• Asian Journal of Business Environment
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• v.2 no.1
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• pp.25-34
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• 2012

It is possible to use pallet for forwarding as chinese cabbages and radishes are general large-scale trading items at the agricultural wholesale market though, however, most of these are forwarded as it have packed in net bags or in P·E bags. Thus, it is still hard for palletizing. The type of packing the product in the net bag makes it difficult for palletizing. It is not a stable shape enough and easily collapsed for pallet loading. Because of this collapsibility, the corrugated cardboard box is being used to enhance forwarding efficiency, but the existing corrugated cardboard box could be crushed easily by moist what is from the agricultural product's property and it also could be squashed by the mass of the loaded box layers on itself. In contrary, the functional waterproof corrugated cardboard box is not collapsed through palletizing and it is efficient for product management with it's ventilation function in respond to pre-cooling effect. Furthermore, because it has various functional shapes as the open type, the partition type and so on, it is effective for maintaining freshness of the product and standardizing the distribution of agricultural product. It is well-known that it is possible to introduce this box to cargo-works of agricultural product. Consequently, the recognition of main distributors about the pallet distribution of the chinese cabbage and the radish was apprehended in this study for activating mechanization of loading and unloading. The survey was conducted to the main distributors such as the forwarder, the auction dealer and the commission merchant with Garak-dong wholesale market as the center. The appropriate packing materials and problems of the existing method for loading and unloading were derived through the survey. Especially, it was focused on analyzing the difference of recognition between the subject groups for the way of using waterproof cardboard corrugated box to deal with the difficult product for packing in normal corrugated box because of the box's absorption of moist from the agricultural product like a chinese cabbage and a radish. Total In the cases of the forwarders and the commission merchants, the net was highly responded as 45%, 74% from each groups for the best packing material for mechanization of distribution and the waterproof corrugated cardboard box was responded as 20%, 22% from each groups as much preferable than multi-stage wooden box. However, for the radish, the waterproof corrugated cardboard box was the best material as 56%, and the auction trader group supported it for 80%. So, the using the waterproof corrugated cardboard box for mechanization of distribution was negative for the chinese cabbage, but it was positive for the radish. The average was 2.42, the standard deviation was 1.24. The negative response(about 55%) was prevailing more than positive response(about 23%). It could be analyzed that even there was the positive recognition for using the waterproof corrugated cardboard box for the radish though the preference for low price of net bag in the chinese cabbage forwarding procedure. Still now, it seems that is a burden for using the waterproof corrugated cardboard box with high price. In the analysis on the recognition differences about using the waterproof corrugated cardboard box for the chinese cabbages and the radish between the forwarders and the commission merchants, generally the negative recognition was prevailing, but the forwarders(2.696) were more positive for using the waterproof corrugated cardboard box than the commission merchants(2.145).

• Korean Journal of Soil Science and Fertilizer
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• v.39 no.6
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• pp.329-333
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• 2006

Most of volcanic ash soils in South Korea are distributed in Jeju province which is an island placed on southern part of Korea and has steep slope mountain area. There are many soils containing high contents of organic matter (OM) derived from volcanic ash in Jejudo, also. Therefore, irrigation and drainage in volcanic ash soil different with general soil which has low OM content have to be applied with another management way, but studies searching appropriate methods for them are set on insufficient situation because the area of volcanic ash soil in South Korea is only 1.3% (130,000ha). This study was conducted for analysis of soil water content and irrigation quantity appropriate for crops cultivated in volcanic ash soil with high OM content. Although soils with different soil color have the same soil texture, soil water characteristics curve by soil color showed the difference of water retention capability by OM content. But, this characteristics classified with soil color could be unified by scaling technique with similitude analysis method which get dimensionless water content using a present water content, a residual water content and saturated water content (or water content at 10kPa). A relation of gravimetric soil water content (GSWC) and dimensionless water content by the results showed a form of power function. The dimensionless water content (DWC) express a relative saturation degree of present water content. This was also expressed by van Genuchten model which describe the relation between relative saturation degrees and matric potentials. These results on soil water characteristics curve (SWCC) of volcanic ash soil will be the basic of irrigation plan in area having high organic contents into soil.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.25 no.1
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• pp.77-86
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• 1980

Asking to change the cropping patterns to save the labor and capitals in paddy rice cultivation, the study was intended to know the weed problems under the various possible cultural systems; namely, direct seeding (in broadcast and row), machine transplanting and hand transplanting. Under the conditions as weedy check plots, paddy yields were significantly variated among cropping systems, and the functions of panicle No. and spikelet No. to the yield were neglected, among others. However, the yield and yield components were narrowed among cropping systems, and the function of spikelets number per area was comparatively improved to the others.

