• Journal of Distribution Science
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• v.22 no.6
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• pp.1-10
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• 2024

Purpose: This study's objective is to examine the impact of firm-specific and macroeconomic factors on the business performance of non-cyclical and cyclical sectors in Indonesian listed firms. The evaluation of business performance holds paramount importance for the achievement and long-term viability of a company. Research Design Data and Methodology: The data for 61 non-cyclicals sector companies and 57 cyclicals sector companies was gathered over a 4-year period from 2018-2021. The model integrates firm size, leverage, and sales growth as firm-specific factors, with real GDP growth and inflation rate as macroeconomic variables. ROA and ROE are indicators of a firm's business performance. The regression models are estimated using the distribution of a dynamic approach with Arellano-Bond Panel Generalized Method of Moments (GMM) estimation. Results: The results of the pooled sample indicate that the historical ROA and ROE have a positive relationship with the business performance of all sectors, including both non-cyclical and cyclical industries. The ROE of non-cyclical enterprises is primarily influenced by firm-specific characteristics and macroeconomic influences. Conclusion: To ensure the successful implementation of the distribution of a dynamic approach towards enhancing corporate business performance, organizations need to take into account a combination of firm-specific factors and macroeconomic factors.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.131-139
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• 2006

Statistical analyses for spatial data are important features for various types of fields. Spatial data are taken at specific locations or within specific regions and their relative positions are recorded. Lattice data are synoptic observation covering an entire spatial region, like cancer rates corresponding to each county in a state. The main purpose of this paper is to detect hotspots for the region with significantly high or low rates. Kulldorff(1997) detected hotspots based on circular spatial scan statistics. We propose a new method to find any shapes of hotspots by use of echelon analysis with spatial scan statistics.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.13
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• pp.5407-5409
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• 2014

Background: In National Cancer Registry Programme (NCRP) reports, various rates are routinely provided for 50 cancer sites of males and 54 cancer sites of females. Very often, depending on our interest, we wish to see these rates for group of cancers like head and neck cancers, oral cancers, and reproductive cancers. In such a situation, the desired rates are calculated independently from the actual data and reported. The question is can we derive the rates for groups of cancers from the published reports when the data is provided only for the individual sites? Objective: In the present paper, an attempt is made to explore the mathematical properties of various rates to derive them directly for the group of cancer sites from the published data when the rates are provided only for the individual sites. Source of data: The cancer incidence data collected by two urban Population Based Cancer Registries (PBCRs), under the network of NCRP for the period of 2006-08 was considered for the study purposes. The Registries included were: Bangalore and Bhopal. Results: In the present communication, we have shown that the crude rate (CR), age specific rates and age-adjuste rates (AAR) all possess additive properties. This means, given the above rates for individual sites, the above rates can be calculated for groups of sites by simply adding them. In terms of formula it can be stated that CR(Site1+Site2+++ SiteN) = CR(Site1)+CR(Site2) +++ CR(SiteN). This formula holds good for age specific rates as well as for AAR. This property facilitates the calculation of various rates for defined groups of cancers by simply adding the above rates for individual sites from which they are made up.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.63-70
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• 2021

Generating a super-resolution meteological data by using a high-resolution deep neural network can provide precise research and useful real-life services. We propose a new technique of generating improved training data for super-resolution deep neural networks. To generate high-resolution meteorological data with domain specific knowledge, Lambert conformal conic projection and objective analysis were applied based on observation data and ERA5 reanalysis field data of specialized institutions. As a result, temperature and humidity analysis data based on domain specific knowledge showed improved RMSE by up to 42% and 46%, respectively. Next, a super-resolution generative adversarial network (SRGAN) which is one of the aritifial intelligence techniques was used to automate the manual data generation technique using damain specific techniques as described above. Experiments were conducted to generate high-resolution data with 1 km resolution from global model data with 10 km resolution. Finally, the results generated with SRGAN have a higher resoltuion than the global model input data, and showed a similar analysis pattern to the manually generated high-resolution analysis data, but also showed a smooth boundary.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1231-1246
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• 2016

Cause-specific hazard model (Prentice et al., 1978) and subdistribution hazard model (Fine and Gray, 1999) are mostly used for the right censored survival data with competing risks. Some other models for survival data with competing risks have been subsequently introduced; however, those models have not been popularly used because the models cannot provide reliable statistical estimation methods or those are overly difficult to compute. We introduce simple and reliable competing risk regression models which have been recently proposed as well as compare their methodologies. We show how to use SAS and R for the data with competing risks. In addition, we analyze survival data with two competing risks using five different models.

