• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.11
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• pp.2101-2106
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• 2009

The power system state estimation and prediction are very important for operation. Because that accidents of the Power system are the cause that many devices and etc are damaged. Currently, almost every power systems have 2nd,3rd back-upsystem for prevention of accident. But prevention of accident by miss-operation, due to operator or miss data, has not acounter plan. Because, we need to estimate the power system for correcting miss data and preventing miss operation by operator. We suggest algorithm for integrity of power system network data.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.3 s.357
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• pp.7-17
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• 2007

An analysis and design theory of an aperture-coupled cavity-fed back-to-back microstrip directional coupler is presented for the efficient and optimized design. For this purpose, an equivalent network is developed, and simple but accurate calculations of circuit element values are described. Design equations of the coupler are derived based on the equivalent circuit. In order to determine various structural design parameters, the evolutionary hybrid optimization method based on the genetic algorithm and Nelder-Mead method is invoked. As a validation check of the proposed theory and optimized design method, a 10 dB directional coupler was designed and fabricated. The measured coupling was 10.3 dB at 3 GHz, and the return loss and isolation were 31.8 dB and 30.5 dB, respectively. The directional coupler also showed very good quadrature phase characteristics. Good agreements between the measured and the design specifications fully validate the proposed network analysis and design procedure.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.7
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• pp.767-777
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• 1999

인과적 메시지 로깅 기법은 정상프로세스를 역전(roll-back)시키거나 메시지의 저장을 위해 프로세스의 수행을 중단시키지 않는 장점을 지니고 있지만, 메시지의 크기가 지나치게 커진다는 단점을 지니고 있다. 본 논문에서는 인과적 메시지 로깅 기법의 이러한 문제점을 해결하기 위하여 로그 상속의 개념을 정의하고 로그 연혁을 이용하여 로그 비용, 특히 로그 크기 면에서 효율적인 로깅 기법을 제안한다. 또한 이 로깅 알고리즘을 이용하여 복구시 메시지의 수와 크기를 줄여 복구시간을 줄이는 효율적인 복구 알고리즘을 제안하고, 제안한 알고리즘이 메시지 로그 크기 면에서 효율적임을 증명한다. 또 제안한 알고리즘의 성능을 검증하기 위하여 두 가지 종류의 모의 실험을 수행하여 기존의 로깅 프로토콜과 메시지 크기 면에서의 성능을 비교한 결과를 제시하였다.Abstract Causal message logging has many good properties such as nonblocking message logging and no rollback propagation. However, it requires a large amount of information to be piggybacked on each message, which may incur severe performance degradation. This paper presents an efficient causal logging algorithm based on the new message log structure, LogOn, which represents the causal inter-process dependency relation with much smaller overhead compared to the existing algorithms. The proposed algorithm is efficient in the sense that it entails no additional information other than LogOn to be carried in each message, while other existing algorithms require extra information other than the message logs. This paper also presents an efficient recovery algorithm to solve the problem of a large amount of data exchanges during the recovery. To verify the performance of our algorithm, we give an analysis of the algorithm and perform two simulations and compare the log size with other causal logging protocols.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.4
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• pp.357-365
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• 2009

A general analysis of a microstrip-to-vertically mounted slotline(VMS) coupler is presented with a view to developing an equivalent circuit, and the efficient evaluation of the related circuit element values. Based on this theory, the effects of frequency and structure parameters such as aperture length and VMS width on the characteristics of the coupler are studied. In order to check the validity of the proposed analysis and design theory, a C-band linearly tapered slot antenna fed by an aperture-coupled back-to-back microstripline-to- VMS coupling structure is optimally designed using a hybrid genetic algorithm. Moreover, the computed characteristics from the network analysis is compared to the measurement and simulation results. The obtained results fully validate the efficiency and accuracy of the proposed network model.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1845-1848
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• 2005

The automatic $CO_2$ welding is a manufacturing process to produce high quality joints for metal and it could provide a capability of full automation to enhance productivity. Despite the widespread use in the various manufacturing industries, the full automation of the robotic $CO_2$ welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this research, an attempt has been made to develop an intelligent algorithm to predict the weld geometry (top-bead width, top-bead height, back-bead width and back-bead height) as a function of key process parameters in the robotic $CO_2$welding. A sensitivity analysis has been conducted and compared the relative impact of three process parameters on bead geometry in order to verify the measurement errors on the values of the uncertainty in estimated parameters.

