• Steel and Composite Structures
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• v.47 no.2
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• pp.167-184
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• 2023

In this study, a new recentering friction device (RFD) to retrofit steel moment frame structures is introduced. The device provides both self-centering and energy dissipation capabilities for the retrofitted structure. A hybrid performance-based seismic design procedure considering multiple limit states is proposed for designing the device and the retrofitted structure. The design of the RFD is achieved by modifying the conventional performance-based seismic design (PBSD) procedure using computational intelligence techniques, namely, genetic algorithm (GA) and artificial neural network (ANN). Numerous nonlinear time-history response analyses (NLTHAs) are conducted on multi-degree of freedom (MDOF) and single-degree of freedom (SDOF) systems to train and validate the ANN to achieve high prediction accuracy. The proposed procedure and the new RFD are assessed using 2D and 3D models globally and locally. Globally, the effectiveness of the proposed device is assessed by conducting NLTHAs to check the maximum inter-story drift ratio (MIDR). Seismic fragilities of the retrofitted models are investigated by constructing fragility curves of the models for different limit states. After that, seismic life cycle cost (LCC) is estimated for the models with and without the retrofit. Locally, the stress concentration at the contact point of the RFD and the existing steel frame is checked being within acceptable limits using finite element modeling (FEM). The RFD showed its effectiveness in minimizing MIDR and eliminating residual drift for low to mid-rise steel frames models tested. GA and ANN proved to be crucial integrated parts in the modified PBSD to achieve the required seismic performance at different limit states with reasonable computational cost. ANN showed a very high prediction accuracy for transformation between MDOF and SDOF systems. Also, the proposed retrofit showed its efficiency in enhancing the seismic fragility and reducing the LCC significantly compared to the un-retrofitted models.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.793-799
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• 2011

A comprehensive analysis of wheel force spectrum is conducted to provide the KTX safety evaluation with structural behaviour of Pre-Stressed Concrete (PSC) box bridge due to various high speeds. The wheel spectrum for KTX locomotive running over road and PSC bridge tracks is compared using irregular track responses with numerical models of 170m approach road track and 40m span length of PSC box bridge The high-speed railway locomotive is used as 38-degree of freedom system. Three displacements (vertical, lateral, and longitudinal) and three rotational components (pitching, rolling, and yawing) for one car-body and two bogies are considered in the 38-degree of freedom model. Three dimensional frame element of finite element method (FEM) is used to model of the simply supported PSC box bridge. The irregulation of rail-way is derived using the experiential spectrum density function under assumption of twelve level tracks conditions based on the normal probability procedure. The dynamic analyses by Runge-Kutta method which are able to analyze the high frequency wheel force spectrum. A dynamic behaviour of KTX due to high speeds until 450km/h developing speed with relative time is analysed and compared the characteristics running over the road and PSC box bridge tracks. Finally, the KTX integrated evaluation method of safety between high speed train and bridge is presented.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2311-2317
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• 2011

In this paper, we study the temperature change of a ${\mu}$-bolometer focal plane array with a capacitive transimpedance amplifier bias cancellation circuit. Thermal analysis is essential to understand the performance of a ${\mu}$-bolometer focal plane array, and to improve the temperature stability of a focal plane array characteristics. In this study, the thermal analyses of a ${\mu}$-bolometer and its two reference detectors are carried out as a function of time. The analyses are done with the $30{\mu}m$ pitch $320{\times}240$ focal plane array operating of 60 Hz frame rate and having a columnwise readout. From the results, the temperature increase of a ${\mu}$-bolometer in FPA by an incident IR is estimated as $0.689^{\circ}C$, while the temperature increase by a pulsed bias as $7.1^{\circ}C$, which is about 10 times larger than by IR. The temperature increase of a reference detector by a train of bias pulses may be increased much higher than that of an active ${\mu}$-bolometer. The suppression of temperature increase in a reference bolometer can be done by increasing the thermal conductivity of the reference bolometer, in which the selection of thermal conductivity also determines the range of CTIA output voltage.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.374-377
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• 2005

Automobile is in charge of most transportation system in modern urban city. However, in fact, cause of problem of road state, environment, and the other reasons, urban transit system is using as Mass Transit nowadays. Nevertheless Urban transit system is considering many kind of safety fact of that system which is increasing continuously nowadays, it occurs various train accident. This paper describes 3D Dimensional Measurement(EDM testing) and tensile testing results of carbody structure for crashed EMU(Electric Multiple Units). Tensile tests were performed on two different types of specimens in order to evaluate the strength changes before and after damages, obtained from plastic deformed area and nondeformed region of the crashed EMU. And Structural analysis of EMU was performed for the criteria of safety assessment. Structural analysis using commercial I-DEAS software provided important information on the stress distribution and load transfer mechanisms as well as the amount of damages during rolling stock crash. The testing results have been used to provide the critical information for the criteria of safety diagnosis.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.9
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• pp.485-490
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• 2006

According to the computerization of railway signaling systems, the interface link between the signaling systems has been replaced by the digital communication channel. At the same time, the importance of the communication link is more pronounced than before. In this paper, new Network-based protocol between railway signaling and SCADA (Supervisory Control and Data Acquisition system) has designed and the overview of designed protocol is briefly represented. And also this paper addresses analysis of newly designed train control systems. Fame error rates of the data transmissions are calculated and compared for the two cases that the CTC (Centralized Traffic Control)/SCADA has an extra data transmission error control (CRC16) besides the inherent error control of the Ethernet and that the CTC/SCADA has no extra data transmission error control. With simulation results it has been verified that the additional error control code contributes to lowering the frame error rate. It will be expected to increase the safety, reliability and efficiency of maintenance of the signaling systems by using the designed protocol for railway signaling system.

