• Journal of the Society of Naval Architects of Korea
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• v.43 no.2 s.146
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• pp.228-235
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• 2006

Nowadays, digital manufacturing has been known to be very effective method in manufacturing fields. It is aimed to estimate process time, to improve operation efficiency, and to prevent bottleneck processes in advance of real manufacturing. This paper addresses a scheduling support system for panel hues in a shipyard through digital manufacturing simulation. The proposed system supports operators to make better decisions on the shop-floor scheduling in panel lines. It ,would provide a complete schedule that is at least as good as any schedule currently obtained. Furthermore, it can evaluate the operator's schedule by simulating it with 3-dimensional models before the work orders and schedules are released.

• The Journal of Fisheries Business Administration
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• v.51 no.1
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• pp.81-96
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• 2020

The purpose of this paper is to analyze factors affecting the net income of regional and industrial fisheries cooperatives in South Korea using panel data. This paper utilizes linear or GLS regression models such as pooled OLS model, fixed effects model, and random effects model to estimate affecting factors of the net income of regional and industrial fisheries cooperatives. After reviewing various tests, we eventually select random effects model. The results, based on panel data between 2013 and 2018 year and 64 fisheries cooperatives, indicate that capital and area dummy variables have positive effects and employment has negative effect on the net income of regional and industrial fisheries cooperatives as predicted. However, debt are opposite with our predictions. Specifically, it turns out that debt has positive effect on the net income of regional and industrial fisheries cooperatives although it has been increased. Additionally, this paper shows that the member of confreres does not show any significant effect on the net income of regional and industrial fisheries cooperatives in South Korea. This study is significant in that it analyzes the major factors influencing changes in the net income that have not been conducted recently for the fisheries cooperatives by region and industry.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.77-88
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• 2021

The stock markets in the BRICS (Brazil, Russia, India, China and South Africa) countries are the leading emerging markets globally. Therefore, it is pertinent to ascertain the critical drivers of stock market development in these economies. The currrent study empirically investigates to identify the linkages between stock market development, key macro-economic factors and institutional factors in the BRICS economies. The study covers the time period from 2000 to 2017. The dependent variable is the country's stock market development and the independent variables consist of six macroeconomic variables and five institutional variables. The study employs a panel cointegration test, Fully Modified OLS (FMOLS), a Pooled Mean Group (PMG) approach and a heterogeneous panel non-causality test.The findings of the study indicate co-integration among the selected variables across the BRICS stock markets. Long-run estimations reveal that five macroeconomic variables and four variables related to institutional quality are positive and statistically significant. Further, short-run causalities between stock market capitalization and selected variables are detected through the test of non-causality in a heterogeneous panel setting. The findings suggest that policymakers in the BRICS countries should enhance robust macroeconomic conditions to support their financial markets and should strengthen the institutional quality drivers to stimulate the pace of stock market development in their countries.

• Korea Real Estate Review
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• v.27 no.1
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• pp.33-44
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• 2017

The continuously rising housing prices in major Chinese cities have raised question about whether inflation is the main reason to drive up housing price to skyrocket in recent years. Based on the provincial panel dataset of China from 2006-2014, this paper investigates the impact of inflation on Chinese housing markets within the frameworks of both static and dynamic panel data models. Our empirical results show evidence that inflation has indeed been a main force of accumulating housing bubbles in these housing markets, especially in eastern region of China. We also find an interesting phenomenon in which Chinese housing bubble is, to a certain extent, affected by market self-adjustment mechanism.

• Asia-Pacific Journal of Business
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• v.11 no.2
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• pp.27-43
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• 2020

Purpose - This study explores the impact of the real estate industry on related industries for the perspective of Chinese steel companies. Design/methodology/approach - The impact of housing prices on the 41 listed steel companies' performance was analyzed by using the panel data model. We used two kinds of housing price indexes that are set in the panel data models to estimate the range of the real estate market, driving the performance growth of steel listed companies. Moreover, the net profit of steel companies is used as the dependent variable. To test the stability of the model, ROA used as a dependent variable for the robustness test. Also, to avoid the time trend of housing prices, this paper selects the growth rate of housing prices as the primary research variable. After Fisher-type testings, there is no unit root problem in both independent and dependent variables. Findings - The results indicated that the rise in the housing price has a positive influence on the steel company performance. When the housing price increases by 1%, the net profit of steel enterprises will increase by 5 to 20 million yuan. Research implications or Originality - In this paper, empirical data at the micro-level and panel model are used to quantify China's real estate industry's driving effect on the iron and steel industry, providing evidence from the microdata level. It helps us to understand further the status and role of China's real estate industry in the economic structure.

