• Journal of the Korean Institute of Landscape Architecture
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• v.34 no.5 s.118
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• pp.39-51
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• 2006

This study was carried out to provide guidance to environmental policy makers when deciding which assessment fields (biotic, abiotic, qualitative, functional) should have priority for ecological preservation and to develop an objective and scientific methodology by introducing the engineering concept of the fuzzy integral. The grant of weights was used the eigenvalues calculated by factor analysis, and the converted values of indicators were obtained in multiplying the arithmetic values and eigenvalues. The results of the appropriateness and reliability of assessment fields were examined over 0.6, and the results showed that the design of questionnaire presented no great problems. When the fuzzy integral was calculated to determine the rankings at ${\lambda}$=1, 2, 3, 4, 5, respectively, they were 0.646, 0.630, 0.943, 1.423, and 1.167 for the biotic field, 1.298, 1.400, 0.901, 0.580, and 1.456 for the abiotic field, 0.714, 0.674, 0.346, 0.674, and 1.610 in the qualitative field and 1.000, 0.973, 0.943, 1.024, and 1.008 in the functional field. The sensitivity to ${\lambda}$ value showed that ${\lambda}=4$ was the most suitable. In comparison with ${\lambda}=0$ (the arithmetic mean), the range of change was narrow. Because the range for ${\lambda}=4$ was narrower than my other values, ${\lambda}=4$ was sure to be available in ranking-decision. The fuzzy integral is expected to be a method for analyzing and filtering human thoughts. In the future, in order to overcome linguistic uncertainty and subjectivity, other fuzzy integral models including Sugeno's method, AHP, and so forth should be used.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.3
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• pp.186-199
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• 2013

We pose pattern classification as a density estimation problem where we consider mixtures of generative models under partially labeled data setups. Unlike traditional approaches that estimate density everywhere in data space, we focus on the density along the decision boundary that can yield more discriminative models with superior classification performance. We extend our earlier work on the recursive estimation method for discriminative mixture models to semi-supervised learning setups where some of the data points lack class labels. Our model exploits the mixture structure in the functional gradient framework: it searches for the base mixture component model in a greedy fashion, maximizing the conditional class likelihoods for the labeled data and at the same time minimizing the uncertainty of class label prediction for unlabeled data points. The objective can be effectively imposed as individual mixture component learning on weighted data, hence our mixture learning typically becomes highly efficient for popular base generative models like Gaussians or hidden Markov models. Moreover, apart from the expectation-maximization algorithm, the proposed recursive estimation has several advantages including the lack of need for a pre-determined mixture order and robustness to the choice of initial parameters. We demonstrate the benefits of the proposed approach on a comprehensive set of evaluations consisting of diverse time-series classification problems in semi-supervised scenarios.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2D
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• pp.241-250
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• 2006

Although walking is an important transport mode which should be promoted, realistic studies about walking is not sufficient. Especially, due to the transportation planning oriented toward automobile, there is not realistic analysis method for walking in the Highway Capacity Manual. Therefore, in this study the fuzzy approximate reasoning was employed to build a model for the analysis of walkways service level. For the input variable the noise level and brightness as well as the pedestrian flow rate were employed and the output variable was the walking satisfaction degree. The fuzzy models were constructed for daytime and nighttime separately. The forecastability analysis for the models were conducted using $R^2$, MAE and MSE. The values of them for the daytime model are 0.802, 0.729 and 0.735 respectively and the values for nighttime are 0.893, 0.878 and 0.860 respectively, so it can be said that the models explain the real situation well. As a result of this study, it can be concluded that the noise level has stronger effects to walking satisfaction then the brightness in night.

• Journal of the Korea Society for Simulation
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• v.5 no.1
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• pp.29-29
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• 1996

This paper describes a new automated simulation system for micromachines whose size range $10^{-6}$ to $10^{-3}$ m. An automic finite element (FE) mesh generation technique, which is bases on the fuzzy knowledge processing and computation al geometry technique, is incorporated into the system, together with one of commerical FE analysis codes, MARC, and one of commerical solid modelers, Designbase. The system allows a geometry model of concern to be automatically converted to different FE models, depending on physical phenomena of micromachines to be analyzed, i,e. electrostatic analysis, stress analysis, modal analysis and so on. The FE models are then automatically analyzed using the FE analysis code. Among a whole process of analysis, the definition of a geometry model, the designation of local node patterns and the assignment of material properties and boundary conditions onto the geometry model are only the interactive process to be done by a user. The interactive operations can be processed in a few minutes. The other processes which are time consuming and labour-intensive in conventional CAE systems are fully automatically performed in a popular engineering workstation environment. This automated simulation system is successfully applied to evaluate an electrostatic micro wobble actuator.

• Computers and Concrete
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• v.21 no.1
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• pp.21-30
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• 2018

In this study, we analyze the behavior of concrete which contains zeolite and diatomite. In order to achieve the goal, we utilize expert system methods. The utilized methods are artificial neural network and adaptive network-based fuzzy inference systems. In this respect, we exploit seven different mixes of concrete. The concrete mixes contain zeolite, diatomite, mixture of zeolite and diatomite. All seven concrete mixes are exposed to 28, 56 and 90 days' compressive strength experiments with 63 specimens. The results of the compressive strength experiments are used as input data during the training and testing of expert system methods. In terms of artificial neural network and adaptive network-based fuzzy models, data format comprises seven input parameters, which are; the age of samples (days), amount of Portland cement, zeolite, diatomite, aggregate, water and hyper plasticizer. On the other hand, the output parameter is defined as the compressive strength of concrete. In the models, training and testing results have concluded that both expert system model yield thrilling medium to predict the compressive strength of concrete containing zeolite and diatomite.

