• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

• Journal of Biosystems Engineering
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• v.5 no.1
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• pp.1-14
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• 1980

Threshing operation may be one of the most important processes in the paddy post-production system as far as the grain loss and labor requirement are concerned . head-feeding type threshers commercially available now in Korea originally were developed for threshing dry paddy in the range of 15 to 17 % in wet basis. However, threshing wet-paddy with the grain moisture content above 20 % has been strongly recommended, especially for new high-yielding Indica -type varieties ; (1) to reduce high grain loss incurred due to the handling operations, and (2) to prevent the quantitative and qualitative loss of milled -rice when unthreshed grains are rewetted due to the rainfall. The objective of this study were to investigate the adaptability of both a head-feeding type thresher and a throw-in type thresher to wet-paddy , and to find out the possiblilities of improving the components of these threshers threshing. Four varieties, Suweon 264 and Milyang 24 as Tongil sister line varieties, minehikari and Jinhueng as Japonica-type varieties, were used at the different levels of the moisture content of grains. Both the feed rate and the cylinder speed were varied for each material and each machine. The thresher output quality , composition of tailing return, and separating loss were analyzed from the sampels taken at each treatment. A separate experiment for measurement opf the power requirement of the head-feeding type thresher was also performed. The results are summarized as follows : 1. There was a difference in the thresher output quality between rice varieties. In case of wet-paddy threshing at 550 rpm , grains with branchlet and torn heads for the Suweon 264 were 12 % and 7 % of the total output in weight, respectively, and for the Minehikari 4.5 % and 2 % respectively. In case of dry paddy threshing , those for the Suweon 264 were 8 % and 5% , and for the Minehikari 4% and 1% respectively. However, those for the Milyang 23 , which is highly susceptable to shattering, were much lower with 1 % and 0.5% respectively, regardless of the moisture content of the paddy. Therefore, it is desirable to breed rice varieties of the same physical properties as well as to improve a thresher adaptable to all the varieties. Torn heads, which increased with the moisture content of rall the varieties except the Milyang 23 , decreased as the cylinder speed increased, but grains with branchlet didnt decrease. The damaged kernels increased with the cylinder speed. 3. The thresher output quality was not affected much by the feed rate. But grains with branchlet and torn heads increased slightly with the feed rate for the head-feeding type thresher since higher resistance lowered at the cylinder speed. 4. In order to reduce grains with branchlet and torn heads in wet-paddy threshing , it is desirable to improve the head-feeding type thresher by developing a new type of cylinder which to not give excess impact on kernels or a concave which has differenct sizes of holes at different locations along the cylinder. 5. For the head-feeding type thresher, there was a difference in separating loss between the varieties. At the cylinder speed of 600 rpm the separating losses for the Minehikari and the Suweon 264 were 1.2% and 0.6% respectively. The separating loss of the head-feeding type thresher was not affected by the moisture content of paddy while that of the Mini-aged thresher increased with the moisture content. 6. From the analysis of the tailings return , to appeared that the tailings return mechanism didn't function properly because lots of single grains and rubbishes were unnecessarily returned. 7. Adding a vibrating sieve to the head-feeding type thresher could increase the efficiency of separation. Consequently , the tailing return mechanism would function properly since unnecessary return could be educed greatly. 8. The power required for the head-feeding type thresher was not affected by the moisture content of paddy, but the average power increased linearly with the feed rate. The power also increased with the cylinder speed.

• Journal of the Korean Institute of Landscape Architecture
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• v.38 no.1
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• pp.39-48
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• 2010

Recreation forests are in the spotlight as the place for personality development, mind and body comfort, companionship, and environment education in forests and valleys. Visitors to recreation forests have been on the increase along with booming in recreation forest building since 1988. Recreation forests are being categorized according to some features such as regional and environmental condition. Recreation forests, however, have not met the expectations of some visitors who want to take a rest with calmness due to the influence of the 5-day-work-week system, increasing interest in rest, leisure, and well-being, and users converge during weekends, summer, and the tourist season. In order to improve visitors' satisfaction efficiently, this study surveyed the level of satisfaction in each cluster based on the precedent study which had classified 85 national or public recreation forests in Korea into clusters. Questionnaires were distributed properly to each cluster and, of the 1,132 questionnaires collected, 1,015 were valid and used for analysis. Reliability of questionnaires and statistical validity of the model were verified. As a result, there are meaningful differences in the ranking of independent variables which affect the level of satisfaction according to clusters. Variables in rest and fatigue recovery have the strongest influence on the level of satisfaction in the clusters of potential factor, internal activation factor, and mixed potential capacity factor. In the use performance and visiting condition factor cluster, appropriateness of visit cost is most influential and, in the education cluster, connectivity with tourist attractions around it is most affective. These results can provide priority in services and maintenance of recreation forests for improving the level of satisfaction and differentiate the distribution of resources according to clusters.

