• 한국정보처리학회논문지
• /
• 제2권3호
• /
• pp.417-424
• /
• 1995

본 논문에서는 정량화(Quantification) 문제를 MFT(Mean Field Theroy)를 통해서 해결하는 기법을 제안한다. 통계학에서 중요한 문제의 하나인 정량화 문제는 주어진 공간에서 대상들간의 유사성에 따라서 최적의 상태를 갖도록 하는 문제이다. 평균장 접근 방법에 기초한 한개의 변수로 표현되는 확률적 시뮬레이티드 아닐링을 제안하고 정량화 문제를 패널티(penalty) 파라메타 항을 첨가한 비한정된 최적화 문제로 변형하 여 MFT를 적용하였다. 또한 연속변수를 갖는 신경회로망에서 실제 값을 계산하는 것 보다 평균장 접근방법으로 계산하는것이 더 빠르게 계산될 수 있음을 확인하였다. 본 논문에서 제안한 방법이 실험결과 해석적인 방법보다 좋은 정량적 결과를 보였다.

• 대한산업공학회지
• /
• 제29권2호
• /
• pp.135-144
• /
• 2003

Traffic performance in a microcellular system is much more affected by cell dwell time and channel holding time in each cell. Cell dwell time of a call is characterized by its mobility pattern, i.e., stochastic changes of moving speed and direction. Cell dwell time provides important information for other analyses on traffic performance such as channel holding time, handover rate, and the average number of handovers per call. In the next generation mobile communication system, the cell size is expected to be much smaller than that of current one to accommodate the increase of user demand and to achieve high bandwidth utilization. As the cell size gets small, traffic performance is much more affected by variable mobility of users, especially by that of pedestrians. In previous work, analytical models are based on simple probability models. They provide sufficient accuracy in a simple second-generation cellular system. However, the role of them is becoming invalid in a picocellular environment where there are rapid change of network traffic conditions and highly random mobility of pedestrians. Unlike in previous work, we propose an improved probability model evolved from so-called Random walk model in order to mathematically formulate variable mobility of pedestrians and analyze the traffic performance. With our model, we can figure out variable characteristics of pedestrian mobility with stochastic correlation. The above-mentioned traffic performance measures are analyzed using our model.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2001년도 추계학술발표논문집 (상)
• /
• pp.535-538
• /
• 2001

Since 1990s, many literatures have shown that connectionist models, such as back propagation, recurrent network, and RBF (Radial Basis Function) outperform the traditional models, MA (Moving Average), AR (Auto Regressive), and ARIMA (Auto Regressive Integrated Moving Average) in time series prediction. Neural based approaches to time series prediction require the enough length of historical measurements to generate the enough number of training patterns. The more training patterns, the better the generalization of MLP is. The researches about the schemes of generating artificial training patterns and adding to the original ones have been progressed and gave me the motivation of developing VTG schemes in 1996. Virtual term is an estimated measurement, X(t+0.5) between X(t) and X(t+1), while the given measurements in the series are called actual terms. VTG (Virtual Tern Generation) is the process of estimating of X(t+0.5), and VTG schemes are the techniques for the estimation of virtual terms. In this paper, the alternative VTG schemes to the VTG schemes proposed in 1996 will be proposed and applied to multivariate time series prediction. The VTG schemes proposed in 1996 are called deterministic VTG schemes, while the alternative ones are called stochastic VTG schemes in this paper.

• 응용통계연구
• /
• 제29권4호
• /
• pp.613-625
• /
• 2016

과학 기술의 발전은 사회를 급격하게 변화시켜 왔다. 특허 자료 분석은 현대 과학 기술의 흐름을 이해하고 미래 유망기술을 예측할 수 있게 한다. 본 연구에서는 기술의 동향을 파악하고자 1985년과 2012년 사이에 미국 특허청에 등록된 특허를 중심으로 특허 인용 네트워크를 분석한다. 주요 기술군을 파악하기 위해 PageRank 알고리즘 외에 다양한 중심성 지표를 이용하고, 통계적 네트워크 모형을 통해 유사한 기술들의 군집을 찾아내고자 한다.

• 산업경영시스템학회지
• /
• 제20권43호
• /
• pp.99-107
• /
• 1997

We consider a multiechelon repairable-item inventory system where several bases are supported by a central depot and the external repair facilities. Unlike METRIC- based models, there are only a finite number of repair channels at each base, central depot and the external repair facilities. It is desired to find repair capacities and spares level which together guarantee a specified service level at minimum cost. Closed queueing network theory is used to model the stochastic process. The purpose of this paper is to derive the steady-state distributions of this system.

• Journal of Communications and Networks
• /
• 제16권1호
• /
• pp.67-80
• /
• 2014

Spectrum sensing is vital for secondary unlicensed nodes to coexist and avoid interference with the primary licensed users in cognitive wireless networks. In this paper, we develop models for bounding interference levels from secondary network to the primary nodes within a spectrum sensing framework. Instead of classical stochastic approaches where Poisson point processes are used to model transmitters, we consider a more practical model which takes into account the medium access control regulations and where the secondary Poisson process is judiciously thinned in two phases to avoid interference with the secondary as well as the primary nodes. The resulting process will be a modified version of the Mat$\acute{e}$rn point process. For this model, we obtain bounds for the complementary cumulative distribution function of interference and present simulation results which show the developed analytical bounds are quite tight. Moreover, we use these bounds to find the operation regions of the secondary network such that the interference constraint is satisfied on receiving primary nodes. We then obtain theoretical results on the primary and secondary throughputs and find the throughput limits under the interference constraint.

