• Journal of Life Science
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• v.26 no.7
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• pp.749-757
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• 2016

This study was carried out to reveal the genetic population structure of the Jeju Island population and the phylogenetic relationship of East Asian populations of the large-footed bat (Myotis macrodactylus) based on the genetic polymorphisms of mitochondrial cytochrome B (CYTB) and NADH dehydrogenase subunit 1 (ND1) gene sequences. A total of fourteen and nine haplotypes were found in the CYTB and ND1 sequences from East Asian bats, respectively. Haplotype distribution showed locality specific patterns. The results from ND1 haplotype analysis showed that the Jeju Island population has four haplotypes: the Mt. Halla and Western subpopulations have three ND1 haplotypes, but the Eastern subpopulation has just a single haplotype Nd03, which is commonly found on this island. The neighbor-joining (NJ) tree showed the closer relationship between Jeju Island and Japan rather than that between Jeju and Gangwon-do Province. The divergence time between the maternal ancestor lineages of Japanese and Chinese populations was estimated to be 0.789±0.063 MYBP. The secondary divergence between Jeju and Japanese bats was calculated about to be 0.168±0.013 MYBP. The Jeju population has immigrated to the island at least fifty thousand years ago. In addition, ND1 haplotype analysis suggested that the insular bats have experienced at least two further genetic differentiation events within this island. Consequently, these findings suggested that the results of this study may play a critical role in understanding the phylogenetic relationship among East Asian bat populations of M. macrodactylus. To prepare more explainable information on evolutionary correlation, analysis is still required to examine using expanded samples from China, Russia, and southern parts of the Korean Peninsula.

• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.1-11
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• 2021

As the Internet developed, various and complex cyber attacks began to emerge. Various detection systems were used outside the network to defend against attacks, but systems and studies to detect attackers inside were remarkably rare, causing great problems because they could not detect attackers inside. To solve this problem, studies on the lateral movement detection system that tracks and detects the attacker's movements have begun to emerge. Especially, the method of using the Remote Desktop Protocol (RDP) is simple but shows very good results. Nevertheless, previous studies did not consider the effects and relationships of each logon host itself, and the features presented also provided very low results in some models. There was also a problem that the model could not explain why it predicts that way, which resulted in reliability and robustness problems of the model. To address this problem, this study proposes an interpretable RDP-based lateral movement detection system using page rank algorithm and SHAP(Shapley Additive Explanations). Using page rank algorithms and various statistical techniques, we create features that can be used in various models and we provide explanations for model prediction using SHAP. In this study, we generated features that show higher performance in most models than previous studies and explained them using SHAP.

• Journal of The Korean Association of Information Education
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• v.24 no.5
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• pp.495-509
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• 2020

This study is a theoretical research to explore ways to effectively learn AI in the age of intelligent information driven by artificial intelligence (hereinafter referred to as AI). The emphasis is on presenting a teaching method to make AI education accessible not only to students majoring in mathematics, statistics, or computer science, but also to other majors such as humanities and social sciences and the general public. Given the need for 'Explainable AI(XAI: eXplainable AI)' and 'the importance of storytelling for a sensible and intelligent machine(AI)' by Patrick Winston at the MIT AI Institute [33], we can find the significance of research on AI storytelling learning model. To this end, we discuss the possibility through a pilot study targeting general students of an university in Daegu. First, we introduce the AI storytelling(AI+ST) learning method[30], and review the educational goals, the system of contents, the learning methodology and the use of new AI tools in the method. Then, the results of the learners are compared and analyzed, focusing on research questions: 1) Can the AI+ST learning method complement algorithm-driven or developer-centered learning methods? 2) Whether the AI+ST learning method is effective for students and thus help them to develop their AI comprehension, interest and application skills.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.1019-1034
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• 2022

Malware attacks become more prevalent in the hyper-connected society of the 4th industrial revolution. To respond to such malware, automation of malware detection using artificial intelligence technology is attracting attention as a new alternative. However, using artificial intelligence without collateral for its reliability poses greater risks and side effects. The EU and the United States are seeking ways to secure the reliability of artificial intelligence, and the government announced a reliable strategy for realizing artificial intelligence in 2021. The government's AI reliability has five attributes: Safety, Explainability, Transparency, Robustness and Fairness. We develop four elements of safety, explainable, transparent, and fairness, excluding robustness in the malware detection model. In particular, we demonstrated stable generalization performance, which is model accuracy, through the verification of external agencies, and developed focusing on explainability including transparency. The artificial intelligence model, of which learning is determined by changing data, requires life cycle management. As a result, demand for the MLops framework is increasing, which integrates data, model development, and service operations. EXE-executable malware and documented malware response services become data collector as well as service operation at the same time, and connect with data pipelines which obtain information for labeling and purification through external APIs. We have facilitated other security service associations or infrastructure scaling using cloud SaaS and standard APIs.

