• 한국통신학회논문지
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• 제21권10호
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• pp.2653-2659
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• 1996

In this paper, DC-free and minimum bandwidth binary line codes with look-up table are proposed and their performances are analyzed. As results of performance analysis, the proposed codes are shown to have spectral nulls at DC and Nyquist frequency. Among the proposed codes, binary line codes of which both codeword digital sum and alternating digital sum are zero have lower code rate but better spectral characteristics. Furthermore, binary line codes which consist of all codewords including those with nonzero digital sum and alternating digital sum have worese spectral characteristics but higher code rate.

• Communications for Statistical Applications and Methods
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• 제26권3호
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• pp.261-272
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• 2019

In this paper, we propose a procedure to build a prediction interval of the sum of dependent binary random variables over a graph to account for the dependence among binary variables. Our main interest is to find a prediction interval of the weighted sum of dependent binary random variables indexed by a graph. This problem is motivated by the prediction problem of various elections including Korean National Assembly and US presidential election. Traditional and popular approaches to construct the prediction interval of the seats won by major parties are normal approximation by the CLT and Monte Carlo method by generating many independent Bernoulli random variables assuming that those binary random variables are independent and the success probabilities are known constants. However, in practice, the survey results (also the exit polls) on the election are random and hardly independent to each other. They are more often spatially correlated random variables. To take this into account, we suggest a spatial auto-regressive (AR) model for the surveyed success probabilities, and propose a residual based bootstrap procedure to construct the prediction interval of the sum of the binary outcomes. Finally, we apply the procedure to building the prediction intervals of the number of legislative seats won by each party from the exit poll data in the $19^{th}$ and $20^{th}$ Korea National Assembly elections.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2016년도 춘계학술대회
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• pp.583-585
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• 2016

The recent advances in wireless networks area have led to new techniques, such as small cells or full-duplex (FD) transmission, have also been developed to further increase the network capacity. Particularly, full-duplex communication promises expected throughput gain by doubling the spectrum compared to half-duplex (HD) communication. Because this technique permits one set of frequencies to simultaneously transmit and receive signals. In this paper, we focus on the binary power control for the users and the base stations in full-duplex multiple cellulars wireless networks to obtain optimal sum-rate under the effect interference and noise. We investigate with a scenario in there one carrier is assigned to only one user in each cell and construct a model for this problem. In this work, we apply the binary power control by the its simplification in the implemented algorithm for both uplink and downlink simultaneously to maximize sum data rate of the system. At first, we realize the 2-cells case separately to check the optimal power allocation whether being binary. Then, we carry on with N-cells case in general through properties of binary power control.

• 한국콘텐츠학회논문지
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• 제2권4호
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• pp.46-50
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• 2002

본 논문은 지문 영상의 전처리중 이진화 수행과정에서 지문 영상의 국부적 밝기 차이에 따른 가장 큰 애로점인 임계치(threshold value) 설정을 대상 지문 영역의 밝기 등에 스스로 적응할 수 있도록 Silt Sum 방법을 응용한 적을 이진화를 수행하였다. 기존의 방법과 비교하여 본 연구에서 제시한 개선된 전처리 방법은 보다 높은 인식 정확도를 제공하며, 이에 따라 실험 결과에서 보는 바와 같이 지문 인식을 위한 특징점 추출 알고리즘에 적용될 수 있다.

• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.585-594
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• 2003

It has been generally recognized that conventional binomial or Poisson model provides poor fits to the actual correlated binary data due to the extra-binomial variation. A number of generalized statistical models have been proposed to account for this additional variation. Among them, beta-binomial, correlated-binomial, and modified-binomial models are binomial-related models which are frequently used in modeling the sum of n correlated binary data. In many situations, it is reasonable to assume that n correlated binary data are exchangeable, which is a special case of correlated binary data. The sum of n exchangeable correlated binary data is modeled relatively well when the above three binomial-related models are applied. But the estimation results of correlation coefficient turn to be quite different. Hence, it is important to identify which model provides better estimates of model parameters(success probability, correlation coefficient). For this purpose, a small-scale simulation study is performed to compare the behavior of above three models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권2호
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• pp.485-499
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• 2021