• International Journal of Computer Science & Network Security
• /
• 제22권7호
• /
• pp.352-358
• /
• 2022

Text mining is an important process used for analyzing the data collected from different sources like videos, audio, social media, and so on. The tools like Natural Language Processing (NLP) are mostly used in real-time applications. In the earlier research, text mining approaches were implemented using long-short memory (LSTM) networks. In this paper, text mining is performed using average-stochastic gradient descent weight-dropped (AWD)-LSTM techniques to obtain better accuracy and performance. The proposed model is effectively demonstrated by considering the internet movie database (IMDB) reviews. To implement the proposed model Python language was used due to easy adaptability and flexibility while dealing with massive data sets/databases. From the results, it is seen that the proposed LSTM plus weight dropped plus embedding model demonstrated an accuracy of 88.36% as compared to the previous models of AWD LSTM as 85.64. This result proved to be far better when compared with the results obtained by just LSTM model (with 85.16%) accuracy. Finally, the loss function proved to decrease from 0.341 to 0.299 using the proposed model

• 한국시뮬레이션학회논문지
• /
• 제19권1호
• /
• pp.113-123
• /
• 2010

첨단 국방과학기술이 발전을 거듭하면서 군사 전문가들은 전투 네트워크 시스템에 대한 관심이 커지고 있다. 소규모 전투끼리 네트워크 형태로 이어지는 전투상황의 실전적 묘사를 위해 청군과 홍군의 배치와 편제무기체계의 특성에 따라 유효사거리와 살상반경을 반영하고 교전 결과 잔여부대의 부대 재조직을 위한 이동규칙을 지정하여 추가 교전의 기회를 제공하였다. 전투시나리오로 실전적으로 모의하므로 기존 모델과의 신뢰도 차이를 추구하였다. 무기체계 배치 변화, 전투원 이동속도, 측/후방공격을 포함하는 전술의 변화, 지형 및 기상 등의 변수가 포함된 향후 연구과제도 제시되었다.

• 한국통신학회논문지
• /
• 제37권3B호
• /
• pp.212-218
• /
• 2012

인터넷 웜은 컴퓨터 네트워크를 이용하여 자기 자신을 자동으로 복제해서 전파하는 프로그램이다. 컴퓨터간의 네트워크 연결이 증가함에 따라 인터넷 웜은 급격해 확산되었고 큰 위협으로 남아있다. 코드 레드, 님다, 슬레머 같은 인터넷 웜의 특성과 이들의 활동을 억제하는 방법을 찾기 위해서 웜이 전파되는 특성을 연구하려는 많은 시도가 있었다. 네트워크 특징들이 인터넷 웜 전파에 미치는 영향은 모델의 간단성과 유사성 때문에 주로 의학계에서 사용되는 전염병 전파 모델을 이용하여 모델링이 되었다. 이런 의학계 모델링은 널리 사용되면서 여러 개선된 모델들이 다양하게 제안되었다. 우리는 이전의 제안된 모델들의 문제점을 분석한 후 통계적 방법을 사용하여 정확도를 높이는 새로운 방법의 웜 전파 모델링을 제안한다.

• Computers and Concrete
• /
• 제32권2호
• /
• pp.149-163
• /
• 2023

One of the main design parameters traditionally utilized in projects of geotechnical engineering is the uniaxial compressive strength. The present paper employed three artificial intelligence methods, i.e., the stochastic fractal search (SFS), the multi-verse optimization (MVO), and the vortex search algorithm (VSA), in order to determine the compressive strength of concrete (CSC). For the same reason, 1030 concrete specimens were subjected to compressive strength tests. According to the obtained laboratory results, the fly ash, cement, water, slag, coarse aggregates, fine aggregates, and SP were subjected to tests as the input parameters of the model in order to decide the optimum input configuration for the estimation of the compressive strength. The performance was evaluated by employing three criteria, i.e., the root mean square error (RMSE), mean absolute error (MAE), and the determination coefficient (R²). The evaluation of the error criteria and the determination coefficient obtained from the above three techniques indicates that the SFS-MLP technique outperformed the MVO-MLP and VSA-MLP methods. The developed artificial neural network models exhibit higher amounts of errors and lower correlation coefficients in comparison with other models. Nonetheless, the use of the stochastic fractal search algorithm has resulted in considerable enhancement in precision and accuracy of the evaluations conducted through the artificial neural network and has enhanced its performance. According to the results, the utilized SFS-MLP technique showed a better performance in the estimation of the compressive strength of concrete (R²=0.99932 and 0.99942, and RMSE=0.32611 and 0.24922). The novelty of our study is the use of a large dataset composed of 1030 entries and optimization of the learning scheme of the neural prediction model via a data distribution of a 20:80 testing-to-training ratio.