• Journal of Korea Water Resources Association
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• v.56 no.11
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• pp.801-813
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• 2023

Recently, as studies about Artificial Neural Network (ANN) are actively progressing, studies for predicting water quality of rivers using ANN are being conducted. However, it is difficult to analyze the operation process inside ANN, because ANN is form of Black-box. Although eXplainable Artificial Intelligence (XAI) is used to analyze the computational process of ANN, research using XAI technology in the field of water resources is insufficient. This study analyzed Multi Layer Perceptron (MLP) to predict Water Temperature (WT), Dissolved Oxygen (DO), hydrogen ion concentration (pH) and Chlorophyll-a (Chl-a) at the Dasan water quality observatory in the Nakdong river using Layer-wise Relevance Propagation (LRP) among XAI technologies. The MLP that learned water quality was analyzed using LRP to select the optimal input data to predict water quality, and the prediction results of the MLP learned using the optimal input data were analyzed. As a result of selecting the optimal input data using LRP, the prediction accuracy of MLP, which learned the input data except daily precipitation in the surrounding area, was the highest. Looking at the analysis of MLP's DO prediction results, it was analyzed that the pH and DO a had large influence at the highest point, and the effect of WT was large at the lowest point.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.385-391
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• 2023

As the use of electric vehicles has increased to minimize carbon emissions, the analyzing the state and performance of lithium-ion batteries that is instrumental in electric vehicles have been important. Comprehensive analysis using not only the voltage, current and temperature of the battery pack, which can affect the condition and performance of the battery, but also the driving data and charging pattern data of the electric vehicle is required. Therefore, a thorough analysis is imperative, utilizing electric vehicle operation data, charging pattern data, as well as battery pack voltage, current, and temperature data, which collectively influence the condition and performance of the battery. Therefore, collection and preprocessing of battery data collected from electric vehicles, collection and preprocessing of data on driver driving habits in addition to simple battery data, detailed design and modification of artificial intelligence algorithm based on the analyzed influencing factors, and A battery analysis and evaluation model was designed. In this paper, we gathered operational data and battery data from real-time electric buses. These data sets were then utilized to train a Random Forest algorithm. Furthermore, a comprehensive assessment of battery status, operation, and charging patterns was conducted using the explainable Artificial Intelligence (XAI) algorithm. The study identified crucial influencing factors on battery status, including rapid acceleration, rapid deceleration, sudden stops in driving patterns, the number of drives per day in the charging and discharging pattern, daily accumulated Depth of Discharge (DOD), cell voltage differences during discharge, maximum cell temperature, and minimum cell temperature. These factors were confirmed to significantly impact the battery condition. Based on the identified influencing factors, a battery analysis and evaluation model was designed and assessed using the Random Forest algorithm. The results contribute to the understanding of battery health and lay the foundation for effective battery management in electric vehicles.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.6
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• pp.67-84
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• 2023

In some areas of the Republic of Korea, the designation and management of conservation areas do not adequately reflect regional characteristics and often impose behavioral regulations without considering the local context. One prominent example is the Busan EDC area. As a result, conflicts may arise, including large-scale civil complaints, regarding the conservation and utilization of these areas. Therefore, for the efficient designation and management of protected areas, it is necessary to consider various ecosystem factors, changes in land use, and regional characteristics. In this study, we specifically focused on the Busan EDC area and applied machine learning techniques to analyze the habitat of regional species. Additionally, we employed Explainable Artificial Intelligence techniques to interpret the results of our analysis. To analyze the regional characteristics of the waterfront area in the Busan EDC district and the habitat of migratory birds, we used bird observations as dependent variables, distinguishing between presence and absence. The independent variables were constructed using land cover, elevation, slope, bridges, and river depth data. We utilized the XGBoost (eXtreme Gradient Boosting) model, known for its excellent performance in various fields, to predict the habitat probabilities of 11 bird species. Furthermore, we employed the SHapley Additive exPlanations technique, one of the representative methodologies of XAI, to analyze the relative importance and impact of the variables used in the model. The analysis results showed that in the EDC business district, as one moves closer to the river from the waterfront, the likelihood of bird habitat increases based on the overlapping habitat probabilities of the analyzed bird species. By synthesizing the major variables influencing the habitat of each species, key variables such as rivers, rice fields, fields, pastures, inland wetlands, tidal flats, orchards, cultivated lands, cliffs & rocks, elevation, lakes, and deciduous forests were identified as areas that can serve as habitats, shelters, resting places, and feeding grounds for birds. On the other hand, artificial structures such as bridges, railways, and other public facilities were found to have a negative impact on bird habitat. The development of a management plan for conservation areas based on the objective analysis presented in this study is expected to be extensively utilized in the future. It will provide diverse evidential materials for establishing effective conservation area management strategies.

