• Nuclear Engineering and Technology
• /
• 제49권6호
• /
• pp.1165-1171
• /
• 2017

The traditional form of parallelism in Monte Carlo particle transport simulations, wherein each individual particle history is considered a unit of work, does not lend itself well to data-level parallelism. Event-based algorithms, which were originally used for simulations on vector processors, may offer a path toward better utilizing data-level parallelism in modern computer architectures. In this study, a simple model is developed for estimating the efficiency of the event-based particle transport algorithm under two sets of assumptions. Data collected from simulations of four reactor problems using OpenMC was then used in conjunction with the models to calculate the speedup due to vectorization as a function of the size of the particle bank and the vector width. When each event type is assumed to have constant execution time, the achievable speedup is directly related to the particle bank size. We observed that the bank size generally needs to be at least 20 times greater than vector size to achieve vector efficiency greater than 90%. When the execution times for events are allowed to vary, the vector speedup is also limited by differences in the execution time for events being carried out in a single event-iteration.

• 대한기계학회논문집
• /
• 제17권11호
• /
• pp.2655-2663
• /
• 1993

In order to evaluate the relative significance of tolerance management of various elements in a VHS VTR tape transport system, the effect of geometric variations of the elements from standard design values on the tape traveling path is studied. The tape is modeled as a string and each element in the tape transport system is modeled as a cylinder whose radius, position vector and orientation vector are specified. An numerical algorithm is proposed to find the coordinates of tape entry points and tape exit points for the elements from which the tape traveling path can be completely described. By using the suggested algorithm, an error sensitivity analysis of tape traveling path due to the geometric variations of tape transport elements is performed for a particular model in the market and the elements demanding relatively strict tolerance management are identified.

• 한국항해항만학회:학술대회논문집
• /
• 한국항해항만학회 2006년도 춘계학술대회 및 창립 30주년 심포지엄(논문집)
• /
• pp.179-183
• /
• 2006

해안지역에서의 인간 활동이 활발해지면서 다양한 시설물과 해빈 보호를 위한 많은 인공 구조물이 설치되었다. 인공 구조물과 더불어 준설과 매립, 항만 공사 등으로 해저 지형이 급변하면서, 인공 구조물 주변에서의 퇴적 기능의 약화는 더욱 심각한 양상을 보이기도 한다. 항만 주변의 인공 구조물들에 대한 퇴적 작용을 이해하기 위해서 간단하고 효과적인 방법 중 하나는 이동 벡터 모델을 이용하여 표층 퇴적물을 분석하는 것이다. 포항항 주변의 해빈 유실이 심각한 지역에 대해서 인공 구조물 주변의 표층 퇴적물에 대한 입도 특성을 이용하여 퇴적물의 이동 양상을 파악해 보았다. 인공 구조물이 있음에도 불구하고 퇴적물은 항만 쪽으로 북상하는 경향이 뚜렷하게 확인되었다.

• 한국세라믹학회지
• /
• 제28권11호
• /
• pp.878-884
• /
• 1991

The mobility and diffusivity in an edge dislocation in an FCC crystal formed by the removal of one half of a (100) plane were evaluated in an applied field by analyzing a vacancy tight binding model using Stark's matrix technique. A model of an edge dislocation in an FCC crystal was constructed for a [100] Burgers vector where vacancy transport along the edge dislocation in an FCC crystal was constructed for a [100] Burgers vector where vacancy transport along the edge of the extrac half plane of ions was considered. The model considered a tight binding approximation of the vacancy to the compressed region of the core and carried the calculation to the limit of an infinite length of dislocation. The diffusivity and the ratio of mobility to diffusivity were found to increase without bounds in the limit where the correlation factor becomes zero. In contrast, as the correlation factor became unity, the diffusivity became zero and the ratio of mobility to diffusivity became unity associated with the uncorrelated limit of 1/kT. This implied that the phenomenon was not unique to the crystal structure but was unique to edge dislocations with vacancy tight binding.

• 한국해안·해양공학회논문집
• /
• 제20권6호
• /
• pp.540-552
• /
• 2008

STA 기법을 이용하여 낙동강 하구역의 계절적인 토사이동경로를 예측하였고 기존의 STA 기법들을 유연하게 적용하기 위해서 eCSedtrend 모형을 사용하였다. 계절적인 토사이동 경로의 분석 결과 낙동강 하구역에서 탁월하게 나타나는 경향은 CB+와 CB-의 경향으로 조사되었다. CB+ 경향의 경우 니질 퇴적물이 분포하는 지역에서 넓게 나타났고 북쪽으로 향하는 수송벡터를 형성하였다. 그리고 CB- 경향의 경우 사질퇴적물이 분포하는 사주 부근에서 대부분 나타나며 계절별로 차이를 보이지만 주로 북쪽으로 향하는 경향벡터를 형성함을 볼 수 있었다.

