• Journal of Korean Society of Transportation
• /
• v.12 no.4
• /
• pp.5-19
• /
• 1994

The new concept of level of service for the airport system indicates that there must be strong stimulation to proceed with the current stereotyped service standards which are being criticised due to their being based on, either physical capacity/volume or temporal/spatial standards that directly incorporates the perception of passengers. Quantitative factors have been dealt with the main service measurements when evaluating level of service. However, the passenger's perception for the provision service at the airport system is definitely important consideration to assess level of service. To enhance the reliability of service level evaluation from the passenger's point of view, this study has adopted three main evaluation factors ; temporal/spatial factors as quantitative measurements and comfort and reasonable service factors as qualitative measurements. The multi-decision model was constructed using Fuzzy Set Theory and applied to a case study at Seoul Kimpo International Airport. Results are presented in terms of passenger satisfaction with a variety of different values.

• Journal of Information Processing Systems
• /
• v.20 no.2
• /
• pp.185-199
• /
• 2024

In this paper, an improved automated spectral clustering (IASC) algorithm is proposed to address the limitations of the traditional spectral clustering (TSC) algorithm, particularly its inability to automatically determine the number of clusters. Firstly, a cluster number evaluation factor based on the optimal clustering principle is proposed. By iterating through different k values, the value corresponding to the largest evaluation factor was selected as the first-rank number of clusters. Secondly, the IASC algorithm adopts a density-sensitive distance to measure the similarity between the sample points. This rendered a high similarity to the data distributed in the same high-density area. Thirdly, to improve clustering accuracy, the IASC algorithm uses the cosine angle classification method instead of K-means to classify the eigenvectors. Six algorithms-K-means, fuzzy C-means, TSC, EIGENGAP, DBSCAN, and density peak-were compared with the proposed algorithm on six datasets. The results show that the IASC algorithm not only automatically determines the number of clusters but also obtains better clustering accuracy on both synthetic and UCI datasets.

• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.2
• /
• pp.823-827
• /
• 2016

Determination of the colorectal cancer stage is possible only after surgery based on pathology results. However, sometimes this may prove impossible. The aim of the present study was to determine colorectal cancer stage using three clustering methods based on preoperative clinical findings. All patients referred to the Colorectal Research Center of Shiraz University of Medical Sciences for colorectal cancer surgery during 2006 to 2014 were enrolled in the study. Accordingly, 117 cases participated. Three clustering algorithms were utilized including k-means, hierarchical and fuzzy c-means clustering methods. External validity measures such as sensitivity, specificity and accuracy were used for evaluation of the methods. The results revealed maximum accuracy and sensitivity values for the hierarchical and a maximum specificity value for the fuzzy c-means clustering methods. Furthermore, according to the internal validity measures for the present data set, the optimal number of clusters was two (silhouette coefficient) and the fuzzy c-means algorithm was more appropriate than the k-means clustering approach by increasing the number of clusters.

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.6
• /
• pp.31-39
• /
• 2011

In order to maintain clean environment in an interior space and an enclosed cattle pen, we have to measure and control environmental factors which are temperature, humidity and CO2, CH4 and so on. Although the measured values are within the normal range, those are increased or decreased sharply by the feces or environmental impacts. In order to take early an appropriate action, we propose an early warning method(EWarM) in this paper, which can recognize the rapidly changing time for the increasing or decreasing rate of the measured values. In addition, we developed fuzzy control system based on an EWarM. We verified that this system based on an EWarM is used for eliminating that impacts through performance evaluation in a variety of environmental situations.

• Journal of the Korean Geotechnical Society
• /
• v.23 no.11
• /
• pp.109-117
• /
• 2007

Uncertainty is pervasive in rock slope stability analysis due to various reasons and subsequently it may cause serious rock slope failures. Therefore, the importance of uncertainty has been recognized and subsequently the probability theory has been used to quantify the uncertainty since 1980's. However, some uncertainties, due to incomplete information, cannot be handled satisfactorily in the probability theory and the fuzzy set theory is more appropriate for those uncertainties. In this study the random variable is considered as fuzzy number and the fuzzy set theory is employed in rock slope stability analysis. However, the previous fuzzy analysis employed the approximate method, which is first order second moment method and point estimate method. Since previous studies used only the representative values from membership function to evaluate the stability of rock slope, the approximated analysis results have been obtained in previous studies. Therefore, the Monte Carlo simulation technique is utilized to evaluate the probability of failure for rock slope in the current study. This overcomes the shortcomings of previous studies, which are employed vertex method. With Monte Carlo simulation technique, more complete analysis results can be secured in the proposed method. The proposed method has been applied to the practical example. According to the analysis results, the probabilities of failure obtained from the fuzzy Monte Carlo simulation coincide with the probabilities of failure from the probabilistic analysis.