• Horticultural Science & Technology
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• v.29 no.6
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• pp.561-567
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• 2011

Cold tolerance of the native Rhododendron species which are on the verge of extinction in Korean nature were compared with the introduced species and its mechanism were studied physiologically with the investigation of the leaf angle, leaf curling, and photosynthetic activity. The degree of cold tolerance measured with the leaf burning after winter season was higher in the native species, Rhododendron brachycarpum and Rhododendron brachycarpum var. roseum than all the introduced species. 'Nova Zembla', an introduced species, showed high sensitivity to the low temperature. Changes in leaf angle by the low temperature were bigger in 2 native species and 'Parker's Pink' than the other introduced species and small comparatively in 'Nova Zembla' and 'Cunningham's White' cultivar. Leaf curling also occurred strongly in 2 native species by the low temperature. While, it was comparatively little and mild in the other introduced species. Therefore these results suggested that the leaf movement such as leaf angle change and curling adapted to the low temperature is positively related to the cold tolerance of 2 native species. By the way, such relationship is not explainable in the cold-sensitive 'Parker's Pink' cultivar showing comparatively stronger leaf movement. Photosynthetic activity measured before the winter season was high in the cold-tolerant R. brachycarpum and its recovery after winter season was faster in the 2 native species and the introduced 'Cynosure' cultivar than the other introduced species. They were the lowest in the most cold-sensitive 'Nova Zembla'. This phenomena occurred similarly even in the stomatal conductivity, suggesting that the movement of water from the roots to the leaves is better and then the leaf burning after winter season become small in the cold-tolerant species. The recovery of photosynthetic activity and stomatal conductivity was comparatively slower in the cold-sensitive 'Parker's Pink'. From the above results, leaf behavior adapted to the low temperature during the winter season and water movement to the leaves are related collectively to the cold tolerance represented as the leaf burning in the Rhododendron species is suggested.

• Journal of the Korean Magnetics Society
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• v.16 no.1
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• pp.23-27
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• 2006

By substituting Mn or Cr for Co in inverse spinel $CoFe_2O_4,\;Mn_xCo_{1-x}Fe_2O_4\;and\;Cr_xCo_{1-x}Fe_2O_4$ and thin films were prepared by sol-gel method and their structural and magnetic properties were investigated. X-ray diffraction indicates that the cubic lattice constant increase for the Mn substitution while it hardly changes for the Cr substitution. Substitution of $Mn^{2+}$ for octahedral $Co^{2+}$ sites can explain the increase of lattice constant in $Mn_xCo_{1-x}Fe_2O_4$. On the other hand, Substitution of $Cr^{3+}$ for octahedral $Co^{2+}$ and subsequent reduction of $Fe^{3+}$ ion into $Fe^{2+}$ are expected to happen. Mossbauer spectroscopy measurements on $Cr_xCo_{1-x}Fe_2P_4$ indicate the existence of tetrahedral $Fe^{2+}$ ions that are created through reduction of tetrahedral $Fe^{3+}$ ions in order to compensate charge imbalance happened by $Cr^{3+}$ substitution for octahedral $Co^{2+}$ sites. On the other hand, no $Fe^{2+}$ ions were detected by Mossbauer spectroscopy for $Mn_xCo_{1-x}Fe_2O_4$. A migration of $Fe^{3+}$ ions from octahedral to tetrahedral sites In $Mn_xCo_{1-x}Fe_2O_4$ was detected by Mossbauer spectroscopy for x>0.47. Vibrating sample magnetometry measurements on the samples at room temperature revealed that the saturation magnetization increases by Mn and Cr substitution for certain range of x, qualitatively explainable in terms of the comparison of spin magnetic moment among the related transition-metal ions.

• Journal of dental hygiene science
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• v.10 no.5
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• pp.307-314
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• 2010

This study aimed to measure such features of emotional responses perceived by students as learning climate, department living stress, and perceived helplessness to analyze their effects on major satisfaction among students at the department of dental hygiene; to do this, a survey was conducted with 431 students, regardless of college year, who were at the department of dental hygiene in four colleges in Gyeonggi Province, Daejeon, and Chungcheong Province. An existing emotion scale which went through the generalization process was used to draw a multiple model in the combination form in order to collect emotional factors affecting college students' satisfaction with their major, which had existed as a hypothetical proposition, and make overall interpretation of relevance through the explainable, predictable modeling process by measuring emotional factors and phenomenal description of the level of general perception. The results showed that major satisfaction was very significantly affected by emotional features among students at the department of dental hygiene, which needs to be treated as an important factor to enhance expertise related to major learning and improve students' living.