• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.247-264
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• 2016

With the rapid acceleration of low-birth rate and population aging, the employment of the neglected groups of people including the middle aged class is a crucial issue in South Korea. In particular, in the 2010s, the number of the middle aged who want to find a new job after retirement age is significantly increasing with the arrival of the retirement time of the baby boom generation (born 1955-1963). Despite the importance of matching jobs to this emerging middle aged class, private job portals as well as the Korean government do not provide any online job service tailored for them. A gigantic amount of job information is available online; however, the current recruiting systems do not meet the demand of the middle aged class as their primary targets are young workers. We are in dire need of a specially designed recruiting system for the middle aged. Meanwhile, when users are searching the desired occupations on the Worknet website, provided by the Korean Ministry of Employment and Labor, users are experiencing discomfort to search for similar jobs because Worknet is providing filtered search results on the basis of exact matches of a preferred job code. Besides, according to our Worknet data analysis, only about 24% of job seekers had landed on a job position consistent with their initial preferred job code while the rest had landed on a position different from their initial preference. To improve the situation, particularly for the middle aged class, we investigate a soft job matching technique by performing the following: 1) we review a user behavior logs of Worknet, which is a public job recruiting system set up by the Korean government and point out key system design implications for the middle aged. Specifically, we analyze the job postings that include preferential tags for the middle aged in order to disclose what types of jobs are in favor of the middle aged; 2) we develope a new occupation classification scheme for the middle aged, Korea Occupation Classification for the Middle-aged (KOCM), based on the similarity between jobs by reorganizing and modifying a general occupation classification scheme. When viewed from the perspective of job placement, an occupation classification scheme is a way to connect the enterprises and job seekers and a basic mechanism for job placement. The key features of KOCM include establishing the Simple Labor category, which is the most requested category by enterprises; and 3) we design MOMA (Middle-aged Occupation Matching Algorithm), which is a hybrid job matching algorithm comprising constraint-based reasoning and case-based reasoning. MOMA incorporates KOCM to expand query to search similar jobs in the database. MOMA utilizes cosine similarity between user requirement and job posting to rank a set of postings in terms of preferred job code, salary, distance, and job type. The developed system using MOMA demonstrates about 20 times of improvement over the hard matching performance. In implementing the algorithm for a web-based application of recruiting system for the middle aged, we also considered the usability issue of making the system easier to use, which is especially important for this particular class of users. That is, we wanted to improve the usability of the system during the job search process for the middle aged users by asking to enter only a few simple and core pieces of information such as preferred job (job code), salary, and (allowable) distance to the working place, enabling the middle aged to find a job suitable to their needs efficiently. The Web site implemented with MOMA should be able to contribute to improving job search of the middle aged class. We also expect the overall approach to be applicable to other groups of people for the improvement of job matching results.

• The korean journal of orthodontics
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• v.32 no.5 s.94
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• pp.327-342
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• 2002

Soft tissue profile is a critical area of interest in the development of an orthodontic treatment and diagnosis. The purpose of this study was to determine the facial profile preference of diversified group and to investigate the relationship between most Preferred facial Profile and existing soft tissue reference lines. A survey instrument of constructed facial silhouettes was evaluated by 894 lay person. The silhouettes had varied nose, lips, chin and soft tissue subnasale point. Seven sets of facial type were computer-generated by an orthodontist to represent distinct facial types. The varied facial profiles were graded on the basis of most preferred to least preferred. Every facial profile were measured by soft tissue reference lines(Ricketts E-line, Burstone B-line) to observe the most preferred facial profile. The results as follows: 1. In reliability test, the childhood group showed lower value than other groups, which means that this group has no concern on facial profile preference. 2. It appears that sexual and age difference made no significant difference in selecting the profile 3. An agreement to least preferred facial profile was higher than an agreement to most preferred facial profile. 4. Coefficient of concordance (Kendall W) was higher in the twentieth group. It means that a profile preference of the twentieth is distinct. 5. A lip protrusion (to Ricketts E-line and Burstone B-line) of most preferred facial profile was similar to measurements of previous study that investigate skeletal and soft tissue of esthetic facial profile of young Korean. So these reference lines can be used valuably in clinics. 6. Profile of excessive lip protrusion or retrusion to E-line & B-line was least preferred. 7. Most preferred profile of all respondents group was straight profile. Profile that showing convex profile was not pre(erred and the least preferred profile was concave profile.

