• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.4
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• pp.120-127
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• 2016

Due to the enlargement and high-rise of reinforced concrete structure, the application of high functional material is required. However, high-strength bar is recently introduced to the country and the material is insufficient to measure the variation of quantity of rebar quantitatively when using high-strength bar. For these reasons, this study is to provide useful data in cost decision making when applying high-strength bar at a stage of architectural project planning. For residence-commerce complex buildings, we set up six types of conditions such as in case of using only rebar, in case of using only high-strength bar, in case of using rebar mixed with high-strength bar and so on. With the standard of study model 1 that applies only SD400 regardless of rebar diameter, the analyzed result of rebar variation and the cost change of construction in other study model is as follows. When the rebar amount and cost in study model I was 100%, each ratio was 88.3% and 90.5% in study model II, 80.2% and 83.4% in study model III, 91.9% and 93.5% in study model IV, 88.9% and 87.7% in study model V and 82.4% and 85.5% in study model VI. Therefore, in case of rebar amount and construction cost, study model III was evaluated as the best that was applied only SD600.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.80-90
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• 2002

Clustering is a useful technique for grouping data points such that points within a single group/cluster have similar characteristics. Many clustering algorithms have been developed and used in engineering applications including pattern recognition and image processing etc. Recently, it has drawn increasing attention as one of important techniques in data mining. However, clustering algorithms such as K-means and Fuzzy C-means suffer from difficulties. Those are the needs to determine the number of clusters apriori and the clustering results depending on the initial set of clusters which fails to gain desirable results. In this paper, we propose a new clustering algorithm, which solves mentioned problems. In our method we use evolutionary algorithm to solve the local optima problem that clustering converges to an undesirable state starting with an inappropriate set of clusters. We also adopt a new measure that represents how well data are clustered. The measure is determined in terms of both intra-cluster dispersion and inter-cluster separability. Using the measure, in our method the number of clusters is automatically determined as the result of optimization process. And also, we combine heuristic that is problem-specific knowledge with a evolutionary algorithm to speed evolutionary algorithm search. We have experimented our algorithm with several sets of multi-dimensional data and it has been shown that one algorithm outperforms the existing algorithms.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.6
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• pp.295-306
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• 2016

The purpose of this paper is both to analyze research trends and to predict industrial flows using the meta-data from the previous studies on data quality. There have been many attempts to analyze the research trends in various fields till lately. However, analysis of previous studies on data quality has produced poor results because of its vast scope and data. Therefore, in this paper, we used a text mining, social network analysis for time series network analysis to analyze the vast scope and data of data quality collected from a Web of Science index database of papers published in the international data quality-field journals for 10 years. The analysis results are as follows: Decreases in Mathematical & Computational Biology, Chemistry, Health Care Sciences & Services, Biochemistry & Molecular Biology, Biochemistry & Molecular Biology, and Medical Information Science. Increases, on the contrary, in Environmental Sciences, Water Resources, Geology, and Instruments & Instrumentation. In addition, the social network analysis results show that the subjects which have the high centrality are analysis, algorithm, and network, and also, image, model, sensor, and optimization are increasing subjects in the data quality field. Furthermore, the industrial connection analysis result on data quality shows that there is high correlation between technique, industry, health, infrastructure, and customer service. And it predicted that the Environmental Sciences, Biotechnology, and Health Industry will be continuously developed. This paper will be useful for people, not only who are in the data quality industry field, but also the researchers who analyze research patterns and find out the industry connection on data quality.

• Journal of Korea Water Resources Association
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• v.50 no.3
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• pp.191-200
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• 2017

A robust parameter set (ROPS) selection framework for an unsteady flow model was developed by combining Pareto optimums obtained by outcomes of model calibration using multi-site observations with the minimax regret approach (MRA). The multi-site calibration problem which is a multi-objective problem was solved by using an aggregation approach which aggregates the weighted criteria related to different sites into one measure, and then performs a large number of individual optimization runs with different weight combinations to obtain Pareto solutions. Roughness parameter structure which can describe the variation of Manning's n with discharges and sub-reaches was proposed and the related coefficients were optimized as model parameters. By applying the MRA which is a decision criterion, the Pareto solutions were ranked based on the obtained regrets related to each Pareto solution, and the top-rated one due to the lowest aggregated regrets of both calibration and validation was determined as the only ROPS. It was found that the determination of variable roughness and the corresponding standardized RMSEs at the two gauging stations varies considerably depending on the combinations of weights on the two sites. This method can provide the robust parameter set for the multi-site calibration problems in hydrologic and hydraulic models.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.3
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• pp.195-201
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• 2016

Eddy current testing (ECT) is commonly applied for the inspection of automated production lines of metallic products, because it has a high inspection speed and a reasonable price. When ECT is applied for the inspection of a metallic object having an uneven target surface, such as the spline gear of a spline shaft, it is difficult to distinguish between the original signal obtained from the sensor and the signal generated by a defect because of the relatively large surface signals having similar frequency distributions. To facilitate the detection of defect signals from the spline gear, implementation of high-order filters is essential, so that the fault signals can be distinguished from the surrounding noise signals, and simultaneously, the pass-band of the filter can be adjusted according to the status of each production line and the object to be inspected. We will examine the infinite impulse filters (IIR filters) available for implementing an advanced filter for ECT, and attempt to detect the flaw signals through optimization of system design parameters for detecting the signals at the system level.

