• Computers and Concrete
• /
• v.34 no.4
• /
• pp.427-446
• /
• 2024

The I-shaped steel reinforced recycled aggregate concrete (SRRC) composite structure has the advantages of high bearing capacity and environmental protection, and the interfacial bond strength is an important theory. To this end, the I-shaped SRRC bond strength and its calculation based on artificial neural network (ANN) will be studied. Firstly, 39 push out tests of I-shaped SRRC were conducted, the load-slip curve has obvious regularity, which is divided into 4 segments by 3 regular points. Three bond strengths were defined based on these three rule points, and the approximate ranges of their values and the laws of influence of each factor on them were found. Secondly, the Elman ANN model used for the prediction of bond strength was established, and the parameters of Elman ANN predicting I-shaped SRRC bond strength were studied, and the effects of detailed parameters on the prediction results were revealed. Finally, the bond strength of SRRC was predicted using Elman and BP (back propagation) neural network models, both of which showed good prediction results. This study is a theoretical basis for the design and fine simulation of I-shaped SRRC composite structures.

• Anatomy & Biological Anthropology
• /
• v.31 no.4
• /
• pp.133-142
• /
• 2018

3D histology is a imaging system for the 3D structural information of cells or tissues. The synchrotron radiation propagation phase contrast micro-CT has been used in 3D imaging methods. However, the simple phase contrast micro-CT did not give sufficient micro-structural information when the specimen contains soft elements, as is the case with many biomedical tissue samples. The purpose of this study is to develop a new technique to enhance the phase contrast effect for soft tissue imaging. Experiments were performed at the imaging beam lines of Pohang Accelerator Laboratory (PAL). The biomedical tissue samples under frozen state was mounted on a computer-controlled precision stage and rotated in $0.18^{\circ}$ increments through $180^{\circ}$. An X-ray shadow of a specimen was converted into a visual image on the surface of a CdWO4 scintillator that was magnified using a microscopic objective lens(X5 or X20) before being captured with a digital CCD camera. 3-dimensional volume images of the specimen were obtained by applying a filtered back-projection algorithm to the projection images using a software package OCTOPUS. Surface reconstruction and volume segmentation and rendering were performed were performed using Amira software. In this study, We found that synchrotron phase contrast imaging of frozen tissue samples has higher contrast power for soft tissue than that of non-frozen samples. In conclusion, synchrotron radiation propagation phase contrast cryo-microCT imaging offers a promising tool for non-destructive high resolution 3D histology.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.33 no.3
• /
• pp.965-974
• /
• 2013

Multiple debris flows occurred on July 27, 2012 in Mt. Umyeon, which resulted in 16 casualties and severe property demage. Accurate reproducing of the propagation and deposition of debris flow is essential for mitigating these disasters. Through applying FLO-2D model to these debris flows and comparing the results with field observations, we seek to evaluate the performance of the model and to analyse the rheological model parameters. Representative yield stress and dynamic viscosity back-calculated for the debris flows in the northern side of Mt. Umyeon are 1022 Pa and 652 $Pa{\cdot}s$, respectively. Numerical results obtained using these parameters reveal that deposition areas of debris flows in Raemian and Shindong-A regions are well reproduced in 63-85% agreement with the field observations. However, the propagation velocities of the flows are significantly underestimated, which is attributable to the inherent limitations of the model that can't take the entrainment of bed material and surface water into account. The debris flow deposition computed in Hyeongchon region where the entrainment is not significant appears to be in very good agreement with the field observation. The sensitivity study of the numerical results on model parameters shows that both sediment volume concentration and roughness coefficient significantly affect the flow thickness and velocity, which underscores the importance of careful selection of these model parameters in FLO-2D modeling.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.16 no.4
• /
• pp.31-40
• /
• 2016

In vehicular ad-hoc networks (VANETs), vehicles sense information on emergency incidents (e.g., accidents, unexpected road conditions, etc.) and propagate this information to following vehicles and a server to share the information. However, this process of emergency message propagation is based on multiple broadcast messages and can lead to broadcast storms. To address this issue, in this work, we use a novel approach to detect the vehicles that are farthest away but within communication range of the transmitting vehicle. Specifically, we discuss a signal-to-noise ratio (SNR)-based linear back-off (SLB) scheme where vehicles implicitly detect their relative locations to the transmitter with respect to the SNR of the received packets. Once the relative locations are detected, nodes that are farther away will set a relatively shorter back-off to prioritize its forwarding process so that other vehicles can suppress their transmissions based on packet overhearing. We evaluate SLB using a realistic simulation environment which consists of a NS-3 VANET simulation environment, a software-based WiFi-IP gateway, and an ITS server operating on a separate machine. Comparisons with other broadcasting-based schemes indicate that SLB successfully propagates emergency messages with latencies and hop counts that is close to the experimental optimal while reducing the number of transmissions by as much as 1/20.

