• Journal of the Society of Naval Architects of Korea
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• v.38 no.1
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• pp.99-107
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• 2001

Manufacture of fishing vessel is needed the effective material for light, strength, fire and corrosion of water in order to improve durability by high-speed and fishing. These fishing vessel can be divided into FRP and AI alloys fishing vessel. FRP fishing vessel is light and effective for strength but highly ignited and susceptible to heat during the manufacturing ship by-produce noxious component for human. In the case of a scrapped ship, it cause environmental pollution. On the other hand, aluminum is a material in return for FRP and has merit of high-strength and lightness. It's more heat proof and durable than FRP and superior to prevent from corrosion. Al alloys fishing vessel development is rising as an urgent matter. But, al alloy has some defect of bad weldability, welding transformation, cracks and overcost of construction. Therefore this study is to develop the new welding joint shape solving aluminum defects and mechanical behavior. First of all, strength was compared and reviewed by analysis of plate, stiffen plate, new model simplified by using plate theory. On the base of this result, plate and new model of temperature distribution, weld residual stress and strength of tensile, compressive force were compared and reviewed by finite element computer program has been developed to deal with heat conduction and thermal elasto plastic problem. Also, new model is proved application possibility and excellent mechanic by strength comparison is established to tensile testing result.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1215-1225
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• 2021

Although Artificial Intelligence (AI) techniques have shown impressive performance in various fields, they are vulnerable to adversarial examples which induce misclassification by adding human-imperceptible perturbations to the input. Previous studies to defend the adversarial examples can be classified into three categories: (1) model retraining methods; (2) input transformation methods; and (3) adversarial examples detection methods. However, even though the defense methods against adversarial examples have constantly been proposed, there is no research to classify the type of adversarial attack. In this paper, we proposed an adversarial attack family classification method based on dimensionality reduction and clustering. Specifically, after extracting adversarial perturbation from adversarial example, we performed Linear Discriminant Analysis (LDA) to reduce the dimensionality of adversarial perturbation and performed K-means algorithm to classify the type of adversarial attack family. From the experimental results using MNIST dataset and CIFAR-10 dataset, we show that the proposed method can efficiently classify five tyeps of adversarial attack(FGSM, BIM, PGD, DeepFool, C&W). We also show that the proposed method provides good classification performance even in a situation where the legitimate input to the adversarial example is unknown.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.873-879
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• 2022

In the future, AI and the metaverse are becoming so powerful that their application areas and influences are swallowing up the world. The advertising field is no exception, and it is becoming more important to predict, analyze, and strategize these future changes. In order to study the future change of advertising according to the development of artificial intelligence and metaverse, literature research related to the development of artificial intelligence and metaverse technology and the resulting change in the advertising environment, in-depth interviews with future and advertising experts, and Delphi technique research method I want to study change. First, through this study, we would like to examine the opinions of experts through in-depth interviews on the development of artificial intelligence and metaverse technology and the changes in the advertising sector in the post-coronavirus era of civilizational transformation. In addition, the Delphi technique is used to determine how important the change is by future advertising technology area, future advertising media area, future advertising form area, future advertising effect area, future advertising application area, and future advertising process area, and at what point in the future it will change. In addition, we want to study how the future advertising form will change in detail. Also, based on this, we would like to propose a countermeasure for the advertising industry.

• Communications of Mathematical Education
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• v.37 no.1
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• pp.65-84
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• 2023

The method of Lagrange multipliers, one of the most fundamental algorithms for solving equality constrained optimization problems, has been widely used in basic mathematics for artificial intelligence (AI), linear algebra, optimization theory, and control theory. This method is an important tool that connects calculus and linear algebra. It is actively used in artificial intelligence algorithms including principal component analysis (PCA). Therefore, it is desired that instructors motivate students who first encounter this method in college calculus. In this paper, we provide an integrated perspective for instructors to teach the method of Lagrange multipliers effectively. First, we provide visualization materials and Python-based code, helping to understand the principle of this method. Second, we give a full explanation on the relation between Lagrange multiplier and eigenvalues of a matrix. Third, we give the proof of the first-order optimality condition, which is a fundamental of the method of Lagrange multipliers, and briefly introduce the generalized version of it in optimization. Finally, we give an example of PCA analysis on a real data. These materials can be utilized in class for teaching of the method of Lagrange multipliers.

