• Nuclear Engineering and Technology
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• v.23 no.1
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• pp.56-65
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• 1991

The LOFT LOCE L2-3 was simulated using the RELAP5/MOD2 Cycle 36.04 code to assess its capability in predicting the thermal-hydraulic phenomena in LBLOCA of a PWR. The reactor vessel was simulated with two core channels and split downcomer modeling for a base case calculation using the frozen code. The result of the base calculation showed that the code predicted the hydraulic behavior, and the blowdown thermal response at high power region of the core reasonably and that the code had deficiencies in the critical How model during subcooled-two-phase transition period, in the CHF correlation at high mass flux and in the blowdown rewet criteria. An overprediction of coolant inventory due to the deficiencies yielded the poor prediction of reflood thermal response. Improvement of the code, RELAP5 / MOD2 Cycle 36.04, based on the sensitivity study increased the accuracy of the prediction of the rewet phenomena.

• Journal of the Korean Geotechnical Society
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• v.23 no.3
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• pp.89-100
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• 2007

In the companion paper, we provided the novel elastic-plastic constitutive model based on the micromechanics theory. Herein, the elastic and elastic-plastic deformation of granular soils is meticulously analyzed. To guarantee high accuracy of the microscopic parameter, the systematic procedure to evaluate the parameters is provided. The analysis of the elastic response during the isotropic and triaxial compression shows that the stress-level dependency of cross-anisotropic elastic moduli is induced by the power relationship of the contact force in the normal contact stiffness, while the evolution of fabric anisotropy is more pronounced during triaxial compression. The micromechanical analysis indicates that the plastic strains are likely to occur at very small strains. The plastic deformation of tangential contacts has an important role in the reduction of soil stiffness during axial loading.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3D
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• pp.331-338
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• 2008

Road pavements consist of layered structure and each layer is made of various materials. The load responses of pavement structures are very sensitive to properties of subbase materials. Successful pavement design, therefore, depends on the method and the accuracy of measuring material properties, and it requires realistic description of the behavior of layered materials. Resilient modulus ($M_R$) is widely used properties representing pavement structure materials. In this study, we collected data for mechanical characteristics of subbase materials that were used in domestic construction and adopted them to form a constitutive equation of subbase $M_R$ value. Proposed model was evaluated through the finite element analysis.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.20 no.1
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• pp.56-65
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• 2024

Cast austenitic stainless steels (CASS) and austenitic stainless steel weldments with a ferrite-austenite duplex structure are widely used in nuclear power plants, incorporating ferrite phase to enhance strength, stress relief, and corrosion resistance. Thermal aging at 290-325℃ can induce embrittlement, primarily due to spinodal decomposition and G-phase precipitation in the ferrite phase. This study evaluates the effects of thermal aging by collecting and analyzing various mechanical properties, such as Charpy impact energy, ferrite microhardness, and tensile strength, from various literature sources. Different model expressions, including hyperbolic tangent and phase transformation equations, are applied to calculate activation energy (Q) of room-temperature impact energies, and the results are compared. Additionally, predictive models for Q based on material composition are evaluated, and the potential of machine learning techniques for improving prediction accuracy is explored. The study also examines the use of ferrite microhardness and tensile strength in calculating Q and assessing thermal embrittlement. The findings provide insights for developing advanced prediction models for the thermal embrittlement behavior of CASS and the weldments of austenitic steels, contributing to the safety and reliability of nuclear power plant components.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.11-20
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• 2024

While artificial neural networks are adept at identifying patterns, they can struggle to distinguish between actual correlations and false associations between extracted facial features and criminal behavior within the training data. These associations may not indicate causal connections. Socioeconomic factors, ethnicity, or even chance occurrences in the data can influence both facial features and criminal activity. Consequently, the artificial neural network might identify linked features without understanding the underlying cause. This raises concerns about incorrect linkages and potential misclassification of individuals based on features unrelated to criminal tendencies. To address this challenge, we propose a novel region-based training approach for artificial neural networks focused on criminal propensity detection. Instead of solely relying on overall facial recognition, the network would systematically analyze each facial feature in isolation. This fine-grained approach would enable the network to identify which specific features hold the strongest correlations with criminal activity within the training data. By focusing on these key features, the network can be optimized for more accurate and reliable criminal propensity prediction. This study examines the effectiveness of various algorithms for criminal propensity classification. We evaluate YOLO versions YOLOv5 and YOLOv8 alongside VGG-16. Our findings indicate that YOLO achieved the highest accuracy 0.93 in classifying criminal and non-criminal facial features. While these results are promising, we acknowledge the need for further research on bias and misclassification in criminal justice applications

• Journal of the Korean Wood Science and Technology
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• v.52 no.4
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• pp.375-382
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• 2024

The study focuses on evaluating the bending creep performance of laminated veneer lumber (LVL) formwork in timber concrete composite (TCC) structures. Timber-framed construction is highlighted for its environmental benefits and seismic resistance, but limitations such as poor tensile strength and brittle failure in bending hinder its use in high-rise buildings. Wood-concrete hybrid structures, particularly those using reinforced concrete slabs with TCC floors, emerge as a potential solution. The research aims to understand the time-dependent behavior of TCC components, considering factors like wood and concrete shrinkage and connection creep. The experiment was conducted in western Japan on the TCC floor designed for use in the Kama-city Inatsuki-higashi compulsory education school. The LVL formwork, measuring 9,000 mm by 900 mm, and concrete is loaded onto it for testing. The creep test periods are examined using concrete loading. It employs a comprehensive creep analysis, adhering to Japanese standards, involving deflection measurements and regression analysis to estimate the creep coefficient. Results indicate substantial deformation after shoring removal, suggesting potential reinforcement needs. The study recommends extending test periods for improved accuracy and recognizing regional climate impacts. Overall, the research provides valuable insights into the potential of LVL formwork in TCC structures, emphasizing safety considerations and paving the way for further experimentation under varied conditions to validate structural integrity.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.39-53
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• 2013

