• Journal of Korea Society of Industrial Information Systems
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• v.24 no.5
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• pp.9-16
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• 2019

In today's global energy market, the importance of green energy is emerging. Hydrogen energy is the future clean energy source and one of the pollution-free energy sources. In particular, the fuel cell method using hydrogen enhances the flexibility of renewable energy and enables energy storage and conversion for a long time. Therefore, it is considered to be a solution that can solve environmental problems caused by the use of fossil resources and energy problems caused by exhaustion of resources simultaneously. The purpose of this study is to efficiently produce hydrogen using plasma, and to study the optimization of DME reforming by checking the reforming reaction and yield according to temperature. The research method uses a 2.45 GHz electromagnetic plasma torch to produce hydrogen by reforming DME(Di Methyl Ether), a clean fuel. Gasification analysis was performed under low temperature conditions ($T3=1100^{\circ}C$), low temperature peroxygen conditions ($T3=1100^{\circ}C$), and high temperature conditions ($T3=1376^{\circ}C$). The low temperature gasification analysis showed that methane is generated due to unstable reforming reaction near $1100^{\circ}C$. The low temperature peroxygen gasification analysis showed less hydrogen but more carbon dioxide than the low temperature gasification analysis. Gasification analysis at high temperature indicated that methane was generated from about $1150^{\circ}C$, but it was not generated above $1200^{\circ}C$. In conclusion, the higher the temperature during the reforming reaction, the higher the proportion of hydrogen, but the higher the proportion of CO. However, it was confirmed that the problem of heat loss and reforming occurred due to the structural problem of the gasifier. In future developments, there is a need to reduce incomplete combustion by improving gasifiers to obtain high yields of hydrogen and to reduce the generation of gases such as carbon monoxide and methane. The optimization plan to produce hydrogen by steam plasma reforming of DME proposed in this study is expected to make a meaningful contribution to producing eco-friendly and renewable energy in the future.

• Journal of Climate Change Research
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• v.5 no.1
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• pp.83-94
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• 2014

Gangnam-gu, where the survey of residents' consciousness has been made in this study, is the district shows the highest rate of the energy consumption and greenhouse gas emission per unit area except some industrial districts such as Gwangyang, Ulsan, and Pohang. The greenhouse gas emission amount of Gangnam-gu is 4,863,765 $tCO_2$ which accounts for 10 % of the total discharging amount of Seoul, 50,330,356 $tCO_2$, which is ranked the top greenhouse gas emission rate in the commercial category and the 2nd place in the household category. The average recognition rate for the 5 subjects of the global warming phenomenons has indicated to be 83.58%. A survey questioning about the main agent to reduce the greenhouse gas, in all age groups except 20s have replied that it should be done by themselves, the residents of Gangnam-gu. For the question of the role of local government to respond to the climate change, the necessity of establishing infrastructure which is suitable for walking and biking. For the other question about the educational facilities to cope with the climate change, many answered the relevant education should be processed from the middle and high schools. For the practical activities in daily life to respond to the climate change, many replies have shown that the energy and resource conservation has been practiced pretty well broadly, but the ecomileage (former carbon mileage) has not been practiced well. Also, many replies have pointed that there were no benefits or rewards for the people who practiced the eco-mileage in their daily lives, which indicates that a kind of incentive is necessary for the efforts to respond to the climate change from the local government to execute the policy substantially and effectively. This study has the purpose to search the political countermeasures to improve the potentiality to reduce the green house gas emission rate through the residents conscious survey about climate change and the political solution by the local government to improve the certain items which showed the lower awareness rate.

• Journal of Nutrition and Health
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• v.56 no.1
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• pp.97-111
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• 2023

Purpose: Childhood obesity has become a social problem due to the social distancing necessitated by the coronavirus disease 2019 pandemic. This study aimed to identify the dietary problems of obese children through various dietary assessment methods and to confirm the usefulness of each method. Methods: The subjects were 88 students in the 4th to 6th grade of elementary school who participated in the nutrition camp organised by the Busan Metropolitan Office of Education, 2020. To evaluate dietary problems and assess diet quality, 24-hour meal records, monthly food intake frequency, and Dietary Screening Test (DST) data were analyzed. Results: Of the subjects, 15.7%, 30.3%, and 53.9% were normal weight, overweight, and obese, respectively. The average age was 11.77 ± 0.77 years and the average body mass index was 23.96 ± 3.01 kg/m². It was observed from the 24-hour meal record method that the overweight and obese subject groups consumed fewer green vegetables (p < 0.001) and white vegetables (p < 0.01) than the normal weight group. In the monthly food intake frequency method, the consumption of ramen (p < 0.01), snacks (p < 0.05), and sausages (p < 0.05) were high in the obese group, and that of anchovies, broccoli, and sweet pumpkin was high in the normal group (p < 0.05). The comparative data from the DST revealed that the overweight and obese groups had less vegetable intake than the normal weight group (p < 0.01) and had higher intakes of dairy products, fast food, and sweet snacks (p < 0.05). Conclusion: The usefulness of each method in the dietary evaluation of obese children was confirmed. To address the problem of obesity, it is necessary to evaluate the dietary problem and approach it with a customized solution tailor-made for the individual subject.

