• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.47-67
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• 2017

Steel plate faults is one of important factors to affect the quality and price of the steel plates. So far many steelmakers generally have used visual inspection method that could be based on an inspector's intuition or experience. Specifically, the inspector checks the steel plate faults by looking the surface of the steel plates. However, the accuracy of this method is critically low that it can cause errors above 30% in judgment. Therefore, accurate steel plate faults diagnosis system has been continuously required in the industry. In order to meet the needs, this study proposed a new steel plate faults diagnosis system using Simultaneous MTS (S-MTS), which is an advanced Mahalanobis Taguchi System (MTS) algorithm, to classify various surface defects of the steel plates. MTS has generally been used to solve binary classification problems in various fields, but MTS was not used for multiclass classification due to its low accuracy. The reason is that only one mahalanobis space is established in the MTS. In contrast, S-MTS is suitable for multi-class classification. That is, S-MTS establishes individual mahalanobis space for each class. 'Simultaneous' implies comparing mahalanobis distances at the same time. The proposed steel plate faults diagnosis system was developed in four main stages. In the first stage, after various reference groups and related variables are defined, data of the steel plate faults is collected and used to establish the individual mahalanobis space per the reference groups and construct the full measurement scale. In the second stage, the mahalanobis distances of test groups is calculated based on the established mahalanobis spaces of the reference groups. Then, appropriateness of the spaces is verified by examining the separability of the mahalanobis diatances. In the third stage, orthogonal arrays and Signal-to-Noise (SN) ratio of dynamic type are applied for variable optimization. Also, Overall SN ratio gain is derived from the SN ratio and SN ratio gain. If the derived overall SN ratio gain is negative, it means that the variable should be removed. However, the variable with the positive gain may be considered as worth keeping. Finally, in the fourth stage, the measurement scale that is composed of selected useful variables is reconstructed. Next, an experimental test should be implemented to verify the ability of multi-class classification and thus the accuracy of the classification is acquired. If the accuracy is acceptable, this diagnosis system can be used for future applications. Also, this study compared the accuracy of the proposed steel plate faults diagnosis system with that of other popular classification algorithms including Decision Tree, Multi Perception Neural Network (MLPNN), Logistic Regression (LR), Support Vector Machine (SVM), Tree Bagger Random Forest, Grid Search (GS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The steel plates faults dataset used in the study is taken from the University of California at Irvine (UCI) machine learning repository. As a result, the proposed steel plate faults diagnosis system based on S-MTS shows 90.79% of classification accuracy. The accuracy of the proposed diagnosis system is 6-27% higher than MLPNN, LR, GS, GA and PSO. Based on the fact that the accuracy of commercial systems is only about 75-80%, it means that the proposed system has enough classification performance to be applied in the industry. In addition, the proposed system can reduce the number of measurement sensors that are installed in the fields because of variable optimization process. These results show that the proposed system not only can have a good ability on the steel plate faults diagnosis but also reduce operation and maintenance cost. For our future work, it will be applied in the fields to validate actual effectiveness of the proposed system and plan to improve the accuracy based on the results.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• Journal of the Earthquake Engineering Society of Korea
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• v.11 no.1 s.53
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• pp.11-19
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• 2007

This paper proposes an optimal design method of distribution and capacities of linear viscous dampers for vibration control of two adjacent buildings. The previous researches have dealt with suboptimal design problem under the assumption that linear viscous dampers are distributed uniformly or proportionally to the sensitivity of the modal damping ratio according to floors, whereas this study deals with global optimization problem in which the damping capacities of each floor are independently selected as design parameters. For this purpose, genetic algorithm to effectively search multiple design variables in large searching domains is adopted and objective function leading to the global optimal solutions is established through the comparison of several optimal design values obtained from different objective functions with control performance and damping capacity. The effectiveness of the proposed method is investigated by comparing the control performance and total damping capacity designed by the proposed method with those of the previous method. In addition, the time history analyses are performed by using three historical earthquakes with different frequency contents, and the simulation results demonstrate that the proposed method is an effective seismic design method for the vibration control of the adjacent structures.

