• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.82-98
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• 2019

The importance of spatial information is rapidly rising. In particular, 3D spatial information construction and modeling for Real World Objects, such as smart cities and digital twins, has become an important core technology. The constructed 3D spatial information is used in various fields such as land management, landscape analysis, environment and welfare service. Three-dimensional modeling with image has the hig visibility and reality of objects by generating texturing. However, some texturing might have occlusion area inevitably generated due to physical deposits such as roadside trees, adjacent objects, vehicles, banners, etc. at the time of acquiring image Such occlusion area is a major cause of the deterioration of reality and accuracy of the constructed 3D modeling. Various studies have been conducted to solve the occlusion area. Recently the researches of deep learning algorithm have been conducted for detecting and resolving the occlusion area. For deep learning algorithm, sufficient training data is required, and the collected training data quality directly affects the performance and the result of the deep learning. Therefore, this study analyzed the ability of detecting the occlusion area of the image using various image quality to verify the performance and the result of deep learning according to the quality of the learning data. An image containing an object that causes occlusion is generated for each artificial and quantified image quality and applied to the implemented deep learning algorithm. The study found that the image quality for adjusting brightness was lower at 0.56 detection ratio for brighter images and that the image quality for pixel size and artificial noise control decreased rapidly from images adjusted from the main image to the middle level. In the F-measure performance evaluation method, the change in noise-controlled image resolution was the highest at 0.53 points. The ability to detect occlusion zones by image quality will be used as a valuable criterion for actual application of deep learning in the future. In the acquiring image, it is expected to contribute a lot to the practical application of deep learning by providing a certain level of image acquisition.

• Korean Journal of Clinical Laboratory Science
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• v.51 no.3
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• pp.309-315
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• 2019

Cervical cancer is the fourth most common malignant neoplasm in women worldwide. Most cases of cervical cancer are caused by an infection by the human papillomavirus. Molecular diagnostic methods have emerged to detect the HPV for sensitivity, specificity, and objectivity. In particular, real-time PCR has been introduced to acquire a more sensitive target DNA or RNA. RNA extraction and complementary DNA synthesis are proceeded before performing real-time PCR targeting RNA. To identify an adequate and sensitive cDNA synthesis kit, this study evaluated the two commonly used kits for cDNA synthesis. The results show that the $R^2$ and efficiency (%) of the two cDNA synthesis kits were similar in the cervical cancer cell lines. On the other hand, the Takara kit compared to Invitrogen kit showed P<0.001 in the $10^2$ and $10^3$ SiHa cell count. The Takara kit compared to the Invitrogen kit showed P<0.001 in the $10^1$ and $10^2$ HeLa cell count. Furthermore, 8, 4, 2, 1, and 0.5 ml of forty exfoliated cell samples were used to compare the cDNA synthesis kits. The Takara kit compared to the Invitrogen kit showed P<0.01 in 8, 4, and 1 ml and P<0.05 in 0.5 mL. The study was performed to identify the most appropriate cDNA synthesis kit and suggests that a cDNA synthesis kit could affect the real-time PCR results.

• Journal of the Korean Society of Radiology
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• v.13 no.1
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• pp.65-71
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• 2019

With regard to current Neck CT, Bismuth shielding boards are often being used to reduce exposure to superficial organs such as the thyroid. However, beam hardening often occurs near superficial organs with Bismuth shielding boards and variations in CT Number, Noise, and Uniformity values occur severely. This study looked into the usefulness of shielding boards made from aluminum and silicone that can be easily obtained and have good machinability by comparing them to the existing Bismuth shielding board. An Aluminum 7.3mm and a Silicone 21.5mm were made with shielding ratios similar to that of the Bismuth(0.06 mmPb). TLD (TLD-100) was placed on the thyroid area of the Phantom (RS-108T) and 5 doses were measured for each. To compare image quality, CT Number and Noise variations in axial images of the thyroid area in Neck CT images were compared. Also, variations in CT Number, Noise, and Uniformity were measured in the AAPM phantom images and compared. In the results, when thyroid doses for each shielding board were compared, the Bismuth shielding board showed a 14% reduction, the Silicone 21.5mm showed a 15% reduction, and the Aluminum 7.3mm showed a 13% reduction compared to the Non-Shield. Statistically, there were no significant differences in comparison with the Bismuth shielding board. In CT Number variations of thyroid area images, variations were largest for the Bismuth shielding board. With Uniformity evaluations of the AAPM phantom, the Bismuth shielding board was found unsuitable and the Aluminum 7.3mm and Silicone 21.5mm satisfied the acceptance criteria. Research results show that the Aluminum 7.3mm and Silicone 21.5mm have a similar shielding ratio to the high-priced Bismuth shielding board that is currently being used clinically and in comparison tests of CT Number attenuation coefficient variations, Noise, and Uniformity which are phantom image evaluation items, they proved to be better than Bismuth shielding boards. If various shielding boards are made using aluminum and silicone, sized appropriately for superficial organs, it would be useful in decreasing patient doses.