• Journal of the Korean Wood Science and Technology
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• v.13 no.1
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• pp.19-62
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• 1985

This study was performed to investigate: (i) the bending processing properties of silk worm oak (Quercus acutissima Carr.) and Korean red pine (Pinus densiflora S. et Z.) by boiling and steaming treatments; (ii) the effects of interrelated factors - sapwood and heartwood, annual ring placement, softening temperature and time, moisture content. and wood defects on bending processing properties; (iii) the changing rates of bending radii after release from a tension strap, and (iv) the improving methods of bending process by treatment with chemicals. The size of specimens tested was $15{\times}15{\times}350mm$ for boiling and steaming treatments and $5{\times}10{\times}200mm$ for treatments with chemicals. The specimens were green for boiling treatments and dried to 15 percent for steaming treatments. The specimens for treatments with chemicals were soaked in saturated urea solution, 35 percent formaldehyde solution, 25 percent polyethylene glycol -400 solution, and 25 percent ammonium hydroxide solution for 5 days and immediately followed the bending process, respectively. The results obtained were as follows: 1. The internal temperature of silk worm oak and Korean red pine by boiling and steaming time was raised slowly to $30^{\circ}C$ but rapidly from $30^{\circ}C$ to $80-90^{\circ}C$ and then slowly from $80-90^{\circ}C$ to $100^{\circ}C$. 2. The softening time required to the final temperature was directly proportional to the thickness of specimen. The time required from $25^{\circ}C$ to $100^{\circ}C$ for 15mm-squared specimen was 9.6-11.2 minutes in silk worm oak and 7.6-8.1 minutes in Korean red pine. 3. The moisture content (M.C.) of specimen by steaming time was increased rapidly first 4 minutes in the both species, and moderately from 4 to 20 minutes and then slowly and constantly in silk worm oak, and moderately from 4 to 15 minutes and then slowly and constantly in Korean red pine. The M.C. of 15mm-squared specimen in 50 minutes of steaming was increased to 18.0 percent in the oak and 22.4 percent in the pine from the initial conditioned M.C. of 15 percent The rate of moisture adsorption measured was therefore faster in the pine than in the oak. 4. The mechanical properties of the both species were decreased significantly with the increase of boiling rime. The decrement by the boiling treatment for 60 minutes was measured to 36.6-45.0 percent in compressive strength, 12.5-17.5 percent in tensile strength, 31.6-40.9 percent in modulus of rupture, and 23.3-34.6 percent in modulus of elasticity. 5. The minimum bending radius (M.B.R.) of sapwood and heartwood was 60-80 mm and 90 mm in silk worm oak, and 260 - 300 mm and 280 - 300 mm in Korean red pine, respectively. Therefore, the both species showed better bending processing properties in sapwood than in heartwood. 6. The M.B.R. of edge-grained and flat-grained specimen in suk worm oak was 60-80 mm, but the M.B.R. in Korean red pine was 240-280 mm and 260-360 mm, respectively. Comparing the M.B.R. of edge-grained with flat-grained specimen, in the pine the edge-grained showed better bending processing property than the flat-grained. 7. The bending processing properties of the both species were improved by the rising of softening temperature from $40^{\circ}C$ to $100^{\circ}C$. The minimum softening temperature for bending was $90^{\circ}C$ in silk worm oak and $80^{\circ}C$ in Korean red pine, and the dependency of softening temperature for bending was therefore higher in the oak than in the pine. 8. The bending processing properties of the both species were improved by the increase of softening time as well as temperature, but even after the internal temperature of specimen reaching to the final temperature, somewhat prolonged softening was required to obtain the best plastic conditions. The minimum softening time for bending of 15 mm-squared silk worm oak and Korean red pine specimen was 15 and 10 minutes in the boiling treatment, and 30 and 20 minutes in the steaming treatment, respectively. 9. The optimum M.C. for bending of silk worm oak was 20 percent, and the M.C. above fiber saturation point rather degraded the bending processing property, whereas the optimum M.C. of Korean red pine needed to be above 30 percent. 10. The bending works in the optimum conditions obtained as seen in Table 24 showed that the M.B.R. of silk worm oak and Korean red pine was 80 mm and 240 mm in the boiling treatment, and 50 mm and 280 mm in the steaming treatment, respectively. Therefore, the bending processing property of the oak was better in the steaming than in the boiling treatment, but that of the pine better in the boiling than in the steaming treatment. 11. In the bending without a tension strap, the radio r/t of the minimum bending radius t to the thickness t of silk worm oak and Korean red pine specimen amounted to 16.0 and 21.3 in the boiling treatment, and 17.3 and 24.0 in the steaming treatment, respectively. But in the bending with a tension strap, the r/t of the oak and the pine specimen decreased to 5.3 and 16.0 in t he boiling treatment, and 3.3 and 18.7 in the steaming treatment, respectively. Therefore, the bending processing properties of the both species were significantly improved by the strap. 12. The effect of pin knot on the degradation of bending processing property was very severe in silk worm oak by side, e.g. 90 percent of the oak specimens with pin knot on the concave side were ruptured when bent to a 100 mm radius but only 10 percent of the other specimens with pin knot on the convex side were ruptured. 13. The changing rate in the bending radius of specimen bent to a 300 mm radius after 30 days of exposure to room temperature conditions was measured to 4.0-10.3 percent in the boiling treatment and 13,0-15.0 percent in the steaming treatment. Therefore, the degree of spring back after release was higher in the steaming than in the boiling treatment. And the changing rate of moisture-proofing treated specimen by expoxy resin coating was only -1.0.0 percent. 14. Formaldehyde, 35 percent solution, and 25 percent polyethylene glycol-400 solution found no effect on the plasticization of the both species, but saturated urea solution and 25 percent ammonium hydroxide solution found significant effect in comparison to non-treated specimen. But the effect of the treatment with chemicals alone was inferior to that of the steaming treatment, and the steaming treatment after the treatment with chemicals improved 10-24 percent over the bending processing property of steam-bent specimen. 15. Three plasticity coefficients - load-strain coefficient, strain coefficient, and energy coefficient - were evaluated to be appropriate for the index of bending processing property because the coefficients had highly significant correlation with the bending radius. The fitness of the coefficients as the index was good at load-strain coefficient, energy coefficient, and strain coefficient, in order.