• Journal of the Korean Solar Energy Society
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• v.37 no.2
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• pp.77-85
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• 2017

Thin crystalline silicon (C-Si) solar cell is expected to be a low price energy source by decreasing the consumption of Si. However, thin c-Si solar cell entails the bowing and crack issues in high temperature manufacturing process. Thus, the conventional tabbing process, based on high temperature soldering (> $250^{\circ}C$), has difficulties for applying to thin c-Si solar cell modules. In this paper, a conductive paste (CP) based interconnection process has been proposed to fabricate thin c-Si solar cell modules with high production yield, instead of existing soldering materials. To optimize the process condition for CP based interconnection, we compared the performance and stability of modules fabricated under various lamination temperature (120, 150, and $175^{\circ}C$). The power from CP based module is similar to that with conventional tabbing process, as modules are fabricated. However, the output of CP based module laminated at $120^{\circ}C$ decreases significantly (14.1% for Damp heat and 6.1% for thermal cycle) in harsh condition, while the output drops only in 3% in the samples process at $150^{\circ}C$, $175^{\circ}C$. The peel test indicates that the unstable performance of sample laminated at $120^{\circ}C$ is attributed to weak adhesion strength (1.7 N) between cell and ribbon compared to other cases (2.7 N). As a result, optimized lamination temperature for CP based module process is $150^{\circ}C$, considering stability and energy consumption during the fabrication.

• The KIPS Transactions:PartA
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• v.12A no.5 s.95
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• pp.451-460
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• 2005

We present a methodology to design energy-efficient data paths using FPGAs. Our methodology integrates domain specific modeling, coarse-grained performance evaluation, design space exploration, and low-level simulation to understand the tradeoffs between energy, latency, and area. The domain specific modeling technique defines a high-level model by identifying various components and parameters specific to a domain that affect the system-wide energy dissipation. A domain is a family of architectures and corresponding algorithms for a given application kernel. The high-level model also consists of functions for estimating energy, latency, and area that facilitate tradeoff analysis. Design space exploration(DSE) analyzes the design space defined by the domain and selects a set of designs. Low-level simulations are used for accurate performance estimation for the designs selected by the DSE and also for final design selection We illustrate our methodology using a family of architectures and algorithms for matrix multiplication. The designs identified by our methodology demonstrate tradeoffs among energy, latency, and area. We compare our designs with a vendor specified matrix multiplication kernel to demonstrate the effectiveness of our methodology. To illustrate the effectiveness of our methodology, we used average power density(E/AT), energy/(area x latency), as themetric for comparison. For various problem sizes, designs obtained using our methodology are on average $25\%$ superior with respect to the E/AT performance metric, compared with the state-of-the-art designs by Xilinx. We also discuss the implementation of our methodology using the MILAN framework.

• Clean Technology
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• v.24 no.3
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• pp.239-248
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• 2018

In this study, pervaporation experiments of water, ethanol and IPA (Isopropyl alcohol) single components and water/ethanol, water/IPA mixtures were carried out using zeolite 4A membranes developed by Fine Tech Co. Ltd. Those membranes were fabricated by hydrothermal synthesis (growth in hydrothermal condition) after uniformly dispersing the zeolite seeds on the tubular alumina supports. They have a pore size of about $4{\AA}$ by ion exchange of $Na^+$ to the LTA structure with Si/Al ratio of 1.0, and shows strong hydrophilic property. Physical characteristics of prepared membranes were evaluated by using SEM (surface morphology), porosimetry (macro- or meso- pore analysis), BET (micropore analysis), and load tester (compressive strength). Pervaporation experiments with various temperature and concentration conditions confirmed that the zeolite 4A membrane can selectively separate water from ethanol and IPA. Water/ethanol separation factor was over 3,000 and water/IPA separation factor was over 1,500 (50 : 50 wt%, initial feed concentration). Pervaporation behaviors of single components and binary mixtures were predicted using ACM (activity coefficient model), GMS (generalized Maxwell Stefan) model and DGM (Dusty Gas Model). The adsorption and diffusion coefficients of the zeolite top layer were obtained by parameter estimation using GA (Genetic Algorithm, stochastic optimization method). All the calculations were carried out using MATLAB 2018a version.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.540-548
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• 2012

