• 대한의용생체공학회:의공학회지
• /
• 제18권4호
• /
• pp.491-498
• /
• 1997

본 연구에서는 졸음운전 방지를 위한 방법으로 기존의 3단계 경고음법과 fuzzy logic을 이용한 가성도 측정 및 제어법을 시뮬레이션으로 비교 및 분석하였다. 각성상태를 제어하는 방법으로 사용되었던 기존의 각성제어지표는 실 차에는 사용될 경우 효과적이지 못하므로 각성상태에 따른 영역별 Nz와 IRI의 상관분포도를 분석하여 기존의 각성제어지표를 수정하였다. Fuzzy 추론으로는 Sugeno의 방법을 사용하였고 멤버쉽함수와 제어규칙 베이스는 수정된 각성제어지표로부터 결정하였다. 시뮬레이션 결과 60이하의 IRI가 발생되는 경우, Nz의 변화에 따라 두 제어방법 모두 small, medium, big의 경고음이 발생되었으나 3단계 경고음법은 다음 단계의 발생영역이 될 때까지 같은 출력만을 발생한다. 그러나 퍼지추론의 출력은 피검자의 각성수준의 변화에 잘 추종하여 변화되었으므로 3단계 경고음법의 문제점을 해겨할 수 있었고 더욱이 퍼지 추론의 출력과 Nz와의 상관계수(r=0.99)가 매우 높았으므로, 실제 운전시 퍼지추론 방법을 이용한 각성도 평가 및 제어에 적용할 경우 3단계 경고음법 보다 효과적일 것으로 기대된다.

• 반도체디스플레이기술학회지
• /
• 제21권3호
• /
• pp.7-11
• /
• 2022

Object detection technology is one of the main research topics in the field of computer vision and has established itself as an essential base technology for implementing various vision systems. Recent DNN (Deep Neural Networks)-based algorithms achieve much higher recognition accuracy than traditional algorithms. However, it is well-known that the DNN model inference operation requires a relatively high computational power. In this paper, we analyze the inference time complexity of the state-of-the-art object detection architecture Yolov7 in various environments. Specifically, we compare and analyze the time complexity of four types of the Yolov7 model, YOLOv7-tiny, YOLOv7, YOLOv7-X, and YOLOv7-E6 when performing inference operations using CPU and GPU. Furthermore, we analyze the time complexity variation when inferring the same models using the Pytorch framework and the Onnxruntime engine.

• 반도체디스플레이기술학회지
• /
• 제21권1호
• /
• pp.75-78
• /
• 2022

Deep Neural Networks (DNN) has become an essential data processing architecture for the implementation of multiple computer vision tasks. Recently, DNN-based algorithms achieve much higher recognition accuracy than traditional algorithms based on shallow learning. However, training and inference DNNs require huge computational capabilities than daily usage purposes of computers. Moreover, with increased size and depth of DNNs, CPUs may be unsatisfactory since they use serial processing by default. GPUs are the solution that come up with greater speed compared to CPUs because of their Parallel Processing/Computation nature. In this paper, we analyze the inference time complexity of DNNs using well-known computer vision library, OpenCV. We measure and analyze inference time complexity for three cases, CPU, GPU-Float32, and GPU-Float16.

• Communications for Statistical Applications and Methods
• /
• 제23권2호
• /
• pp.131-146
• /
• 2016

In research on behavioral studies, significant attention has been paid to the stage-sequential process for longitudinal data. Latent class profile analysis (LCPA) is an useful method to study sequential patterns of the behavioral development by the two-step identification process: identifying a small number of latent classes at each measurement occasion and two or more homogeneous subgroups in which individuals exhibit a similar sequence of latent class membership over time. Maximum likelihood (ML) estimates for LCPA are easily obtained by expectation-maximization (EM) algorithm, and Bayesian inference can be implemented via Markov chain Monte Carlo (MCMC). However, unusual properties in the likelihood of LCPA can cause difficulties in ML and Bayesian inference as well as estimation in small samples. This article describes and addresses erratic problems that involve conventional ML and Bayesian estimates for LCPA with small samples. We argue that these problems can be alleviated with a small amount of prior input. This study evaluates the performance of likelihood and MCMC-based estimates with the proposed prior in drawing inference over repeated sampling. Our simulation shows that estimates from the proposed methods perform better than those from the conventional ML and Bayesian method.

• Smart Structures and Systems
• /
• 제13권1호
• /
• pp.81-98
• /
• 2014

This paper presents the elastic buckling of smart lightweight column structures integrated with a pair of surface piezoelectric layers using artificial intelligence. The finite element modeling of Smart lightweight columns is found using $ANSYS^{(R)}$ software. Then, the first buckling load of the structure is calculated using eigenvalue buckling analysis. To determine the accuracy of the present finite element analysis, a compression study is carried out with literature. Later, parametric studies for length variations, width, and thickness of the elastic core and of the piezoelectric outer layers are performed and the associated buckling load data sets for artificial intelligence are gathered. Finally, the application of soft computing-based methods including artificial neural network (ANN), fuzzy inference system (FIS), and adaptive neuro fuzzy inference system (ANFIS) were carried out. A comparative study is then made between the mentioned soft computing methods and the performance of the models is evaluated using statistic measurements. The comparison of the results reveal that, the ANFIS model with Gaussian membership function provides high accuracy on the prediction of the buckling load in smart lightweight columns, providing better predictions compared to other methods. However, the results obtained from the ANN model using the feed-forward algorithm are also accurate and reliable.