• Journal of Korea Trade
• /
• 제25권1호
• /
• pp.18-33
• /
• 2021

Purpose - This paper investigates the causal relationship between logistics infrastructure development and the economic growth of Korea. Considering the industrial and economic structure of Korea, it is likely that logistics infrastructure is positively associated with the economic growth of the country. Design/methodology - The causal relationship between logistics infrastructure and economic development is estimated using Vector Autoregressive (VAR) and Vector Error Correction Model (VECM) considering long-run equilibrium between the two factors. To this end, a dataset consisting of 7 logistics infrastructure proxies and 5 economic growth indicators covering the period of 1990-2017 is used. Findings - It was found that causality, in general, runs from logistics infrastructure development to economic growth. Specifically, the results indicate that maritime transport is positively associated with the economic growth of Korea in terms of GDP and international trade. In addition, other modes of transport also have a positive impact on either the GDP or international trade of Korea. Originality/value - While existing studies in this area are based on either regional observations or a specific mode of transport, this study presents empirical evidence on causality between logistics infrastructure and the economic growth of Korea using a more comprehensive dataset. In addition, the findings in this paper can provide valuable implications for transport infrastructure development policies.

• 한국ITS학회 논문지
• /
• 제5권2호
• /
• pp.1-10
• /
• 2006

본 논문에서는 강인한 음성인식 기술의 하나인 모델 파라미터 변환 기법 중 Carnegie Mellon University(1996)에서 Moreno가 제안한 최신 VTS(Vector Taylor Series) 알고리즘을 이용하여 주어진 잡음 환경에서 실험하였다. 이러한 VTS 알고리즘의 성능평가를 위해서 기존의 잡음 처리 방법 중 CMN(Cepstral Mean Normalization) 기법을 도입하였으며, 데시벨별로 설정한 백색 잡음과 거리잡음을 환경잡음으로 주어졌을 때의 인식률을 비교하였다. 또한 기존 Moreno가 제안한 실험환경의 인식 결과와 본 논문에서의 실험결과를 비교 분석하였다. 인식 알고리즘으로는 실시간 구현이 가능한 이산HMM(Hidden Markov Model)을 사용하였다.

• 무역학회지
• /
• 제47권2호
• /
• pp.69-88
• /
• 2022

This paper aims to predict Busan's regional product and employment using the logistic regression models and machine learning models. The following are the main findings of the empirical analysis. First, the OLS regression model shows that the main industries such as electricity and electronics, machine and transport, and finance and insurance affect the Busan's income positively. Second, the binomial logistic regression models show that the Busan's strategic industries such as the future transport machinery, life-care, and smart marine industries contribute on the Busan's income in large order. Third, the multinomial logistic regression models show that the Korea's main industries such as the precise machinery, transport equipment, and machinery influence the Busan's economy positively. And Korea's exports and the depreciation can affect Busan's economy more positively at the higher employment level. Fourth, the voting ensemble model show the higher predictive power than artificial neural network model and support vector machine models. Furthermore, the gradient boosting model and the random forest show the higher predictive power than the voting model in large order.

• 한국ITS학회 논문지
• /
• 제10권1호
• /
• pp.84-90
• /
• 2011

본 논문은 텔레매틱스 환경에서 문장독립형 화자인증을 이용한 VoIP 음성 보안통신기술을 제안한다. 보안통신을 위해 송신측에서는 화자의 음성정보로부터 생성된 공개키를 통해 음성 패킷을 암호화하여 수신측에 전송함으로써 중간자 공격에 대항한다. 수신측에서는 수신된 암호화된 음성패킷을 복호화한 후에 추출된 음성 특징과 송신측으로부터 수신받은 음성키를 비교하여 화자인증을 수행한다. 제안된 방식에서는 Gaussian Mixture Model(GMM)-supervector를 Bayesian information criterion (BIC) 방식과 Mahalanobis distance (MD) 방식을 이용한 Support Vector Machine (SVM) 커널에 적용하여 문장독립형 화자인증 정확도를 향상시켰다.

• Journal of information and communication convergence engineering
• /
• 제21권1호
• /
• pp.9-16
• /
• 2023

The natural language on social network platforms has a certain front-to-back dependency in structure, and the direct conversion of Chinese text into a vector makes the dimensionality very high, thereby resulting in the low accuracy of existing text classification methods. To this end, this study establishes a deep learning model that combines a big data ultra-deep convolutional neural network (UDCNN) and long short-term memory network (LSTM). The deep structure of UDCNN is used to extract the features of text vector classification. The LSTM stores historical information to extract the context dependency of long texts, and word embedding is introduced to convert the text into low-dimensional vectors. Experiments are conducted on the social network platforms Sogou corpus and the University HowNet Chinese corpus. The research results show that compared with CNN + rand, LSTM, and other models, the neural network deep learning hybrid model can effectively improve the accuracy of text classification.