The Weapon Target Allocation(WTA) problem is the NP-Complete problem. The WTA problem is that the threatful air targets are assigned by weapon of allies for killing the targets. A good solution of NP-complete problem is heuristic algorithms. Genetic algorithms are commonly used heuristic for global optimization, and it is good solution on the diverse problem domain. But there has been very little research done on the generation of their initial population. The initialization of population is one of the GA step, and it decide to initial value of individuals. In this paper, we propose to the population initialization method to improve a Genetic Algorithm. When it initializes population, the proposed algorithm reflects the characteristics of the WTA problem domain, and inherits the dominant gene. In addition, the search space widely spread in the problem space to find efficiently the good quality solution. In this paper, the proposed algorithm to verify performance examine that an analysis of various properties and the experimental results by analyzing the performance compare to other algorithms. The proposed algorithm compared to the other initialization methods and a general genetic algorithm. As a result, the proposed algorithm showed better performance in WTA problem than the other algorithms. In particular, the proposed algorithm is a good way to apply to the variety of situation WTA problem domain, because the proposed algorithm can be applied flexibly to WTA problem by the adjustment of RMI.

• Therapeutic Science for Rehabilitation
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• v.8 no.4
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• pp.33-39
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• 2019

Objective : The purpose of this study is to examine the effect of successful intervention program on SOC strategy and successful aging of the elderly. Methods : Three elderly individuals aged ${\geq}65$ living in W-si, Gangwon-do, were included. A successful aging program using an SOC strategy was conducted twice a week for 6 sessions. To verify the effectiveness of the program, the Korean versions of the SOC scale and the successful aging scale were used. Descriptive statistics were used for general characteristics, and the Wilcoxon signed-rank test was used to compare successful aging and SOC strategies. Results : After 3 weeks for successful aging program, the SOC strategy levels of the subjects were constant or improved. In addition, the average score of successful aging level increased by 11.4 points. Conclusion : This study show that the successful aging program using the SOC strategy improved the level of SOC strategy and the level of successful aging. The results of this study can be used as basic data to develop programs that can promote SOC strategy and successful aging. Also, the result of this study can be used as a basic data for the development of guidelines for occupational therapists in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1D
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• pp.107-117
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• 2008

Private financing is playing an increasing role in public infrastructure construction projects worldwide. However, private investors/operators are exposed to the financial risk of low profitability due to the inaccurate estimation of facility demand, operation income, maintenance costs, etc. From the operator's perspective, a sound and thorough financial feasibility study is required to establish the appropriate capital structure of a project. Operators tend to reduce the equity amount to minimize the level of risk exposure, while creditors persist to raise it, in an attempt to secure a sufficient level of financial involvement from the operators. Therefore, it is important for creditors and operators to reach an agreement for a balanced capital structure that synthetically considers both profitability and repayment capacity. This paper presents an optimal capital structure model for successful private infrastructure investment. This model finds the optimized point where the profitability is balanced with the repayment capacity, with the use of the concept of utility function and multi-objective GA (Generic Algorithm)-based optimization. A case study is presented to show the validity of the model and its verification. The research conclusions provide a proper capital structure for privately-financed infrastructure projects through a proposed multi-objective model.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.993-1000
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• 2023

Recently, enviromentally friendly natural substances derived from plants have been attracting attention as cosmetic materials, and research on various physiological activities of natural substances is being actively conducted. This study investigated the antioxidant, anti-inflammatory, moisturizing, and antibacterial effects of three types of extracts of mixtures containing different mixing ratios, Polygala tenuifolia, Angelica dahurica, and Elsholtzia splendens, known to have various physiological activities. The mixing ratio is 7 conditions (M1, 1:1:1; M2, 0.5:1.5:1; M3, 1.5:0.5:1; M4, 0.1:0.95:0.95; M5, 0.5:0.5:2; M6, 0.95 :1.95:0.1; M7, 1.45:0.1:1.45), and the optimal mixing ratio was confirmed for use as a cosmetic material. DPPH and ABTS radical scavenging activities showed scavenging abilities of 75.37% and 99.19%, respectively, at 1,000 ㎍/mL of M6. At a concentration of 200 ㎍/mL of M6, it showed 50% of nitric oxide production inhibition compared to the lipopolysaccharide-treated that induced an inflammatory response. It was confirmed that M3 and M6 produced hyaluronic acid 1.47 and 1.49 times higher than the control at a concentration of 50 ㎍/mL, respectively. Through the disc diffiusion test, the clear zone was 9.75 mm at 8 ㎍/mL of M6, confirming the inhibition of growth of staplylococcus aureus strain. Based on the above results, it is believed that the mixed extract of Polygala tenuifolia, Angelica dahurica, and Elsholtzia splendens can be used as a functional natural material for cosmetics.