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| 1 | Radial growth response of Pinus densiflora and Quercus spp.to topographic and climatic factors in South Korea显示文摘Aims This study aimed to develop radial growth models and to predict the potential spatial distribution of Pinus densiflora(Japanese red pine)and Quercus spp.(Oaks)in South Korea,considering topographic and climatic factors.Methods We used a dataset of diameter at breast height and radial growth estimates of individual trees,topographic and climatic factors in systematic sample plots distributed over the whole of South Korea.On the basis that radial growth is attributed primarily to tree age,we developed a radial growth model employing tree age as an explanatory variable.We estimated standard growth(SG),defined as radial growth of the tree at age 30,to eliminate the influence of tree age on radial growth.In addition,SG estimates including the Topographic Wetness Index,temperature and precipitation were calculated by the Generalized Additive Model.Important Findings As a result of variogram analysis of SG,we found spatial autocorrelation between SG,topographic and climatic factors.Incremental temperature had negative impacts on radial growth of P.densiflora and positive impacts on that of Quercus spp.Precipitation was associated with positive effects on both tree species.Based on the model,we found that radial growth of P.densiflora would be more vulnerable than that of Quercus spp.to climatic factors.Through simulation with the radial growth model,it was predicted that P.densiflora stands would be gradually replaced with Quercus spp.stands in eastern coastal and southern regions of South Korea in the future.The models developed in this study will be helpful for understanding the impact of climatic factors on tree growth and for predicting changes in distribution of P.densiflora and Quercus spp.due to climate change in South Korea. | Jae Gyun Byun Woo Kyun Lee Moonil Kim Doo Ahn Kwak Hanbin Kwak Taejin Park Woo Hyuk Byun Yowhan Son Jung Kee Choi Young Jin Lee Joachim Saborowski Dong Jun Chung Jin Hyun Jung | 2013 | Journal of Plant Ecology2013，6，5： | 3 |
| 2 | Crustal growth history of the Korean Peninsula: Constraints from detrital zircon ages in modern river sediments显示文摘U-Pb analyses were carried out on detrital zircon grains from major river-mouth sediments draining South Korea to infer provenance characteristics and the crustal growth history of the southern Korean Peninsula, using a laser ablation inductively coupled plasma mass spectrometer(LA-ICP-MS). The Korean Peninsula is located in the East Asian continental margin and mainly comprises three Precambrian massifs and two metamorphic belts in between them. We obtained 515 concordant to slightly discordant zircon ages ranging from ca. 3566 to ca. 48 Ma. Regardless of river-mouth location, predominance of Mesozoic(249e79 Ma) and Paleoproterozoic(2491e1691 Ma) ages with subordinate Archean ages indicates that the zircon ages reflect present exposures of plutonic/metamorphic rocks in the drainage basins of the South Korean rivers and the crustal growth of the southern Korean Peninsula was focused in these two periods. Comparison of detrital zircon-age data between the North and South Korean river sediments reveals that the Paleoproterozoic zircon age distributions of both regions are nearly identical,while the Neoproterozoice Paleozoic ages exist and the Mesozoic ages are dominant in southern Korean Peninsula. This result suggests that Precambrian terrains in Korea record the similar pre-Mesozoic magmatic history and that the influence of Mesozoic magmatism was mainly focused in South Korea. | Taejin Choi Yong Il Lee Yuji Orihashi | 2016 | Geoscience Frontiers2016，7，5： | 3 |
| 3 | Investigation of drying characteristics of low rank coal of bubbling fluidization through experiment using lab scale显示文摘Lignite is a low rank coal which is evenly distributed throughout the world and accounts for 45% of the total coal reserves. As it has a higher moisture content, its moisture content must be reduced in order to utilize it in power plant. In the present work, experiments on lignite has been done using a lab scale fluidized-bed reactor. Drying lignite through fluidized-bed reactor has a higher drying rate because there is good contact between particles and gas in the fluidized-bed reactor. Fluidized-bed drying can use air of 1.5 times of the minimum fluidizing velocity performance at bubbling fluidized-bed. Experiments have been performed on coal particle sizes of 0.3–1 mm, 1–1.18 mm and 1.18–2.8 mm, with operating temperatures being 100°C, 125°C and 150°C, respectively. It is found that fluidization has a higher drying rate due to the heat transfer rate through air velocity. Hence, moisture content in lignite can be dried to a desired value with a time interval of 10 min. The experiment through fluidized-bed reactor is expected to be useful for saving money and time. | DoMan JEON TaeJin KANG HyungTaek KIM SiHyun LEE SangDo KIM | 2011 | Science China(Technological Sciences)2011，54，7： | 2 |