• Proceedings of the KSR Conference
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• 2003.05a
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• pp.567-573
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• 2003

Korean High Speed Train (KHST) has been tested on high speed line in JungBu site since it was developed in 2002. The data acquisition system was developed to accomplish successfully this on-line test for proving the dynamic Performance of KHST. This system was consist of the personal computers based on National Instrument PXI modules and the test programs based on Labview 6i. This paper shows that this system is efficient to acquire the test data through the multi-channels connected the accelerometers which located in long distance places and flexible to change and add channels for data acquisition. The dynamic analysis of an on-line test is very complicate because the environmental conditions, as examples radius of curve, inclination of the track, tunnels, bridges, and so forth, and running conditions, as examples driving, braking, the number of working motors, and so forth, have an effect on the results. Therefor, the analysis method is important and this paper proposes the efficient procedure graphically, showing the proposed method simplify the accelerations of 5th bogie frame acquired during the on-line test for KHST.

• ETRI Journal
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• v.36 no.4
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• pp.662-672
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• 2014

Human action recognition is used in areas such as surveillance, entertainment, and healthcare. This paper proposes a system to recognize both single and continuous human actions from monocular video sequences, based on 3D human modeling and cyclic hidden Markov models (CHMMs). First, for each frame in a monocular video sequence, the 3D coordinates of joints belonging to a human object, through actions of multiple cycles, are extracted using 3D human modeling techniques. The 3D coordinates are then converted into a set of geometrical relational features (GRFs) for dimensionality reduction and discrimination increase. For further dimensionality reduction, k-means clustering is applied to the GRFs to generate clustered feature vectors. These vectors are used to train CHMMs separately for different types of actions, based on the Baum-Welch re-estimation algorithm. For recognition of continuous actions that are concatenated from several distinct types of actions, a designed graphical model is used to systematically concatenate different separately trained CHMMs. The experimental results show the effective performance of our proposed system in both single and continuous action recognition problems.

• The Journal of Korea Robotics Society
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• v.14 no.4
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• pp.357-362
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• 2019

This paper proposes a model and train method that can real-time detect objects and distances estimation based on a monocular camera by applying deep learning. It used YOLOv2 model which is applied to autonomous or robot due to the fast image processing speed. We have changed and learned the loss function so that the YOLOv2 model can detect objects and distances at the same time. The YOLOv2 loss function added a term for learning bounding box values x, y, w, h, and distance values z as 클래스ification losses. In addition, the learning was carried out by multiplying the distance term with parameters for the balance of learning. we trained the model location, recognition by camera and distance data measured by lidar so that we enable the model to estimate distance and objects from a monocular camera, even when the vehicle is going up or down hill. To evaluate the performance of object detection and distance estimation, MAP (Mean Average Precision) and Adjust R square were used and performance was compared with previous research papers. In addition, we compared the original YOLOv2 model FPS (Frame Per Second) for speed measurement with FPS of our model.

• Proceedings of the KSR Conference
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• 2009.05b
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• pp.437-443
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• 2009

Generally speaking, the uniqueness of Korea for the high-speed railway rolling-stock is mainly perceived through the travel over the high-speed electric railway by passengers, which consists of the organization of space in the coach and its interior design. For such purpose, it is necessary to build a series of independent cultures with high-speed railway i.e. to have the development of design technologies oriented to harmonizing human beings, their society and culture with the systematic integration of production technologies to support the culture and its substructure foundation which constitute the frame of superstructure in the society, by defining the relationships with travelers on the high-speed railway as well as enhancing the comfort and convenience of high-speed railway through drawing up the development of links with the systematic integration among lines of technologies on the basis of 'User Train Interface. This thesis aims at introducing the study carried out to build the evaluation standards of performance outputs for 'HEMU-400X rolling-stock Design' derived from the performance for the first year in the research 'Development of Technologies for the Next-Generation High-Speed Railway' as part of "Development Project for the Future Railway Technologies" from 2007.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.43.1-43.1
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• 2019

In this study, we apply a deep learning model to denoising solar magnetograms. For this, we design a model based on conditional generative adversarial network, which is one of the deep learning algorithms, for the image-to-image translation from a single magnetogram to a denoised magnetogram. For the single magnetogram, we use SDO/HMI line-of-sight magnetograms at the center of solar disk. For the denoised magnetogram, we make 21-frame-stacked magnetograms at the center of solar disk considering solar rotation. We train a model using 7004 paris of the single and denoised magnetograms from 2013 January to 2013 October and test the model using 1432 pairs from 2013 November to 2013 December. Our results from this study are as follows. First, our model successfully denoise SDO/HMI magnetograms and the denoised magnetograms from our model are similar to the stacked magnetograms. Second, the average pixel-to-pixel correlation coefficient value between denoised magnetograms from our model and stacked magnetogrmas is larger than 0.93. Third, the average noise level of denoised magnetograms from our model is greatly reduced from 10.29 G to 3.89 G, and it is consistent with or smaller than that of stacked magnetograms 4.11 G. Our results can be applied to many scientific field in which the integration of many frames are used to improve the signal-to-noise ratio.