• Journal of Energy Engineering
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• v.28 no.2
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• pp.18-35
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• 2019

The Korea District Heating Corporation operates a gas engine generator with a capacity of $4500m^3 /day$ of biogas generated from the sewage treatment plant of the Nanji Water Recycling Center and 1,500 kW. However, the actual operation experience of the biogas power plant is insufficient, and due to lack of accumulated technology and know-how, frequent breakdown and stoppage of the gas engine causes a lot of economic loss. Therefore, it is necessary to prepare technical fundamental measures for stable operation of the power plant In this study, a series of process problems of the gas engine plant using the biogas generated in the sewage treatment plant of the Nanji Water Recovery Center were identified and the optimization of the actual operation was made by minimizing the problems in each step. In order to purify the gas, which is the main cause of the failure stop, the conditions for establishing the quality standard of the adsorption capacity of the activated carbon were established through the analysis of the components and the adsorption test for the active carbon being used at present. In addition, the system was applied to actual operation by applying standards for replacement cycle of activated carbon to minimize impurities, strengthening measurement period of hydrogen sulfide, localization of activated carbon, and strengthening and improving the operation standards of the plant. As a result, the operating performance of gas engine # 1 was increased by 530% and the operation of the second engine was increased by 250%. In addition, improvement of vent line equipment has reduced work process and increased normal operation time and operation rate. In terms of economic efficiency, it also showed a sales increase of KRW 77,000 / year. By applying the strengthening and improvement measures of operating standards, it is possible to reduce the stoppage of the biogas plant, increase the utilization rate, It is judged to be an operational plan.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.3
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• pp.73-84
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• 2019

Despite decades of work experience, workers at small- and medium-sized enterprises(SME) here have yet to make inroads into the self-employed sector that utilizes the job competency they have accumulated at work after retirement. Unlike large companies, SME do not have a proper system for improving the long-term job competency of their employees as they focus on their immediate performance. It is necessary to analyse the independent variables affecting the job competency of employees of SME to derive practical implications for the personnel of SME. In the preceding studies, there are independent variable analyses that affect job competency in specialized industries, such as health care, public officials and IT, but the analysis of workers at SME is insufficient. This study set the person-job fit and organizational justice based on the prior studies of the independent variables that affect the job competency of SME general workers as a dependent variable. The sub-variables of each variable derived knowledge, skills, experience, and desire for person-job fit, and distribution, procedural and deployment justice for organizational justice, respectively. The survey of employees of SME in Korea was conducted from February to March 2019 by Likert 5 scales, and the survey was retrieved from 323 people and analyzed in a demonstration using the SPSS and AMOS statistics package. Among the four sub-independent variables of person-job fit, knowledge, skills and experience were shown to have a significant impact on the job competency, and desire was not shown to be so. Among the three sub-independent variables of organizational justice, deployment justice has a significant impact on job competency, but distribution and procedural justices have not. Personnel managers of SME need to improve the job competency of their employees by appropriately utilizing independent variables such as knowledge, skills, experience and deployment at each stage, including recruitment, deployment, and promotion. Future job competency modeling studies are needed to overcome the limitations of this study, which fails to objectively measure job competency.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.1
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• pp.1-18
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• 2021

This study was intended to present measures for the improvement of the apartment house landscaping supervision system by examining the adequacy of landscaping supervision, which is aimed at improving the quality of landscape plants and facilities in apartment house landscaping sites. Additionally, this study aims to identify the problems occurring in the process of the performance of landscaping supervision and to provide the evidence for legislative activities and revision of the laws currently being pushed forward for the mandatory deployment of apartment house landscaping supervision personnel. The results of the analysis showed that no landscaping supervision personnel was deployed to apartment complexes with less than 1,500 households and that the landscaping comprised 19% to 46% of the entire construction process. The civil engineering firm performed the landscaping supervision, which made it impracticable to fully focus on the construction quality in the field of landscaping. The quality control in terms of landscape plants revealed differences in quality control, depending on the competence and experience of the civil engineer supervising the personnel, where the landscaping supervision personnel was not deployed. The apartment houses landscaping supervision activity index was analyzed, and the results showed that the supervision activity index for apartment house A was 72.0, B was 70.4, and apartment houses C to G ranged from 38.7 to 46.9, which suggested that the difference in quality control, process control, and technical support affected the construction quality and occurrence of defects.The improvement of landscaping process quality control and process management will be carried out more smoothly and the rate of defects will be drastically reduced if the landscaping supervision personnel placement threshold is lowered from 1,500 households to 300 households in complexes. The results of this study are expected to be useful in promoting and re-establishing the landscaping industry based on the improvement of construction quality in the field of landscaping in connection with the construction of apartment houses.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.1
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• pp.48-61
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• 2022