• Journal of the Korean GEO-environmental Society
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• v.13 no.8
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• pp.25-33
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• 2012

Although it is important to reflect the accurate information of the ground condition in the tunnel design, the analysis and design are conducted by limited information because it is very difficult to consider various geographies and geotechnical conditions. When the tunnel is under construction, examination of accurate safety and prediction of behavior are overcome the limits of predicting behavior by Artificial Neural Network in this study. First, construct the suitable structure after the data of field was made sure by the multi-layer back propagation, then apply with algorithm. Employ the result of measured data from database, and consider the influence factor of tunnel, like supporting pattern, RMR, Q, the types of rock, excavation length, excavation shape, excavation over, to carry out the reliable analysis through field applicability of Artificial Neural Network. After studying, using the ANN model to predict the shearing displacement, convergence displacement, underground displacement, Rock bolt output follow the excavation over of tunnel construction field, then determine the field applicability with ANN through field measured value and comparison analysis when tunnel is being constructed.

• Journal of the Korean Geosynthetics Society
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• v.21 no.2
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• pp.21-30
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• 2022

Shear strength is the most important indicator in the evaluation of rock slope stability. It is generally estimated by comparing the results of existing literature data, back analysis, experiments and etc. There are additional variables related to the state of discontinuity to consider in the shear strength of the rock slope. It is difficult to determine whether these variables exist through drilling, and it is also difficult to find an exact relationship with shear strength. In this study, the data calculated through back analysis were used. The relationship between previously considered variables was applied to deep learning and the possibility for estimating shear strength of rock slope was explored. For comparison, an existing simple linear regression model and a deep learning algorithm, a deep neural network(DNN) model, were used. Although each analysis model derived similar prediction results, the explanatory power of DNN was improved with a small differences.

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.3
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• pp.214-221
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• 1998

A new control approach using neural network for the robust position control of a BRUSHLESS direct drive(BLDD) motor is presented. The linear quadratic controller plus feedforward neural network is employed to obtain the robust BLDD motor system approximately linearized using field-orientation method for an AC servo. The neural network is trained in on-line phases and this neural network is composed by a feedforward recall and error back-propagation training. Since the total number of nodes are only eight, this system will be easily realized by the general microprocessor. During the normal operation, the input-output response is sampled and the weighting value is trained by error back-propagation at each sample period to accommodate the possible variations in the parameters or load torque. And the state space analysis is performed to obtain the state feedback gains systematically. In addition, the robustness is also obtained without affecting overall system response.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.297-303
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• 2023

Pediatric Computed Tomography (CT) examinations can often result in exam failures or the need for frequent retests due to the difficulty of cooperation from young patients. Deep Learning Image Reconstruction (DLIR) methods offer the potential to obtain diagnostically valuable images while reducing the retest rate in CT examinations of pediatric patients with high radiation sensitivity. In this study, we investigated the possibility of applying DLIR to reduce artifacts caused by respiration or motion and obtain clinically useful images in pediatric chest CT examinations. Retrospective analysis was conducted on chest CT examination data of 43 children under the age of 7 from P Hospital in Gyeongsangnam-do. The images reconstructed using Filtered Back Projection (FBP), Adaptive Statistical Iterative Reconstruction (ASIR-50), and the deep learning algorithm TrueFidelity-Middle (TF-M) were compared. Regions of interest (ROI) were drawn on the right ascending aorta (AA) and back muscle (BM) in contrast-enhanced chest images, and noise (standard deviation, SD) was measured using Hounsfield units (HU) in each image. Statistical analysis was performed using SPSS (ver. 22.0), analyzing the mean values of the three measurements with one-way analysis of variance (ANOVA). The results showed that the SD values for AA were FBP=25.65±3.75, ASIR-50=19.08±3.93, and TF-M=17.05±4.45 (F=66.72, p=0.00), while the SD values for BM were FBP=26.64±3.81, ASIR-50=19.19±3.37, and TF-M=19.87±4.25 (F=49.54, p=0.00). Post-hoc tests revealed significant differences among the three groups. DLIR using TF-M demonstrated significantly lower noise values compared to conventional reconstruction methods. Therefore, the application of the deep learning algorithm TrueFidelity-Middle (TF-M) is expected to be clinically valuable in pediatric chest CT examinations by reducing the degradation of image quality caused by respiration or motion.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.4
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• pp.711-719
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• 1994

This paper deals with the development of an efficient algorithm which can consider the bond-slip effect in numerical analysis of reinforced concrete structures. Unlike the classical bond-link or bond-zone element using double nodes, the proposed model is considering the bond-slip effect without taking double nodes by incorporation of the equivalent steel stiffness. Moreover after calculation of nodal displacements, the deformation of steel at each node can be found out through the back-substitution technique from first up to final steel element using a governing equation constructed based on the equilibrium at each node of steel and the compatibility condition between steel and concrete.