• Journal of agriculture & life science
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• v.46 no.3
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• pp.119-127
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• 2012

For the promotion of the evaporative cooling efficiency of hot air in greenhouse in summer, a fog sprayer consisted of a high volume spraying two-fluid nozzle with turbo fan and a blowing fan was set up at 2.2 m height from bottom of small glass greenhouse and tested to estimate the possibility of the greenhouse cooling. The mean droplet size and the volume sprayed by one of fog sprayer were $29{\mu}m$ and $160m{\ell}/min$. All the droplets sprayed and blown by the fog sprayer were evaporated within 2 m radius. The result from the cooling test that two sprayers set up in glass greenhouse of plane area $228m^2$ was represented lower cooling effect that the temperature and relative humidity of inside air of greenhouse were $28.8^{\circ}C$ and 87.5% when those of outside air of greenhouse were $30.2^{\circ}C$ and 81.2%. Through investigation of literatures and results of the cooling test, it was estimated that the water spraying rate of evaporative cooling of single span greenhouse with 50% light curtain and with air change rate of 1 volume/min was $10m{\ell}/min/m^2$ so that the inside air temperature may cool down $2{\sim}3^{\circ}C$ on the basis of $35^{\circ}C$ atmospheric temperature in summer of south korean area.

• (The)Study of the Eastern Classic
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• no.67
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• pp.297-325
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• 2017

In this paper, the ideological features of Mohism were examined through the analysis into the viewpoint of Mohism on justice and benefit and "universal love" based on it. Even before the viewpoint on justice and benefit became a main agenda in Confucianism, Mohism and the Hundred Schools of Thought, there had been discussions on it, and the relation between "justice" and "benefit" was generally understood as that of means and ends(本末) or that of the thing and its functions(體用). What succeeded to this tendency and set it as an individual's moral standard was the viewpoint of Confucianism including Confucius. Of course, the Confucian view was focused on the politicians or leaders of those times. Compared to which, Mohism represented the stance of their group members and pursued the interest of groups and the society rather than that of individuals. Accordingly, while Confucianism considered "justice" more important than "benefit", Mohism could understand both of them unificatively. The crucial reason why Mohism could be most active during the Warring States Period is that it had its metaphysical basis on "the disposition of Providence." Accompanying this, the viewpoint of Mohism on justice and benefit was internally reflected in its key arguments including "universal love." That is so-called "Jianxiangai, Jiaoxiangli", that is to say, "that loving each other is namely benefiting each other." On the other hand, the fact that the viewpoint of Mohism on justice and benefit, and furthermore, the ideological foundation of its ten main arguments including universal love was "the disposition of Providence" became a double-edged sword. It was because it could be easily accepted by the laborers, farmers, and craftsmen consisting of Mohism of those times, but it instead became the reason for falling into ruins since the establishment of the feudal empire of Qin and Han(秦漢). In the feudal empire, the ideology and activities of Mohism as an individual group couldn't be embraced. For example, the way to set "Heaven"(the heavenly king) above "the sovereign ruler" might be a decisive limit to the legitimacy and rationality of the regime. Moreover, the arguments by Mohism, such as "Jieyong", "Jiezang", "Feiyue" and others couldn't be taken easily by the privileged class. Therefore, Mohism couldn't do any activities as an academic school until Seojedongjeom(西勢東漸) during the Qing dynasty later, and it was different from Confucianism. In brief, ideas of Mohism including universal love ended up as an utopian idea historically, but the conception of sharing mutual interest along with mutual love and consideration with Confucianism from the position of the relatively disadvantaged in the society has a value worthy of being appreciated even today.

