• Journal of Life Science
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• 제29권8호
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• pp.854-860
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• 2019

This study investigated the optimum pH conditions for efficient extraction of protein from defatted Tenebrio molitor (TM) larvae. We examined the anti-inflammatory effect of protein derived from defatted TM larvae obtained by an alkaline extraction method. Six extraction pH values (7, 8, 9, 10, 11, and 12) and three precipitation pH values (2, 4, and 6) were used. The protein content, browning degree, and recovery yield of the protein obtained under each pH condition were determined. For efficient extraction of protein from defatted TM larvae, a combination of an extraction pH of 9 and precipitation pH of 4 resulted in a 32.4% recovery yield based on the extraction value and degree of browning. To determine whether the protein ameliorated inflammation by inhibition of macrophage activation by lipopolysaccharides (LPS), we measured nitric oxide (NO), cyclooxygenase-2 (COX-2), and inducible nitric oxide synthase (iNOS) expression in LPS-stimulated raw 264.7 macrophage cells. The protein markedly inhibited the production of NO without cytotoxicity and reduced the expression level of COX-2 and iNOS protein through the regulation of mitogen-activated protein kinases (MAPKs) and nuclear factor kappa B ($NF-{\kappa}B$) signaling. These results suggested that protein derived from TM larvae could have potential applications in anti-inflammatory therapeutic agents and protein supplements.

• Korean Chemical Engineering Research
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• 제57권5호
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• pp.637-651
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• 2019

In the event of a major accident such as an explosion in a refinery or a petrochemical plant, it has caused a serious loss of life and property and has had a great impact on the insurance market. In the case of catastrophic incidents occurring in process industries such as refinery and petrochemical plants, only the proximate causes of loss have been drawn and studied from inspectors or claims adjustors responsible for claims of property insurers, incident cause investigators, and national forensic service workers. However, it has not been done well for conducting root cause analysis (RCA) and identifying the factors that contributed to the failure and establishing preventive measures before leading to chemical plant's catastrophic incidents. In this study, the criteria of warning signs on CCPS catastrophic incident waning sign self-assessment tool which was derived through the RCA method and the contribution factor analysis method using the swiss cheese model principle has been reviewed first. Secondly, in order to determine the major incident warning signs in an actual chemical plant, 614 recommendations which have been issued during last the 17 years by loss control engineers of global reinsurers were analyzed. Finally, in order to facilitate the assessment index for catastrophic incident warning signs, the criteria for the catastrophic incident warning sign index at chemical plants were grouped by type and classified into upper category and lower category. Then, a catastrophic incident warning sign index for a chemical plant was developed using the weighted values of each category derived by applying the analytic hierarchy process (pairwise comparison method) through a questionnaire answered by relevant experts of the chemical plant. It is expected that the final 'assessment index for catastrophic incident warning signs' can be utilized by the refinery and petrochemical plant's internal as well as external auditors to assess vulnerability levels related to incident warning signs, and identify the elements of incident warning signs that need to be tracked and managed to prevent the occurrence of serious incidents in the future.

• The Korean Journal of Air & Space Law and Policy
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• 제33권2호
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• pp.169-197
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• 2018

The purpose of this study is to present a reasonable and concrete standard for the Korean aviation insurance compulsory subscription system. Through this, we aim to improve the current revision of laws and regulations, and ultimately create an environment in which the safety and property of the Korean people who use aircraft with appropriate aviation insurance can be secured. In particular, by reviewing the aviation business law and its new laws and regulations enacted in 2017, the legislative improvement direction of aviation insurance will be proposed. In order to maintain the continuous growth of the air transportation industry and to make amicable compensation for the victims, considering the characteristics of the total accident, instantness, and giganticness of air accidents in which a lot of people and property are lost in the event of an accident, adequate insurance coverage is essential. In this respect, the compulsory insurance to amend the principle of freedom of contract, which is the great principle of the modern judicial system, will be persuasive. However, in comparison with foreign legislation, the legal provisions on Korea's obligation to comply with aviation insurance need to be revised around the following issues: First, it is reasonable to enforce the regulation of the mandatory aviation insurance by legislation from the Congress not by administrative regulations. Because it will force the monetary obligations of the individual such as common air carriers. Second, our law regulations respond to various kinds of air damages by using the phrase "limit of liability stipulated in international conventions". However, as we have seen in the text, the range of compensation are various according to the use of legal instruments in international conventions such as the Montreal Convention, which governs the compensation of passengers for damages to passengers today. Third, in countries with narrow territories, such as Korea, there are big differences in flying time and insurable risk between domestic and international transportation. Therefore, it is necessary to divide domestic transportation and international transportation even in the obligation to join the insurance. This dual discipline has the advantage for rookies in air carrier market who mainly start their business from domestic service. Fourth, according to Korean law, the regulations of automobile loss insurance is applicable to the aviation mandatory insurance of unmanned aerial vehicle accident which is lack of persuasion. In the future, it will be appropriate to discipline insurance for unmanned aerial vehicles with unlimited potential for development from a long-term perspective.

