• Journal of Korean Society of Disaster and Security
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• v.15 no.3
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• pp.79-88
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• 2022

Cable-supported bridges, as important large infrastructures, require a long-term and systematic maintenance strategy. In particular, various methods have been proposed to secure safety for the bridges, such as installing various types of sensor on members in the bridges, and setting management thresholds. It is evidently necessary to propose a strategic plan to efficiently manage increasing number of cable-supported bridges and data collected from a number of sensors. This study aims to develop an analysis tool that can automatically remove abnormal signals and calculate statistical results for the purpose of efficiently analyzing a wide range of data collected from a long span bridge measurement system. To develop the tool, basic information such as the types and quantity of sensors installed in long span bridges and signal characteristics of the collected data were analyzed. Thereafter, the Humpel filtering method was used to determine the presence or absence of an abnormality in the signal and then filtered. The statistical results with filtered data were shown. Finally, one cable-stayed bridge and one suspension bridge currently in use were chosen as the target bridges to verify the performance of the developed tool. Signal processing and statistical analysis with the tool were performed. The results are similar to the results reported in the existing work.

• Biotechnology and Bioprocess Engineering:BBE
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• v.9 no.6
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• pp.459-464
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• 2004

The objective of this study is to improve cephalosporin C (CPC) production byoptimization of medium and culture conditions. A statistical method was introduced to optimize the main culture medium. The main medium for CPC production was optimized using a statistical method. Glucose and corn steep liquor (CSL) were found to be the most effective factors for CPC production. Glucose and CSL were optimized to 2.84 and $6.68\%$, respectively. CPC produc­tion was improved $50\%$ by feeding of $5\%$ rice oil at day 3rd and 5th day during the shake flask culture of C acremonium M25. The effect of agitation speeds on CPC production in a 2.5-L bio­reactor was also investigated with fed-batch mode. The maximum cell mass (54.5 g/L) was obtained at 600 rpm. However, the maximum CPC production (0.98 g/L) was obtained at 500 rpm. At this condition, the maximum CPC production was improved about $132\%$ compared to the re­sult with batch flask culture.

• Journal of Microbiology and Biotechnology
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• v.19 no.4
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• pp.378-386
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• 2009

A sequential optimization strategy, based on statistical experimental designs, is employed to enhance the production of alkaline protease by a Bacillus pseudofirmus local isolate. To screen the bioprocess parameters significantly influencing the alkaline protease activity, a 2-level Plackett-Burman design was applied. Among 15 variables tested, the pH, peptone, and incubation time were selected based on their high positive significant effect on the protease activity. A near-optimum medium formulation was then obtained that increased the protease yield by more than 5-fold. Thereafter, the response surface methodology(RSM) was adopted to acquire the best process conditions among the selected variables, where a 3-level Box-Behnken design was utilized to create a polynomial quadratic model correlating the relationship between the three variables and the protease activity. The optimal combination of the major medium constituents for alkaline protease production, evaluated using the nonlinear optimization algorithm of EXCEL-Solver, was as follows: pH of 9.5, 2% peptone, and incubation time of 60 h. The predicted optimum alkaline protease activity was 3,213 U/ml/min, which was 6.4 times the activity with the basal medium.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.6
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• pp.698-705
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• 2008

When electromagnetic waves propagate through atmosphere, waves are affected by various factors. Atmosphere normally consists of different molecular species, water vapours, rain, fog, snow and small suspended particles called aerosols. The distributions of atmosphere molecules, water vapours, rain rate, snowfall and aerosol are dependent on geometrical regions or environment. In order to predict propagation characteristics in atmospheric environment, statistical analysis of the relevant parameters such as temperature, humidity, atmospheric pressure, wind speed, areosol and rainfall is crucial. In this paper, we performed a long-term statistical analysis for the atmospheric parameters in domestic environments and analyzed the propagation characteristics through atmosphere based on that.

• Journal of Power System Engineering
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• v.21 no.3
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• pp.37-44
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• 2017

In this study, failure analysis of the angular pin for molding machines to aluminum component molding was carried out. SM45C steel was used for the angular pin, it was surface hardened by the induction surface hardening heat treatment. The cross section of damaged angular pin was observed, and micro Vickers hardness value from the fractured part was measured. Brittle fracture was occurred from the fracture surface of angular pin, therefore, impact toughness value was evaluated by V-notch Charpy impact test. It was confirmed that the impact absorption energy was high when was tempered at a high temperature for a long time, and the toughness was slightly increased. Also, 2-parameter Weibull statistical analysis was investigated in order to evaluate the reliability of the measured micro Vickers hardness values and absorbed energy. The micro Vickers hardness and absorbed energy well followed a two-parameter Weibull probability distribution, respectively. The reverse design against angular pin was proposed as possible by using test results.

