• 전기전자학회논문지
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• 제23권1호
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• pp.302-305
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• 2019

Non-orthogonal multiple access (NOMA) has been considered for the fifth generation (5G) mobile networks to provide high system capacity and low latency. We calculate the channel capacity for the weak channel user in NOMA and the channel capacity region for NOMA. In this paper, Gaussian mixture channel is compared to the additive white Gaussian noise (AWGN) channel. Gaussian mixture channel is modeled when we assume the practical signal modulation for the inter user interference, such as the binary phase shift keying (BPSK) modulation. It is shown that the channel capacity with BPSK inter user interference is better than that with Gaussian inter user interference. We also show that the channel capacity region with BPSK inter user interference is larger than that with Gaussian inter user interference. As a result, NOMA could perform better in the practical environments.

• International Journal of Reliability and Applications
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• 제1권2호
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• pp.145-154
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• 2000

In this paper, tests of statistical hypotheses for the expectation and variance of Gaussian fuzzy population are discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권4호
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• pp.2038-2057
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• 2017

Gaussian learning is a new technology in the computational intelligence area. However, this technology weakens the learning ability of a particle swarm and achieves a lack of diversity. Thus, this paper proposes a vector Gaussian learning strategy and presents an effective approach, named particle swarm optimization based on vector Gaussian learning. The experiments show that the algorithm is more close to the optimal solution and the better search efficiency after we use vector Gaussian learning strategy. The strategy adopts vector Gaussian learning to generate the Gaussian solution of a swarm's optimal location, increases the learning ability of the swarm's optimal location, and maintains the diversity of the swarm. The method divides the states into normal and premature states by analyzing the state threshold of the swarm. If the swarm is in the premature category, the algorithm adopts an inertia weight strategy that decreases linearly in addition to vector Gaussian learning; otherwise, it uses a fixed inertia weight strategy. Experiments are conducted on eight well-known benchmark functions to verify the performance of the new approach. The results demonstrate promising performance of the new method in terms of convergence velocity and precision, with an improved ability to escape from a local optimum.

• 감성과학
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• 제17권2호
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• pp.63-76
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• 2014

대부분의 인지적 과제에서 관찰되는 반응시간 자료의 분포는 정적으로 편포되어 나타남에도 불구하고, 반응시간을 종속측정치로 하는 대다수의 연구들은 표본 평균에 근거한 집중경향치 분석에 의존한다. 본 연구에서는 반응시간 자료의 분포특성에 분석의 초점을 맞추어 실험적 처치의 효과를 구체적으로 추론하는 방법을 소개하였다. 평균 반응시간의 변화는 그 분포상 가우시안 및 지수 분포가 혼합된 형태로 나타난다고 가정할 수 있으며, 최대우도 추정법에 근거한 ex-Gaussian 모형 검증을 통해 반응시간 분포 특성을 수치화된 파라미터로 산출하고 확률밀도함수를 구현할 수 있다. 분석 사례를 위해 두 가지 고전적 시각탐색과제에서 얻어진 반응시간 자료를 사용하였으며, ex-Gaussian 함수를 통해 탐색배열의 항목개수의 증가가 초래하는 평균 반응시간의 지연효과에 대한 해석을 시도하였다. 수리적 모형을 통한 반응시간 분포 분석은 고전적 집중경향치 분석의 한계를 넘어 반응시간을 활용한 다양한 이론 및 개인차 연구에서 활용될 수 있을 것으로 기대된다.

• Wind and Structures
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• 제34권5호
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• pp.421-435
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• 2022

To investigate the non-Gaussian properties of fluctuating wind pressures and the error margin of extreme wind loads on a long-span curved roof with matching and mismatching ratios of turbulence integral scales to depth (L_u^x/D), a series of synchronized pressure tests on the rigid model of the complex curved roof were conducted. The regions of Gaussian distribution and non-Gaussian distribution were identified by two criteria, which were based on the cumulative probabilities of higher-order statistical moments (skewness and kurtosis coefficients, S_k and K_u) and spatial correlation of fluctuating wind pressures, respectively. Then the characteristics of fluctuating wind-loads in the non-Gaussian region were analyzed in detail in order to understand the effects of turbulence integral-scale. Results showed that the fluctuating pressures with obvious negative-skewness appear in the area near the leading edge, which is categorized as the non-Gaussian region by both two identification criteria. Comparing with those in the wind field with matching L_u^x/D, the range of non-Gaussian region almost unchanged with a smaller L_u^x/D, while the non-Gaussian features become more evident, leading to higher values of S_k, Ku and peak factor. On contrary, the values of fluctuating pressures become lower in the wind field with a smaller L_u^x/D, eventually resulting in underestimation of extreme wind loads. Hence, the matching relationship of turbulence integral scale to depth should be carefully considered as estimating the extreme wind loads of long-span roof by wind tunnel tests.