• Korean Journal of Poultry Science
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• 제46권2호
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• pp.105-115
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• 2019

In this study, viability, growth performance and egg production performance were investigated to determine the productive characteristics of 12 Korean domestic chicken varieties which have been collected and conserved for over 25 years in National Institute of Animal Science, Rural Development Administration, Korea. The 1,134 hens were tested and their production traits including survival rate, body weight, age at first egg laying, hen-day and hen-housed egg production, egg weight, and egg quality were measured. Survival rate was the highest in Korean Rhode-D and Korean Native Chicken (KNC) White and the lowest in Korean Cornish Brown (92.2% and 54.3%, respectively). Body weights from 4 to 50 wks were consistently high in the order of Korean Cornish, Korean Rhode, KNC, Korean Ogye, and Korean Leghorn breeds. There was no significant difference in weight between varieties within a breed. Age at first egg laying was the shortest in Korean Leghorn, while it was the longest in Korean Cornish. The hen-day egg production from $1^{st}$ egg to 57 wks was the highest in Korean Leghorn-K, and the lowest in Korean Cornish Brown. Egg weight was the heaviest in Korean Leghorn-F and the lightest in KNC White. The Haugh unit was the highest in Korean Rhode-C and the lowest in Korean Ogye. Taken together, these results suggest that it is desirable that the Korean Cornish variety is improved as the Korean meat breed because of its excellent growth ability, the Korean Leghorn variety is improved as the Korean layer breed because of its good laying performance, and the Korean Rhode and KNC varieties are improved as strong viable breeds because of their good survival rate.

• Journal of Intelligence and Information Systems
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• 제25권1호
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Journal of Intelligence and Information Systems
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• 제25권1호
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• pp.127-137
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• 2019

Recidivism prediction has been a subject of constant research by experts since the early 1970s. But it has become more important as committed crimes by recidivist steadily increase. Especially, in the 1990s, after the US and Canada adopted the 'Recidivism Risk Assessment Report' as a decisive criterion during trial and parole screening, research on recidivism prediction became more active. And in the same period, empirical studies on 'Recidivism Factors' were started even at Korea. Even though most recidivism prediction studies have so far focused on factors of recidivism or the accuracy of recidivism prediction, it is important to minimize the prediction misclassification cost, because recidivism prediction has an asymmetric error cost structure. In general, the cost of misrecognizing people who do not cause recidivism to cause recidivism is lower than the cost of incorrectly classifying people who would cause recidivism. Because the former increases only the additional monitoring costs, while the latter increases the amount of social, and economic costs. Therefore, in this paper, we propose an XGBoost(eXtream Gradient Boosting; XGB) based recidivism prediction model considering asymmetric error cost. In the first step of the model, XGB, being recognized as high performance ensemble method in the field of data mining, was applied. And the results of XGB were compared with various prediction models such as LOGIT(logistic regression analysis), DT(decision trees), ANN(artificial neural networks), and SVM(support vector machines). In the next step, the threshold is optimized to minimize the total misclassification cost, which is the weighted average of FNE(False Negative Error) and FPE(False Positive Error). To verify the usefulness of the model, the model was applied to a real recidivism prediction dataset. As a result, it was confirmed that the XGB model not only showed better prediction accuracy than other prediction models but also reduced the cost of misclassification most effectively.