• Journal of Venture Innovation
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• v.5 no.3
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• pp.81-98
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• 2022

In the recent COVID-19 pandemic, urban agriculture is attracting attention as a healing concept. In 2020, 1,848,000 people participated in urban agriculture activities in Korea. Therefore, this study was conducted to empirically analyze the factors affecting the intention to participate in urban agriculture, which is rapidly increasing. The theoretical basis of this study is the experiential economy theory of Pine II and Gilmore and the experiential theory of Schmitt. As independent variables, a total of five variables were set as the four elements of Pine II and Gilmore's experiential economy theory, namely, educational, entertainment, escapist, and aesthetic experiences, and relational experience reclassified using Schmitt's theory. Interest was set as a mediating variable between these independent variables and the dependent variable, intention to participate in urban agriculture. For empirical analysis, data were collected through a survey. Based on the significant 314 samples of the collected data, the hypothesis was tested through statistical analysis. First, as a result of testing the influence relationship between the independent and dependent variables, educational, entertainment, and escapist experiences had a significant positive (+) effect on the intention to participate in urban agriculture. The impact of the influence was in the order of entertainment experience, escapist experience, and educational experience. There was no significant influence relationship between aesthetic experience, relational experience and intention to participate in urban agriculture. On the other hand, as a result of this study, interest introduced as a mediating variable was found to play a mediating role between entertainment, escapist, aesthetic experiences and intention to participate in urban agriculture. The mediating effect of interest was not tested between educational, relational experiences and intention to participate in urban agriculture. This study approached urban agriculture participation from the concept of healing and analyzes the factors affecting participation in urban agriculture activities empirically based on a theoretical framework by combining and analyzing the representative Pine II and Gilmore theories and Schmitt theories. It had academic significance. In addition, it was meaningful to suggest that the healing concept approach is directional in relation to urban agriculture by revealing that entertainment and escapist experiences are important influencing variables in decision-making to participate in urban agriculture in practice.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.

• The Journal of Korean Society for Radiation Therapy
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• v.26 no.2
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• pp.329-336
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• 2014

Purpose : To evaluate the dose on a contralateral breast and the usefulness of shielding according to the distance between the contralateral breast and the side of the beam by breast size when patients who got breast implant receive radiation therapy. Materials and Methods : We equipped 200 cc, 300 cc, 400 cc, and 500 cc breast model on the human phantom (Rando-phantom), acquired CT images (philips 16channel, Netherlands) and established the radiation treatment plan, 180 cGy per day on the left breast (EclipseTM ver10.0.42, Varian Medical Systems, USA) by size. We set up each points, A, B, C, and D on the right(contralateral) breast model for measurement by size and by the distance from the beam and attached MOSFET at each points. The 6 MV, 10 MV and 15 MV X-ray were irradiated to the left(target) breast model and we measured exposure dose of contralateral breast model using MOSFET. Also, at the same condition, we acquired the dose value after shielding using only Pb 2 mm and bolus 3 mm under the Pb 2 mm together. Results : As the breast model is bigger from 200 cc to 500 cc, The surface of the contralateral breast is closer to the beam. As a result, from 200 cc to 500 cc, on 180 cGy basis, the measurement value of the scattered ray inclined by 3.22 ~ 4.17% at A point, 4.06 ~ 6.22% at B point, 0.4~0.5% at C point, and was under 0.4% at D point. As the X-ray energy is higher, from 6 MV to 15 MV, on 180 cGy basis, the measurement value of the scattered ray inclined by 4.06~5% at A point, 2.85~4.94% at B point, 0.74~1.65% at C point, and was under 0.4% at D point. As using Pb 2 mm for shield, scattered ray declined by average 9.74% at A and B point, 2.8% at C point, and is under 1% at D point. As using Pb 2 mm and bolus together for shield, scattered ray declined by average 9.76% at A and B point, 2.2% at C point, and is under 1% at D point. Conclusion : Commonly, in case of patients who got breast implant, there is a distance difference by breast size between the contralateral breast and the side of beam. As the distance is closer to the beam, the scattered ray inclined. At the same size of the breast, as the X-ray energy is higher, the exposure dose by scattered ray tends to incline. As a result, as low as possible energy wihtin the plan dose is good for reducing the exposure dose.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of Korean Institute of Fire Investigation
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• v.9 no.1
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• pp.103-121
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• 2006

