• Journal of the Korean Institute of Rural Architecture
• /
• v.23 no.4
• /
• pp.63-71
• /
• 2021

The purpose of this study was to investigate whether the standard models for rural housing and wooden housing model have performance for hygrothermal and to propose a way of improvement relevant to hygrothermal performance for those models. All of the models to be analyzed were found to have some parts that were absent of stability in terms of performance for hygrothermal. In the process of analyzing the causes and proposing improvement measures, the following conclusions were derived. Fist, The exterior surface of the structure should be composed of a structure with good moisture permeability, and for the interior surface, a variable vapor retarder paper should be applied in consideration of the reverse condensation phenomenon in summer. Second, in terms of performance for hygrothermal, applications of external insulation plaster finish to the exterior wall or of ventilation method using a rafter vent on the roof should be avoided. Third, a rain screen method with a ventilation layer should be applied to the exterior wall, and a method of constructing ventilation layer separated from the insulation layer with a vapor retarder paper should be applied to the roof. Fourth, the application of insulation materials having capillary action, such as wood fiber insulation board or cellulose insulation board, contributes to more stable performance for hygrothermal.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.1252-1252
• /
• 2022

Temporary work that utilizes temporary equipment (e.g., system scaffold and system pipe support) in construction work is one of the most vulnerable work from a safety perspective in South Korea. Typically, temporary equipment is reused at construction sites. The Korea Occupational Safety and Health Agency announced guidelines regarding the performance standards for reusable temporary equipment to prevent the accidental collapse of temporary facilities. Nevertheless, temporary facilities' collapse still occurs, which could be attributed to a degradation in the performance due to the reuse of temporary equipment. Therefore, this study investigated the performance of simple temporary structures assembled with new and reused equipment. To this end, an experimental module was designed based on previous research cases, and two experimental models were constructed, in which one was assembled using new equipment (Model A), and the other was built using reused equipment (Model B). To determine the performance of each model, a load test was conducted to measure the maximum load that each model could withstand. The experimental results revealed that the maximum load of Model B was 15% lower than that of Model A. This indicates that there is a meaningful performance difference between those two models. Based on this result, the authors decided to perform additional tests with more realistic models than previous ones. The new experimental module was designed to ensure compliance with the Korean design guidelines. In this presentation, the authors show details of the first tests and their results and plan for the additional test.

• Structural Engineering and Mechanics
• /
• v.36 no.2
• /
• pp.225-241
• /
• 2010

In this study, a hybrid search algorithm combining genetic programming with orthogonal least squares (GP/OLS) is utilized to generate prediction models for compressive strength of high performance concrete (HPC) mixes. The GP/OLS models are developed based on a comprehensive database containing 1133 experimental test results obtained from previously published papers. A multiple least squares regression (LSR) analysis is performed to benchmark the GP/OLS models. A subsequent parametric study is carried out to verify the validity of the models. The results indicate that the proposed models are effectively capable of evaluating the compressive strength of HPC mixes. The derived formulas are very simple, straightforward and provide an analysis tool accessible to practicing engineers.

• Proceedings of the KIEE Conference
• /
• 1987.07b
• /
• pp.1026-1030
• /
• 1987

This paper proposes analytic models of the large scale ESS's control system which has the multiprocessor architecture. The performance indices such as the ringback tone delay, busy tone delay, queue length and processor idletime are investigated through the analytic model. The system bottleneck is also analyzed. For the validation of analytic models, its simulation is performed using the SDL/SIM package for the case of 100,000 subscribers. From computer simulation, the results of analytic models are shown to be similar to the results of simulation models, which validates the analytic models.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.4
• /
• pp.73-80
• /
• 2016

Most fault prediction models have class imbalance problems because training data usually contains much more non-fault class modules than fault class ones. This imbalanced distribution makes it difficult for the models to learn the minor class module data. Data imbalance is much higher when severity-based fault prediction is used. This is because high severity fault modules is a smaller subset of the fault modules. In this paper, we propose severity-based models to solve these problems using the three sampling methods, Resample, SpreadSubSample and SMOTE. Empirical results show that Resample method has typical over-fit problems, and SpreadSubSample method cannot enhance the prediction performance of the models. Unlike two methods, SMOTE method shows good performance in terms of AUC and FNR values. Especially J48 decision tree model using SMOTE outperforms other prediction models.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2018.06a
• /
• pp.63-66
• /
• 2018