• 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.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.35-52
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• 2019

As public services are provided in various forms, including e-government, the level of public demand for public service quality is increasing. Although continuous measurement and improvement of the quality of public services is needed to improve the quality of public services, traditional surveys are costly and time-consuming and have limitations. Therefore, there is a need for an analytical technique that can measure the quality of public services quickly and accurately at any time based on the data generated from public services. In this study, we analyzed the quality of public services based on data using process mining techniques for civil licensing services in N city. It is because the N city's building license complaint service can secure data necessary for analysis and can be spread to other institutions through public service quality management. This study conducted process mining on a total of 3678 building license complaint services in N city for two years from January 2014, and identified process maps and departments with high frequency and long processing time. According to the analysis results, there was a case where a department was crowded or relatively few at a certain point in time. In addition, there was a reasonable doubt that the increase in the number of complaints would increase the time required to complete the complaints. According to the analysis results, the time required to complete the complaint was varied from the same day to a year and 146 days. The cumulative frequency of the top four departments of the Sewage Treatment Division, the Waterworks Division, the Urban Design Division, and the Green Growth Division exceeded 50% and the cumulative frequency of the top nine departments exceeded 70%. Higher departments were limited and there was a great deal of unbalanced load among departments. Most complaint services have a variety of different patterns of processes. Research shows that the number of 'complementary' decisions has the greatest impact on the length of a complaint. This is interpreted as a lengthy period until the completion of the entire complaint is required because the 'complement' decision requires a physical period in which the complainant supplements and submits the documents again. In order to solve these problems, it is possible to drastically reduce the overall processing time of the complaints by preparing thoroughly before the filing of the complaints or in the preparation of the complaints, or the 'complementary' decision of other complaints. By clarifying and disclosing the cause and solution of one of the important data in the system, it helps the complainant to prepare in advance and convinces that the documents prepared by the public information will be passed. The transparency of complaints can be sufficiently predictable. Documents prepared by pre-disclosed information are likely to be processed without problems, which not only shortens the processing period but also improves work efficiency by eliminating the need for renegotiation or multiple tasks from the point of view of the processor. The results of this study can be used to find departments with high burdens of civil complaints at certain points of time and to flexibly manage the workforce allocation between departments. In addition, as a result of analyzing the pattern of the departments participating in the consultation by the characteristics of the complaints, it is possible to use it for automation or recommendation when requesting the consultation department. In addition, by using various data generated during the complaint process and using machine learning techniques, the pattern of the complaint process can be found. It can be used for automation / intelligence of civil complaint processing by making this algorithm and applying it to the system. This study is expected to be used to suggest future public service quality improvement through process mining analysis on civil service.