• Journal of Broadcast Engineering
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• v.26 no.7
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• pp.844-854
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• 2021

Semi-supervised learning (SSL) and few-shot learning (FSL) have shown impressive performance even then the volume of labeled data is very limited. However, SSL and FSL can encounter a significant performance degradation if the diversity gap between the labeled and unlabeled data is high. To reduce this diversity gap, we propose a novel scheme that relies on an autoencoder for generating pseudo examples. Specifically, the autoencoder is trained on a specific class using the available labeled data and the decoder of the trained autoencoder is then used to generate N samples of that specific class based on N random noise, sampled from a standard normal distribution. The above process is repeated for all the classes. Consequently, the generated data reduces the diversity gap and enhances the model performance. Extensive experiments on MNIST and FashionMNIST datasets for SSL and FSL verify the effectiveness of the proposed approach in terms of classification accuracy and robustness against adversarial attacks.

• 한국생물공학회:학술대회논문집
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• 2001.11a
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• pp.886-889
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• 2001

C-terminus specific immobilization often results in a increased structural stability resistant to various denaturation factors. In order to elucidate the immobilization effect on the c-terminus in molecular level, we made over 200 protein data set from Protein Data Bank(PDB), analyzed c-terminus structure of each protein, and investigated the structural relationship with the stabilizing factors such as hydrogen bond, ion pairs, cation pi, disulfide bond, solvation free energy, surface area, flexibility and so on.

• Journal of the Korean Wood Science and Technology
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• v.26 no.4
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• pp.86-91
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• 1998

The drainage was used to determine the specific filtration resistance for wood and non-wood fibers. The drainage rate is also affected by factors that can be changed on consistency, pressure drop across the mat, basis weight, additives, and viscosity. Recent development of theoretical studies in flow through porous media and filtration operation emphasize the urgent need for more accurate data for porosity and specific filtration resistance. This study was investigated to determine specific filtration resistance of Hw, Sw-BKP and Kenaf fiber by filtration experimental. Freeness levels selected were 150,250,and $350m\ell$ CSF. The average specific filtration resistance decreased as freeness increased and resistance of Sw-BKP was greater than that of Hw-BKP. The filtrate and porosity increased and specific filtration resistance decreased as particle size of fiber increased.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.733-736
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• 1996

Neural Network(NN) is known to be suitable for forecasting corporate bankruptcy because of discriminant capability. Bankruptcy prediciton on NN by now has mostly been studied based on financial indices at specific point of time. However, the financial profile of corporates fluctuates within a certain range with the elapse of time. Besides, we need a lot of data of different bankrupt types in order to apply NN for better bankruptcy prediciton. Therefore, we have decided to focus on textiles and clothing industries for bankruptcy prediction with limited data. One part of the collected data was used for training and calibration, and the other was used for verification. The model makes a learning with extended data from financial indices at specific point of time. The trained model has been tested and we could get a high hitting ratio relatively.

• Korean Management Science Review
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• v.14 no.2
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• pp.91-111
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• 1997

Neural Network(NN) is known to be suitable for forecasting corporate bankruptcy because of discriminant capability. Bandkruptcy prediction on NN by now has mostly been studied based on financial indices at specific point of time. However, the financial profile of corporates fluctuates within a certain range with the elapse of time. Besides, we need a lot of data of different bankrupt types in order to apply NN for better bankruptcy prediction. Therefore, We have decided to focus on textile and clothing industries for bankruptcy prediction with limited data. One part of the collected data was used for training and calibration, and the other was used for verification. The model makes a learning with extended data from financial indices at specific point of time. The trained model has been tested and we could get a high hitting ratio relatively.