• Health Policy and Management
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• v.12 no.2
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• pp.1-22
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• 2002

Since the early 1980s scholars have applied latent structure and other type of finite mixture models from various academic fields. Although the merits of finite mixture model are well documented, the attempt to apply the mixture model to medical service has been relatively rare. The researchers aim to try to fill this gap by introducing finite mixture model and segmenting inpatients DB from one general hospital. In section 2 finite mixture models are compared with clustering, chi-square analysis, and discriminant analysis based on Wedel and Kamakura(2000)'s segmentation methodology schemata. The mixture model shows the optimal segments number and fuzzy classification for each observation by EM(expectation-maximization algorism). The finite mixture model is to unfix the sample, to Identify the groups, and to estimate the parameters of the density function underlying the observed data within each group. In section 3 and 4 we illustrate results of segmenting 4510 patients data including menial and ratio scales. And then, we show AHP can be identify the attractiveness of each segment, in which the decision maker can select the best target segment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2824-2837
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• 2019

Container technologies are widely used in infrastructures to deploy and manage applications in cloud computing environment. As containers are light-weight software, the cluster of cloud applications can easily scale up or down to provide Internet-based services. Container-based applications can well deal with fluctuate workloads by dynamically adjusting physical resources. Current works of scheduling applications often construct applications' performance models with collected historical training data, but these works with static models cannot self-adjust physical resources to meet the dynamic requirements of cloud computing. Thus, we propose a self-adaptive automatic container scheduling framework AutoScale for cloud applications, which uses a feedback-based approach to adjust physical resources by extending, contracting and migrating containers. First, a queue-based performance model for cloud applications is proposed to correlate performance and workloads. Second, a fuzzy Kalman filter is used to adjust the performance model's parameters to accurately predict applications' response time. Third, extension, contraction and migration strategies based on predicted response time are designed to schedule containers at runtime. Furthermore, we have implemented a framework AutoScale with container scheduling strategies. By comparing with current approaches in an experiment environment deployed with typical applications, we observe that AutoScale has advantages in predicting response time, and scheduling containers to guarantee that response time keeps stable in fluctuant workloads.

• Steel and Composite Structures
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• v.48 no.5
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• pp.531-545
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• 2023

Composite dowels are implemented as a powerful alternative to headed studs for the efficient combination of Ultra High-Performance Concrete (UHPC) with high-strength steel in novel composite structures. They are required to provide sufficient shear resistance and ensure the transmission of tensile forces in the composite connection in order to prevent lifting of the concrete slab. In this paper, the load bearing capacity of puzzle-shaped and clothoidal-shaped dowels encased in UHPC specimen were investigated based on validated experimental test data. Considering the influence of the embedment depth and the spacing width of shear dowels, the characteristics of UHPC square plate on the load bearing capacity of composite structure, 240 numeric models have been constructed and analyzed. Three artificial intelligence approaches have been implemented to learn the discipline from collected experimental data and then make prediction, which includes Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), Adaptive Neuro-Fuzzy Inference System (ANFIS) and an Extreme Learning Machine (ELM). Among the factors, the embedment depth of composite dowel is proved to be the most influential parameter on the load bearing capacity. Furthermore, the results of the prediction models reveal that ELM is capable to achieve more accurate prediction.

• Geomechanics and Engineering
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• v.31 no.3
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• pp.291-304
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• 2022

Maximum shear modulus (G_max or G₀) is an important soil property useful for many engineering applications, such as the analysis of soil-structure interactions, soil stability, liquefaction evaluation, ground deformation and performance of seismic design. In the current study, bender element (BE) tests are used to evaluate the effect of the void ratio, effective confining pressure, grading characteristics (D₅₀, C_u and C_c), anisotropic consolidation and initial fabric anisotropy produced during specimen preparation on the G_max of sand-gravel mixtures. Based on the tests results, an empirical equation is proposed to predict G_max in granular soils, evaluated by the experimental data. The artificial neural network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) models were also applied. Coefficient of determination (R²) and Root Mean Square Error (RMSE) between predicted and measured values of G_max were calculated for the empirical equation, ANN and ANFIS. The results indicate that all methods accuracy is high; however, ANFIS achieves the highest accuracy amongst the presented methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.11
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• pp.1477-1485
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• 2018

There are two major issues for activated sludge process in sewage treatment plant. One is how to make sewage be more clean and the other is the energy saving in sewage treatment process. The major monitoring sewage qualities are chemical oxygen demand, phosphorus, nitrogen, suspended solid in effluent. These are transmitted to the national TMS(Telemetry Monitoring System) at every hour. If these exceed the environmental standard, the environmental charges imposed. So, these water qualities are to be controlled below the environmental standard in operation of sewage treatment plant. And recently, the energy saving is also important in process operation. Over 50% energy is consumed in blowers and motors for injection oxygen into aeration tank. So, with the water qualities to be controlled below the environmental standard, the energy saving also is to be accomplished for efficient plant management. Almost researches are aimed to control water quality without considering energy saving. AI techniques have been used for control water quality. AI modeling simulator provided the optimal control inputs(blower speed, waste sludge, return sludge) for control water quality. Blower speed is the main control input for activated sludge process. To make sewage be more clean, the excessive blower speed is supplied, but water quality is not better than the previous. In results, non necessary energy is consumed. In this paper we propose a new method that the energy saving also is to be accomplished with the water qualities to be controlled below the environmental standard for efficient plant management. Water qualities in only aeration tank are used the inputs of fuzzy models. Outputs of these models are chemical oxygen demand, phosphorus, nitrogen, suspended solid in effluent and have the environmental standards. In test, we found this method could save 10% energy than the previous methods.