• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1196-1204
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• 2022

In this paper, we propose an optimization algorithm that maximizes the network lifetime in wireless ad-hoc networks using the broadcast transmission method. The optimization algorithm proposed in this paper applies tabu search algorithm, a metaheuristic method that improves the local search method using the memory structure. The proposed tabu search algorithm proposes efficient encoding and neighborhood search method to the network lifetime maximization problem. By applying the proposed method to design efficient broadcast routing, we maximize the lifetime of the entire network. The proposed tabu search algorithm was evaluated in terms of the energy consumption of all nodes in the broadcast transmission occurring in the network, the time of the first lost node, and the algorithm execution time. From the performance evaluation results under various conditions, it was confirmed that the proposed tabu search algorithm was superior to the previously proposed metaheuristic algorithm.

• 전자공학회논문지 IE
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• v.43 no.3
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• pp.13-21
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• 2006

Ryu et al. proposed a multiple target angle tracking algorithm using the angular measurement obtained from the signal subspace estimated by the output of sensor array. Ryu's algorithm has good features that it has no data association problem and simple structure. But its performance is seriously degraded in the low signal-to-noise ratio, and it uses the angular measurement obtained from the signal subspace of sampling time, even though the signal subspace is continuously updated by the output of sensor array. For improving the tracking performance of Ryu's algorithm, a measurement fusion method is derived based on ML(Maximum Likelihood) in this paper, and it admits us to use the angular measurements obtained form the adjacent signal subspaces as well as the signal subspace of sampling time. The new target angle tracking algorithm is proposed using the derived measurement fusion method. The proposed algorithm has a better tracking performance than that of Ryu's algorithm and it sustains the good features of Ryu's algorithm.

• Journal of IKEEE
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• v.8 no.1 s.14
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• pp.47-53
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• 2004

We present the design of ternary flip-flop which is based on ternary logic so as to process ternary data. These flip-flops are composed with ternary voltage mode NMAX, NMIN, INVERTER gates. These logic gate circuits are designed using CMOS and obtained the characteristics of a lower voltage, lower power consumption as compared to other gates. These circuits have been simulated with the electrical parameters of a standard 0.35um CMOS technology and 3.3Volts supply voltage. The architecture of proposed ternary flip-flop is highly modular and well suited for VLSI implementation, only using ternary gates.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.91-96
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• 2014

This paper proposed an adaptive threshold method for detecting flame candidate regions in a infrared image and it adapts according to the contrast and intensity changes in the image. Conventional flame detection systems uses fixed threshold method since surveillance environment does not change, once the system installed. But it needs a adaptive threshold method as requirements of surveillance system has changed. The proposed adaptive threshold algorithm uses the dynamic behavior of flame as featured parameter. The test result is analysed by comparing test result of proposed adaptive threshold algorithm and conventional fixed threshold method. The analysed data shows, the proposed method has 91.42% of correct detection rate and false detection is reduced by 20% comparing to the conventional method.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.416-426
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• 2003

In this paper we propose a new method for choosing the initial values of Expectation-Maximization(EM) algorithm that has been used in various applications for clustering. Conventionally, the initial values were chosen randomly, which sometimes yields undesired local convergence. Later, K-means clustering method was employed to choose better initial values, which is currently widely used. However the method using K-means still has the same problem of converging to local points. In order to resolve this problem, a new method of initializing values for the EM process. The proposed method not only strengthens the characteristics of EM such that the number of iteration is reduced in great amount but also removes the possibility of falling into local convergence.

• Computational Structural Engineering
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• v.11 no.1
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• pp.205-216
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• 1998

A solution method is presented to solve the eigenvalue problem arising in the dynamic analysis of nonclassicary damped structural systems with multiple eigenvalues. The proposed method is obtained by applying the modified Newton-Raphson technique and the orthonormal condition of the eigenvectors to the linear eigenproblem through matrix augmentation of the quadratic eigenvalue problem. In the iteration methods such as the inverse iteration method and the subspace iteration method, singularity may be occurred during the factorizing process when the shift value is close to an eigenvalue of the system. However, even though the shift value is an eigenvalue of the system, the proposed method provides nonsingularity, and that is analytically proved. Since the modified Newton-Raphson technique is adopted to the proposed method, initial values are need. Because the Lanczos method effectively produces better initial values than other methods, the results of the Lanczos method are taken as the initial values of the proposed method. Two numerical examples are presented to demonstrate the effectiveness of the proposed method and the results are compared with those of the well-known subspace iteration method and the Lanczos method.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.184-196
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• 2021

This paper proposes statistics adaptive linear regression-based object size prediction method for object detection. YOLOv2 and YOLOv3, which are typical deep learning-based object detection algorithms, designed the last layer of a network using statistics adaptive exponential regression model to predict the size of objects. However, an exponential regression model can propagate a high derivative of a loss function into all parameters in a network because of the property of an exponential function. We propose statistics adaptive linear regression layer to ease the gradient exploding problem of the exponential regression model. The proposed statistics adaptive linear regression model is used in the last layer of the network to predict the size of objects with statistics estimated from training dataset. We newly designed the network based on the YOLOv3tiny and it shows the higher performance compared to YOLOv3 tiny on the UFPR-ALPR dataset.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.08a
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• pp.475-478
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• 1998

음성에 공존하는 유/무성음 성분을 추정하는 알고리즘을 제안하였다. 유성음 성분은 주기성을 띤 사인곡선의 형태로 표현되며, 무성음 성분은 자동회기의 결과로 표현된다. 두 성분을 각각 차례대로 추정할 경우 한 성분에 대한 추정치의 정확도가 나머지 성분의 추정에도 영향을 주기 때문에 제안된 알고리즘은 두 성분을 공동으로 추정한다. 실제 ML 추정치는 구하기 어려워 이에 근접하는 추정치를 선형 방정식들을 interative 방법으로 풀어 구현하였다. 예비 시험결과 제안한 알고리즘이 정확하고 효율적으로 두 성분을 추정함을 알 수 있었고, 합성된 데이터 뿐만 아니라 실제 음성 데이터를 이용한 실험에서도 좋은 결과를 보여주었다.

• The Korean Journal of Applied Statistics
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• v.6 no.1
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• pp.67-78
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• 1993

A new diagnostic statistic for detecting outliers and influential observations in linear models is suggested and studied in this paper. The proposed statistic is a weighted sum of two measures; one is for detecting outliers and the other is for detecting influential observations. The merit of this statistic is that it is possible to distinguish outliers from influential observations. We have done some Monte-Carlo Simulation to find the probability distribution of this statistic.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.05a
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• pp.329-334
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• 1997

기포막 제한 및 기포 군집 이론에 의한 해석적 접근을 통해 축방향 출력분포가 임계열속에 미치는 영향을 파악하고, 이를 근거로 임계열속 발생지점에서의 엔탈피 변화를 고려하여 축방향 출력분포에 따른 임계열속 예측치 보정 모델을 개발하였다. 제안된 모델의 검증을 위해 cosine 형태의 축방향 출력분포를 갖는 임계열속 측정치와 비교하였으며, 그 결과 제안된 모델은 측정치에 대해 평균 1.0072, 표준편차 9.98%의 예측 성능을 나타냈다.