• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.3
• /
• pp.226-232
• /
• 2016

The research for the improvement in math and science scores is active by the brain exercises, stress reliefs, and emotion sensitized illuminations. This principle is based on the following facts that the most effective brain turns are supported with the circumstances not only when the brain wave should keep stability and comfort in science criticism, but also when minimized stress and comfortable illumination should be adjusted in solving math problem. In this paper, in order to effectively learn mathematics and science, the most optimized simulating tests in learning conditions are conducted by using a stress relief. However, depending on the users' tastes, the effectiveness on favorite music or colors therapy have no convergency but many differentiations. Therefore, in this paper, in order to solve this problem, the proposed optimal illumination and music therapy treatment using fuzzy inference method.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.49 no.2
• /
• pp.38-43
• /
• 2012

It is necessary to measure cracks on concrete slab surface accurately in concrete structure maintenance for the stability of the structure. However, in real world, the process is done by time consuming and ineffective manual inspection. Although there have been some studies to provide computerized inspection methods, they are vulnerable to rugged surface or noise due to the influence of the light or environmental reasons. In this paper, we propose a new method that extracts not only undistorted cracks but minute cracks that were often regarded as noise. We extract candidate crack areas by applying fuzzy method with R, G, and B channel values of concrete slab structure. Then further refinement processes are performed with SOM algorithm and density based cutoff to remove noise. Experiment verifies that the proposed method is sufficiently useful in various crack images.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.5
• /
• pp.1886-1900
• /
• 2018

This paper proposes a confronting feedback control structure and controllers for positive output elementary super lift Luo converters (POESLLCs) working in discontinuous conduction mode (DCM). The POESLLC offers the merits like high voltage transfer gain, good efficiency, and minimized coil current and capacitor voltage ripples. The POESLLC working in DCM holds the value of not having right half pole zero (RHPZ) in their control to output transfer function unlike continuous conduction mode (CCM). Also the DCM bestows superlative dynamic response, eliminates the reverse recovery troubles of diode and retains the stability. The proposed control structure involves two controllers respectively to control the voltage (outer) loop and the current (inner) loop to confront the time-varying ON/OFF characteristics of variable structured systems (VSSs) like POESLLC. This study involves two different combination of feedback controllers viz. the proportional integral controller (PIC) plus sliding mode controller (SMC) and the fuzzy logic controller (FLC) plus SMC. The state space averaging modeling of POESLLC in DCM is reviewed first, then design of PIC, FLC and SMC are detailed. The performance of developed controller combinations is studied at different working states of the POESLLC system by MATLAB-Simulink implementation. Further the experimental corroboration is done through implementation of the developed controllers in PIC 16F877A processor. The prototype uses IRF250 MOSFET, IR2110 driver and UF5408 diodes. The results reassured the proficiency of designed FLC plus SMC combination over its counterpart PIC plus SMC.

• The Journal of Korean Institute of Information Technology
• /
• v.18 no.5
• /
• pp.55-64
• /
• 2020

Estimating the state of charge (SOC) of a battery is essential for increasing the stability and reliability of a photovoltaic system. In this study, battery SOC estimation methods were proposed using artificial neural networks (ANNs) with gradient descent (GD), Levenberg-Marquardt (LM), and scaled conjugate gradient (SCG), and an adaptive neuro-fuzzy inference system (ANFIS). The charge start voltage and the integrated charge current were used as input data and the SOC was used as output data. Four models (ANN-GD, ANN-LM, ANN-SCG, and ANFIS) were implemented for battery SOC estimation and compared using MATLAB. The experimental results revealed that battery SOC estimation using the ANFIS model had both the highest accuracy and highest convergence speed.

• Journal of Korea Spatial Information System Society
• /
• v.8 no.3
• /
• pp.1-14
• /
• 2006

Recently, to efficiently support the real-time requirements of LBS and Telematics services, interest in the spatial main memory DBMS is rising. In the spatial main memory DBMS, because all spatial data can be lost when the system failure happens, the recovery system is very important for the stability of the database. Especially, disk I/O in executing the log and the checkpoint becomes the bottleneck of letting down the total system performance. Therefore, it is urgently necessary to research about the recovery system to reduce disk I/O in the spatial main memory DBMS. In this paper, we study an efficient recovery system for the spatial main memory DBMS. First, the pre-commit log method is used for the decrement of disk I/O and the improvement of transaction concurrency. In addition, we propose the fuzzy-shadow checkpoint method for the recovery system of the spatial main memory DBMS. This method can solve the problem of duplicated disk I/O on the same page of the existing fuzzy-pingpong checkpoint method for the improvement of the whole system performance. Finally, we also report the experimental results confirming the benefit of the proposed recovery system.

