• Interaction and multiscale mechanics
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• v.5 no.1
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• pp.27-40
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• 2012

The two-dimensional incompressible flow of a linear viscoelastic fluid we considered in this research has rapidly oscillating initial conditions which contain both the large scale and small scale information. In order to grasp this double-scale phenomenon of the complex flow, a multiscale analysis method is developed based on the mathematical homogenization theory. For the incompressible flow of a linear viscoelastic Maxwell fluid, a well-posed multiscale system, including averaged equations and cell problems, is derived by employing the appropriate multiple scale asymptotic expansions to approximate the velocity, pressure and stress fields. And then, this multiscale system is solved numerically using the pseudospectral algorithm based on a time-splitting semi-implicit influence matrix method. The comparisons between the multiscale solutions and the direct numerical simulations demonstrate that the multiscale model not only captures large scale features accurately, but also reflects kinetic interactions between the large and small scale of the incompressible flow of a linear viscoelastic fluid.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.204-208
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• 1990

The digital instrumentation and control system for the large scale system like the power plant must have the form of the heirachical structure. Because most large scale system have many control and process signals and it is distributed in the vade region, it is necessary to partition them into several subsystems. Therefore, the role of SCS(Supervisory Control System) having the functions of controlling and monitoring for the status of subsystems is very important. In this paper, new SCS for the effective control of the large scale system is proposed.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.695-699
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• 2005

Propriety of PERT network for planning and management in large-scale systems was investigated. We also review the example of small and middle size system to compare the appropriateness of PERT analysis method. For comparing the appropriateness of PERT analysis method the calculation time, and the variances were estimated. Eventualy, we proposed the criteria of PERT analysis method for planning and management large-scale systems.

• Korean Journal of Ecology and Environment
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• v.53 no.4
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• pp.336-343
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• 2020

Large-scale cultivation of Microcystis aeruginosa in different light conditions was conducted for verifying the cell growth in a greenhouse system. Environmental and chemical parameters of the large-scale culture medium were measured for analyzing the interaction between M. aeruginosa and its symbiotic bacteria. During cultivation, a difference in cell growth pattern was observed between control (natural light) and light-limited groups (reduction of blue, green, and blue/green light, respectively). Comparing the control group, the light reduced groups showed slow and delayed cell growth through the cultivation period. Also, there is differences in the consuming pattern of total nitrogen and total phosphorus which indicated that the possibility of interaction between M. aeruginosa and symbiotic bacteria.

• Journal of Electrical Engineering and Technology
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• v.11 no.3
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• pp.724-732
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• 2016

In this paper, a sampled-data observer-based decentralized fuzzy control technique is proposed for a class of nonlinear large-scale systems, which can be represented to a Takagi-Sugeno fuzzy system. The premise variable is assumed to be measurable for the design of the observer-based fuzzy controller, and the closed-loop system is obtained. Based on an exact discretized model of the closed-loop system, the stability condition is derived for the closed-loop system. Also, the stability condition is converted into the linear matrix inequality (LMI) format. Finally, an example is provided to verify the effectiveness of the proposed techniques.

• The Transactions of the Korean Institute of Power Electronics
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• v.14 no.5
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• pp.365-371
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• 2009

This paper studies the optimal operation schemes for large scale wind farm. With few operation experiences and fundamental technology for the wind farm, there is a difficult to establish the grid code which is the standard for connecting wind farm to power system. Analysis of the grid code and the operation of other nations for wind farm is used to propose the optimal operation schemes for large-scale wind farm considering the characteristic of our power system, by analyzing the influence of power system by wind farm at Cheju island.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.119-126
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• 2024

The volume of genomic data is constantly increasing in various modern industries and research fields. This growth presents new challenges and opportunities in terms of the quantity and diversity of genetic data. In this paper, we propose a distributed cloud system for integrating and managing large-scale gene databases. By introducing a distributed data storage and processing system based on the Hadoop Distributed File System (HDFS), various formats and sizes of genomic data can be efficiently integrated. Furthermore, by leveraging Spark on YARN, efficient management of distributed cloud computing tasks and optimal resource allocation are achieved. This establishes a foundation for the rapid processing and analysis of large-scale genomic data. Additionally, by utilizing BigQuery ML, machine learning models are developed to support genetic search and prediction, enabling researchers to more effectively utilize data. It is expected that this will contribute to driving innovative advancements in genetic research and applications.

• Journal of the Korean Solar Energy Society
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• v.31 no.1
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• pp.8-14
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• 2011

Recently, a number of large-scale photovoltaic(PV) power generation system has been installed all over the world. Thus, in order to improve the system efficiency, the optimal design of the large-scale PV systems has become an important issue. DC cable loss of PV array is one of the design factors related to the system efficiency. This paper introduces the array design method of a 500kW Photovoltaic power plant. Three types of the PV array are suggested. Also, string cables, sub-array cables and array cables are designed within 1% of voltage drop in the line, and the DC cable losses are analyzed. The results of this paper show that the DC cable loss of large-scale PV array can be reduced by adopting a proper sub-array design method.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.359-370
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• 2024

The primary goal of this study is to evaluate the migration of fine granular materials into overlying layers under cyclic loading using a modified large-scale triaxial system as a physical model test. Samples prepared for the modified large-scale triaxial system comprised a 60 mm thick gravel layer overlying a 120 mm thick subgrade layer, which could be either tailings or railway sand. A quantitative analysis of the migration of fine granular materials was based on the mass percentage and grain size of migrated materials collected in the gravel. In addition, the cyclic characteristics, i.e., accumulated axial strain and excess pore water pressure, were evaluated. As a result, the total migration rate of the railway sand sample was found to be small. However, the total migration rate of the sample containing tailings in the subgrade layer was much higher than that of the railway sand sample. In addition, the migration analysis revealed that finer tailings particles tended to be migrated into the upper gravel layer easier than coarser tailings particles under cyclic loading. This could be involved in significant increases in excess pore water pressure at the last cycles of the physical model test.