• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.4
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• pp.175-182
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• 2021

There have been many instances of damage to buildings with soft stories, and it is important to consider vertically irregular buildings when evaluating the seismic performance of existing buildings. However, because conventional methods do not easily reflect vertical irregularities with sufficient accuracy, it is possible to underestimate or overestimate the seismic performance of buildings with vertical irregularities. This study aims to develop a seismic performance evaluation method for vertically irregular buildings using the stiffness-based soft story ratio (SSR), which is a parameter that represents the ratio of the demand and the capacity for displacement and refers to the ratio of displacement concentration in buildings. The seismic performance evaluation method developed in this study is compared with the conventional seismic performance evaluation method for four piloti buildings, using the first-floor column as a variable. Conventional seismic performance evaluation methods often overestimate the seismic performance for models in which vertical irregularities are maximized. However, results of the proposed seismic performance evaluation method are identical to those from a detailed evaluation for all models. Therefore, it is considered that the proposed seismic performance evaluation method can provide more precise seismic performance evaluation results than conventional methods in the case of piloti buildings, where vertical irregularities are maximized.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1183-1193
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• 2021

Low flow affects various fields such as river water supply management and planning, and irrigation water. A sufficient period of flow data is required to calculate the Flow Duration Curve. However, in order to calculate the Flow Duration Curve, it is essential to secure flow data for more than 30 years. However, in the case of rivers below the national river unit, there is no long-term flow data or there are observed data missing for a certain period in the middle, so there is a limit to calculating the Flow Duration Curve for each river. In the past, statistical-based methods such as Multiple Regression Analysis and ARIMA models were used to predict sulfur in the unmeasured watershed, but recently, the demand for machine learning and deep learning models is increasing. Therefore, in this study, we present the DNN technique, which is a machine learning technique that fits the latest paradigm. The DNN technique is a method that compensates for the shortcomings of the ANN technique, such as difficult to find optimal parameter values in the learning process and slow learning time. Therefore, in this study, the Flow Duration Curve applicable to the unmeasured watershed is calculated using the DNN model. First, the factors affecting the Flow Duration Curve were collected and statistically significant variables were selected through multicollinearity analysis between the factors, and input data were built into the machine learning model. The effectiveness of machine learning techniques was reviewed through statistical verification.

• Journal of the Korean Geotechnical Society
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• v.38 no.11
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• pp.107-118
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• 2022

The social need for stable hydrogen storage technologies that respond to the increasing demand for hydrogen energy is increasing. Among them, underground hydrogen storage is recognized as the most economical and reasonable storage method because of its vast hydrogen storage capacity. In Korea, low-depth hydrogen storage using artificial protective structures is being considered. Further, establishing corresponding safety standards and ground stability evaluation is becoming essential. This study evaluated the hydro-mechanical behavior of the ground during a hydrogen gas leak from a low-depth underground hydrogen storage facility through the HM coupled analysis model. The predictive reliability of the simulation model was verified through benchmark experiments. A parameter study was performed using a metamodel to analyze the sensitivity of factors affecting the surface uplift caused by the upward infiltration of high-pressure hydrogen gas. Accordingly, it was confirmed that the elastic modulus of the ground was the largest. The simulation results are considered to be valuable primary data for evaluating the complex analysis of hydrogen gas explosions as well as hydrogen gas leaks in the future.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.109-116
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• 2021

Traditionally, the engineering field has been dominated by face-to-face education focused on experimental practice, but demand for online learning has soared due to the rapid development of IT technology and Internet communication networks and recent changes in the social environment such as COVID-19. In order for efficient online education to be conducted in the engineering field, where the proportion of experimental practice is relatively high compared to other fields, virtual laboratory practice content that can replace actual experimental practice is very necessary. In this study, we developed a line tracer model and a virtual experimental software to simulate it for efficient online learning of microprocessor applications that are essential not only in the electric and electronic field but also in the overall engineering field where IT convergence takes place. In the developed line tracer model, the user can set various hardware parameter values in the desired form and write the software in assembly language or C language to test the operation on the computer. The developed line tracer virtual experimental software has been used in actual classes to verify its operation, and is expected to be an efficient virtual experimental practice tool in online non-face-to-face classes.

• Journal of Powder Materials
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• v.30 no.4
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• pp.324-331
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• 2023

Lithium (Li) is a key resource driving the rapid growth of the electric vehicle industry globally, with demand and prices continually on the rise. To address the limited reserves of major lithium sources such as rock and brine, research is underway on seawater Li extraction using electrodialysis and Li-ion selective membranes. Lithium lanthanum titanate (LLTO), an oxide solid electrolyte for all-solid-state batteries, is a promising Li-ion selective membrane. An important factor in enhancing its performance is employing the powder synthesis process. In this study, the LLTO powder is prepared using two synthesis methods: sol-gel reaction (SGR) and solid-state reaction (SSR). Additionally, the powder size and uniformity are compared, which are indices related to membrane performance. X-ray diffraction and scanning electron microscopy are employed for determining characterization, with crystallite size analysis through the full width at half maximum parameter for the powders prepared using the two synthetic methods. The findings reveal that the powder SGR-synthesized powder exhibits smaller and more uniform characteristics (0.68 times smaller crystal size) than its SSR counterpart. This discovery lays the groundwork for optimizing the powder manufacturing process of LLTO membranes, making them more suitable for various applications, including manufacturing high-performance membranes or mass production of membranes.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.337-345
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• 2023

