• 인지과학
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• 제20권4호
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• pp.535-554
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• 2009

모바일 장비에서 수집되는 정보는 개인의 기억을 보조하기 위한 수단으로 활용될 수 있지만, 그 양이 너무 많아 사용자가 효과적으로 검색하기에는 어려움이 있다. 데이터를 사람의 기억과 유사한 에피소드 방식으로 저장하기 위해 중요 이벤트인 랜드마크를 탐지하는 것이 필요하다. 본 논문에서는 사용자에게 새로운 서비스를 제공하기 위해서 다양한 컨텍스트 로그 정보로부터 자동으로 랜드마크를 찾아내는 속성별 베이지안 랜드마크 예측 모델을 제안한다. 랜드마크 예측 정확도를 높이기 위해 요일별, 주간별로 데이터를 나누고 다시 수집된 경로에 따른 속성으로 분류하여 학습을 통해 베이지안 네트워크를 생성하였다. 노키아의 로그데이터로 실험한 결과, 베이지안 네트워크를 사용한 방법이 SVM을 사용한 방법보다 예측성능이 높았으며, 주간별 및 요일별로 설계한 베이지안 네트워크에 비해 제안한 방법인 속성별 베이지안 네트워크의 성능이 가장 우수하였다.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제23권5호
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• pp.310-315
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• 2017

Software-Defined Network의 등장은 하드웨어적인 네트워크 기능들을 소프트웨어적인 형태의 모듈로 Controller에 보다 유연하게 적용시키도록 함으로써 전통적인 네트워크의 구조를 변화시키고 있다. 이러한 환경 속에서 최근 네트워크 트래픽에 대한 Quality of Service 및 자원관리와 같은 다양한 관점에서의 네트워크 관리정책에 대한 연구개발이 진행되고 있고, 이러한 관리정책을 뒷받침 할 수 있는 네트워크 모니터링에 대한 기법들 또한 제시되어 왔다. 이에 본 논문에서는 기계 학습 기법인 Naive Bayesian Classification을 통하여 Flow를 분류한 후, 전송 지연 측정 모듈을 통하여 효율적인 전송경로를 선정하는 기법을 제안한다. 이는 다양한 대역폭을 갖는 여러 경로들로 이루어진 네트워크상에서 효율적인 경로 분배 역할을 할 수 있고, 부하를 분산시킴으로써 보다 원활한 네트워크 환경 및 서비스 품질을 제공할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권12호
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• pp.5819-5840
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• 2018

Considering the topology of hierarchical tree structure, each cluster in WSNs is faced with various attacks launched by malicious nodes, which include network eavesdropping, channel interference and data tampering. The existing intrusion detection algorithm does not take into consideration the resource constraints of cluster heads and sensor nodes. Due to application requirements, sensor nodes in WSNs are deployed with approximately uncorrelated security weights. In our study, a novel and versatile intrusion detection system (IDS) for the optimal defense strategy is primarily introduced. Given the flexibility that wireless communication provides, it is unreasonable to expect malicious nodes will demonstrate a fixed behavior over time. Instead, malicious nodes can dynamically update the attack strategy in response to the IDS in each game stage. Thus, a multi-stage intrusion detection game (MIDG) based on Bayesian rules is proposed. In order to formulate the solution of MIDG, an in-depth analysis on the Bayesian equilibrium is performed iteratively. Depending on the MIDG theoretical analysis, the optimal behaviors of rational attackers and defenders are derived and calculated accurately. The numerical experimental results validate the effectiveness and robustness of the proposed scheme.

• Safety and Health at Work
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• 제3권2호
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• pp.92-100
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• 2012

Objectives: The French National Occupational Diseases Surveillance and Prevention Network (RNV3P) is a French network of occupational disease specialists, which collects, in standardised coded reports, all cases where a physician of any specialty, referred a patient to a university occupational disease centre, to establish the relation between the disease observed and occupational exposures, independently of statutory considerations related to compensation. The objective is to compare the relevance of disproportionality measures, widely used in pharmacovigilance, for the detection of potentially new disease ${\times}$ exposure associations in RNV3P database (by analogy with the detection of potentially new health event ${\times}$ drug associations in the spontaneous reporting databases from pharmacovigilance). Methods: 2001-2009 data from RNV3P are used (81,132 observations leading to 11,627 disease ${\times}$ exposure associations). The structure of RNV3P database is compared with the ones of pharmacovigilance databases. Seven disproportionality metrics are tested and their results, notably in terms of ranking the disease ${\times}$ exposure associations, are compared. Results: RNV3P and pharmacovigilance databases showed similar structure. Frequentist methods (proportional reporting ratio [PRR], reporting odds ratio [ROR]) and a Bayesian one (known as BCPNN for "Bayesian Confidence Propagation Neural Network") show a rather similar behaviour on our data, conversely to other methods (as Poisson). Finally the PRR method was chosen, because more complex methods did not show a greater value with the RNV3P data. Accordingly, a procedure for detecting signals with PRR method, automatic triage for exclusion of associations already known, and then investigating these signals is suggested. Conclusion: This procedure may be seen as a first step of hypothesis generation before launching epidemiological and/or experimental studies.

• Structural Engineering and Mechanics
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• 제26권3호
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• pp.251-262
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• 2007

This paper examines the application of artificial neural networks (ANN) to the response prediction of geometrically nonlinear truss structures. Two types of analysis (deterministic and probabilistic analyses) are considered. A three-layer feed-forward backpropagation network with three input nodes, five hidden layer nodes and two output nodes is firstly developed for the deterministic response analysis. Then a back propagation training algorithm with Bayesian regularization is used to train the network. The trained network is then successfully combined with a direct Monte Carlo Simulation (MCS) to perform a probabilistic response analysis of geometrically nonlinear truss structures. Finally, the proposed ANN is applied to predict the response of a geometrically nonlinear truss structure. It is found that the proposed ANN is very efficient and reasonable in predicting the response of geometrically nonlinear truss structures.

