• Journal of the Earthquake Engineering Society of Korea
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• v.6 no.6
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• pp.41-48
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• 2002

This study is on the structural control experiment for a small scale three-story building structure employing on active mass damper subjected to earthquake loading. $\mu$-synthesis controllers, which belong to robust control strategies, were designed and their performance were experimentally verified. Frequency-dependent weighting functions corresponding to disturbance input and controlled output were defined and combined to produce optimal $\mu$-synthesis controllers. The experiment result shows 60-70% reduction in RMS responses under the band-limited white noise excitation and 30-45% reduction in peak responses under the scaled earthquake excitations. Good agreement was obtained between the simulations based on the identified mathematical model and experimental results. And the simulations for the system with uncertainties show that the designed controllers are robust within a specified range of uncertainties.

• Smart Structures and Systems
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• v.18 no.1
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• pp.53-74
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• 2016

This paper presents an experimental study on constructing a tunable secondary suspension for high-speed trains using magneto-rheological fluid dampers (referred to as MR dampers hereafter), in the interest of improving lateral ride comfort. Two types of MR dampers (type-A and type-B) with different control ranges are designed and fabricated. The developed dampers are incorporated into a secondary suspension of a full-scale high-speed train carriage for rolling-vibration tests. The integrated rail vehicle runs at a series of speeds from 40 to 380 km/h and with different current inputs to the MR dampers. The dynamic performance of the two suspension systems and the ride comfort rating of the rail vehicle are evaluated using the accelerations measured during the tests. In this way, the effectiveness of the developed MR dampers for attenuating vibration is assessed. The type-A MR dampers function like a stiffness component, rather than an energy dissipative device, during the tests with different running speeds. While, the type-B MR dampers exhibit significant damping and high current input to the dampers may adversely affect the ride comfort. As part of an ongoing investigation on devising an effective MR secondary suspension for lateral vibration suppression, this preliminary study provides an insight into dynamic behavior of high-speed train secondary suspensions and unique full-scale experimental data for optimal design of MR dampers suitable for high-speed rail applications.

• Structural Engineering and Mechanics
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• v.83 no.5
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• pp.655-669
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• 2022

Passive vibration control devices like tuned liquid column dampers (TLCD) not only significantly reduce buildings' vibrations but also can serve as a water storage facility. The recently introduced modified form of TLCD known as tuned liquid column ball damper (TLCBD) suppressed external vibration efficiently compared to traditional TLCD. For excellent performance, the mass ratio of TLCBD should be in the range of 5% to 7%, which does not include the mass of the ball. This additional mass of the ball increases the overall structure mass. Therefore, in this paper, an effort is made to reduce the mass of TLCBD. For this purpose, a new modified version of TLCBD known as tuned liquid column hollow ball damper (TLCHBD) is proposed. The existing mathematical modeling of TLCBD is used for this new damper by updating the numerical values of the mass and mass moment of the ball. Analytically the optimal design parameters are obtained. Numerically the TLCHBD is investigated with a single degree of freedom structure under harmonic and seismic loadings. It is found that TLCHBD performance is similar to TLCBD in both loadings' cases. To validate the numerical results, an experimental study is conducted. The mass of the ball of TLCHBD is reduced by 50% compared to the ball of TLCBD. Both the arrangements are studied with a multi-degree of freedom structure under harmonic and seismic loadings using a shake table. The results of the experimental study confirm the numerical findings. It is found that the performance behavior of both the dampers is almost similar under harmonic and seismic loadings. In short, the TLCHBD is lighter in weight than TLCBD but has a similar vibration suppression ability.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.119-124
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• 2023

In this study, experimental approach of the measurement of condensation and evaporation heat transfer coefficients is discussed for mixed refrigerants using in the ultra low-temperature cooling system for semiconductor etching process. An experimental apparatus was described performing the condensation and evaporation heat transfer measurements for mixed refrigerants. The mixed refrigerant used in this study was composed of the optimal mixture determined in previous research, with a composition of Ar:R14:R23:R218 = 0.15:0.4:0.15:0.3. The experiments were conducted over a temperature range from -82℃ to 15℃ and at pressures ranging from 18.5 bar to 5 bar. The convection heat transfer coefficients of the mixed refrigerant were measured at flow rates corresponding to actual operating conditions. The condensation heat transfer coefficient ranged from approximately 0.7 to 0.9 kW/m2K, while the evaporation heat transfer coefficient ranged from 1.0 to 1.7 kW/m2K. The detailed discussion of the experimental methods, procedures, and results were described in this paper.