Monitoring river microplastics is a challenging task since it is a time-consuming and high-cost process. The use of a physical model to have a better understanding of river microplastics' behaviors can complement the challenging monitoring process. However, there have been very limited studies on modeling river microplastics. In this study, therefore, we evaluated the applicability of one commonly used river water quality model, i.e., the Water Quality Analysis Simulation Program (WASP), in simulating the microplastic concentration in the river environment. We simulated the microplastic concentration in the Anyangcheon stream using the WASP's biochemical oxygen demand (BOD) and suspended solid (SS) variables as possible surrogate variables for the microplastics. Simulation analyses indicate that the SS state variable performs better than the BOD state variable to mimic the observed concentrations of microplastics. This is because of the characteristics of each water quality parameter; the BOD variable, a biochemical indicator, is inappropriate for modeling the behaviors of microplastics, which have generally constant biochemical features. In contrast, the SS variable, which has similar physical behaviors, followed the observed patterns of the microplastic concentrations well. To build a more advanced and accurate model for simulating the microplastic concentration, comprehensive and long-term monitoring studies of the river microplastics under different environmental conditions are needed, and the unit of microplastic concentration should be carefully addressed before its modeling application.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.543-551
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• 2023

As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.

• KDI Journal of Economic Policy
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• v.12 no.4
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• pp.47-69
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• 1990

Using an aggregator model, we look into the possibilities for substitution between Korea's exports, imports, domestic sales and domestic inputs (particularly labor), and substitution between disaggregated export and import components. Our approach heavily draws on an economy-wide GNP function that is similar to Samuelson's, modeling trade functions as derived from an integrated production system. Under the condition of homotheticity and weak separability, the GNP function would facilitate consistent aggregation that retains certain properties of the production structure. It would also be useful for a two-stage optimization process that enables us to obtain not only the net output price elasticities of the first-level aggregator functions, but also those of the second-level individual components of exports and imports. For the implementation of the model, we apply the Symmetric Generalized McFadden (SGM) function developed by Diewert and Wales to both stages of estimation. The first stage of the estimation procedure is to estimate the unit quantity equations of the second-level exports and imports that comprise four components each. The parameter estimates obtained in the first stage are utilized in the derivation of instrumental variables for the aggregate export and import prices being employed in the upper model. In the second stage, the net output supply equations derived from the GNP function are used in the estimation of the price elasticities of the first-level variables: exports, imports, domestic sales and labor. With these estimates in hand, we can come up with various elasticities of both the net output supply functions and the individual components of exports and imports. At the aggregate level (first-level), exports appear to be substitutable with domestic sales, while labor is complementary with imports. An increase in the price of exports would reduce the amount of the domestic sales supply, and a decrease in the wage rate would boost the demand for imports. On the other hand, labor and imports are complementary with exports and domestic sales in the input-output structure. At the disaggregate level (second-level), the price elasticities of the export and import components obtained indicate that both substitution and complement possibilities exist between them. Although these elasticities are interesting in their own right, they would be more usefully applied as inputs to the computational general equilibrium model.

• Korean Journal of Ecology and Environment
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• v.39 no.2 s.116
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• pp.198-208
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• 2006

This research was to apply a multi-metric approach, so called the Index of Biological Integrity (IBI) as a tool for biological evaluations of water environments, to a wadable stream. For the study, we surveyed 5 sampling locations in Kap Stream during August 2004 ${\sim}$ September 2005. We also compared the biological data with long-term water quality data, obtained from the Ministry of Environment, Korea and physical habitat conditions based on the Quantitative Habitat Evaluation Index (QHEI). We used ten metric systems for the IBI model to evaluate biological stream health. Overall IBI values in Kap Stream averaged 24 (range: 20${\sim}$30, n=5), indicating a "fair ${\sim}$ poor" conditions according to the modified criteria of Karr (1981) and US EPA(1993). Exclusive of 4th survey, average IBI values at the upstream reach (S1 ${\sim}$ S3)and downstream reach (S4 ${\sim}$ S5) were 20 and 24, respectively. However, in 4th survey the averages were 21 and 20 in the upstream and downstream reaches, respectively. This difference was larger in the upstream than in the downstream because of physical condition disturbed during summer monsoon. Values of the QHEI varied from 75(fair condition) to 148 (good condition) and values of QHEI in the S3 were significantly (P=0.001, n=5) lower than other sites. Biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) were greater by 3 ${\sim}$ 8 fold in the downstream than in the upstream reach. We believe that present IBI approach applied in this study may be used as a key tool to set up specific goals for restoration of Kap Stream.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.1-23
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• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.