• Journal of Biomedical Engineering Research
• /
• v.24 no.5
• /
• pp.465-471
• /
• 2003

The purpose of this research was to evaluate chronic stress using physiological parameters. Wistar rats were exposed to the sound stress for 14 days. Biosignals were acquired hourly. To develop a fuzzy inference system which can integrate physiological parameters. the parameters of the system were adjusted by the adaptive neuro-fuzzy inference system. Of the training dataset, input dataset was the physiological parameters from the biosignals and output dataset was the target values from the cortisol production. Physiological parameters were integrated using the fuzzy inference system. then 24-hour results were analyzed by the Cosinor method. Chronic stress was evaluated from the degree of circadian rhythm disturbance. Suppose that the degree of stress for initial rest period is 1. Then. the degree of stress after 14-day sound stress increased to 1.37, and increased to 1.47 after the 7-day recovery period. That is, the rat was exposed to 37%-increased amount of stress by the 14-day sound and did not recover after the 7-day recovery period.

• Science of Emotion and Sensibility
• /
• v.12 no.1
• /
• pp.77-86
• /
• 2009

A comfort evaluation of car seat clothing has been proposed for high comforts interior seat clothing. Car seat covers have received wide spread attention due to their man-machine interface working. And then, it will be necessary for measurements on delicate basic mechanical-properties, which closely relate with human touch feeling of its materials. In this research, we have utilized $KES-FB^{(R)}$(Kawabata Evaluation System) series, $^ST300{(R)}$ analogue softness tester and friction tester for measurement a physical properties. In order to consider both kansei and physical properties on interior seat covers, we firstly have established subjective words of judgement for the seat covers. Secondly, related them to the objective measurement of physical properties. Each kansei-language has clearly defined as 'Softness', 'Elasticity', 'Volume' and 'Stickiness' for the adjectives of leather car seat covers. These technical terms have correlated to physical properties in other words, h (mm), bending moment ($gf^*$cm/cm), To-Tm (mm) and ${\mu}$. At this time, fuzzy logic has utilized to predict the value of kansei language through physical values. On the basis of this result, finally it is possible to predict quality index of car seat covers using neural networks technique. In short, we develop a quality evaluation system of car seat clothing combining four physical quantities with kansei engineering.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.5
• /
• pp.645-651
• /
• 2021

With the advent of the Fourth Industrial Revolution, modern society uses a diverse pool of devices. In this context, there is increasing interest in removing various kinds of noise arising in data transmission. However, it is difficult to restore image that damaged by mixed noise, and a digital filter that effectively restores an image according to the characteristics of the noise is required. In this paper, we propose a digital switching filter algorithm to remove mixed noise generated during digital image transmission. The proposed algorithm switches the filtering process through noise judgment and reconstructs the image using fuzzy weights and combined weights based on the pixel values inside the mask. To evaluate the proposed algorithm, we compared it with existing filter algorithms through simulation. Filtering results were expanded and compared for visual evaluation, and PSNR comparison was used for quantitative evaluation.

• Journal of Digital Convergence
• /
• v.15 no.5
• /
• pp.443-451
• /
• 2017

This paper's objectives are to investigate the criteria for ergonomic evaluation of indoor bike coordinated with virtual images, and to compare HMD-based VR bike with 2D-based one. 12 experts performed delphi method with an aim to determine ergonomic evaluation criteria that were classified into 4 categories(Usability, Emotion, User Values, Reality). 2D-based bike and HMD-based one were compared according to part of final criteria through fuzzy-logic question performed by 20 subjects. Though there were no confidential difference in usability, HMD-based VR bicycle resulted in greater scores than 2D-based one in elements related with emotion, user value and reality statistically. It is expected that this research results will be used as references to evaluate ergonomic design of other indoor exercise equipments combined with VR or AR.

• Steel and Composite Structures
• /
• v.51 no.1
• /
• pp.25-41
• /
• 2024

The measurement of pile bearing capacity is crucial for the design of pile foundations, where in-situ tests could be costly and time needed. The primary objective of this research was to investigate the potential use of fuzzy-based techniques to anticipate the maximum weight that concrete driven piles might bear. Despite the existence of several suggested designs, there is a scarcity of specialized studies on the exploration of adaptive neuro-fuzzy inference systems (ANFIS) for the estimation of pile bearing capacity. This paper presents the introduction and validation of a novel technique that integrates the fire hawk optimizer (FHO) and equilibrium optimizer (EO) with the ANFIS, referred to as ANFIS_FHO and ANFIS_EO, respectively. A comprehensive compilation of 472 static load test results for driven piles was located within the database. The recommended framework was built, validated, and tested using the training set (70%), validation set (15%), and testing set (15%) of the dataset, accordingly. Moreover, the sensitivity analysis is performed in order to determine the impact of each input on the output. The results show that ANFIS_FHO and ANFIS_EO both have amazing potential for precisely calculating pile bearing capacity. The R² values obtained for ANFIS_FHO were 0.9817, 0.9753, and 0.9823 for the training, validating, and testing phases. The findings of the examination of uncertainty showed that the ANFIS_FHO system had less uncertainty than the ANFIS_EO model. The research found that the ANFIS_FHO model provides a more satisfactory estimation of the bearing capacity of concrete driven piles when considering various performance evaluations and comparing it with existing literature.