• Journal of Korea Trade
• /
• v.24 no.8
• /
• pp.81-100
• /
• 2020

Purpose - Based on complexity theory, this study develops a configurational model to predict the profitability of Halal cosmetics firms in the Indonesian and Malaysian markets. The proposed research model involves two level configurations-industry context and selling strategies-to predict high and low scores of a firm's profitability. The industry context configuration model comprises industry stability, product homogeneity, price sensitivity, and switching cost. Selling strategies include customer-focused, competitor-focused, and margin-focused approaches. Design/methodology - This is the first empirical study that calculates causal models using a combination of industry context and selling strategy factors to predict profitability. Data obtained from the marketing managers of cosmetics firms are used to test the proposed configurational model using fuzzy-set qualitative comparative analysis (fsQCA). It contributes to the current knowledge of business marketing by identifying the factors necessary to achieve profitability using analysis of condition (ANC). Findings - The results revealed that unique and distinct models explain the conditions for high and low profitability in the Indonesian and Malaysian halal cosmetic markets. While customer-focused selling strategy is necessary to attain a higher profit in both the markets, margin-focused selling strategy appears to be an essential factor only in Malaysia. Complexity of the interactions of selling strategies with industry factors and differences between across two study markets confirmed that complexity theory can support the research configurational model. The theoretical and practical implications are also illustrated. Originality/value - Despite the rapid growth of the global halal industry, there is little knowledge about the halal cosmetic market. This study contributes to the current literature of the halal market by performing a set of asymmetric analytical approaches using a complex theoretical model. It also deepens our understating of how the Korean firms can approach the Muslim consumer's needs to generate more beneficial turnover/revenue.

• Geomechanics and Engineering
• /
• v.37 no.4
• /
• pp.307-321
• /
• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.9 no.2
• /
• pp.7-12
• /
• 2009

The new approach was proposed which uses feature extraction based on fuzzy integral in the process of face verification using morphological shape decomposition. The centre of area was used with image pixels related with structure element and its weight in an attempt to consider neighborhood information. Therefore the morphological operators were defined for feature extraction. And then the number of decomposition images were more about 4 times than the conventional. Finally in the simulations with the extractions for face verification it was proved that the approach in this paper was even more good than the conventional in stability of feature extraction and threshold value.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.28 no.9
• /
• pp.1-7
• /
• 2014

Brushless Direct Current(BLDC) Motor is essential to measure a rotor position because of that this motor type needs to synchronize the rotor's position and changeover phase current instead of a brush and commutator used on the existing dc motor. Recently, many researches have studied on sensorless control drive for BLDC motor. The conventional control methods are a compensation value dq, Kalman filter, Fuzzy logic, Neurons neural network, and the like. These methods has difficulties of detecting BEMF accurately at low speed because of low BEMF voltage and switching noise. And also, the operation is long and complex. So, it is required a high-performance microprocessor. Therefore, it is not suitable for a small BLDC motor sensorless drive. This paper presents control methods suitable for economic small BLDC motor sensorless drive which are an improved design of the BEMF detection circuit, simplifying a complex algorithm and computation time reduction. The improved motor sensorless drive is verified stability and validity through being designed, manufactured and analyzed.

• Proceedings of the KSR Conference
• /
• 2006.11a
• /
• pp.189-197
• /
• 2006

Maglev using EMS becomes unstable by unexpected big air-gap disturbance. The main causes of the unexpected air-gap disturbance are step-wise rail joint and large distance between rail splices. For the stable operation of the Maglev, the conventional system uses the threshold method, which selects one gap sensor among two gap sensors installed on the magnet to read the gap between magnet and guide rail. But the threshold method with a wide bandwidth makes the discontinuous air-gap signal at the rail joints because of the offset in air gap sensors and/or the step-wise rail joins. Further more, in the case of the one with a narrow bend-width, it makes Maglev system unstable because of frequent alternation. In this paper, a new method using fuzzy rule to reduce air-gap disturbances proposed to improve the stability of Maglev system. It treats the air-gap signal from dual gap sensors effectively to make continuous signal without air gap disturbance. Simulation and experiment results proved that the proposed scheme was effective to reduce air-gap disturbance from dual gap sensors in rail joints.