• Nuclear Engineering and Technology
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• 제17권3호
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• pp.216-223
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• 1985

원자력 발전소로부터 방출되는 기체상 방사성 물질에 의한 환경 영향을 평가함에 있어서 방사성 물질의 대기중에서의 수송과 확산을 기술하는 모델로써 Gaussian plume mode띠 널리 사용되고 있다. Gaussian plume model은 평탄한 지형에 적용하도록 만들어진 모델이므로 대부분의 국토가 복잡한 산악으로 구성된 한극의 경우에 적용하기 위해서는 모델의 수정이 필요하다. 본 논문에서는 2차원적 x-ｚ 평면에서 확산방정식을 해석한 numerical diffusion model과 Gaussian plume model을 비교하여, Gaussian plume mode에서 가장 중요한 변수인 dispersion coefficient를 지형의 높이에 대하여 보정하였다. 보정된 dispersion coefficient 값을 Gaussian plume mode에 적용시켜 계산을 수행한 결과를 보면, 산악지역에서의 방사성 물질의 농도는 평지에서보다 낮게 나타나고 있다.

• 산업경영시스템학회지
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• 제33권3호
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• pp.130-136
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• 2010

In this study, we propose a new method for generating candidate solutions based on both the Cauchy and the Gaussian probability distributions in order to use the merit of the solutions generated by these distributions. The Cauchy probability distribution has larger probability in the tail region than the Gaussian distribution. Thus, the Cauchy distribution can yield higher probabilities of generating candidate solutions of large-varied variables, which in turn has an advantage of searching wider area of variable space. On the contrary, the Gaussian distribution can yield higher probabilities of generating candidate solutions of small-varied variables, which in turn has an advantage of searching deeply smaller area of variable space. In order to compare and analyze the performance of the proposed method against the conventional method, we carried out experiments using benchmarking problems of real valued functions. From the result of the experiment, we found that the proposed method based on the Cauchy and the Gaussian distributions outperformed the conventional one for most of benchmarking problems, and verified its superiority by the statistical hypothesis test.

• 한국정보과학회논문지:소프트웨어및응용
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• 제35권7호
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• pp.417-423
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• 2008

본 논문에서는 각 음원이 시간적 구조를 가졌을 경우 음원들을 분리해내는 확률적 음원분리 방법을 제안한다. 이를 위해 각 음원의 시간적 구조를 가우시안 프로세스(Gaussian process)로 모델링하고 기존의 음원분리 문제를 유사-가능도 최대화 문제(pseudo-likelihood maximization)로 공식화한다. 본 알고리즘을 통해 얻어진 데이타의 유사-가능도는 정규 분포이며 이는 가우시안 프로세스 회귀방법(Gaussian process regression)을 통해 쉽게 계산이 가능하다. 음원분리의 역혼합 행렬은 경도(gradient) 기반최적화 기법을 통해 데이타의 유사-가능도를 최대화하는 해를 찾음으로써 구해진다. 여러 실험을 통하여 제안 알고리듬이 몇 가지 특정 상황에서 기존의 분리 알고리듬들에 비해 우수한 성능을 보임을 확인 할 수 있다.

• 대한조선학회논문집
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• 제55권6호
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• pp.466-473
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• 2018

Most frequency domain-based approaches assume that structural response should be a Gaussian random process. But a lot of non-Gaussian processes caused by multi-excitation and non-linearity in structural responses or load itself are observed in many real engineering problems. In this study, the effect of non-Normality on fatigue damages are discussed through case study. The accuracy of four frequency domain methods for non-Gaussian processes are compared in the case study. Power-law and Hermite models which are derived for non-Gaussian narrow-banded process tend to estimate fatigue damages less accurate than time domain results in small kurtosis and in case of large kurtosis they give conservative results. Weibull model seems to give conservative results in all environmental conditions considered. Among the four methods, Benascuitti-Tovo model for non-Gaussian process gives the best results in case study. This study could serve as background material for understanding the effect of non-normality on fatigue damages.

• Current Optics and Photonics
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• 제7권6호
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• pp.701-707
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• 2023

In laser imaging, accurate extraction of the laser's center is essential. Several methods exist to extract the laser's center in an image, such as the geometric mean, the parabolic curve fitting, and the Gaussian curve fitting, etc. The Gaussian curve fitting is the most suitable because it is based on the physical properties of the laser. The width of the Gaussian laser beam depends on the distance from the laser source to the target object. It is assumed in general that the distance remains constant at a laser spot resulting in a symmetric Gaussian model for the laser image. However, on a curved surface of the object, the distance is not constant; The laser beam is narrower on the side closer to the focal point of the laser light and wider on the side closer to the laser source, which causes the distribution of the laser beam to skew. This study presents a modified Gaussian model in the laser imaging to incorporate the slant angle of a curved object. The proposed method is verified with simulation and experiments.