• Journal of Korea Water Resources Association
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• v.56 no.spc1
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• pp.1049-1058
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• 2023

The flow passing through river-crossing structures such as weirs and low-fall dams is dominated by rapidly varied flow including hydraulic jump. The intense unsteadiness of flow velocity and free surface profile affects the stability of such hydraulic structures. In particular, the steady hydraulic jump generated at high Froude number conditions includes remarkably air entrainment, making the flow characteristics more complicated. In this study, a large-eddy simulation was performed for turbulence effect and the hybrid VoF technique to simulate the steady hydraulic jump at the Froude number of 7.3 and the Reynolds number of 15,700. The results of the numerical simulation showed that the instantaneous maximum pressure and time-average pressure distribution calculated on the bottom surface downstream of the structure could be reasonably well reproduced being in good agreement with the experimental values. However, the instantaneous minimum pressure distribution in the direct downstream of the structure shows the opposite pattern to the target experimental measurement value. However, the numerical simulation performed in this study is considered to reasonably predict the minimum pressure distributions observed in various experiments conducted at similar conditions. The vertical distributions of flow velocity and air concentration computed in the center of the hydraulic jump were found to be in good agreement with the experimental results measured under similar conditions, showing self-similarity. These results show that the large eddy simulation and hybrid VoF techniques applied in this study can reproduce the hydraulic jump with strong air entrainment and the resulting intense free surface and pressure fluctuations at high Froude number conditions.

• Korean Association of Arts Management
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• no.50
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• pp.5-31
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• 2019

The main purpose of this study was to use Deep Learning based Topic Modeling and Semantic Network Analysis to examine research trend of arts management-related papers in korea. For this purpose, research subjects such as 'The Journal of Cultural Policy', 'The Journal of Cultural Economics', 'The Journal of Culture Industry', 'The Journal of Arts Management', and 'The Journal of Human Content', which are the registered journal of the National Research Foundation of Korea directly or indirectly related to arts management field. From 1988 to 2017, a total of 2,110 domestic journals' signature, abstract, and keyword were analyzed. We tried Big Data analysis such as Topic Modeling and Semantic Network Analysis to examine changes in trends in arts management. The analysis program used open software R and standard statistical software SPSS. Based on the results of the analysis, the implications and limitations of the study and suggestions for future research were discussed. And the potential for development of convergent research such as Arts & Artificial Intelligence and Arts & Big Data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.207-216
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• 2024

As big data was built due to the 4th Industrial Revolution, personalized services increased rapidly. As a result, the amount of personal information collected from online services has increased, and concerns about users' personal information leakage and privacy infringement have increased. Online service providers provide privacy policies to address concerns about privacy infringement of users, but privacy policies are often misused due to the long and complex problem that it is difficult for users to directly identify risk items. Therefore, there is a need for a method that can automatically check whether the privacy policy is safe. However, the safety verification technique of the conventional blacklist and machine learning-based privacy policy has a problem that is difficult to expand or has low accessibility. In this paper, to solve the problem, we propose a safety verification technique for the privacy policy using the GPT-3.5 API, which is a generative artificial intelligence. Classification work can be performed evenin a new environment, and it shows the possibility that the general public without expertise can easily inspect the privacy policy. In the experiment, how accurately the blacklist-based privacy policy and the GPT-based privacy policy classify safe and unsafe sentences and the time spent on classification was measured. According to the experimental results, the proposed technique showed 10.34% higher accuracy on average than the conventional blacklist-based sentence safety verification technique.