• Structural Engineering and Mechanics
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• v.71 no.5
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• pp.545-552
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• 2019

The analysis and design of complex structures like sandwich-panel elements are difficult; the use of finite element method for the analysis is complicated and time consuming when non-linear effects are considered. On the other hand, artificial neural network (ANN) models can capture the non-linear effects and its application requires lesser computational demand. Two ANN models were trained, tested and validated to compute the force for a given displacement of a sandwich-type roof element; 2555 force and element deformation pairs were used for training the ANN models. For the models trained without considering the damping effect, there were two values in the input layer: maximum displacement and current displacement, and for the model considering damping, displacement from the previous step was used as an additional input. Totally, 400 ANN models were trained. Results show that there is a good agreement between the experimental and simulated data, and the models showed a good performance with a mean square error value of 4548.85. Both the ANN models could simulate the inelastic behaviour, loss of rigidity, and evolution of permanent displacements. The models could also interpolate and extrapolate, which enables them to be used as an analysis and design tool for such complex elements.

• Asia pacific journal of information systems
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• v.26 no.1
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• pp.66-79
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• 2016

The availability of detailed data on customers' online behaviors and advances in big data analysis techniques enable us to predict consumer behaviors. In the past, researchers have built purchase prediction models by analyzing clickstream data; however, these clickstream-based prediction models have had several limitations. In this study, we propose a new method for purchase prediction that combines information theory with machine learning techniques. Clickstreams from 5,000 panel members and data on their purchases of electronics, fashion, and cosmetics products were analyzed. Clickstreams were summarized using the 'entropy' concept from information theory, while 'random forests' method was applied to build prediction models. The results show that prediction accuracy of this new method ranges from 0.56 to 0.83, which is a significant improvement over values for clickstream-based prediction models presented in the past. The results indicate further that consumers' information search behaviors differ significantly across product categories.

• Journal of The Korean Society of Agricultural Engineers
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• v.47 no.3
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• pp.29-37
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• 2005

This study was carried out to file up structural design data for optimizing Pleurotus eryngii growing houses. Design data are including current farm status of Pleurotus eryngii growing houses in the aspect of structural configuration as well as environmental conditions to be controlled and maintained inside. A structural analysis was performed for the on-farm structures as well as some structures modified and suggested through field survey and analysis. The results are summarized as follows. According to the results of status analysis, Pleurotus eryngii growing houses were categorized as arch-roofed simple type and sandwich panel type. Though the size of Pleurotus eryngii cultivation facilities were considerably diverse, the basic dimensions of Pleurotus eryngii cultivation facilities showed relatively similar pattern: more or less of 20m of length, $6.6\~7.0m$ of width, $4.6\~5.0m$ of peak height, $1.2\~1.6m$ of bed width, and 4 layers of bed. In the aspect of spatial use of cultivation facilities, suggested models were shown to be mostly reasonable in the aspect of heating and cooling, micro-meteorological stability, land use efficiency per unit floor area, etc.. Especially, the standard models suggested so far were thought to be not efficient in its surface area and spatial volume per unit floor area as well as its uneffective structural design in the area around ceiling. In the results of structural analysis for the models suggested through this study by using those section frames to be found on farms, the panel type structures of both single span and double span were estimated to be over designed, whereas arch-roofed pipe houses were mostly found to be under-designed.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.129-134
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• 1995

The ordinary least square estimator of the disturbance variance in the pooled cross-sectional and time series regression model is shown to be asymptotically unbiased without any restrictions on the regressor matrix when the disturbances follow an equicorrelated error component models.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.40 no.4
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• pp.351-359
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• 2004

Aluminum honeycomb sandwich panel (AHSP) not only have high flexural rigidity and strength per density but also excellence in anti-vibration and anti-noise properties. Their properties are very useful for build airplane and high speed crafts, which need lighter-weighted and more strengthed element. Recently, the AHSP is regarded as a promising strength member of light structures like the hull of high speed crafts. Generally, the core shape of aluminum sandwich panel (ASP) is the hexagonal shape of honeycomb. But, in this paper, authors proposed the ASP with pyramid core, as the ASP model of new type, and analysed the structural and vibrational characteristics for aluminum pyramid sandwich panel (APSP) as this new ASP type, according to the thickness variation of core and face, the height variation of core. The applied sandwich models have isotropic and symmetrical aluminum faces and pyramid cores. And, the applied boundary conditions are simple, fixed and free support.