• Journal of Environmental Impact Assessment
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• v.28 no.5
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• pp.492-506
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• 2019

Heavy metal contaminants in the shooting range are mostly present in a warhead circle or a metal fragment present as a particle, these fine metal particles are weathered for a long period of time is very likely that the surface is present as an oxide or carbon oxide. In particular, lead which is a representative contaminant in the shooting range soil, is present as more fine particles because it increases the softness and is stretched well. Therefore, by physical washing experiment, we conducted a degree analysis, concentration of heavy metals by cubic diameter, composition analysis of metallic substances, and assessment of applicability of gravity, magnetism and floating selection. The experimental results FESEM analysis and the measurement results lead to the micro-balance was confirmed thatthe weight goes outless than the soil ofthe same size in a thinly sliced and side-shaped structure according to the dull characteristics it was confirmed that the high specific gravity applicability. In addition, the remediation efficiency evaluation results using a hydrocyclone applied to this showed a cumulative remediation efficiency of 71%,twice 80%, 3 times 91%. On the other hand, magnetic sifting showed a low efficiency of 17%,floating selection -35mesh (0.5mm)target soil showed a relatively high efficiency to 39% -10mesh (2mm) efficiency was only 16%. The target treatment diameter of soil washing should be 2mm to 0.075mm, which is applied to the actual equipment by adding an additional input classification, which would require management as additional installation costs and processes are constructed. As a result, it is found that the soilremediation of shooting range can be separately according to the size of the warhead. The size is larger than the gravel diameter to most 5.56mm, so it is possible to select a specific gravity using a high gravity. However, the contaminants present in the metal fragments were found to be processed by separating using a hydrocyclone of the soil washing according to the weight is less than the soil of the same particle size in a thinly fragmented structure.

• Economic and Environmental Geology
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• v.53 no.6
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• pp.687-694
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• 2020

Two oxidizing agents (KMnO4, H2O2), and one neutralizing agent (NaOH) were applied to evaluate Mn removal in mine drainage. A Mn2+ solution and artificial mine drainage were prepared to identify the Fe2+ influence on Mn2+ removal. The initial concentrations of Mn2+ and Fe2+ were 0.1 mM and 1.0 mM, respectively. The injection amount of oxidizing and neutralizing agents were set to ratios of 0.1, 0.67, 1.0, and 2.0 with respect to the Mn2+ mole concentration. KMnO4 exhibited a higher removal efficiency of Mn2+ than did H2O2 and NaOH, where approximately 90% of Mn2+ was removed by KMnO4. A black MnO2 was precipitated that indicated the oxidation of Mn2+ to Mn4+ after an oxidizing agent was added. In addition, MnO2 (pyrolusite) is a stable precipitate under pH-Eh conditions in the solution. However, relatively low removal ratios (6%) of Mn2+ were observed in the artificial mine drainage that included 1.0 mM of Fe2+. The rapid oxidation tendency of Fe2+ as compared to that of Mn2+ was determined to be the main reason for the low removal ratios of Mn2+. The oxidation of Fe2+ showed a decrease of Fe concentration in solution after injection of the oxidizing and neutralizing agents. In addition, Mn7+ of KMnO4 was reduced to Mn2+ by Fe2+ oxidation. Thus, the concentrations of Mn increased in artificial mine drainage. These results revealed that the oxidation method is more effective than the neutralization method for Mn removal in solution. It should also be mentioned that to achieve the Mn removal in mine drainage, Fe2+ removal must be conducted prior to Mn2+ oxidation.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.67-86
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• 2020