• Design & Manufacturing
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• v.17 no.4
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• pp.1-7
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• 2023

In the fabrication of curved multi-display glass for automotive use, the surface roughness of the mold is a critical quality factor. However, the difficulty in detecting micro-cutting signals in a micro-machining environment and the absence of a standardized model for predicting micro-cutting forces make it challenging to intuitively infer the correlation between cutting variables and actual surface roughness under machining conditions. Consequently, current practices heavily rely on machining condition optimization through the utilization of cutting models and experimental research for force prediction. To overcome these limitations, this study employs a surface roughness prediction formula instead of a cutting force prediction model and converts the surface roughness prediction formula into experimental data. Additionally, to account for changes in surface roughness during machining runtime, the theory of position variables has been introduced. By leveraging artificial neural network technology, the accuracy of the surface roughness prediction formula model has improved by 98%. Through the application of artificial neural network technology, the surface roughness prediction formula model, with enhanced accuracy, is anticipated to reliably perform the derivation of optimal machining conditions and the prediction of surface roughness in various machining environments at the analytical stage.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Journal of Life Science
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• v.32 no.11
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• pp.872-881
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• 2022

In this study, we optimized the composition of the indole-3-acetic acid (IAA) production medium using response surface methodology on Pantoea agglomerans SRCM 119864 isolated from soil. IAA-producing P. aglomerans SRCM 119864 was identified by 16S rRNA gene sequencing. There are 11 intermediate components known to affect IAA production, hence the effect of each component on IAA production was investigated using a Plackett-Burman design (PBD). Based on the PBD, sucrose, tryptone, and sodium chloride were selected as the main factors that enhanced the IAA production at optimal L-tryptophan concentration. The predicted maximum IAA production (64.34 mg/l) was obtained for a concentration of sucrose of 13.38 g/l, of tryptone of 18.34 g/l, of sodium chloride of 9.71 g/l, and of L-tryptophan of 6.25 g/l using a the hybrid design experimental model. In the experiment, the nutrient broth medium supplemented with 0.1% L-tryptophan as the basal medium produced 45.24 mg/l of IAA, whereas the optimized medium produced 65.40 mg/l of IAA, resulting in a 44.56% increase in efficiency. It was confirmed that the IAA production of the designed optimal composition medium was very similar to the predicted IAA production. The statistical significance and suitability of the experimental model were verified through analysis of variance (ANOVA). Therefore, in this study, we determined the optimal growth medium concentration for the maximum production of IAA, which can contribute to sustainable agriculture and increase crop yield.

• Journal of the Microelectronics and Packaging Society
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• v.27 no.1
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• pp.45-54
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• 2020

Recently, market demands of miniaturization, high interconnection density, and fine pitch of PCBs continuously keep increasing. Therefore, SLP (substrate like PCB) technology using a modified semi additive process (MSAP) has attracted great attention. In particular, SLP technology is essential for the development of high-capacity batteries and 5G technology for smartphones. In this study, the reliability of the microvia of hybrid SLP, which is made of conventional HDI (high density interconnect) and MSAP technologies, was investigated by experimental and numerical analysis. Through thermal cycling reliability test using IST (interconnect stress test) and finite element numerical analysis, the effects of various parameters such as prepreg properties, thickness, number of layers, microvia size, and misalignment on microvia reliability were investigated for optimal design of SLP. As thermal expansion coefficient (CTE) of prepreg decreased, the reliability of microvia increased. The thinner the prepreg thickness, the higher the reliability. Increasing the size of the microvia hole and the pad will alleviate stress and improve reliability. On the other hand, as the number of prepreg layers increased, the reliability of microvia decreased. Also, the larger the misalignment, the lower the reliability. In particular, among these parameters, CTE of prepreg material has the greatest impact on the microvia reliability. The results of numerical stress analysis were in good agreement with the experimental results. As the stress of the microvia decreased, the reliability of the microvia increased. These experimental and numerical results will provide a useful guideline for design and fabrication of SLP substrate.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.56-63
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• 2016

This paper presents a novel method for detecting a ghost key at the membrane type keyboard, which has additional resistive coating to the membrane film. Also, the optimal ratio of resistances for detecting a ghost key was designed based on the characteristics of the membrane film. The optimal ratio of resistances was considered to be able to detect the worst case (i.e., difference voltage between normal key and ghost key is minimum). The ability of the proposed methods are evaluated by simulation studies in this paper. In order to verify the proposed method, the experiment was carried out with a designed circuit and A/D (analog to digital) in MCU (micro controller unit). The proposed method is implemented into the membrane type keyboard and is verified by experimental results.