• Journal of IKEEE
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• v.25 no.2
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• pp.261-266
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• 2021

This paper suggests design methodology for thermal infrared data correction algorithms considering environmental temperature variations. First, a thermal infrared measurement model is suggested by a parameter-dependent first-order input-output equation using the relationship between infrared measurement data and model environmental parameters. In order to compensate the influence of environmental temperatures on infrared data, a compensation function is identified. Through experiments, the proposed algorithm is shown to reduce the influence of environmental temperatures on the infrared data effectively.

• Journal of Soil and Groundwater Environment
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• v.12 no.1
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• pp.44-52
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• 2007

This study reviewed the overall process of application of contaminant fate and transport model as part of risk assessment. Site characterization and establishment of a conceptual model prior to establishing or selecting a appropriate model were described. Types of models, model selection guidance, and generic site conditions for model application were presented, the process of model calibration, validation, and sensitivity analysis were reviewed. Objectives of modeling should be defined before model selection, and the complexity of selected models should balance the quantity and quality of available input data with the desired model output. If model output is highly sensitive to an assumed or default value of input parameter, or fate and transport models cannot be adequately calibrated or validated, consideration should be given to other options such as using measured data or using another model.

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.4
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• pp.589-605
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• 1996

Recycling of used products and components has been considered as one of promising strategies for resolving environmental problems. In this respect, most manufacturing companies begin to consider possible recycling (e.q., reuse or re-production) of the components contained in their products. The primary objective of this research is to develop a multiple criteria decision making model for systematic management of recycle-oriented manufacturing components. The production planning problem of recycle-oriented manufacturing components is first formulated as a multiobjective mixed 0-1 integer programming model with three conflicting objectives. An interactive multiple criteria decision making method is then developed for solving the mathematical model. Also, an Input/Output analysis software is developed to help practitioners apply the model to real problems without much knowledge on computers and mathematical programming. A numerical example is used in examining the validity of the proposed model and to investigate the impact of the input variables on recycling production strategy.

• Journal of Korea Trade
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• v.25 no.2
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• pp.58-74
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• 2021

Purpose - This paper research on the embodied carbon emission in Sino-Korea trade. It calculates and analyzes the carbon emission coefficient and specific carbon emissions in Sino-Korea trade from 2005 to 2014. Design/methodology - This paper conducted an empirical analysis for embodied carbon emission in Sino-Korea trade during the years 2005-2014, using a multi-region input-output model. First, direct and complete CO2 emission coefficient of the two countries were calculated and compared. On this basis, combined with the world input-output table, the annual import and export volume and sector volume of embodied carbon emission are determined. Then through the comparative analysis of the empirical results, the reasons for the carbon imbalance in Sino-Korea trade are clarified, and the corresponding suggestions are put forward according to the environmental protection policies being implemented by the two countries. Findings - The results show that South Korea is in the state of net trade export and net embodied carbon import. The carbon emission coefficient of most sectors in South Korea is lower than that of China. However, the reduction of carbon emission coefficient in China is significantly faster than that in South Korea in this decade. The change of Korea's complete CO2 emission coefficient shows that policy factors have a great impact on environmental protection. The proportion of intra industry trade between China and South Korea is relatively large and concentrated in mechanical and electrical products, chemical products, etc. These sectors generally have large carbon emissions, which need to be noticed by both countries. Originality/value - To the best knowledge of the authors, this study is the first attempt to research the embodied carbon emission of ten consecutive years in Sino-Korea Trade. In addition, In this paper, some mathematical methods are used to overcome the error problem caused by different statistical caliber in different databases. Finally, the accurate measurement of carbon level in bilateral trade will provide some reference for trade development and environmental protection.

• Journal of Korean Society on Water Environment
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• v.33 no.6
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• pp.769-779
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• 2017

Nutrient (i.e., nitrogen and phosphorus) budgets are required under a 'Livestock Excreta Survey'. A nutrient budget is one of the agri-environmental indicators that calculates the difference between the inputs and outputs of the amount of nutrients within a certain boundary and for a certain time period (e.g., 1 year). In this study, a nutrients budget model was developed to effectively determine the surplus of nutrients within a region in Korea. The C# program language was used in order to facilitate the deployment of a graphical user interface (GUI) and to enhance compatibility. Also, the model was developed on Windows OS, which is the commonly used operating system in Korea. The model was based on the OECD/Eurostat nutrient budget method, and it was modified to consider manure composting procedures as well. There are key features of the nutrient budget model, including directly use of the original data sets from various input and output sources, and a collectively exchange of the address in different formats. The model can quickly show the results of various spatial and temporal resolutions with the same data, as well as perform a sensitivity analysis with coefficients and easily compareresults using tables and graphs. Further, it would be necessary to study the extension of the scope of utilization, such as the application of various nutrient budget methods. It would also be helpful to investigate both pre and postprocessing information such as linking input data through online systems.

