• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.11a
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• pp.217-218
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• 2019

Fire resistance ceiling system is the structure of which the ceiling installed under the slave of the structure has the fire resistance performance. Because of having the fire resistance performance, fire resistive coatings on steel beams can be reduced and large span structures can be constructed. So, it have advantages of convenience for construction, shorten for construction time and cost reducing. In foreign country, it is general that one system consisting of slave and ceiling is constructed as a fire resistance system, in these cases, gypsum boards are mostly used as ceiling materials. The purpose of this study was to explain the possibility of expanding the use of gypsum boards by securing fire resistance performance of these ceilings.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.09a
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• pp.191-198
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• 2003

Flat slab system has been adopted in many buildings constructed recently because of the advantage of reduced floor heights to meet the economical and architectural demands. Structural engineers commonly use the equivalent frame method(EFM) with equivalent beams proposed by Jacob S. Grossman in practical engineering for the analysis of flat slab structures. However, in many cases, when it is difficult to use the EFM, it is necessary to use a refined finite element model for an accurate analysis. But it would take significant amount of computational time and memory if the entire building structure were subdivided into a finer mesh. An efficient analytical method is proposed in this study to obtain accurate results in significantly reduced computational time. The proposed method employs super elements developed using the matrix condensation technique and fictitious beams are used in the development of super elements to enforce the compatibility at the interfaces of super elements. The stiffness degradation of flat slab system considered in the EFM was taken into account by reducing the elastic modulus of floor slabs in this study. Static and dynamic analyses of example structures were peformed and the efficiency and accuracy of the proposed method were verified by comparing the results with those of the refined finite element model and the EFM.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.5
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• pp.433-441
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• 2009

Generally, the events in nature have multi-disciplinary characteristics. To solve this problems, these days loosely coupled methods are widely applied because of advantage of solvers which are already developed and well proved. Those solvers use different mesh system, so transformation and mapping of data are vital in the field of fluid-structure interaction(FSI). In this paper, the interpolation of deformation which is used globally and compactly supported radial basis functions(RBF), and mapping of force which use principle of virtual work are examined for computing time and accuracy to compare ability with simple 3-D problem. As the results, interpolation scheme of compactly supported radial basis functions are useful to interpolation and mapping for large-scale airplane in FSI with a k-dimensional tree(kd-tree) which is a space-partitioning data structure for organizing points in a k-dimensional space.

• Smart Structures and Systems
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• v.6 no.3
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• pp.197-209
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• 2010

The structural state of a bridge is currently examined by visual inspection or by wired sensor techniques, which are relatively expensive, vulnerable to inclement conditions, and time consuming to undertake. In contrast, wireless sensor networks are easy to deploy and flexible in application so that the network can adjust to the individual structure. Different sensing techniques have been used with such networks, but the acoustic emission technique has rarely been utilized. With the use of acoustic emission (AE) techniques it is possible to detect internal structural damage, from cracks propagating during the routine use of a structure, e.g. breakage of prestressing wires. To date, AE data analysis techniques are not appropriate for the requirements of a wireless network due to the very exact time synchronization needed between multiple sensors, and power consumption issues. To unleash the power of the acoustic emission technique on large, extended structures, recording and local analysis techniques need better algorithms to handle and reduce the immense amount of data generated. Preliminary results from utilizing a new concept called Acoustic Emission Array Processing to locally reduce data to information are presented. Results show that the azimuthal location of a seismic source can be successfully identified, using an array of six to eight poor-quality AE sensors arranged in a circular array approximately 200 mm in diameter. AE beamforming only requires very fine time synchronization of the sensors within a single array, relative timing between sensors of $1{\mu}s$ can easily be performed by a single Mote servicing the array. The method concentrates the essence of six to eight extended waveforms into a single value to be sent through the wireless network, resulting in power savings by avoiding extended radio transmission.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.654-656
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• 1998

This paper presents an optimal identification method of nonlinear and complex system that is based on fuzzy-neural network(FNN). The FNN used simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM Algorithm to find initial parameters of membership function. And then to obtain optimal parameters, we use the genetic algorithm. Genetic algorithm is a random search algorithm which can find the global optimum without converging to local optimum. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance of the FNN, we use the time series data for 9as furnace and the sewage treatment process.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.413-418
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• 1998

We often use induction motor in the hard environment including vibration and high ambient temperature, or in maintenance free operation, because induction motor has durable and simple structure. However, when we use it as servo actuator or accurate speed control motor, we have to equip sensor such as encoder and tachogenerator with the motor control system. And generally those sensor's abilities against bad environment are less than the induction motor itself, So if we can remove these sensors from the system, it'll have more environmental resistance, and the cost will also be reduced. Actually this removal has been achieved in limited field. However, that needs complex calculations and a certain elapse time for data processing. In our study, we intended to estimate the rotational speed from the motor current instead of speed sensor, easily and rapidly in comparison to former methods.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.565-580
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• 2006

Set in constant increase and period current of lively technical development of railroad use and construction of cable stayed bridge railway bridge, one of bridge form of most suitable that think side police officer and the material enemy of bridge that use long rail, is increasing laying stress on the foreign countries. Main tower fixing department of this cable stayed bridge is consisted of main tower flange that support bearing plate, bay ring plate bearing plate, support end rib and diaphragm etc, as stress transmission mechanic that tensility of cable socket into normal force of main tower, and is used this time. These structural elements is very complex the structure and direction of load delivered from socket specially calbe particularly be different, and need FEM analysis that use Thick Shell element for suitable arrangement of mutual stress flowing grasping and absence that follow hereupon because all of the each support plate angle that suport this differ.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.464-471
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• 2009

Robot and application of IT skill of construction industry are slow comparatively than another thing industry by the feature. This research proposes progress management and real time information gathering through construction automation and RFID focused on steel structure construction. Building for RTPM system, must consider various variables and surrounding situation in construction field and it is the most important and difficult matter that draw right requirement and grasp relation between this requirements to accomplish one suitable task considering these environment. Therefore, in this study analyzes requirement and target for RTPM system based on scenario that is easy to draw requirement and apply this to use case model. Presented method suggests that represent relation between goals and way that refines goal systematically from requirement of RTPM system. And it could express for visualization through the Way that attaches nonfunctional elements of system with system internal goal.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.421-427
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• 2013

The dependence between wind speeds in multiple wind sites has a considerable impact on the reliability of power systems containing wind energy. This paper presents a new method to generate dependent wind speed time series (WSTS) based on copulas theory. The basic feature of the method lies in separating multivariate WSTS into dependence structure and univariate time series. The dependence structure is modeled through the use of copulas, which, unlike the cross-correlation matrix, give a complete description of the joint distribution. An autoregressive moving average (ARMA) model is applied to represent univariate time series of wind speed. The proposed model is illustrated using wind data from two sites in Canada. The IEEE Reliability Test System (IEEE-RTS) is used to examine the proposed model and the impact of wind speed dependence between different wind regimes on the generation system reliability. The results confirm that the wind speed dependence has a negative effect on the generation system reliability.