| 4 | Characterization of parylene deposition process for the passivation of organic light emitting diodes显示文摘 | Taejin Lee Junho Lee Chinho Park | 2002 | Korean Journal of Chemical Engineering2002，，4： | 1 |
| 5 | Identification of RFID tags in framed slotted ALOHA with robust estimation and binary selection 显示文摘 | Jongho P Min Youngchung Lee Taejin | 2007 | IEEE Communications Letters2007，11，5： | 1 |
| 6 | Efficient Explanation and Evaluation Methodology Based on Hybrid Feature Dropout显示文摘AI-related research is conducted in various ways,but the reliability of AI prediction results is currently insufficient,so expert decisions are indispensable for tasks that require essential decision-making.XAI(eXplainable AI)is studied to improve the reliability of AI.However,each XAI methodology shows different results in the same data set and exact model.This means that XAI results must be given meaning,and a lot of noise value emerges.This paper proposes the HFD(Hybrid Feature Dropout)-based XAI and evaluation methodology.The proposed XAI methodology can mitigate shortcomings,such as incorrect feature weights and impractical feature selection.There are few XAI evaluation methods.This paper proposed four evaluation criteria that can give practical meaning.As a result of verifying with the malware data set(Data Challenge 2019),we confirmed better results than other XAI methodologies in 4 evaluation criteria.Since the efficiency of interpretation is verified with a reasonable XAI evaluation standard,The practicality of the XAI methodology will be improved.In addition,The usefulness of the XAI methodology will be demonstrated to enhance the reliability of AI,and it helps apply AI results to essential tasks that require expert decision-making. | Jingang Kim Suengbum Lim Taejin Lee | 2023 | Computer Systems Science & Engineering2023，47，10： | 0 |
| 7 | Explainable Artificial Intelligence-Based Model Drift Detection Applicable to Unsupervised Environments显示文摘Cybersecurity increasingly relies on machine learning(ML)models to respond to and detect attacks.However,the rapidly changing data environment makes model life-cycle management after deployment essential.Real-time detection of drift signals from various threats is fundamental for effectively managing deployed models.However,detecting drift in unsupervised environments can be challenging.This study introduces a novel approach leveraging Shapley additive explanations(SHAP),a widely recognized explainability technique in ML,to address drift detection in unsupervised settings.The proposed method incorporates a range of plots and statistical techniques to enhance drift detection reliability and introduces a drift suspicion metric that considers the explanatory aspects absent in the current approaches.To validate the effectiveness of the proposed approach in a real-world scenario,we applied it to an environment designed to detect domain generation algorithms(DGAs).The dataset was obtained from various types of DGAs provided by NetLab.Based on this dataset composition,we sought to validate the proposed SHAP-based approach through drift scenarios that occur when a previously deployed model detects new data types in an environment that detects real-world DGAs.The results revealed that more than 90%of the drift data exceeded the threshold,demonstrating the high reliability of the approach to detect drift in an unsupervised environment.The proposed method distinguishes itself fromexisting approaches by employing explainable artificial intelligence(XAI)-based detection,which is not limited by model or system environment constraints.In conclusion,this paper proposes a novel approach to detect drift in unsupervised ML settings for cybersecurity.The proposed method employs SHAP-based XAI and a drift suspicion metric to improve drift