Recommender Systems have been huge influence users and business more and more. Recently the importance of E-commerce has been reached rapid growth greatly in world-wide COVID-19 pandemic. Recommender system is the center of E-commerce lively. Top ranked E-commerce managers mentioned that recommender systems have a major influence on customer's purchase such as about 50% of Netflix, Amazon sales from their recommender systems. Most algorithms have been focused on improving accuracy of recommender system regardless of novelty, diversity, serendipity etc. Recommender systems with only high accuracy cannot satisfy business long-term profit because of generating sales polarization. In addition, customers do not experience enjoyment of shopping from only focusing accuracy recommender system because customer's preference is changed constantly. Therefore, recommender systems with various values need to be developed for user's high satisfaction. Reranking is the most useful methodology to realize diversity of recommender system. In this paper, diversity of recommender system is represented through constructing high similarity with users who have different preference using each user's purchased item's category algorithm. It is distinguished from past research approach which is changing the algorithm of recommender system without user's diversity preference level. We tried to discover user's diversity preference level and observed the results how the effect was different according to user's diversity preference level. In addition, graph-based recommender system was used to show diversity through user's network, not collaborative filtering. In this paper, Amazon Grocery and Gourmet Food data was used because the low-involvement product, such as habitual product, foods, low-priced goods etc., had high probability to show customer's diversity. First, a bipartite graph with users and items simultaneously is constructed to make graph-based recommender system. However, each users and items unipartite graph also need to be established to show diversity of recommender system. The weight of each unipartite graph has played crucial role changing Jaccard Distance of item's category. We can observe two important results from the user's unipartite network. First, the user's diversity preference level is observed from the network and second, dissimilar users can be discovered in the user's network. Through the research process, diversity of recommender system is presented highly with small accuracy loss and optimalization for higher accuracy is possible controlling diversity ratio. This paper has three important theoretical points. First, this research expands recommender system research for user's satisfaction with various values. Second, the graph-based recommender system is developed newly. Third, the evaluation indicator of diversity is made for diversity. In addition, recommender systems are useful for corporate profit practically and this paper has contribution on business closely. Above all, business long-term profit can be improved using recommender system with diversity and the recommender system can provide right service according to user's diversity level. Lastly, the corporate selling low-involvement products have great effect based on the results.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.30 no.4
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• pp.342-352
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• 2020

Objectives: The characteristics of research workers are different from those working in the manufacturing industry. Furthermore, the reagents used change according to the research due to the characteristics of the laboratory, and the amounts used vary. In addition, since the working time changes almost every day, it is difficult to adjust the time according to exposure standards. There are also difficulties in setting standards as in the manufacturing industry since laboratory environments and the types of experiments performed are all different. For these reasons, the measurement of the working environment of research workers is not realistically carried out within the legal framework, there is a concern that the accuracy of measurement results may be degraded, and there are difficulties in securing data. The exposure evaluation based on an eight-hour time-weighted average used for measuring the working environment to be studied in this study may not be appropriate, but it was judged and consequently applied as the most suitable method among the recognized test methods. Methods: The investigation of the use of chemical substances in the research laboratory, which is the subject of this study, was conducted in the order of carrying out work environment measurement, sample analysis, and result analysis. In the case of the use of chemical substances, after organizing the substances to be measured in the working environment, the research workers were asked to write down the status, frequency, and period of use. Work environment measurement and sample analysis were conducted by a recognized test method, and the results were compared with the exposure standards (TWA: time weighted average value) for chemical substances and physical factors. Results: For the substances subject to work environment measurement, the department of chemical engineering was the most exposed, followed by the department of chemistry. This can lead to exposure to a variety of chemicals in departmental laboratories that primarily deal with chemicals, including acetone, hydrogen peroxide, nitric acid, sodium hydroxide, and normal hexane. Hydrogen chloride was measured higher than the average level of domestic work environment measurements. This can suggest that researchers in research activities should also be managed within the work environment measurement system. As a result of a comparison between the professional science and technology service industry and the education service industry, which are the most similar business types to university research laboratories among the domestic work environment measurements provided by the Korea Safety and Health Agency, acetone, dichloromethane, hydrogen peroxide, sodium hydroxide, nitric acid, normal hexane, and hydrogen chloride are items that appear higher than the average level. This can also be expressed as a basis for supporting management within the work environment measurement system. Conclusions: In the case of research activity workers' work environment measurement and management, specific details can be presented as follows. When changing projects and research, work environment measurement is carried out, and work environment measurement targets and methods are determined by the measurement and analysis method determined by the Ministry of Employment and Labor. The measurement results and exposure standards apply exposure standards for chemical substances and physical factors by the Ministry of Employment and Labor. Implementation costs include safety management expenses and submission of improvement plans when exposure standards are exceeded. The results of this study were presented only for the measurement of the working environment among the minimum health management measures for research workers, but it is necessary to prepare a system to improve the level of safety and health.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.6
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• pp.135-154
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• 2020

