• The Journal of Korean Academy of Prosthodontics
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• v.50 no.4
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• pp.258-270
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• 2012

Purpose: Use of inappropriate statistical methods may lead to incorrect conclusions and a waste of valuable resources. The goal of this study was to assess the frequency and the types of several common statistical errors in the published articles of the Journal of the Korean Academy of Prosthodontics (JKAP) for a 5-year period. Materials and methods: Of 336 articles in the JKAP published from 2006 to 2010, 255 articles using statistics were reviewed and classified by statistical method and year. The frequency and types of the statistical methods were examined, and the statistical errors were evaluated by the appropriateness of the experimental design, assumption check, independent outcomes, proper sample size and suitable use of statistical method. Statistical guidelines were completed based on the appropriateness. Results: Of the 255 articles using statistics, 193 articles (75.9%) used inferential statistics and 153 articles used SPSS statistical software (60.0%). Of the articles using inferential statistics, the three most frequently used statistical methods were ANOVA (41.5%), t-test (20.0%), and the nonparametric method (16.9%). The average rate of statistical errors was 61.2 percent, similar to the rate reported by several studies completed for the medical journal. Conclusion: After the whole analysis of the difference among the groups, post-hoc tests for the pairwise comparisons are required. The optimal sample size calculation is an essential part of this study protocol. To minimize the occurrence of statistical errors, statistical guidelines were developed according to each statistical test procedure and will contribute to the academic improvement in the JKAP.

• Journal of Animal Science and Technology
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• v.49 no.4
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• pp.471-480
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• 2007

The aim of the present study was to determine the effects of a low-energy finisher diet on feed and growth efficiencies and carcass traits of ‘high’-market weight (MW) finishing pigs and thereby to extrapolate optimal dietary energy level for the high-MW swine. A total of 160 (Yorkshire × Landrace) × Duroc-crossbred finishing gilts and barrows weighing approximately 90 kg were fed a low-energy (3,200 kcal DE/kg) diet (LE) or control (3,400 kcal) diet (CON) ad libitum in 16 pens up to 135- and 125-kg live weights, respectively, at which the animals were slaughtered and their carcasses were analyzed [2 (sex) × 2 (diet) factorial experimental design]. Average daily gain, average daily feed intake and feed efficiency did not differ between the two sex or diet groups. Backfat thickness was less (P<0.05) in LE (22.4 mm) than in CON group (24.3 mm) in gilts, but not in barrows (24.4 ± 0.4 mm). The percentage of C- & D-grade carcasses was over 90% because of the ‘over-weight’ problem in gilts, whereas in barrows, percentages of A plus B grades and C plus D grades were 79% and 21%, respectively. The yield percentage of each trimmed primal cut per total trimmed cuts (w/w) did not differ between the two sex or diet groups. Physicochemical characteristics of longissimus muscle including color (lightness and redness), pH, drip loss and chemical composition, which overally were within the range of normal carcass, also did not differ between the two sex or diet groups. In conclusion, both LE and CON are judged to be adequate for the high-MW swine during the latter finishing period. If fat deposition of a given herd of high-MW pigs needs to be suppressed by a dietary treatment, the energy content of the diet will have to be reduced to a level lower than 3,200 kcal DE/kg.

• Korean Journal of Environmental Biology
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• v.38 no.4
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• pp.658-666
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• 2020

The purpose of this study was to select a suitable planting substrate for multilayered plantings in an apartment landscape space. The experiment was conducted between May to October 2019, at the National Institute of Horticultural and Herbal Science. Planting substrate was prepared in six repetitions of eight treatment zones using mulching material, horticultural soil, bottom ash, and subgrade soil. Rosa hybrid 'Barkarole' and Hydrangea macrophylla 'Nikko Blue' were selected as the experimental plants. We investigated the monthly variation and effect of the substrate type on the growth (plant height, number of branches, leaf length, leaf width, and plant area of the substrates) of the plants. In R. hybrid 'Barkarole' grown in 20 cm of horticultural soil and 10 cm of bottom ash, the plants were taller(102.2±5.8 cm), had more branches (5.5±0.6 each), longer leaves (10.9±1.0 cm), and greater leaf width (6.2±0.5 cm) and plant area (4077.1±416.6 cm²)(p<0.05). H. macrophylla 'Nikko Blue' showed the best growth from 3cm of mulching, 20cm of horticultural topsoil, and 10cm of bottom ash, which resulted in taller plants (43.6±2.1 cm), more branches (4.9±0.8 each), longer leaves (7.2±0.5 cm), and greater leaf width(4.3±0.3 cm) and plant area (344.5±43.2 cm²). Through this study, it was possible to propose an optimal planting substrate for shrubs for multi-layered landscaping.

• Clean Technology
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• v.28 no.1
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• pp.79-93
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• 2022

In this study, an electronics industrial wastewater activated sludge model (e-ASM) to be used as a Water Digital Twin was calibrated based on real high-tech electronics industrial wastewater treatment measurements from lab-scale and pilot-scale reactors, and examined for its treatment performance, effluent quality prediction, and optimal process selection. For specialized modeling of a high-tech electronics industrial wastewater treatment system, the kinetic parameters of the e-ASM were identified by a sensitivity analysis and calibrated by the multiple response surface method (MRS). The calibrated e-ASM showed a high compatibility of more than 90% with the experimental data from the lab-scale and pilot-scale processes. Four electronics industrial wastewater treatment processes-MLE, A2/O, 4-stage MLE-MBR, and Bardenpo-MBR-were implemented with the proposed Water Digital Twin to compare their removal efficiencies according to various electronics industrial wastewater characteristics. Bardenpo-MBR stably removed more than 90% of the chemical oxygen demand (COD) and showed the highest nitrogen removal efficiency. Furthermore, a high concentration of 1,800 mg L^-1 T MAH influent could be 98% removed when the HRT of the Bardenpho-MBR process was more than 3 days. Hence, it is expected that the e-ASM in this study can be used as a Water Digital Twin platform with high compatibility in a variety of situations, including plant optimization, Water AI, and the selection of best available technology (BAT) for a sustainable high-tech electronics industry.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.