Although non-binary low-density parity-check (NB-LDPC) codes have better error-correction capability than that of binary LDPC codes, their decoding complexity is significantly higher. Therefore, it is crucial to reduce the decoding complexity of NB-LDPC while maintaining their error-correction capability to adopt them for various applications. The extended min-sum (EMS) algorithm is widely used for decoding NB-LDPC codes, and it reduces the complexity of check node (CN) operations via message truncation. Herein, we propose a low-cost CN processing method to reduce the complexity of CN operations, which take most of the decoding time. Unlike existing studies on low complexity CN operations, the proposed method employs quick selection algorithm, thereby reducing the hardware complexity and CN operation time. The experimental results show that the proposed selection-based CN operation is more than three times faster and achieves better error-correction performance than the conventional EMS algorithm.

• Asian Journal for Public Opinion Research
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• 제5권1호
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• pp.15-40
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• 2017

This paper addresses the zero-sum of soft power against the backdrop of the rise of China and the relative "decline" of America. It attempts to find out that whether the "decline" of America's soft power is caused by the rise of China's soft power, and whether China's rise could guarantee with certainty the growth of soft power. In light of the particularity of South Korea, that is, its economy relies on China and its security relies on the US, this paper chooses South Korea as the entry point for the study. Based on the Pew data from a South Korean opinion poll, this paper conducts bivariate correlation and binary logistic regression respectively, to explore the existence of zero-sum "competitions" between China's and America's soft power.

• 전자공학회논문지B
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• 제28B권12호
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• pp.1-8
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• 1991

Given the set of minimal paths between two vertices in a network, this paper shows that the system reliability can be obtained by using a Binary Decision Digram. It can be done by transforming a Boolean sum of products into a Binary Decision Diagram and the reliability can be directly derived from it. The resulting reliability expression is compact and requires relatively fewer arithmetic opersations in its evaluation. Several examples are given to show that the method using Binary Decision Diagram is conceptually casy, simple and efficient.

• 방송공학회논문지
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• 제20권2호
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• pp.257-264
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• 2015

본 논문에서는 개선된 이진 블록 매칭 방법을 사용한 제한된 1비트 변환 (Constrained One-bit Transform : C1BT) 알고리듬을 제안한다. 이진 영상 기반의 움직임 예측은 기존의 정합 오차 기준인 SAD (Sum of Absolute Difference)를 사용한 움직임 예측의 방대한 계산량을 줄이기 위해 정합오차 기준을 이진 논리 연산을 사용하는 NNMP (Number of Non-Matching Points)를 사용하여 움직임 예측의 속도를 향상시키고 하드웨어 구현을 용이하게 하는 방법이다. C1BT 알고리듬은 기존의 1BT알고리듬이 원본의 픽셀값을 1개의 비트만으로 표현하며 발생하는 단점을 보완하여 이진 영상 기반의 움직임 예측의 정확도를 향상시킨 알고리듬이다. 이 논문에서는 이진 블록 기반의 움직임 예측이 작은 크기의 정합블록을 사용할 때 더욱 정확한 정확도를 갖는 방법을 제안하였다. 기존의 C1BT기반의 움직임 예측의 결과와 제안하는 알고리듬의 결과를 비교해 보았을 때 PSNR측면에서 더 우수한 성능을 갖는 것을 볼 수 있다.

• 한국지능시스템학회논문지
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• 제14권2호
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• pp.216-221
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• 2004

This paper describes an optimal synthesis method of binary neural network for pattern recognition. Our objective is to minimize the number of connections and the number of neurons in hidden layer by using a Newly Expanded and Truncated Learning Algorithm (NETLA) for the multilayered neural networks. The synthesis method in NETLA uses the Expanded Sum of Product (ESP) of the boolean expressions and is based on the multilayer perceptron. It has an ability to optimize a given binary neural network in the binary space without any iterative learning as the conventional Error Back Propagation (EBP) algorithm. Furthermore, NETLA can reduce the number of the required neurons in hidden layer and the number of connections. Therefore, this learning algorithm can speed up training for the pattern recognition problems. The superiority of NETLA to other learning algorithms is demonstrated by an practical application to the approximation problem of a circular region.