• Journal of the Korean earth science society
• /
• v.41 no.6
• /
• pp.647-657
• /
• 2020

Infrasound signals associated with the 29 May 2004 offshore Uljin earthquake (Mw 5.1) were recorded at infrasound arrays of CHNAR (epicentral distance of 321 km) and TJNAR (256 km). Back-azimuths, indicating the directions to source locations, varied more than 28° broadly for the long-lasting signals over several minutes. From the analysis of the back-projecting location method and attenuation correction for infrasound propagation, the infrasound waves were to be generated by the interaction (diffraction) between seismic waves and topography in an area of ~4,600 ㎢ connecting the Samcheok-Uljin-Pohang regions. The maximum sound source pressure (BSP) was estimated to be 11.1 Pa. This result was consistent with the peak sound pressure (PSP) calculated by the Rayleigh integral approximation to the peak ground acceleration (PGA) dataset. In addition, the minimum PGA that was detectable at the two arrays was estimated to be ~3.0 cm s-2. Although the earthquake occurred offshore, diffracted infrasound signals were effectively generated by ground motions when seismic surface waves passed through high-topographic regions in the eastern Korean Peninsula. The relationship between infrasound source pressure and PGA can be applicable to characterize the ground motions in areas with insufficient seismological observatories.

• Geotechnical Engineering
• /
• v.10 no.4
• /
• pp.17-28
• /
• 1994

It is well known that human brain has the advantage of handling disperse and parallel distributed data efficiently. On the basic of this fact, artificial neural networks theory was developed and has been applied to various fields of science successfully. In this study, error back propagation algorithm which is one of the teaching technique of artificial neural networks is applied to predict ultimate bearing capacity of pile foundations. For the verification of applicability of this system, a total of 28 data of model pile test results are used. The 9, 14 and 21 test data respectively out of the total 28 data are used for training the networks, and the others are used for the comparison between the predicted and the measured. The results show that the developed system can provide a good matching with model pile test results by training with data more than 14. These limited results show the possibility of utilizing the neural networks for pile capacity prediction problems.

• Journal of the Institute of Convergence Signal Processing
• /
• v.4 no.4
• /
• pp.15-22
• /
• 2003

A film scanner is one of the input devices for ac acquiring high resolution and high qualify of digital images from the existing optical film. Recently the demand of film scanners have risen for experts of image printing and editing fields. However, due to the nonlinear characteristic of light source and sensor, colors of the original film image do not correspond to the colors of the scanned image. Therefore color correction for the scanned digital image is essential in film scanner. In this paper, neural network method is applied for the color correction to CIE L/sup *//a/sup *//b/sup */ color model data converted from RGB color model data. Also a film scanner hardware with 12 bit color resolution for each R, G, B and 2400 dpi is implemented by using the TMS320C32 DSP chip and high resolution line sensor. An experimental result shows that the average color correction rate is 79.8%, which is an improvement of 43.5% than our previous method, polygonal regression method.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.37 no.4
• /
• pp.350-358
• /
• 2009

This paper describes a result of the guidance and control for re-entry vehicle during TAEM phase. TAEM phase (Terminal Aerial Energy Management phase) has many conditions, such as density, velocity, and so on. Under these conditions, we have optimized trajectory and other states for guidance in TAEM phase. The optimized states consist of 7 variables, down-range, cross range, altitude, velocity, flight path angle, vehicle's azimuth and flight range. We obtained the optimized reference trajectory by DIDO tool, and used feedback linearization with neural network for control re-entry vehicle. By back propagation algorithm, vehicle dynamics is approximated to real one. New command can be decided using the approximated dynamics, delayed command input and plant output, NARMA-L2. The result by this control law shows a good performance of tracking onto the reference trajectory.

• Proceedings of the KSR Conference
• /
• 2011.10a
• /
• pp.656-662
• /
• 2011

Recently, rolling stock technology has rapidly developed. Nevertheless, ride quality and vibration vibration on vehicle still need more study. EMU to be operated on SMRT Line 7(SR001) extension section is applied to oil-free reciprocating air compressor considering maintainability and convenience of inspection. But reciprocating air compressor compresses the air by back-and-forth motion of piston, and spreads the vibration to surrounding structures by its force of inertia. Optimum design of mounting bracket is able to reduce the vibration. As a result, we analyzed the frequency spectrum on vibration upon value by measuring vibration during operation of air compressor. On this study, we comprehend the vibration transmission process of reciprocating air compressor and consider the measure for reducing vibration by minimizing propagation of vibration.

• Journal of Biosystems Engineering
• /
• v.37 no.4
• /
• pp.271-278
• /
• 2012

Worldwide trends in animal welfare have resulted in an increased interest in individual management of sows housed in groups within hog barns. Estrus detection has been shown to be one of the greatest determinants of sow productivity. Purpose: We conducted this study to develop a method that can automatically detect the estrus state of a sow by selecting optimal texture parameters from images of a sow's pudendum and by optimizing the number of neurons in the hidden layer of an artificial neural network. Methods: Texture parameters were analyzed according to changes in a sow's pudendum in estrus such as mucus secretion and expansion. Of the texture parameters, eight gray level co-occurrence matrix (GLCM) parameters were used for image analysis. The image states were classified into ten grades for each GLCM parameter, and an artificial neural network was formed using the values for each grade as inputs to discriminate the estrus state of sows. The number of hidden layer neurons in the artificial neural network is an important parameter in neural network design. Therefore, we determined the optimal number of hidden layer units using a trial and error method while increasing the number of neurons. Results: Fifteen hidden layers were determined to be optimal for use in the artificial neural network designed in this study. Thirty images of 10 sows were used for learning, and then 30 different images of 10 sows were used for verification. Conclusions: For learning, the back propagation neural network (BPN) algorithm was used to successful estimate six texture parameters (homogeneity, angular second moment, energy, maximum probability, entropy, and GLCM correlation). Based on the verification results, homogeneity was determined to be the most important texture parameter, and resulted in an estrus detection rate of 70%.