South Korea is quite vegetation rich country which has 63% forests and 16% cropland area. Massive NOx emissions from megacities, therefore, are easily combined with BVOCs emitted from the forest and cropland area, then produce high ozone concentration. BVOCs emissions have been estimated using well-known emission models, such as BEIS (Biogenic Emission Inventory System) or MEGAN (Model of Emission of Gases and Aerosol from Nature) which were developed using non-Korean emission factors. In this study, we ran MEGAN v2.1 model to estimate BVO Cs emissions in Korea. The MO DIS Land Cover and LAI (Leaf Area Index) products over Korea were used to run the MEGAN model for June 2012. Isoprene and Monoterpenes emissions from the model were inter-compared against the enclosure chamber measurements from Taehwa research forest in Korea, during June 11 and 12, 2012. For estimating emission from the enclosed chamber measurement data. The initial results show that isoprene emissions from the MEGAN model were up to 6.4 times higher than those from the enclosure chamber measurement. Monoterpenes from enclosure chamber measurement were up to 5.6 times higher than MEGAN emission. The differences between two datasets, however, were much smaller during the time of high emissions. More inter-comparison results and the possibilities of improving the MEGAN modeling performance using local measurement data over Korea will be presented and discussed.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.191-204
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• 2015

According to the '2013 Performance Assessment Report on the Financial Program' from the National Assembly Budget Office, the unfilled recruitment ratio of Software(SW) Developers in South Korea was 25% in the 2012 fiscal year. Moreover, the unfilled recruitment ratio of highly-qualified SW developers reaches almost 80%. This phenomenon is intensified in small and medium enterprises consisting of less than 300 employees. Young job-seekers in South Korea are increasingly avoiding becoming a SW developer and even the current SW developers want to change careers, which hinders the national development of IT industries. The Korean government has recently realized the problem and implemented policies to foster young SW developers. Due to this effort, it has become easier to find young SW developers at the beginning-level. However, it is still hard to recruit highly-qualified SW developers for many IT companies. This is because in order to become a SW developing expert, having a long term experiences are important. Thus, improving job continuity intentions of current SW developers is more important than fostering new SW developers. Therefore, this study surveyed the job continuity intentions of SW developers and analyzed the factors associated with them. As a method, we carried out a survey from September 2014 to October 2014, which was targeted on 130 SW developers who were working in IT industries in South Korea. We gathered the demographic information and characteristics of the respondents, work environments of a SW industry, and social positions for SW developers. Afterward, a regression analysis and a decision tree method were performed to analyze the data. These two methods are widely used data mining techniques, which have explanation ability and are mutually complementary. We first performed a linear regression method to find the important factors assaociated with a job continuity intension of SW developers. The result showed that an 'expected age' to work as a SW developer were the most significant factor associated with the job continuity intention. We supposed that the major cause of this phenomenon is the structural problem of IT industries in South Korea, which requires SW developers to change the work field from developing area to management as they are promoted. Also, a 'motivation' to become a SW developer and a 'personality (introverted tendency)' of a SW developer are highly importantly factors associated with the job continuity intention. Next, the decision tree method was performed to extract the characteristics of highly motivated developers and the low motivated ones. We used well-known C4.5 algorithm for decision tree analysis. The results showed that 'motivation', 'personality', and 'expected age' were also important factors influencing the job continuity intentions, which was similar to the results of the regression analysis. In addition to that, the 'ability to learn' new technology was a crucial factor for the decision rules of job continuity. In other words, a person with high ability to learn new technology tends to work as a SW developer for a longer period of time. The decision rule also showed that a 'social position' of SW developers and a 'prospect' of SW industry were minor factors influencing job continuity intensions. On the other hand, 'type of an employment (regular position/ non-regular position)' and 'type of company (ordering company/ service providing company)' did not affect the job continuity intension in both methods. In this research, we demonstrated the job continuity intentions of SW developers, who were actually working at IT companies in South Korea, and we analyzed the factors associated with them. These results can be used for human resource management in many IT companies when recruiting or fostering highly-qualified SW experts. It can also help to build SW developer fostering policy and to solve the problem of unfilled recruitment of SW Developers in South Korea.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.