• 대한산업공학회지
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• 제42권4호
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• pp.249-255
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• 2016

Recently, transaction data is accumulated everywhere very rapidly. Association analysis methods are usually applied to analyze transaction data, but the methods have several problems. For example, these methods can only consider one-way relations among items and cannot reflect domain knowledge into analysis process. In order to overcome defect of association analysis methods, we suggest a transaction data analysis method based on probabilistic graphical model (PGM) in this study. The method we suggest has several advantages as compared with association analysis methods. For example, this method has a high flexibility, and can give a solution to various probability problems regarding the transaction data with relationships among items.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 추계학술대회 논문집
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• pp.1033-1036
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• 2005

The effect of surface roughness on the contact fatigue was investigated in this study. To accomplish this goal, contact analysis based on the influence functions and the rectangular patch solution was performed to obtain the subsurface stress. Mesoscopic multiaxial fatigue criterion is then applied to predict fatigue damage. Suitable counting method and damage rule were used to evaluate the fatigue life of random loading caused by rough surface. As a result of the analysis, relationship between the life and roughness as well as the creack initiation depth was revealed. Below the critical roughness, It is observed that the fatigue life has hardly changed and creack is initiated around the depth at which the maximum shear stress occurs. Different behavior, however, is observed in case that the roughness is above the critical value.

• Structural Engineering and Mechanics
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• 제67권2호
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• pp.175-183
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• 2018

This research deals with the wave dispersion analysis of functionally graded double-layered nanobeam systems (FG-DNBSs) considering the piezoelectric effect based on nonlocal strain gradient theory. The nanobeam is modeled via Euler-Bernoulli beam theory. Material properties are considered to change gradually along the nanobeams' thickness on the basis of the rule of mixture. By implementing a Hamiltonian approach, the Euler-Lagrange equations of piezoelectric FG-DNBSs are obtained. Furthermore, applying an analytical solution, the dispersion relations of smart FG-DNBSs are derived by solving an eigenvalue problem. The effects of various parameters such as nonlocality, length scale parameter, interlayer stiffness, applied electric voltage, relative motions and gradient index on the wave dispersion characteristics of nanoscale beam have been investigated. Also, validity of reported results is proven in the framework of a diagram showing the convergence of this model's curve with that of a previous published attempt.

• 대한교통학회:학술대회논문집
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• 대한교통학회 2000년도 제37회 학술발표회논문집
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• pp.89-115
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• 2000

Traffic signal setting policies and traffic assignment procedures are mutually dependent. The combined signal control and traffic assignment problem deals with this interaction. With the total travel time minimization objective, gradient based local search methods are implemented. Deterministic user equilibrium is the selected user route choice rule, Webster's delay curve is the link performance function, and green time per cycle ratios are decision variables. Three implemented solution codes resulting in six variations include intersections operating under multiphase operation with overlapping traffic movements. For reference, the iterative approach is also coded and all codes are tested in four example networks at five demand levels. The results show the numerical gradient estimation procedure performs best although the simplified local searches show reducing the large network computational burden. Demand level as well as network size affects the relative performance of the local and iterative approaches. As demand level becomes higher, (1) in the small network, the local search tends to outperform the iterative search and (2) in the large network, vice versa.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.447-450
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• 2003

In this research, we propose an automatic knowledge acquisition and composite knowledge expression mechanism based on machine learning and relational database. Most of traditional approaches to develop a knowledge base and inference engine of expert systems were based on IF-THEN rules, AND-OR graph, Semantic networks, and Frame separately. However, there are some limitations such as automatic knowledge acquisition, complicate knowledge expression, expansibility of knowledge base, speed of inference, and hierarchies among rules. To overcome these limitations, many of researchers tried to develop an automatic knowledge acquisition, composite knowledge expression, and fast inference method. As a result, the adaptability of the expert systems was improved rapidly. Nonetheless, they didn't suggest a hybrid and generalized solution to support the entire process of development of expert systems. Our proposed mechanism has five advantages empirically. First, it could extract the specific domain knowledge from incomplete database based on machine learning algorithm. Second, this mechanism could reduce the number of rules efficiently according to the rule extraction mechanism used in machine learning. Third, our proposed mechanism could expand the knowledge base unlimitedly by using relational database. Fourth, the backward inference engine developed in this study, could manipulate the knowledge base stored in relational database rapidly. Therefore, the speed of inference is faster than traditional text -oriented inference mechanism. Fifth, our composite knowledge expression mechanism could reflect the traditional knowledge expression method such as IF-THEN rules, AND-OR graph, and Relationship matrix simultaneously. To validate the inference ability of our system, a real data set was adopted from a clinical diagnosis classifying the dermatology disease.