• Korean Journal of Culture and Social Issue
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• v.14 no.4
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• pp.39-69
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• 2008

The present study examined the overall receptivity of community members to migrant wives in Korea. A total of 23 community members from two regions, Ansan, an urban area and Youngam, a rural area, were selected for the interview based on their prior experience with migrant wives. Data were collected via a semi-structured interview method. The participants provided their personal feelings and thoughts on a variety of issues involving migrant wives, such as how they perceive migrant wives' original culture and lifestyles, the nature of their interaction experience with migrant wives, their overall evaluation of migrant wives, their opinions about migrant wives' cultural adaptation, and their opinions about the on-going transformation of the Korean society into a multi-racial, multi-cultural society. Interview results indicated that, although the participants' attitude toward migrant wives was positive, the overall receptivity to migrant wives was largely based on the traditional sex-role stereotypes typically found in the Korean society. Moreover, the vast majority of the participants endorsed a narrow-minded, uni-directional perspective on cultural adaptation which puts far greater emphasis on migrant wives' assimilation into the host culture than reciprocal influence process between the two cultures. Implications of the study and directions for future research were discussed.

• Economic and Environmental Geology
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• v.35 no.6
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• pp.491-508
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• 2002

While about 80% of Jeju soils are classified as Andisols, the soils derived from volcanic ash in Dangsanbong are not Andisols. There is a significant difference of precipitation in localities of Jeju island. The study area is characterized by the lowest amount of annual rainfall in Jeju Island, and by the layered silicates as dominant solid phase in clay fraction. The purpose of this study was to characterize the mineralogy of the non-Andie soils in detail, especially hydroxy-interlayered silicates. Two major soil horizons are recognized in the soil profile developed in the Dangsanbong area, which can be designated as A and C. The soil pH($H_{2}0$), ranges from 6.6 to 7.3 increasing with depth, is higher than that of typical Andisols(pH<6.0). While the pH(NaF), ranges from 9.49 to 9.81, indicates that significant amount of amorphous phases might be present as exchanging complexes. It is estimated to about 1.542.88 wt% by using chemical selective dissolution. The organic content of surface horizon is about 2 wt%. This soil are composed of quartz, feldspar and olivine as major constituents with minor of silicate clays. Quartz is frequently observed in A and distinctly decreases in its amount with depth, while olivine is dominant phase in C and rarely observed in A. In the <0.2$\mu\textrm{m}$ size fraction, smectite and kaolinite/smectite interstratification are dominant with minor of illite. The amounts of smectite decrease with depth, while the amounts of kaolinite/smecite interstratification increase with depth, which indicates the trend of mineral transformation with increasing the degree of weathering. The proportion of kaolinite in kaolinite/smectite interstratification is about 85%, and is not changed significantly through the profile. In the 2-0.2$\mu\textrm{m}$size fraction, vermiculite, smectite, illite and kaolinite are major components with minor of chlorite. Most of chlorite are interstratified with smectite. Chlorite which is not interstratified with smectite occurs only in surface horizon. The proportion of the chlorite in the chlorite/smectite interstratification is 59-70(%) and increases with depth. Hydroxy-interlayered vermiculite(HIV) with hydroxy-Fe/AI in their interlayers occurs in both A and C horizon. The amounts of hydroxy-Fe/AI decrease with depth. Hydroxy-interlayered smectite(HIS) of which interlayers might be composed of hydroxy-Mg/Al occurs only in C horizon. As the results of mineralogical investigation for the soil profile in the study area, clay minerals might be changed and evolved through the following weathering sequences: 1) Smectite Kaolinite, HIS, Vermiculite, 2) Vermiculite HIV Chlorite.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• Korean Journal of Medicinal Crop Science
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• v.9 no.2
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• pp.156-165
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• 2001

Exogeneous application of pokeweed antiviral protein (PAP), a ribosomal-inacivating protein in the cell wall of Phytolacca americana (pokeweed) protects heterologous plants from viral and fungal infection. A cDNA clone of PAP introduced into Scrophularia buergeriana Miquel by thransformation with Agrobacterium tumefaciences. For plant transformation, explants were precultured on shoot induction medium without kanamycin for 2-5 day, and then they were cocultured with Agrobacterium for 10 minutes. The explants were placed on co culture medium in dark condition, $28^{\circ}C$ for 2days. After explants were washed in MS liquid medium, they were transferred into selection medium including kanamycin 50mg/L (MS salts+1mg/ l BAP+2mg/ l TDZ+0,2mg/ l NAA+MS vitamin+3% sucrose+0.8% agar, pH5.8). From PCR analysis, NPT II band was confirmed in transgenic plant genome and showed resistance against fungi in antifungal activity test. Micro assay to which protein extracted from transgenic line were added, revealed hyphae growth inhibition and no spore germination at high concentration. The characteristics of inhibited hyphae was represented transparent and thin. Expression of PAP in transgenic plants offers the possibility of developing resistance to viral and fungal infection.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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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.