• The Korean Journal of Rehabilitation Nursing
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• v.2 no.2
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• pp.257-270
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• 1999

The purpose of this study is to investigate factors related to the intent of using home nursing of chronic disease patients who got out of a university hospital. For the purpose, the study selected 153 patients who were hospitalized and left K university hospital with diagnoses of cancer, hypertension, diabetes and cerebral vascular accident and ordered to be discharged and performed interviews with them and surveys on their medical records to obtain the following results. For this study a direct-interview survey and medical record review was conducted from June 28 to Aug. 30, 1998. The frequency and mean values were computed to find the characteristics of the study subjects, and $X^2$-test, t-test, factor analysis and multiple logistic regession analysis were applied for the analysis of the data. The following results were obtained. 1) When characteristics of the subjects were examined, men and women occupied for 58.8% and 41.2%, respectively. The subjects were 41.3 years old in aver age and had the monthly aver age earning of 0.99 million won or below, which was the most out of the total subjects at 34.6%. Among the total, 87.6% resided in cities and 12.4 in counties. The most left the hospital with diagnosis of cancer at 51.6%, followed by hyper tension at 24.2%, diabetes at 13.7% and cerebral vascular accident at 7.2%. 2) 93.5% of the selected patients had the intent of using home nursing and 6.5%, didn't. Among those patients having the intent, 85.6% had the intent of paying for home nursing and 14.4%, didn't. The subjects expected that the nursing would be paid 9,143 won in aver age and 47.7% of them preferred national authorities as the main servers. 86.3% of the subjects thought that home nursing business had the main advantage of making it possible to learn nursing methods at home and thereby contributing to improving the ability of patients and their facilities to solve health problems. 3) Relations between the intent of use and characteristics of the subjects such as demography-related social, home environment, disease and physical function characteristics did not show statistically significant differences among one another. Compared to those who had no intent of using home nursing, the group having the intent had more cases of male patients, the age of 39 or below, residence in cities, 5 family member s or more, no existence of home nursing servers, leaving the hospital from a non-hospitalized building, disease development for five months or below, hospitalization for ten days or more, non-hospitalization with in the recent one month, two times or over of hospitalization, leaving the hospital with no demand of special treatment, operation underwent, poor results of treatment, leaving the hospital with demand of rehabilitation services, physical disablement and high evaluation point of daily life. 4) Among those patients having the intent of using home nursing, 47.6% demanded technical nursing and 55.9%, supportive nursing. As technical nursing,' inject into a blood vessel ' and 'treat pustule and teach basic prevention methods occupied for 57.4%, respectively, topping the list. Among demands of supportive nursing, 'observe patients 'status and refer them to hospitals or community resources as available, if necessary' was the most with percent age point of 59.5. Regarding the intent of paying for home nursing, 39.2% of those patients wishing to use the nursing responded paying for technical services and 20.2, supportive services. In detail, 70.0% wanted to pay for a service stated as 'inject into a blood vessel', highest among the former services and 30.7%, a service referred to as 'teaching exercises needed to make the body of patients move', highest among the latter. When this was analyzed in terms of a relation between the need(the need for home nursing) and the demand(the intent of paying for home nursing), The rate of the need to the demand was found two or three times higher in technical nursing(0.82) than in supportive nursing(0.35). In aspects of tech ical nursing, muscle injection(1.26, the 1st rank) was highest in the rate while among aspects of supportive nursing, a service referred to as 'teach exercises needed for making patients move their bodies normally'(0.58, the 1st rank). 5) factors I(satisfaction with hospital services), II(recognition of disease state), III(economy) and IV(period of disease) occupied for 34.4, 13.8, 11.9 and 9.2 percents, respectively among factors related to the intent by the subjects of using home nursing, totaled 59.3%. In conclusion, most of chronic disease patients have the intent of using hospital-based home nursing and satisfaction with hospital services is a factor affecting the intent most. Thus a post-management system is needed to continue providing health management to those patients after they leave the hospital. Further, supportive services should be provided in order that those who are satisfied with hospital services return to their community and live their in dependent lives. Based on these results, the researcher would make the following recommendation. 1) Because home nursing becomes more and more needed due to a sharp increase in chronic disease patients and elderly people, related rules and regulations should be made and implemented. 2) Hospital nurses specializing in home nursing should be cultivated.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.