• Journal of Energy Engineering
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• v.13 no.4
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• pp.242-258
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• 2004

The duplex burnable poison absorbers concept was suggested by Korea Atomic Energy Research Institute. This BP rod is composed of inner region of natural U-Gd$_2$O$_3$ and outer shell of enriched UO$_2$-Er$_2$O$_3$. It is expected that this burnable absorber has same reactivity control capability with gadolinia burnable absorber used in extened fuel cycle. In order to evaluate the nuclear feasibility of duplex BPs, the nuclear design characteristics were compared with that of four types of burnable absorbers; gadolinia, erbia, IFBA, dysprosia duplex BP on 24 months fuel cycle for Korean Standard Nuclear Power plants. According to the evaluation results of nuclear characteristics, the duplex BPs were better than other BPs on k-infinitives, reactivity holddown worth (RHW), pin power peaking and moderator temperature coefficient (MTC). The possibility of nuclear core design was also confirmed based on the optimized fuel assemblies which were searched for a sensitivity analysis. Characteristics of core design with duplex BPs was compared with that of reference core with gadolinia BPs for cycle length, power peaking and MTC. The duplex BP core had a little longer cycle length by 4 to 7 days because of increased amount of fissile in enriched uranium at the outer shell of duplex BP In case of power peaking F$\_$Q/ of duplex BP core was reduced from 1.5773 to 1.5335. MTC was also less -0.48 pcm/C than that of reference core. Finally, evaluation of fuel cycle economy was performed for the manufacturing feasibility test and fuel cost evaluation with duplex BPs. Fuel cycle economy of duplex BP core almost was equivalent with that of gadolinia BP core.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.281-286
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• 2021

The International Maritime Organization recently proposed a policy to establish a preemptive response strategy for exhaust gas pollution on board ships according to the recent strengthening of the sulfur content regulations. Discussions on improving the fuel oil quality and reducing emissions are also ongoing. Fuel oil quality information, which is one of the main concerns internationally, is increasing as the sulfur content standard is being applied from the current 3.5% to 0.5% by 2020. From the perspective of shipping companies and recipients, the essential quality of fuel oil is also requested for domestic and international fuel oil information, basic properties, correlation information between characteristics for application of solid ships and ships. The current standard for the basic quality of fuel oil is generally used, but the nature and composition of the fuel oil are very complex, and the interpretation of the basic quality is complicated because there are many cases outside the scope of the basic standard. Various factors were analyzed for the basic quality of fuel oil in terms of the basic quality of fuel oil, optimization of operation in ships, and fuel efficiency in ships. Moreover, the possibility of applying the standard according to the dilution was suggested.

• Microbiology and Biotechnology Letters
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• v.47 no.3
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• pp.390-400
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• 2019

In this study, strains isolated from soil samples collected from Busan, Changwon, and Jeju Island were examined to verify their abilities of phosphate solubilization and nitrogen fixation, production of indole-3-acetic acid (IAA), siderophore, and 1-aminocylopropane-1-carboxylyic acid (ACC) deaminase in order to select strains that promote plant growth and play a role in biocontrol of pests or pathogens. According to the results of this study, most of the isolated strains were found to have ability of phosphate solubilization, nitrogen fixation, IAA production, siderophore production, and production of ACC deaminase. These isolated strains might help plant growth by directly improving absorption of nutrients essential for phosphate solubilization and nitrogen fixation. In addition, they can promote plant growth and control resistance to plant diseases through extracellular enzyme activity and antifungal activity. In addition, most of the selected strains were found to survive in various environmental conditions such as temperature, salinity, and pH. Therefore, Pseudomonas plecoglossicida ANG14, Pseudarthrobacter equi ANG28, Beijerinckia fluminensis ANG34, and Acinetobacter calcoaceticus ANG35 were finally selected through a comparative advantage analysis to suggest their potential as novel biological agents. Further studies are necessary in order to prove their efficacy as novel biological agents through formulation and optimization of effective microorganisms, their preservation period, and crop cultivation tests.

• Journal of the Korean Institute of Gas
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• v.23 no.1
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• pp.27-35
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• 2019

In order to solve the global warming and reduce greenhouse gas emissions, $CO_2$ capture technology was developed by applying oxy-fuel combustion. But there has been such a problem that its economic efficiency is low due to the high price of oxygen gases. ASU is known to be most suitable method to produce large quantity of oxygen, to reduce the oxygen production cost, the efficiency of ASU need to be improved. To improve the efficiency of ASU, exergy analysis can be used. The exergy analysis provides the information of used energy in the process, the location and size of exergy destruction. In this study, the exergy analysis was used for process developing and optimization of large scale ASU. The process simulation of ASU was conducted, the results were used to calculate the exergy. As a result, to reduce the exergy loss in the cold box of ASU, a lower operating pressure process was suggested. It was confirmed the importance of heat leak and heat loss reduction of cold box. Also, the unit process of ASU which requires thermal integration was confirmed.

• Korean Chemical Engineering Research
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• v.59 no.2
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• pp.191-199
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• 2021

A distillation column, which is a main facility of the chemical process, separates the desired product from a mixture by using the difference of boiling points. The distillation process requires the optimization and the prediction of operation because it consumes much energy. The target process of this study is difficult to operate efficiently because the composition of feed flow is not steady according to the supplier. To deal with this problem, we could develop a data-driven model to predict operating conditions. However, data preprocessing is essential to improve the predictive performance of the model because the raw data contains outlier and noise. In this study, after optimizing the predictive model based long-short term memory (LSTM) and Random forest (RF), we used a low-pass filter and one-class support vector machine for data preprocessing and compared predictive performance according to the method and range of the preprocessing. The performance of the predictive model and the effect of the preprocessing is compared by using R2 and RMSE. In the case of LSTM, R2 increased from 0.791 to 0.977 by 23.5%, and RMSE decreased from 0.132 to 0.029 by 78.0%. In the case of RF, R2 increased from 0.767 to 0.938 by 22.3%, and RMSE decreased from 0.140 to 0.050 by 64.3%.