• Environmental and Resource Economics Review
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• v.16 no.2
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• pp.313-336
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• 2007

The paper is concerned with, firstly, estimating the North Korean input-output table in which energy sectors like electricity and petro products are specified and, secondly, computing the effect of an investment in the energy sector on North Korean economy, by using the estimated input-output table and applying CGE analysis. The 4,000 million dollar investment on North Korean electricity industry produces 368 million dollar worth of output and 156 million dollar worth of value added. The 150 million dollar investment on petro industry creates about 20.5 million dollar worth of output and 9.65 million dollar worth of value added in North Korea.

• Environmental and Resource Economics Review
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• v.22 no.1
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• pp.31-52
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• 2013

The recent analyses of carbon emissions embodied in international trade are related with discussions on who is responsible for the carbon emissions causing global warming. Some authors insist that the countries importing carbon-intensive goods should share the responsibility with the suppliers of those goods. In order to determine which countries are net importers of carbon dioxide embodied in traded goods, we need to construct the multi-regional input-output (MRIO) model incorporating national input-output tables and data on bilateral trades. The paper calculates consumption-based as well as production-based inventories by using MRIO model whose global database is GTAP version 8 to get the picture of carbon footprints in international trades of Korea and other regions in the world.

• Steel and Composite Structures
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• v.46 no.6
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• pp.731-744
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• 2023

Vehicle load information is an important role in operating and ensuring the structural health of cable-stayed bridges. In this regard, an efficient and economic method is proposed for vehicle load detection based on the observed cable tension and vehicle position using a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), a robust program for modeling and considering both geometric and material nonlinearities of bridge structures subjected to vehicle load with low computational costs. With the superiority of GNN, the proposed model is demonstrated to precisely capture complex nonlinear correlations between the input features and vehicle load in the output. Four popular machine learning methods including artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machines (SVM) are refereed in a comparison. A case study of a cable-stayed bridge with the typical truck is considered to evaluate the model's performance. The results demonstrate that the GNN-based model provides high accuracy and efficiency in prediction with satisfactory correlation coefficients, efficient determination values, and very small errors; and is a novel approach for vehicle load detection with the input data of the existing monitoring system.

• Journal of Environmental Science International
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• v.6 no.3
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• pp.205-211
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• 1997

For the prediction of multi-site rainfall with radar data and ground meteorological data, a rainfall prediction model was proposed, which uses the neural network theory, a kind of artifical Intelligence technique. The Input layer of the prediction model was constructed with current ground meteorological data, their variation, moving vectors of rain- fall field and digital terrain of the measuring site, and the output layer was constructed with the predicted rainfall up to 3 hours. In the application of the prediction model to the Pyungchang river basin, the learning results of neural network prediction model showed more Improved results than the parameter estimation results of an existing physically based model. And the proposed model comparisonally well predicted the time distribution of ralnfall.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1017-1030
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• 2016

This study focuses on the system identification of reinforced concrete bridges using vector autoregressive model (VAR). First, the time series output response from a bridge establishes the autoregressive (AR) models. AR models are one of the most accurate methods for stationary time series. Burg's algorithm estimates the autoregressive coefficients (ARCs) at p-lag by reducing the sum of the forward and the backward errors. The computed ARCs are assembled in the state system matrix and the eigen-system realization algorithm (ERA) computes: the eigenvector matrix that contains the vectors of the mode shapes, and the eigenvalue matrix that contains the associated natural frequencies. By taking advantage of the characteristic of the AR model with ERA (ARMERA), civil engineering can address problems related to damage detection. Operational modal analysis using ARMERA is applied to three experiments. One experiment is coupled with an artificial neural network algorithm and it can detect damage locations and extension. The neural network uses a specific number of ARCs as input and multiple submatrix scaling factors of the structural stiffness matrix as output to represent the damage.