• 한국농공학회논문집
• /
• 제65권4호
• /
• pp.33-43
• /
• 2023

In this study, it was developed and verified an application method of fuzzy-logic theory to the rating process of agricultural fill dam safety. A fuzzy-logic is very famous logical system when some decision making is made on the status of a lack of information. Three proxies were selected and configured membership functions (MFs) and these MFs were activated in the process of fuzzification procedures. Fuzzified vlaues were passed through the rule-based inference system, then fire strength could classified among cases of the rule-based inference system. To obtain final results, Mandani-type was adapted in the defuzzification process. As the results, it was shown the developed system can give a correct results that was compared with Matlab - fuzzy inference function. More ever it could perform the detailed analysis and improvement on the infrastructure safety rating process using classical diagnosis method.

• 한국수학교육학회지시리즈A:수학교육
• /
• 제55권2호
• /
• pp.193-208
• /
• 2016

This study analyzes the likelihood principle and elicits an educational implication. As a result of analysis, this study shows that Frequentist and Bayesian interpret the principle differently by assigning different role to that principle from each other. While frequentist regards the principle as 'the principle forming a basis for statistical inference using the likelihood ratio' through considering the likelihood as a direct tool for statistical inference, Bayesian looks upon the principle as 'the principle providing a basis for statistical inference using the posterior probability' by looking at the likelihood as a means for updating. Despite this distinction between two methods of statistical inference, two statistics schools get clues to compromise in a regard of using frequency prior probability. According to this result, this study suggests the statistics education that is a help to building of students' critical eye by their comparing inferences based on likelihood and posterior probability in the learning and teaching of updating process from frequency prior probability to posterior probability.

• 컴퓨터교육학회논문지
• /
• 제21권3호
• /
• pp.51-58
• /
• 2018

본 연구에서는 컴퓨터 오락추구 행동의 예측 분석을 목적으로 한국아동 청소년패널조사(KCYPS) 데이터를 대상으로 베이지안 추론을 사용하여 컴퓨터 오락추구 행동과 관련 변수들의 상호의존성과 인과관계를 조사하였다. 이를 위해 일반 베이지안 네트워크를 통한 마코프 블랭킷(Markov Blanket)을 추출하였다. 또한 변수들의 확률을 변화시켜 컴퓨터 오락추구 행동에 대한 변수들의 영향 정도를 분석하였다. 연구결과, 컴퓨터 오락추구 행동은 관련 변수들(학교학습활동, 비행-흡연, 비행-조롱, 팬덤활동, 학교규칙)의 값을 조정하였을 때 유의미하게 변화되는 것으로 나타났다. 본 연구의 결과로 베이지안 추론은 청소년의 컴퓨터 오락추구 행동을 예측하고 조절하는 등 교육 분야에서 활용될 수 있음을 제시하였다.

• 한국전자통신학회논문지
• /
• 제9권10호
• /
• pp.1079-1085
• /
• 2014

변화하는 연속적인 데이터가 대량으로 유입되는 스트림 형태의 센서 데이터에 대한 분석은 궁극적으로 상황인식에 도달할 수 있어야 한다. 본 연구에서 가변적이며 연속적으로 입수되는 센서 데이터 스트림을 분석하여 상황을 추론하는 방안을 제안한다. 연속적인 스트림 형태를 가지는 센서 데이터를 분류하기 위하여 센서로 부터 보내온 각 센서 데이터에 내포된 값들을 평가하고, 시간에 따른 변화를 토대로 신뢰도를 계산하였다. 각 데이터들이 구성하는 상황요인을 설정하였고 각 요인들의 변화를 추정할 수 있도록 함으로써 상황 추론이 가능함을 보였다.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제50권9호
• /
• pp.430-438
• /
• 2001

In this study, we introduce the adaptive Polynomial Neuro-Fuzzy Networks(PNFN) architecture generated from the fusion of fuzzy inference system and PNN algorithm. The PNFN dwells on the ideas of fuzzy rule-based computing and neural networks. Fuzzy inference system is applied in the 1st layer of PNFN and PNN algorithm is employed in the 2nd layer or higher. From these the multilayer structure of the PNFN is constructed. In order words, in the Fuzzy Inference System(FIS) used in the nodes of the 1st layer of PNFN, either the simplified or regression polynomial inference method is utilized. And as the premise part of the rules, both triangular and Gaussian like membership function are studied. In the 2nd layer or higher, PNN based on GMDH and regression polynomial is generated in a dynamic way, unlike in the case of the popular multilayer perceptron structure. That is, the PNN is an analytic technique for identifying nonlinear relationships between system's inputs and outputs and is a flexible network structure constructed through the successive generation of layers from nodes represented in partial descriptions of I/O relatio of data. The experiment part of the study involves representative time series such as Box-Jenkins gas furnace data used across various neurofuzzy systems and a comparative analysis is included as well.