• Smart Structures and Systems
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• v.16 no.5
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• pp.937-960
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• 2015

Tuned mass dampers (TMDs) have been a prevalent vibration control device for suppressing excessive vibration because of environmental loadings in contemporary tall buildings since the mid-1970s. A TMD must be tuned to the natural frequency of the primary structure to be effective. In practice, a TMD may be assembled in situ, simultaneously with the building construction. In such a situation, the respective dynamic properties of the TMD device and building cannot be identified to determine the tuning status of the TMD. For this purpose, a methodology was developed to obtain the parameters of the TMD and primary building on the basis of the eigenparameters of any two complex modes of the combined building-TMD system. The theory was derived in state-space to characterize the nonclassical damping feature of the system, and combined with a system identification technique to obtain the system eigenparameters using the acceleration measurements. The proposed procedure was first demonstrated using a numerical verification and then applied to real, experimental data of a large-scale building-TMD system. The results showed that the procedure is capable of identifying the respective parameters of the TMD and primary structure and is applicable in real implementations by using only the acceleration response measurements of the TMD and its located floor.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.7
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• pp.1270-1275
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• 2009

The structure of a power distribution system will change in a loop configuration such as in the case of a smart grid. If power distribution system changes radial to loop form, the structure may have to be changed significantly. Therefore, we analyzed the reliability indices and calculated a CIC(Customer Interruption Cost) for the loop power distribution system. The power distribution system reliability depends on the protection scheme. This study is applied to the current protection scheme method and is compared with each model. When the CIC was evaluated, most studies performed calculations only for sustained interruptions. However, in actuality, momentary interruption frequencies occurred more than sustain interruptions. Thus, it is occurred the CIC additively. Therefore, we evaluated a CIC including momentary interruption, for each model, and then compared with MAIFI(Momentary Average Interruption Frequency Index)

• Smart Structures and Systems
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• v.15 no.3
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• pp.863-879
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• 2015

This paper experimentally investigates the effectiveness and applicability of the time delay control (TDC) algorithm, which is simple and robust to unknown system dynamics and disturbance, for an active mass damper (AMD) system to mitigate the excessive vibration of a building structure. To this end, the theoretical background including the mathematical formulation of the control system is first described; and then, a thorough experimental study using a shaking table system with a small-scale three-story building structural model is conducted. In the experimental tests, the performance of the proposed control system is examined by comparing its structural responses with those of the uncontrolled system in the free vibration and forced vibration cases. It is clearly verified from the test results that the TDC algorithm embedded AMD system can effectively reduce the structural response of the building structure.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.1
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• pp.57-73
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• 2021

The purpose of this study is to analyze the diffusion period and productivity of smartwork in an organization. Firms are increasingly interested in smartwork for non contact work and working from home because of the corona 19. The smartwork is a new technology that changes face-to-face work in an organization. It helps the work of individuals and organizations regardless of time and place. The theoretical background describes the complexity, system thinking, diffusion theory, smart work, organizational resistance, and productivity. This study analyzes the diffusion period and productivity of smart work through business simulation techniques. A simulation study progresses four stages. There are problem definition, hypothesis establishment and causal loop diagram, model construction and verification, and policy evaluation. The simulation models contain an individual's resistance variables organizational investment and leadership variables related to the operation of smartwork. The organizational investment variables include organizational culture, legal system, implement systems and technology investment. The individual resistance variables include cognitive, attitude, structure and technological resistance. The leadership includes leadership interest variables and performance linkage variables. The simulation executed the changes of a people number adopting smart work and the organizational productivity monthly. As a result of the simulation, many organization members have accepted the smart work innovation after 20 months. The organizational productivity through smart work showed very high value after 16 months. In scenario analysis, the individuals' awareness and attitude resistance showed very important variables to productivity and a personal change of smart work adoption. Meanwhile, The organizational investment showed that the high driving-force increased not productivity and the low driving-force showed decreased low productivity. Also, leadership variables showed a powerful driver for changing smart work productivity. The implication of the study has suggested extending complexity, diffusion theory and organization resistance theory based on simulation methods.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.12
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• pp.561-568
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• 2013

Instrumentation and control system in nuclear power plant performs protecting, controling and monitoring safety operation of Nuclear Power Plant. As cyber attack to the control equipment of instrumentation and control system can cause reactor shutdown and radiation release, it is required to design the instrumentation and control system considering cyber security in accordance with regulatory guides and industrial standards. In this paper, we proposed a design method of uni-directional communication structure which is required in the design of defense-in-depth model according to regulatory guides and industrial standards and we implemented a communication board with the proposed method. This communication board was tested in various test environments and test items and we concluded it can provide uni-directional communication structure required to design of defense-in-depth model against cyber attack by analyzing the results. The proposed method and implemented communication board were applied in the design of SMART (system-integrated modular advanced reactor) I&C (instrumentation and control) systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.4
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• pp.49-59
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• 2017