• Journal of Intelligence and Information Systems
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• 제25권1호
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of Korean Society of Forest Science
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• 제108권1호
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• pp.67-76
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• 2019

With an increasing demand of timber production, the use of heavy machinery in forest management has significantly increased, causing the changes of soil physical properties and the decline of long-term site productivity. This study was conducted to evaluate the effects of logging slash (non-slash, slash $7.3kg/m^2$, and slash $11.5kg/m^2$) and machine passes(harvester 1 pass and forwarder 1 to 10 passes) on soil physical properties at 10 cm, 20 cm and 30 cm soil depths in harvester and forwarder operations and also to estimate the degree of soil surface disturbance. The results indicated that soil bulk density in the non-slash treatment site increased 10 %~29 % (25~139 % in soil penetration resistance) at all soil depths, compared with the slash treatment site(slash $11.5kg/m^2$). Therefore, the creation of a slash mat could be an effective way to minimize the changes of soil physical properties. In addition, 92 % of total soil compaction in slash treatment site was created within harvester 1 pass and forwarder 5 passes. In non-slash treatment site, 84 % of total soil compaction was created within first harvester and forwarder passes. The results showed that slash treatment was effective to reduce soil compaction caused by machine passes and also it is necessary to create designed forwarding trails for minimizing soil compaction area at timber harvesting sites.

• Journal of the Society of Cosmetic Scientists of Korea
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• 제45권1호
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• pp.57-67
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• 2019

Particulate matters with a diameter of < $10{\mu}m$ (PM10) exert oxidative stress and inflammatory events in various organs. The purpose of this study was to examine the molecular mechanism of reactive oxygen species (ROS) production induced by PM10 in the human epidermal keratinocytes (HEKs). When cultured HEKs were exposed to PM10, ROS production was induced and it was inhibited by apocynin, an antioxidant. The mRNA expression of NADPH oxidase (NOX) family was analyzed in order to examine their role in PM10-induced ROS production. PM10 increased the mRNA expression of NOX1, NOX2, dual oxidase (DUOX) 1 and DUOX2. HEKs expressed DUOX1 and DUOX2 at higher levels compared to other NOXs. The mRNA expression of dual oxidase maturation factors, DUOXA1 and DUOXA2, was also increased by PM10. We examined whether these calcium-dependent enzymes, DUOX1 and DUOX2, mediate the PM10-induced ROS production. A selective intracellular calcium chelator, BAPTA-AM, attenuated ROS production induced by PM10 or calcium ionophore A23187. The small intereference RNA (siRNA)-mediated down-regulation of DUOX2, but not DUOX1, attenuated the ROS production induced by PM10. PM10 increased the expression of inflammatory cytokines such as interleukin $(IL)-1{\beta}$, IL-6, IL-8 and interferon $(IFN)-{\gamma}$. SiRNA-mediated down-regulation of DUOX2 suppressed the PM10-induced expression of $IFN-{\gamma}$ but not other cytokines. This study suggests that DUOX2 plays a crucial role in ROS production and inflammatory response in PM10-exposed keratinocytes.

• Korean Journal of Remote Sensing
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• 제35권3호
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• pp.347-358
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• 2019

Lambertian cloud model (Lambertian Cloud Model) is the simplified cloud model which is used to effectively retrieve the vertical ozone distribution of the atmosphere where the clouds exist. By using the Lambertian cloud model, the optical characteristics of clouds required for radiative transfer simulation are parametrized by Optical Centroid Cloud Pressure (OCCP) and Effective Cloud Fraction (ECF), and the accuracy of each parameter greatly affects the radiation simulation accuracy. However, it is very difficult to generalize the vertical ozone error due to the OCCP error because it varies depending on the radiation environment and algorithm setting. In addition, it is also difficult to analyze the effect of OCCP error because it is mixed with other errors that occur in the vertical ozone calculation process. This study analyzed the ozone retrieval error due to OCCP error using two methods. First, we simulated the impact of OCCP error on ozone retrieval based on Optimal Estimation. Using LIDORT radiation model, the radiation error due to the OCCP error is calculated. In order to convert the radiation error to the ozone calculation error, the radiation error is assigned to the conversion equation of the optimal estimation method. The results show that when the OCCP error occurs by 100 hPa, the total ozone is overestimated by 2.7%. Second, a case analysis is carried out to find the ozone retrieval error due to OCCP error. For the case analysis, the ozone retrieval error is simulated assuming OCCP error and compared with the ozone error in the case of PROFOZ 2005-2006, an OMI ozone profile product. In order to define the ozone error in the case, we assumed an ideal assumption. Considering albedo, and the horizontal change of ozone for satisfying the assumption, the 49 cases are selected. As a result, 27 out of 49 cases(about 55%)showed a correlation of 0.5 or more. This result show that the error of OCCP has a significant influence on the accuracy of ozone profile calculation.