• Environmental Engineering Research
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• 제23권1호
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• pp.107-113
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• 2018

Air quality and pollution have recently become a major concern; vehicle emissions significantly pollute the air, especially in large and crowded cities. There are various factors that affect vehicle emissions; this research aims to find the most influential factors affecting $CO_2$ and $NO_x$ emissions using Adaptive Neural Fuzzy Inference System (ANFIS) as well as a systematic approach. The modified ANFIS (MANFIS) was developed to enhance modelling and Root Mean Square Error was used to evaluate the model performance. The results show that percentages of $CO_2$ from trucks represent the best input combination to model. While for $NO_x$ modelling, the best pair combination is the vehicle delay and percentage of heavy trucks. However, the final MANFIS structure involves two inputs, three membership functions and nine rules. For $CO_2$ modelling the triangular membership function is the best, while for $NO_x$ the membership function is two-sided Gaussian.

• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.341-352
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• 2003

Collecting data through experiment, the observers are an import source of measurement error and the inference on the measure of agreement, say kappa, is necessary. The models commonly used are complicated general mixture model, which have many nuisance parameters. Orthogonalization of parameters reduce the effect of nuisance parameter. Orthogonalization of estimating function gives the same effect as the parameter orthogonalization. In this study, the method for orthogonalization of estimating equation is studied and applied to the Beta-binomial model to examine the properties of the estimate of kappa. As a result, the likelihood function is insensitive to the change of the nuisance parameter and bias is smaller than the result of m.1.e. when kappa has extreme values

• 한국지능시스템학회논문지
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• 제7권1호
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• pp.16-26
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• 1997

China is one of the few countries where natural disaster strike frequently and cause heavy damage. In this paper, we mathematically develop two models to assess fuzzy risk of natural disaster in China. One is to assess the risk based on database of historical disaster effects by using information diffusion method relevant in fuzzy information analysis. In another model, we give an overview over advanced method to calculate the risk of release, exposure and consequence assessent, where information distribution technique is used to calculate basic fuzzy relationships showing historical experience of natural disasters, and fuzzy approximate inference is employed to study loss risk based on these basic relationships. We also present an examples to show how to use the first model. Result show that the model is effective for natural disaster risk assessment.

• 논리연구
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• 제13권2호
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• pp.83-116
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• 2010

이 글에서 우리는 토론 교육 특히 공학도를 위한 토론 교육에 필요한 논리 교수 학습 모형 을 제공한다. 이를 위하여 먼저 기존에 사용되고 있는 토론 관련 교재의 논증 개념과 논증 모형을 비판적으로 검토한다. 다음으로 토론에 필요한 기본 논증과 이에 대한 훈련 모형을 제공한다. 마지막으로 이공계 학생들 특히 공대 학생들을 위한 논증 방식 특히 토론에 사용될 논증 방식으로 가설 추론과 최선의 선택으로의 추론 모형을 제공한다.

• Industrial Engineering and Management Systems
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• 제13권1호
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• pp.29-42
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• 2014

Extensive research has been conducted on supplier evaluation and selection as a strategic and crucial component of supply chain management in recent years. However, few articles in the previous literature have been dedicated to the use of fuzzy inference systems as an aid in decision-making. Therefore, this essay attempts to demonstrate the application of this method in evaluating suppliers, based on a comprehensive framework of qualitative and quantitative factors besides the effect of gradual coverage distance. The purpose of this study is to investigate the applicability of the numerous measures and metrics in a multi-objective optimization problem of the supply chain network design with the aim of managing the allocation of orders by coordinating the production lines to satisfy customers' demand. This work presents a dynamic non-linear programming model that examines the important aspects of the strategic planning of the manufacturing in supply chain. The effectiveness of the configured network is illustrated using a sample, following which an exact method is used to solve this multi-objective problem and confirm the validity of the model, and finally the results will be discussed and analyzed.

• Journal of Electrical Engineering and Technology
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• 제8권5호
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• pp.1163-1168
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• 2013

In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied for modeling and simulation of DC characteristic of cylindrical Silicon Nanowire Transistor (SNWT). Device Geometry parameters, terminal voltages, temperature and output current were selected as the main factors of modeling. The results obtained are compared with numerical method and a good match has been observed between them, which represent accuracy of model. Finally, we imported the ANFIS model as a voltage controlled current source in a circuit simulator like HSPICE and simulated a SNWT inverter and common-source amplifier by this model.

• 제어로봇시스템학회논문지
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• 제7권6호
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• pp.494-501
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• 2001

In this paper an ANFIS(Adativo Neuro-Fuzzy Inference System)- based fault detection and diagnosis for a closed loop control system is proposed. The proposed diagnostic system contains two ANFIS. One is run as a parallel model within the model in closed loop control(MCL) and the other is run as a series-parallel model within the process in closed loop(PCL) for the generation of relevant symptoms for fault diagnosis. These symptoms are further processed by another classification logic with simple rules and neural network for process and controller fault diagnosis. Experimental results for a DC shunt motor control system illustrate the effectiveness of the proposed diagnostic scheme.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.302-305
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• 1996

In this paper, an optimal identification method using fuzzy-neural networks is proposed for modeling of nonlinear complex systems. The proposed fuzzy-neural modeling implements system structure and parameter identification using the intelligent schemes together with optimization theory, linguistic fuzzy implication rules, and neural networks(NNs) from input and output data of processes. Inference type for this fuzzy-neural modeling is presented as simplified inference. To obtain optimal model, the learning rates and momentum coefficients of fuzz-neural networks(FNNs) and parameters of membership function are tuned using genetic algorithm(GAs). For the purpose of its application to nonlinear processes, data for route choice of traffic problems and those for activated sludge process of sewage treatment system are used for the purpose of evaluating the performance of the proposed fuzzy-neural network modeling. The show that the proposed method can produce the intelligence model w th higher accuracy than other works achieved previously.

• 한국지구과학회지
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• 제35권5호
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• pp.362-374
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• 2014

This study investigated the initial mass function (IMF) and star formation history of high-mass stars in the Small Magellanic Cloud (SMC) using a population synthesis technique. We used the photometric survey catalog of Lee (2013) as the observable quantities and compare them with those of synthetic populations based on Bayesian inference. For the IMF slope (${\Gamma}$) range of -1.1 to -3.5 with steps of 0.1, five types of star formation models were tested: 1) continuous; 2) single burst at 10 Myr; 3) single burst at 60 Myr; 4) double bursts at those epochs; and 5) a complex hybrid model. In this study, a total of 125 models were tested. Based on the model calculations, it was found that the continuous model could simulate the high-mass stars of the SMC and that its IMF slope was -1.6 which is slightly steeper than Salpeter's IMF, i.e., ${\Gamma}=-1.35$.

• Journal of information and communication convergence engineering
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• 제11권1호
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• pp.24-29
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• 2013

In this paper, we propose a fuzzy inference model for a navigation algorithm for a mobile robot that intelligently searches goal location in unknown dynamic environments. Our model uses sensor fusion based on situational commands using an ultrasonic sensor. Instead of using the "physical sensor fusion" method, which generates the trajectory of a robot based upon the environment model and sensory data, a "command fusion" method is used to govern the robot motions. The navigation strategy is based on a combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance based on a hierarchical behavior-based control architecture. To identify the environments, a command fusion technique is introduced where the sensory data of the ultrasonic sensors and a vision sensor are fused into the identification process. The result of experiment has shown that highlights interesting aspects of the goal seeking, obstacle avoiding, decision making process that arise from navigation interaction.