Smart sensing plays a key role in a variety of IoT applications, and its importance is growing more and more together with the development of artificial intelligence. Therefore the importance of smart sensors cannot be overemphasized. However, most studies related to smart sensors have been focusing on specific application purposes, for example, security, energy saving, monitoring, and there are not much effort on researches on how to efficiently configure various types of smart sensors to be needed in the future. In this paper, a component-based framework with hierarchical structure for efficient construction of smart sensor is proposed and its application to smart home is designed and implemented. The proposed method shows that various types of smart sensors to be appeared in the near future can be configured through the design and development of necessary components within the proposed software framework. In addition, since it has a layered architecture, the configuration of the smart sensor can be expanded by inserting the internal or external layers. In particular, it is possible to independently design the internal and external modules when designing an IoT application service through connection with the external device layer. A small-scale smart home system is designed and implemented using the proposed method, and a home cloud operating as an external layer, is further designed to accommodate and manage multiple smart homes. By developing and thus adding the components of each layer, it will be possible to efficiently extend the range of applications such as smart cars, smart buildings, smart factories an so on.

• Steel and Composite Structures
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• v.46 no.1
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• pp.107-114
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• 2023

A new intelligent adaptive control scheme was proposed that combines observer disturbance-based adaptive control and fuzzy adaptive control for a composite structure with a mass-adjustable damper. The most important advantage is that the control structures do not need to know the uncertainty limits and the interference effect is eliminated. Three adjustable parameters in LMI are used to control the gain of the 2D fuzzy control. Binary performance indices with weighted matrices are constructed to separately evaluate validation and training performance using the revalidation learning function. Determining the appropriate weight matrix balances control and learning efficiency and prevents large gains in control. It is proved that the stability of the control system can be ensured by a linear matrix theory of equality based on Lyapunov's theory. Simulation results show that the multilevel simulation approach combines accuracy with high computational efficiency. The M-TMD system, by slightly reducing critical joint load amplitudes, can significantly improve the overall response of an uncontrolled structure.

• International journal of advanced smart convergence
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• v.10 no.3
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• pp.51-58
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• 2021

In this paper, we propose a deep learning classifier for estimating the number of layers in the Earth's structure. When installing a grounding system, knowledge of the subsurface in the area is absolutely necessary. The subsurface structure can be modeled by the earth parameters. Knowing the exact number of layers can significantly reduce the amount of computation to estimate these parameters. The classifier consists of a feedforward neural network. Apparent resistivity curves were used to train the deep learning classifier. The apparent resistivity at 20 equally spaced log points in each curve are used as the features for the input of the deep learning classifier. Apparent resistivity curve data sets are collected either by theoretical calculations or by Wenner's measurement method. Deep learning classifiers are coded by Keras, an open source neural network library written in Python. This model has been shown to converge with close to 100% accuracy.

• 한국신재생에너지학회:학술대회논문집
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• 2011.05a
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• pp.76.1-76.1
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• 2011

With the growing penetration of distributed generation including from renewable sources, smart grid power system is needed to address the reliability problem. One important feature of smart grid is demand response. In order to design a demand response program, it is indispensable to understand how consumer reacts upon the change of electricity price. In this paper, we construct an econometrics model to estimate the hourly price elasticity of demand. This panel model utilizes the hourly load data obtained from KEPCO for the period from year 2005 to 2009. The hourly price elasticity of demand is found to be statistically significant for all the sample under investigation. The samples used for this analysis is from the past historical data under the price structure of three different time zones for each season. The result of the analysis of this time of use pricing structure would allow the policy maker design an appropriate incentive program. This study is important in the sense that it provides a basic research information for designing future demand response programs.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.3
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• pp.177-183
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• 2022

To measure the size and weight of the fish, we developed an automatic fish size measurement system using a deep neural network, where the YOLO (You Only Look Once)v3 model was used. To detect fish, an IP camera with infrared function was installed over the fish pool to acquire image data and used as input data for the deep neural network. Using the bounding box information generated as a result of detecting the fish and the structure for which the actual length is known, the size of the fish can be obtained. A GUI (Graphical User Interface) program was implemented using LabVIEW and RTSP (Real-Time Streaming protocol). The automatic fish size measurement system shows the results and stores them in a database for future work.