent binary output of ADC. A symmetric map is analyzed first, and then asymmetric map is followed to provide addition read DBNS to DSP circuitry. The simulation results are shown for the Double-Base Integer Encoder (DBIE) of the 6-bit ADC to demonstrate an effectiveness of the Constraint algorithm, using $0.18{\mu}\;m$ CMOS technology. The DBIE’s processing speed of the ADC is fast compared to the FAT tree encoder circuit by 0.95 GHz.

• 한국경영과학회지
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• 제30권4호
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• pp.27-43
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• 2005

This paper presents a new hybrid data mining technique using error pattern modeling to improve classification accuracy when the data type of a target variable is binary. The proposed method increases prediction accuracy by combining two different supervised learning methods. That is, the algorithm extracts a subset of training cases that are predicted inconsistently by both methods, and models error patterns from the cases. Based on the error pattern model, the Predictions of two different methods are merged to generate final prediction. The proposed method has been tested using practical 10 data sets. The analysis results show that the performance of proposed method is superior to the existing methods such as artificial neural networks and decision tree induction.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2008년도 추계학술대회 및 정기총회
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• pp.1-7
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• 2008

The purpose of this paper is to develop a new classification method for predicting consumer choice based on genetic algorithm, and to validate its prediction power over existing methods. To serve this purpose, we propose a hybrid model, and discuss its methodological characteristics in comparison with other existing classification methods. Also, to assess the prediction power of the model, we conduct a series of experiments employing survey data of consumer choices of MP3 players. The results show that the suggested model in this paper is statistically superior to the existing methods such as logistic regression model, artificial neural network model and decision tree model in terms of prediction accuracy. The model is also shown to have an advantage of providing several strategic information of practical use for consumer choice.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 하계종합학술대회논문집
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• pp.518-521
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• 1998

This paper describes a 3.3V-65MHz 12BIT CMOS current-mode DAC designed with a 8 MSB current matirx stage and a 4 LSB binary weighting stage. The linearity errors caused by a voltage drop of the ground line and a threshold voltage mismatch of transistors have been reduced by the symmetrical routing method with ground line and the tree structure bias circuit, respectively. In order to realize a low glitch energy, a cascode current switch ahs been employed. The simulation results of the designed DAC show a coversion rate of 65MHz, a powr dissipation of 71.7mW, a DNL of .+-.0.2LSB and an INL of .+-.0.8LSB with a single powr supply of 3.3V for a CMOS 0.6.mu.m n-well technology.

• International Journal of Contents
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• 제2권1호
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• pp.34-38
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• 2006

In this paper, an overview of anti-collision algorithm for RFID system of a standard EPC Class1 protocol is presented, and the binslotted dynamic search algorithm (BDS) based upon the slotted ALOHA and binary tree procedure is proposed and analyzed. Also, the performance is evaluated as comparing the BDS algorithm with the standard bin-slotted algorithm (BSA) through the simulation program. The performance of the proposed BDS algorithm is improved by dynamically identifying the collided-bit position and the collided bins stored in the stack of the reader. As the results, the number of request command that a reader send to tags in the reader s interrogation zone and the total recognition time are decreased to 59% as compared with BSA algorithm. Therefore, the tag identification performance is fairly improved by resolving a collision problem using the proposed BDS algorithm.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2001년도 봄 학술발표논문집 Vol.28 No.1 (B)
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• pp.70-72
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• 2001

최근 멀티미디어 응용의 증가에 따라 그래픽 데이터를 위한 내용 기반 검색 기술에 대한 연구가 활발히 진행되고 있다. 또한 인터넷 응용분야에서 3차원 그래픽 데이터베이스 사용의 필요성이 대두되고 활용되고 있다. 대부분의 3차원 그래픽 시스템은 사용자에게 그래픽은 검색이 대상이 아니라 단순히 보여주는 역할로 주로 사용되고 있다. 3차원 그래픽객체는 어떤 객체들로 구성되여 있으며 그들의 크기는 어떠한지 등의 정보를 포함하고 있다. 따라서 3차원 그래픽 객체에서는 2차원 그래픽 객체에서는 2차원 이미지보다 의미객체에 대한 정확한 정보를 더 많이 얻어 낼 수 있다. 이러한 사실 때문에 2차원 이미지의 특징추출의 방법과는 다른 형식의 접근이 필요하다. 본 논문에서는 3차원 그래픽으로 모델링 된 3차원 객체들을 대상으로 객체가 이루는 X, Y, Z축상의 비율과 윤곽형태에 대한 SPBT(Space Partitioning Binary Tree)의 결과값으로 특징을 추출하고 샘플 데이터를 통해서 이들간의 클러스터링과 실제 예제 질의를 토한 비교분석을 통해 객체간의 유사검색이 가능하도록 하는 특징추출 방법을 제안하였다. 본 논문에서는 제시한 모양기반 특징추출 방법은 웹상의 다양한 3차원 객체정보의 자동분류나 3차원 그래픽 데이터베이스를 위한 인덱스 구축 등에 활용될 수 있을 것이다.

• Communications for Statistical Applications and Methods
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• 제13권1호
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• pp.151-165
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• 2006

It is important to estimate the true misclassification rate of a given classifier when an independent set of test data is not available. Cross-validation and bootstrap are two possible approaches in this case. In related literature bootstrap estimators of the true misclassification rate were asserted to have better performance for small samples than cross-validation estimators. We compare the two estimators empirically when the classification rule is so adaptive to training data that its apparent misclassification rate is close to zero. We confirm that bootstrap estimators have better performance for small samples because of small variance, and we have found a new fact that their bias tends to be significant even for moderate to large samples, in which case cross-validation estimators have better performance with less computation.