• 콘크리트학회논문집
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• 제17권3호
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• pp.369-374
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• 2005

이 연구의 목적은 화상처리 기법과 신경회로망을 이용하여 다섯가지 균열 패턴 즉, 횡방향, 종방향, 대각선($-45^{\circ}$) 대각선($+45^{\circ}$) 그리고 비방향성 균열의 패턴을 인식할 수 있는 기법을 제안하는 것이다. 제안된 화상처리 알고리즘과 인공 신경회로망 모델은 MATLAB 언어를 이용하여 구현하였다. 인공 신경회로망의 입력층에 들어갈 패턴인자는 Total projection technique를 통해 구하였으며, 인공 신경회로망의 구조(은닉층의 수와 은닉노드의 수)와 가중치 값은 가상 균열 화상을 사용하여 학습을 통해 결정하였다. 인공 신경회로망의 학습은 Bayesian regularization 기법을 도입함으로써 과적합 문제가 발생하지 않도록 하였으며, 이 연구에서 제안한 기법의 적합성을 판정하기 위하여 총 38개의 실제 균열 화상을 사용하여 시험하였다. 검증 시험 결과내에서는 이 연구에서 제안한 기법이 사람의 균열 패턴 인식결과와 정확히 일치하는 결과것으로 나타났다.

• Geomechanics and Engineering
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• 제22권4호
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• pp.291-303
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• 2020

The deformation of the rock surrounding a tunnel manifests due to the stress redistribution within the surrounding rock. By observing the deformation of the surrounding rock, we can not only determine the stability of the surrounding rock and supporting structure but also predict the future state of the surrounding rock. In this paper, we used grey system theory to analyse the factors that affect the deformation of the rock surrounding a tunnel. The results show that the 5 main influencing factors are longitudinal wave velocity, tunnel burial depth, groundwater development, surrounding rock support type and construction management level. Furthermore, we used seismic prospecting data, preliminary survey data and excavated section monitoring data to establish a neural network learning model to predict the total amount of deformation of the surrounding rock during tunnel collapse. Subsequently, the probability of a change in deformation in each predicted section was obtained by using a Bayesian method for detecting change points. Finally, through an analysis of the distribution of the change probability and a comparison with the actual situation, we deduced the survey mark at which collapse would most likely occur. Surface collapse suddenly occurred when the tunnel was excavated to this predicted distance. This work further proved that the Bayesian method can accurately detect change points for risk evaluation, enhancing the accuracy of tunnel collapse forecasting. This research provides a reference and a guide for future research on the probability analysis of tunnel collapse.

• 한국멀티미디어학회논문지
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• 제6권1호
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• pp.136-147
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• 2003

주어진 문제에 대하여 최적의 성능을 가지는 신경회로망을 얻기 위해서는 학습을 통한 매개변수의 최적화 (parameter optimization)와 모델 선택을 통한 구조 최적화(structure optimization )의 통합적인 과정이 필요하다. 본 논문에서는, 각 세부 방법들의 특성을 고려하여, 공통의 특성을 갖는 방법들을 결합함으로써 효율적이면서도 일반화 성능을 높이는 총체적인 신경회로망 최적화 방법을 제안한다. 먼저 다양한 오차 함수를 사용할 수 있는 자연 기울기 강하 학습에 적응적 정규화 방법을 도입함으로써 가중치 매개변수(weight parameter)들을 최적화한다. 그리고 이렇게 최적화된 매개변수(parameter)들에 자연 프루닝(natural pruning)을 적용하여 불필요한 요소들을 제저하여 최적화 된 구조를 생성한다. 반복적인 과정에 의하여 후보 모델들을 구성하고 베이시안 정보 기준(Bayesian Information Criterion: BIC )을 이 용하여 최적의 모델을 평가하여 선택하는 방법을 제안하였다. 벤치마크 데이터에 대한 실험을 통하여 제안하는 방법의 구조 최적화 능력과 일반화 성능의 우수성을 보였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1988년도 전기.전자공학 학술대회 논문집
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• pp.624-627
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• 1988

This paper describes the implementation of recognition of speaker - dependent Korean spoken continuous digits. The recognition system can be divided into two parts, acoustic - phonetic processor and lexical decoder. Acoustic - phonetic processor calculates the feature vectors from input speech signal and the performs frame labelling and phone labelling. Frame labelling is performed by Bayesian classification method and phone labelling is performed using labelled frame and posteriori probability. The lexical decoder accepts segments (phones) from acoustic - phonetic processor and decodes its lexical structure through phone network which is constructed from phonetic representation of ten digits. The experiment carried out with two sets of 4continuous digits, each set is composed of 35 patterns. An evaluation of the system yielded a pattern accuracy of about 80 percent resulting from a word accuracy of about 95 percent.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2000년도 학술발표회 발표논문집
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• pp.384-389
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• 2000

A artificial neural network model was developed to analyze and forecast the flow fluctuation at small streams in the Balan watershed. Backpropagation neural networks were found to perform very well in forecasting daily streamflows. In order to deal with slow convergence and an appropriate structure, two algorithms were proposed for speeding up the convergence of the backpropagation method, and the Bayesian Information Criterion(BIC) was proposed for obtaining the optimal number of hidden nodes. From simulations using daily flows at the HS#3 watershed of the Balan Watershed Project, which is 412,5 ㏊ in size and relatively steep in landscape, it was found that those algorithms perform satisfactorily.