We are living in more comfortable circumstances with the social developments and the improvement of the standard of living, but, on the other hand, we are exposed to an increase of the occurrences of tires on account of large-sized, higher stories, deeper underground building and the use of various energy resources. The materials of the floor in a residence modern society have been going through various alterations in accordance with the uses of a residence and are now used as final goods in interioring the bottom of apartments, houses and shops. There are so many kinds of materials you usually come in contact with, but in the first place, we need to make an experiment on the spread of the fire with the hypocaust used as the floors of apartments, etc. and the floor covers you usually can get easily. We, scientific investigators, can get in contact with the accidents caused by incendiarism or an accidental fire closely connected with petroleum stuffs on the floor materials that give rise to lots of problems. on this account, I'd like to propose that we conduct an experiment on fire shapes by each petroleum stuff and that discriminate an accidental tire from incendiarism. In an investigation, it seems that finding a live coal could be an essential part of clearing up the cause of a tire but it could not be the cause of a fire itself. And besides, all sorts of tire cases or fire accidents have some kind of legislation and standard to minimize and at an early stage cope with the damage by tires. That is to say, we are supposed to install each kind of electric apparatus, automatic alarm equipment, automatic fire extinguisher in order to protect ourselves from the danger of fires and check them at any time and also escape urgently in case of fire-outbreaking or build a tire-proof construction to prevent flames from proliferating to the neighboring areas. Namely, you should take several factors into consideration to investigate a cause of a case or an accident related to fire. That means it's not in reason for one investigator or one investigative team to make clear of the starting part and the cause of a tire. accordingly, in this thesis, explanations would be given set limits to the judgement and verification on the cause of a fire and the concrete tire-spreading part through investigation on the very spot that a fire broke out. The fire-discernment would also be focused on the early stage fire-spreading part fire-outbreaking resources, and I think the realities of police tire investigations and the problems are still a matter of debate. The cause of a fire must be examined into by logical judgement on the basis of abundant scientific knowledge and experience covering the whole of fire phenomena. The judgement of the cause should be made with fire-spreading situation at the spot as the central figure and in case of verifying, you are supposed to prove by the situational proof from the traces of the tire-spreading to the fire-outbreaking sources. The causal relation on a fire-outbreak should not be proved by arbitrary opinion far from concrete facts, and also there is much chance of making mistakes if you draw deduction from a coincidence. It is absolutely necessary you observe in an objective attitude and grasp the situation of a tire in the investigation of the cause. Having a look at the spot with a prejudice is not allowed. The source of tire-outbreak itself is likely to be considered as the cause of a tire and that makes us doubt about the results according to interests of the independent investigators. So to speak, they set about investigations, the police investigation in the hope of it not being incendiarism, the fire department in the hope of it not being problems in installments or equipments, insurance companies in the hope of it being any incendiarism, electric fields in the hope of it not being electric defects, the gas-related in the hope of it not being gas problems. You could not look forward to more fair investigation and break off their misgivings. It is because the firing source itself is known as the cause of a fire and civil or criminal responsibilities are respected to the firing source itself. On this occasion, investigating the cause of a fire should be conducted with research, investigation, emotion independent, and finally you should clear up the cause with the results put together.

• 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.