The emergence of the internet technology and SNS has increased the information flow and has changed the way people to communicate from one-way to two-way communication. Users not only consume and share the information, they also can create and share it among their friends across the social network service. It also changes the Social Media behavior to become one of the most important communication tools which also includes Social TV. Social TV is a form which people can watch a TV program and at the same share any information or its content with friends through Social media. Social News is getting popular and also known as a Participatory Social Media. It creates influences on user interest through Internet to represent society issues and creates news credibility based on user's reputation. However, the conventional platforms in news services only focus on the news recommendation domain. Recent development in SNS has changed this landscape to allow user to share and disseminate the news. Conventional platform does not provide any special way for news to be share. Currently, Social News Service only allows user to access the entire news. Nonetheless, they cannot access partial of the contents which related to users interest. For example user only have interested to a partial of the news and share the content, it is still hard for them to do so. In worst cases users might understand the news in different context. To solve this, Social News Service must provide a method to provide additional information. For example, Yovisto known as an academic video searching service provided time dependent metadata from the video. User can search and watch partial of video content according to time dependent metadata. They also can share content with a friend in social media. Yovisto applies a method to divide or synchronize a video based whenever the slides presentation is changed to another page. However, we are not able to employs this method on news video since the news video is not incorporating with any power point slides presentation. Segmentation method is required to separate the news video and to creating time dependent metadata. In this work, In this paper, a time dependent metadata-based framework is proposed to segment news contents and to provide time dependent metadata so that user can use context information to communicate with their friends. The transcript of the news is divided by using the proposed story segmentation method. We provide a tag to represent the entire content of the news. And provide the sub tag to indicate the segmented news which includes the starting time of the news. The time dependent metadata helps user to track the news information. It also allows them to leave a comment on each segment of the news. User also may share the news based on time metadata as segmented news or as a whole. Therefore, it helps the user to understand the shared news. To demonstrate the performance, we evaluate the story segmentation accuracy and also the tag generation. For this purpose, we measured accuracy of the story segmentation through semantic similarity and compared to the benchmark algorithm. Experimental results show that the proposed method outperforms benchmark algorithms in terms of the accuracy of story segmentation. It is important to note that sub tag accuracy is the most important as a part of the proposed framework to share the specific news context with others. To extract a more accurate sub tags, we have created stop word list that is not related to the content of the news such as name of the anchor or reporter. And we applied to framework. We have analyzed the accuracy of tags and sub tags which represent the context of news. From the analysis, it seems that proposed framework is helpful to users for sharing their opinions with context information in Social media and Social news.

• Journal of Korean Society of Steel Construction
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• v.21 no.2
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• pp.155-164
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• 2009

In this study, a computationally efficient parallel axial-flexural plastic hinge model is proposed for nonlinear dynamic progressive collapse analysis of welded steel moment frames. To this end, post-yield flexural behavior and the interaction of bending moment and axial force of the double-span beams in the column's missing event was first investigated by using material and geometric nonlinear parametric finite element analysis. A piece-wise linear parallel point hinge model that captures the moment-axial tension interaction was then proposed and applied to nonlinear dynamic progressive collapse analysis of welded steel moment frames with the use of the OpenSees Program. The accuracy as well as the efficiency of the proposed model was verified based on the inelastic dynamic finite element analysis results. The importance of including the catenary action effects for proper progressive collapse resistant analysis and design was also emphasized.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.5
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• pp.611-617
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• 2010

A complex system involves a large number of design variables, and its operation is non-linear. To explore the characteristics in its design space, a Kriging meta-model can be utilized; this model has replaced expensive computational analysis that was performed in traditional parametric design optimization. In this study, a Kriging meta-model with a computational orthogonal array for the design of experiments was developed to optimize the fatigue life of a turbine blade whose behavior under cyclic rotational loads is significantly non-linear. The results not only show that the maximum fatigue life is improved but also indicate that the accuracy of computational analysis is achieved. In addition, the robustness of the results obtained by six-sigma optimization can be verified by comparison with the results obtained by performing Monte Carlo simulations.

• Journal of IKEEE
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• v.13 no.2
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• pp.140-149
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• 2009

Traffic anomaly detection is one of important technology that should be considered in network security and administration. In this paper, we propose an abnormal traffic detection mechanism that includes traffic monitoring and traffic analysis. We develop analytical passive monitoring system called WISE-Mon which can inspect traffic behavior. We establish a criterion by analyzing the characteristics of a traffic training set. To detect abnormal traffic, we derive a hyperplane by using Fisher linear discriminant and chi-square distribution as well as the analyzed characteristics of traffic. Our mechanism can support reliable results for traffic anomaly detection and is compatible to real-time detection. In addition, since the trend of traffic can be changed as time passes, the hyperplane has to be updated periodically to reflect the changes. Accordingly, we consider the self-learning algorithm which reflects the trend of the traffic and so enables to increase the pliability of detection probability. Numerical results are presented to validate the accuracy of proposed mechanism. It shows that the proposed mechanism is reliable and relevant for traffic anomaly detection.