This paper analyzes the 5,521 samples of the small and medium-sized businesses(SMBs) obtained from the Korea Credit Guarantee Fund. From January 2014 to September 2019, 85% of the SMBs have 5 or fewer full-time employees. The proportion of SMBs is overwhelmed by the elderly men, and most founders are the CEO. Also, about 87% of the workplace types are rented, while 64% of the CEO's residence types are owner-occupation. 47% of the financial grade score is less than 10 points out of 100 and 80% of SMBs have less than 200 million won of the loan guarantee. In particular, the total guarantee loan amount or the days of net guarantee have significantly positive relations with the working period of the CEO in the same industry, the number of employees, the operation period of SMBs, and the corporate business type. In the case of the financial grading score which has the highest weight in overall credit rating gets higher with the higher number of employees, the longer the operation period, and the corporate business type. However, the quantified non-financial grading score has no significant relationship with other explanatory variables, except for the corporate business type. This implies that a non-financial grade score is measured by other determinants that are not observed by the Korea credit guarantee fund. The pure non-financial grade score has positive relations with the working period of the CEO. Overall, this paper would help Korean SMBs upgrade their credit ratings and expand the money supply when there is no standardized credit rating model or no publicly available evaluation criteria for SMBs. We expect this paper provides important insights for further research and policy-makers for SMBs. In particular, to address the financial needs of thin-filers such as SMBs, technology-based financial services (TechFin) would use alternative data to evaluate the financial capabilities of thin-filers and to develop new financial services.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.6
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• pp.27-42
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• 2020

Startup accelerators have emerged as new investment entities that help early startups, which are not easy to survive continuously due to lack of funds, commercialization capabilities, and experiences. As their positive performance on early startups and the ecosystem has been proven, the number of early startups which want to receive their investment is also increasing. However, they are vaguely preparing to attract accelerators' investment because they do not have any information on what factors the accelerators consider important. In addition, researches on startup accelerators are also at an early level, so there are no remarkable prior studies on factors that decide on investment. Therefore, this study aims to help startups prepare for investment attraction by looking at what factors are important for accelerators to invest, and to provide meaningful implications to academia. In the preceding study, we derived five upper level categories, 26 lower level accelerators' investment determinants through the qualitative meta-synthesis method, secondary data analysis, observation on US accelerators and in-depth interviews. In this study, we want to derive important implications by deriving priorities of the accelerators' investment determinants. Therefore, we used AHP that are evaluated as the suitable methodology for deriving importance and priority. The analysis results show that accelerators value market-related factors most. This means that startups that are subject to investment by accelerators are early-stage startups, and many companies have not fully developed their products or services. Therefore, market-related factors that can be evaluated objectively seem to be more important than products (or services) that are still ambiguous. Next, it was found that the factors related to the internal workforce of startups are more important. Since accelerators want to develop their businesses together with start-ups and team members through mentoring, ease of collaboration with them is very important, which seems to be important. The overall priority analysis results of the 26 investment determinants show that 'customer needs' and 'founders and team members' understanding of customers and markets' (0.62) are important and high priority factors. The results also show that startup accelerators consider the customer-centered perspective very important. And among the factors related to startups, the most prominent factor was the founder's openness and execution ability. Therefore, it can be confirmed that accelerators consider the ease of collaboration with these startups very important.