detection reliability.It is versatile and suitable for various realtime data analysis contexts beyond DGA detection environments.This study significantly contributes to theMLcommunity by addressing the critical issue of managing ML models in real-world cybersecurity settings.Our approach is distinguishable from existing techniques by employing XAI-based detection,which is not limited by model or system environment constraints.As a result,our method can be applied in critical domains that require adaptation to continuous changes,such as cybersecurity.Through extensive validation across diverse settings beyond DGA detection environments,the proposed method will emerge as a versatile drift detection technique suitable for a wide range of real-time data analysis contexts.It is also anticipated to emerge as a new approach to protect essential systems and infrastructures from attacks. | Yongsoo Lee Yeeun Lee Eungyu Lee Taejin Lee | 2023 | Computers, Materials & Continua2023，76，8： | 0 |
| 8 | Adversarial Attack-Based Robustness Evaluation for Trustworthy AI显示文摘Artificial Intelligence(AI)technology has been extensively researched in various fields,including the field of malware detection.AI models must be trustworthy to introduce AI systems into critical decisionmaking and resource protection roles.The problem of robustness to adversarial attacks is a significant barrier to trustworthy AI.Although various adversarial attack and defense methods are actively being studied,there is a lack of research on robustness evaluation metrics that serve as standards for determining whether AI models are safe and reliable against adversarial attacks.An AI model’s robustness level cannot be evaluated by traditional evaluation indicators such as accuracy and recall.Additional evaluation indicators are necessary to evaluate the robustness of AI models against adversarial attacks.In this paper,a Sophisticated Adversarial Robustness Score(SARS)is proposed for AI model robustness evaluation.SARS uses various factors in addition to the ratio of perturbated features and the size of perturbation to evaluate robustness accurately in the evaluation process.This evaluation indicator reflects aspects that are difficult to evaluate using traditional evaluation indicators.Moreover,the level of robustness can be evaluated by considering the difficulty of generating adversarial samples through adversarial attacks.This paper proposed using SARS,calculated based on adversarial attacks,to identify data groups with robustness vulnerability and improve robustness through adversarial training.Through SARS,it is possible to evaluate the level of robustness,which can help developers identify areas for improvement.To validate the proposed method,experiments were conducted using a malware dataset.Through adversarial training,it was confirmed that SARS increased by 70.59%,and the recall reduction rate improved by 64.96%.Through SARS,it is possible to evaluate whether an AI model is vulnerable to adversarial attacks and to identify vulnerable data types.In addition,it is expected that improved models can be achieved by improving resistance to adversarial attacks via methods such as adversarial training. | Eungyu Lee Yongsoo Lee Taejin Lee | 2023 | Computer Systems Science & Engineering2023，47，11： | 0 |
| 9 | Covariant open string field theory on multiple Dp-branes显示文摘We study covariant open bosonic string field theories on multiple Dp-branes by using the deformed cubic string field theory, which is equivalent to string field theory in the proper-time gauge. Constructing the Fock space representations of the three-string vertex and the four-string vertex on multiple Dp-branes, we obtain the field theoretical effective action in the zero-slope limit. On multiple D0-branes, the effective action reduces to the Banks-Fishler-Shenker-Susskind(BFSS) matrix model. We also discuss the relation between open string field theory on multiple D-instantons in the zero-slope limit and the Ishibashi-Kawai-Kitazawa-Tsuchiya(IKKT) matrix model.The covariant open string field theory on multiple Dp-branes could be useful to study the non-perturbative properties of quantum field theories in(p+1)-dimensions in the framework of the string theory. The non-zero-slope corrections may be evaluated systematically by using covariant string field theory. | Taejin Lee | 2018 | Chinese Physics C2018，42，11： | 0 |
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