• Journal of the Korean Geotechnical Society
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• v.40 no.3
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• pp.41-54
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• 2024

Regular monitoring plays a crucial role in ensuring the safety of geotechnical structures. Currently, nondestructive methods are employed to monitor such structures to minimize the impact, e.g., sensor-based accelerometers, displacement meters, image-based lasers, and drone imaging. These technologies can observe surface changes; however, they frequently suffer difficulties in terms of identifying changes in internal properties. To monitor changes in internal properties, in situ geotechnical investigations can be employed. A nondestructive test that can be used for this purpose is the spectral analysis of surface wave (SASW) test using geophones. The SASW test is a nondestructive method; however, due to the time required for data interpretation and the difficulty in analyzing the data, it is challenging to use the SASW test for monitoring applications that require frequent observations. However, it is possible to apply the first-step analysis, which yields the dispersion curve, for monitoring rather than the complete SASW analysis, which yields the shear wave velocity. Thus, this paper presents a fundamental study on the phase difference that derives the dispersion curve to utilize the SASW test for monitoring. The reliability of each phase difference interval is examined to determine the boundary to the subjected monitor. The study used phase difference data obtained using a geophone from a single-layered, homogeneous ground site to evaluate reliable boundaries. The findings of this study are expected to improve the utility of monitoring by identifying the ideal boundary for phase difference data.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.100-107
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• 2024

Currently, in Korea, due to the rapid aging and deterioration of facilities, the minimum Maintenance Level and Performance Level' of facilities are required by the 'Facility Safety Act' or 'Infrastructure Management Act'. Since infrastructure assets have a long lifespan and the pattern of deterioration over time is complex, it is very difficult to maintain infrastructure as 'minimum maintenance state' or 'minimum performance state' by the current way of management. 'Asset Management' shall be performed not only by a technical perspective, but also by an accounting perspective such as cost and asset value. However, due to lack of awareness of 'asset management' among stakeholder, only technical perspective management is being carried out in practice. In order to effectively manage infrastructure assets, complex consideration of various asset value factors such as budget and service as well as safety and durability are required. In this paper, we presented a theory to evaluate and quantify the road network value for efficient asset management of the road network. We also presented a method of simulation to apply the theory presented in this paper. Through simulation and the results derived from this study, it is possible to specify the budget for the future national asset management, and to optimize the strategy for the management of old road facilities.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.1
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• pp.49-56
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• 2024

An automated dynamic structural analysis module stands as a crucial element within a structural integrated mitigation system. This module must deliver prompt real-time responses to enable timely actions, such as evacuation or warnings, in response to the severity posed by the structural system. The finite element method, a widely adopted approximate structural analysis approach globally, owes its popularity in part to its user-friendly nature. However, the computational efficiency and accuracy of results depend on the user-provided finite element mesh, with the number of elements and their quality playing pivotal roles. This paper introduces a computationally efficient adaptive mesh generation scheme that optimally combines the h-method of node movement and the r-method of element division for mesh refinement. Adaptive mesh generation schemes automatically create finite element meshes, and in this case, representative strain values for a given mesh are employed for error estimates. When applied to dynamic problems analyzed in the time domain, meshes need to be modified at each time step, considering a few hundred or thousand steps. The algorithm's specifics are demonstrated through a standard cantilever beam example subjected to a concentrated load at the free end. Additionally, a portal frame example showcases the generation of various robust meshes. These examples illustrate the adaptive algorithm's capability to produce robust meshes, ensuring reasonable accuracy and efficient computing time. Moreover, the study highlights the potential for the scheme's effective application in complex structural dynamic problems, such as those subjected to seismic or erratic wind loads. It also emphasizes its suitability for general nonlinear analysis problems, establishing the versatility and reliability of the proposed adaptive mesh generation scheme.

• The Journal of the Acoustical Society of Korea
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• v.27 no.4
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• pp.163-170
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• 2008

Most of receiving rooms for the measurement of floor impact sound have rectangular shapes with couple of meters of dimension, with reflective finishing, no furniture, no curtains. Modal overlaps in those condition are the major reason for the low reproducibility, and as a matter of course, the low credibility. It is the major purpose of this study that searching for a better measurement method which mitigate the effect of modal overlap on measurement. Two ways of methods are tested. One is the way described in ISO standards which enables controlling the room modes of receiving rooms, the other is the way which enables to get more precise spatial averages in receiving rooms with room modes. It is not easy maintaining the reverberation time of low frequency bands in the range between 1s and 2s, though it is proven to be effective controlling the room modes with base traps. Space-time average SPL's through combinations of rotating microphones are easy to measure, and have good consistencies with average SPL of entire receiving room.