Recently, a convolutional neural network (CNN) models at single image super-resolution have been very successful. Residual learning improves training stability and network performance in CNN. In this paper, we compare four convolutional neural network models for super-resolution (SR) to learn nonlinear mapping from low-resolution (LR) input image to high-resolution (HR) target image. Four models include general CNN model, global residual learning CNN model, local residual learning CNN model, and the CNN model with global and local residual learning. Experiment results show that the results are greatly affected by how skip connections are connected at the basic CNN network, and network trained with only global residual learning generates highest performance among four models at objective and subjective evaluations.

• Communications for Statistical Applications and Methods
• /
• v.28 no.1
• /
• pp.39-57
• /
• 2021

Time series prediction is an area of great interest to many people. Algorithms for time series prediction are widely used in many fields such as stock price, temperature, energy and weather forecast; in addtion, classical models as well as recurrent neural networks (RNNs) have been actively developed. After introducing the attention mechanism to neural network models, many new models with improved performance have been developed; in addition, models using attention twice have also recently been proposed, resulting in further performance improvements. In this paper, we consider time series prediction by introducing attention twice to an RNN model. The proposed model is a method that introduces H-attention and T-attention for output value and time step information to select useful information. We conduct experiments on stock price, temperature and energy data and confirm that the proposed model outperforms existing models.

• Computers and Concrete
• /
• v.17 no.3
• /
• pp.353-386
• /
• 2016

Realistic assessment of the performance of reinforced concrete structural members like columns is needed for designing new structures or maintenance of the existing structural members. This assessment requires analytical capability of employing proper material models and cyclic rules and considering various load and displacement patterns. A computer application was developed to analyze the non-linear, cyclic flexural performance of reinforced concrete structural members under various types of loading paths including non-sequential variations in axial load and bi-axial cyclic load or displacement. Different monotonic material models as well as hysteresis rules, were implemented in a fiber-based moment-curvature and in turn force-deflection analysis, using proper assumptions on curvature distribution along the member, as in plastic-hinge models. Performance of the program was verified against analytical results by others, and accuracy of the analytical process and the implemented models were evaluated in comparison to the experimental results. The computer application can be used to predict the response of a member with an arbitrary cross section and various type of lateral and longitudinal reinforcement under different combinations of loading patterns in axial and bi-axial directions. On the other hand, the application can be used to examine analytical models and methods using proper experimental data.

• Journal of Korean Institute of Industrial Engineers
• /
• v.29 no.2
• /
• pp.165-171
• /
• 2003

Visual search in an unstructured search field is a fruitful research area for computational modeling. Search models that describe relationship between search time and probability of target detection have been used for prediction of human search performance and provision of ideal goals for search training. Until recently, however, most of models were focused on detecting a single target in a search field, although, in practice, a search field includes multiple targets and search models for multiple targets may differ from search models for a single target. This study proposed a random search model for multiple targets, generalizing a random search model for a single target which is the most typical search model. To test this model, human search data were collected and compared with the model. This model well predicted human performance in visual search for multiple targets. This paper also proposed how to determine optimal stopping time in multiple-target search.

• Journal of the Korean Society for Precision Engineering
• /
• v.24 no.4 s.193
• /
• pp.93-101
• /
• 2007

Aluminum alloy which is one of the light materials has been tried to apply to light weight vehicle body. In order to do that, welding technology is very important. In case of the aluminum laser welding, the strength of welded part is reduced due to porosity, underfill, and magnesium loss. To overcome these problems, laser welding of aluminum with filler wire was suggested. In this study, experiment about laser welding of AA5182 aluminum alloy with AA5356 filler wire was performed according to process parameters such as laser power, welding speed and wire feed rate. The tensile strength was measured to find the weldability of laser welding with filler wire. The models to estimate tensile strength were suggested using three regression models and one neural network model. For regression models, one was the multiple linear regression model, another was the second order polynomial regression model, and the other was the multiple nonlinear regression model. Neural network model with 2 hidden layers which had 5 and 3 nodes respectively was investigated to find the most suitable model for the system. Estimation performance was evaluated for each model using the average error rate. Among the three regression models, the second order polynomial regression model had the best estimation performance. For all models, neural network model has the best estimation performance.