• Steel and Composite Structures
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• 제27권3호
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• pp.255-271
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• 2018

This paper deals with the transient dynamic analysis and elastic wave propagation in a functionally graded graphene platelets (FGGPLs)-reinforced composite thick hollow cylinder, which is subjected to shock loading. A micromechanical model based on the Halpin-Tsai model and rule of mixture is modified for nonlinear functionally graded distributions of graphene platelets (GPLs) in polymer matrix of composites. The governing equations are derived for an axisymmetric FGGPLs-reinforced composite cylinder with a finite length and then solved using a hybrid meshless method based on the generalized finite difference (GFD) and Newmark finite difference methods. A numerical time discretization is performed for the dynamic problem using the Newmark method. The dynamic behaviors of the displacements and stresses are obtained and discussed in detail using the modified micromechanical model and meshless GFD method. The effects of the reinforcement of the composite cylinder by GPLs on the elastic wave propagations in both displacement and stress fields are obtained for various parameters. It is concluded that the proposed micromechanical model and also the meshless GFD method have a high capability to simulate the composite structures under shock loadings, which are reinforced by FGGPLs. It is shown that the modified micromechanical model and solution technique based on the meshless GFD method are accurate. Also, the time histories of the field variables are shown for various parameters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1205-1223
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• 2018

Image enhancement is an emerging method for analyzing the images clearer for interpretation and analysis in the spatial domain. The goal of image enhancement is to serve an input image so that the resultant image is more suited to the particular application. In this paper, a novel method is proposed based on Mamdani fuzzy inference system (FIS) using multiple fuzzy membership functions. It is observed that the shape of membership function while converting the input image into the fuzzy domain is the essential important selection. Then, a set of fuzzy If-Then rule base in fuzzy domain gives the best result in image contrast enhancement. Based on a different combination of membership function shapes, a best predictive solution can be determined which can be suitable for different types of the input image as per application requirements. Our result analysis shows that the quality attributes such as PSNR, Index of Fuzziness (IOF) parameters give different performances with a selection of numbers and different sized membership function in the fuzzy domain. To get more insight, an optimization algorithm is proposed to identify the best combination of the fuzzy membership function for best image contrast enhancement.

• Advances in Automotive Engineering
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• 제1권1호
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• pp.105-121
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• 2018

The hybrid electric powertrain is a robust solution that allows for major improvements in both fuel economy and emission reduction. In the present study, a through-the-road hybrid vehicle model with an electric motor driving the rear axle and an Internal Combustion Engine (ICE) driving the front axle has been constructed. We then present a systematic method for the determination of a real time applicable optimal Energy Management Strategy (EMS) for a hybrid road vehicle. More precisely, we compare the performance of rule-based EMS strategies to an optimization-based strategy, namely ECMS (Equivalent Consumption Minimization Strategy). The comparison is conducted in parallel with a parameterization of the size of the internal combustion engine and the implementation of a Continuously Variable Transmission (CVT) that allows following the line of best fuel economy. For the FTP-75 driving cycle, the constrained engine On-off control algorithm is shown to offer a 28% improvement potential of fuel consumption compared to the conventional internal combustion engine while the ECMS strategy achieves an improved potential of nearly 33%.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권9호
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• pp.531-538
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• 2005

This paper proposes an expert system which is able to enhance the accuracy and productivity by determining the test strategy based on heuristic rules for test of the hybrid electronic circuit board producted massively in production line. The test heuristic rules are obtained from test system designer, test experts and experimental results. The guarding method separating the tested device with circumference circuit of the device is adopted to enhance the accuracy of measurements in the test of analog devices. This guarding method can reduce the error occurring due to the voltage drop in both the signal input line and the measuring line by utilizing heuristic rules considering the device impedance and the parallel impedance. Also, PSA(Parallel Signature Analysis) technique Is applied for test of the digital devices and circuits. In the PSA technique, the real-time test of the high integrated device is possible by minimizing the test time forcing n bit output stream from the tested device to LFSR continuously. It is implemented in Visual C++ computer language for the purpose of the implementation of the inference engine using the dynamic memory allocation technique, the interface with the electronic circuit database and the hardware direct control. Finally, the effectiveness of the builded expert system is proved by simulating the several faults occurring in the mounting process the electronic devices to the surface of PCB for a typical hybrid electronic board and by identifying the results.

• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.177-184
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• 2021

All over the world, people are affected by many chronic diseases and medical practitioners are working hard to find out the symptoms and remedies for the diseases. Many researchers focus on the feature detection of the disease and trying to get a better health recommendation system. It is necessary to detect the features automatically to provide the most relevant solution for the disease. This research gives the framework of Health Recommendation System (HRS) for identification of relevant and non-redundant features in the dataset for prediction and recommendation of diseases. This system consists of three phases such as Pre-processing, Feature Selection and Performance evaluation. It supports for handling of missing and noisy data using the proposed Imputation of missing data and noise detection based Pre-processing algorithm (IMDNDP). The selection of features from the pre-processed dataset is performed by proposed ensemble-based feature selection using an expert's knowledge (EFS-EK). It is very difficult to detect and monitor the diseases manually and also needs the expertise in the field so that process becomes time consuming. Finally, the prediction and recommendation can be done using Support Vector Machine (SVM) and rule-based approaches.