• Journal of Korean Society of Water and Wastewater
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• v.29 no.4
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• pp.519-531
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• 2015

It has been widely accepted that the pressure management of water distribution systems using pressure reducing valves(PRVs) would be an effective method for controlling leakages. A pressure reducing valve (PRV) regulates outlet pressure regardless of fluctuating flow and varying inlet pressure, thereby reducing leakage and mitigating the stress on the water distribution system. However, the operation of a PRV is vulnerable to its mechanical condition and hydraulic operability. In this research, the effect of PRVs installed in water distribution system are evaluated in terms of hydraulic pressure reduction and mechanical performance by analyzing measured pressure data with statistical approach. A statistical approach using the moving average filter and frequency analysis based on fourier transform is presented to detect abnormally operated PRVs that have been densely installed in water distribution system. The result shows that the proposed approach can be a good performance evaluation method by simply measuring pressures for the PRVs.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2002.05a
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• pp.104-109
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• 2002

In this paper, a statistical analysis of fatigue crack growth behavior under constant amplitude loads has been carried out. Fatigue crack growth tests were conducted on sixteen pre-cracked compact tension (CT) specimens of the pressure vessel (SPV50) steel in controlled identical load and environmental conditions. The assessment of the statistical distribution of fatigue crack growth experimental data obtained from SPV50 steel was studied and also the correlation of the parameter C and m in the Paris-Erdogan law was discussed. The probability distribution function of fatigue crack growth life seems to follow the 3-parameter Weibull. The fatigue crack growth rate seems to follow the 3-parameter Weibull and the log-normal distribution. The coefficient of variation (COV) of fatigue crack growth life was observed to decrease as the crack grows. A strong negative linear correlation exists between the coefficient C and the exponent m in Paris model. Fatigue crack growth rate data shows a normal distribution for both m and logC.

• Journal of the Korean Institute of Gas
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• v.10 no.4 s.33
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• pp.29-33
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• 2006

Estimation of process safety is important in the chemical process design. Prediction for flash points of flammable substances used in chemical processes is the one of the methods for estimating process safety. Flash point is the property used to examine the potential for the fire and explosion hazards of flammable substances. In this paper, multivariate statistical analysis methods(partial least squares(PLS) quadratic partial least squares(QPLS)) using experimental data is suggested for predicting flash points of flammable substances of binary systems. The prediction results are compared with the values calculated by laws of Raoult and Van Laar equation.

• Smart Structures and Systems
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• v.6 no.4
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• pp.363-372
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• 2010

Accurate peak response estimation of a seismically excited structure with frictional damping system (FDS) is very difficult since the structure with FDS shows nonlinear behavior dependent on the structural period, loading characteristics, and relative magnitude between the frictional force and the excitation load. Previous studies have estimated the peak response of the structure with FDS by replacing a nonlinear system with an equivalent linear one or by employing the response spectrum obtained based on nonlinear time history and statistical analysis. In case that earthquake excitation is defined probabilistically, corresponding response of the structure with FDS becomes to have probabilistic distribution. In this study, nonlinear time history analyses were performed for the structure with FDS subjected to artificial earthquake excitation generated using Kanai-Tajimi filter. An equation for the probability density function (PDF) of the displacement response is proposed by adapting the PDF of the normal distribution. Coefficients of the proposed PDF are obtained by regression of the statistical distribution of the time history responses. Finally, the correlation between the resulting PDFs and statistical response distribution is investigated.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.5
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• pp.828-834
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• 1997

The knowledge of material stress-strain behavior is an essential requirement for design and analysis of deformation processes. Empirical stress-strain relationship and constitutive equations describing material behavior during deformation are being widely used, despite suffering some drawbacks in terms of ease of development, accuracy and speed. In the present study, back-propagation neural networks are used to model and predict the flow stresses of a HSLA steel under conditions of constant strain, strain rate and temperature. The performance of the network model is comparedto those of statistical models on rate equations. Well-trained network model provides fast and accurate results, making it superior to statistical models.