• The Journal of Korean Academy of Prosthodontics
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• v.62 no.1
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• pp.64-71
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• 2024

The most critical aspect of full-arch prosthodontic treatment is evaluating whether the patient's vertical occlusal dimension is appropriate, and if necessary, restoring it through increasing vertical dimension. If the vertical occlusal dimension is too low, it can lead to reduced chewing efficiency, as well as not only aesthetic concerns but also potential issues like hyperactivity of muscles and posterior displacement of the mandible. This report is about the patient dissatisfied with pronunciation and aesthetics due to an inappropriate vertical occlusal dimension resulting from prior prosthetic interventions, underwent full-arch prosthodontic restoration treatment. Through the utilization of digital diagnostic apparatus, a comprehensive evaluation was undertaken for patient's vertical occlusal dimension, occlusal plane orientation, and the condition of prosthetic restorations. Through 3D facial scanning, the facial landmarks were discerned, and subsequently, the new occlusal plane was established. This provided the foundation for a digitally guided diagnostic wax-up. An elevation of 5 mm from the incisor was determined. Comprehensive dental rehabilitation was then executed for all remaining teeth, excluding the maxillary four incisors. The treatment protocol followed a systematic approach by initially creating implant-supported restorations on both sides of the dental arch to establish a stable occlusal contact. Subsequently, prosthetic restorations for the natural dentition were generated. Diagnostic and treatment planning were established through the utilization of facial scanning. This subsequently led to a reduction in treatment complexity and an expedited treatment timeline.

• Information Systems Review
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• v.25 no.1
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• pp.129-143
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• 2023

In the era of big data, the field of artificial intelligence is showing remarkable growth, and in particular, the image classification learning methods by deep learning are becoming an important area. Various studies have been actively conducted to further improve the performance of CNNs, which have been widely used in image classification, among which a representative method is the Convolutional Recurrent Neural Network (CRNN) algorithm. The CRNN algorithm consists of a combination of CNN for image classification and RNNs for recognizing time series elements. However, since the inputs used in the RNN area of CRNN are the flatten values extracted by applying the convolution and pooling technique to the image, pixel values in the same phase in the image appear in different order. And this makes it difficult to properly learn the sequence of arrangements in the image intended by the RNN. Therefore, this study aims to improve image classification performance by proposing a novel hybrid method of CNN and RNN applying the concepts of encoder and decoder. In this study, the effectiveness of the new hybrid method was verified through various experiments. This study has academic implications in that it broadens the applicability of encoder and decoder concepts, and the proposed method has advantages in terms of model learning time and infrastructure construction costs as it does not significantly increase complexity compared to conventional hybrid methods. In addition, this study has practical implications in that it presents the possibility of improving the quality of services provided in various fields that require accurate image classification.

• Smart Media Journal
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• v.13 no.4
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• pp.33-48
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• 2024

With the advent of 5G, characterized by Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), efficient network management and service provision are becoming increasingly critical. This paper proposes a novel approach to address key challenges of 5G networks, namely ultra-high speed, ultra-low latency, and ultra-reliability, while dynamically optimizing network slicing and resource allocation using machine learning (ML) and deep learning (DL) techniques. The proposed methodology utilizes prediction models for network traffic and resource allocation, and employs Federated Learning (FL) techniques to simultaneously optimize network bandwidth, latency, and enhance privacy and security. Specifically, this paper extensively covers the implementation methods of various algorithms and models such as Random Forest and LSTM, thereby presenting methodologies for the automation and intelligence of 5G network operations. Finally, the performance enhancement effects achievable by applying ML and DL to 5G networks are validated through performance evaluation and analysis, and solutions for network slicing and resource management optimization are proposed for various industrial applications.