• Journal of Korean Association for Spatial Structures
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• v.23 no.4
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.181-187
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• 2024

There are two kinds of well-known CNN methods, the group equivariant CNN and the CNN using steerable filters, which have excellent classification performances for randomly rotated objects in image space. This paper describes their mathematical structures and introduces implementation methods. We implement them, including the existing CNN, which have the same number of filters, then compare and analyze their performances by simulating them with the randomly rotated MNIST. According to the experimental results, the steerable CNN, which shows a classification improvement over the others, has a relatively small number of parameters to learn, so performance degradation is relatively small even when the size of the training dataset is reduced.

• Journal of Korean Association for Spatial Structures
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• v.24 no.2
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• pp.83-90
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• 2024

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.4
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• pp.223-231
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• 2024

For the OPR1000, a standard power plant in Korea, an analytical model of the containment building considering voids and deterioration was built with multilayer shell elements. Voids were placed in the vulnerable parts of the analysis model, and the deterioration effects of concrete and rebar were reflected in the material model. To check the impact of voids and deterioration on the seismic performance of the containment building, iterative push-over analysis was performed on four cases of the analytical model with and without voids and deterioration. It was found that the effect of voids with a volume ratio of 0.6% on the seismic performance of the containment building was insignificant. The effect of strength reduction and cross-sectional area loss of reinforcement due to deterioration and the impact of strength increase of concrete due to long-term hardening offset each other, resulting in a slight increase in the lateral resistance of the containment building. To determine the limit state that adequately represents the seismic performance of the containment building considering voids and deterioration, the Ogaki shear strength equation, ASCE 43-05 low shear wall allowable lateral displacement ratio, and JEAC 4601 shear strain limit were compared and examined with the analytically derived failure point (ultimate point) in this study.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.4
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• pp.110-122
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• 2014

As multimedia technologies develop, three-dimensional (3D) technologies are attracting increasing attention from researchers. In particular, video quality assessment (VQA) has become a critical issue in stereoscopic image/video processing applications. Furthermore, a human visual system (HVS) could play an important role in the measurement of stereoscopic video quality, yet existing VQA methods have done little to develop a HVS for stereoscopic video. We seek to amend this by proposing a 3D visual attention (3DVA) model which simulates the HVS for stereoscopic video by combining multiple perceptual stimuli such as depth, motion, color, intensity, and orientation contrast. We utilize this 3DVA model for pooling on significant regions of very poor video quality, and we propose no-reference (NR) stereoscopic VQA (SVQA) method. We validated the proposed SVQA method using subjective test scores from our results and those reported by others. Our approach yields high correlation with the measured mean opinion score (MOS) as well as consistent performance in asymmetric coding conditions. Additionally, the 3DVA model is used to extract information for the region-of-interest (ROI). Subjective evaluations of the extracted ROI indicate that the 3DVA-based ROI extraction outperforms the other compared extraction methods using spatial or/and temporal terms.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.157-164
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• 2024

In this paper, we propose a discussion that the feasibility of deploying a deep learning-based detector on the resource-limited board. Although many studies evaluate the detector on machines with high-performed GPUs, evaluation on the board with limited computation resources is still insufficient. Therefore, in this work, we implement the deep-learning detectors and deploy them on the compact board by parsing and optimizing a detector. To figure out the performance of deep learning based detectors on limited resources, we monitor the performance of several detectors with different H/W resource. On COCO detection datasets, we compare and analyze the evaluation results of detection model in On-Board and the detection model in On-GPU in terms of several metrics with mAP, power consumption, and execution speed (FPS). To demonstrate the effect of applying our detector for the military area, we evaluate them on our dataset consisting of thermal images considering the flight battle scenarios. As a results, we investigate the strength of deep learning-based on-board detector, and show that deep learning-based vision models can contribute in the flight battle scenarios.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.6
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• pp.465-474
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• 2014

To validate the accuracy of the boiling heat flux partitioning model, an experiment was performed to investigate how the wall heat flux is divided into the three heat transfer modes of evaporation, quenching, and single-phase convection during subcooled nucleate boiling on a vertical wall. For the experimental partitioning of the wall heat flux, the wall heat flux and liquid-vapor distributions were simultaneously obtained using synchronized infrared thermometry and the total reflection technique. Boiling experiments of water with subcooling of $10^{\circ}C$ were conducted under atmospheric pressure, and the results obtained at the wall superheat of $12^{\circ}C$ and average heat flux of $283kW/m^2$were analyzed. There was a large difference in the heat flux partitioning results between the experiment and correlation, and the bubble departure diameter and bubble influence factor, which account for a portion of the surrounding superheated liquid layer detached by the departure of a bubble, were found to be important fundamental boiling parameters.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.3
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• pp.58-65
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• 2002

In order to reduce blocking artifacts in inter-frame coded images, we propose a new image restoration algorithm, which directly processes differential images before reconstruction. We note that blocking artifact in inter-frame coded images is caused by both 8$\times$8 DCT and 16$\times$16 macroblock based motion compensation, while that of intra-coded images is caused by 8$\times$8 DCT only. According to the observation, we Propose a new degradation model for differential images and the corresponding restoration algorithm that utilizes additional constraints and convex sets for discontinuity inside blocks. The proposed restoration algorithm is a modified version of standard regularization that incorporate!; spatially adaptive lowpass filtering with consideration of edge directions by utilizing a part of DCT coefficients. Most of video coding standard adopt a hybrid structure of block-based motion compensation and block discrete cosine transform (BDCT). By this reason, blocking artifacts are occurred on both block boundary and block interior For more complete removal of both kinds of blocking artifacts, the restored differential image must satisfy two constraints, such as, directional discontinuities on block boundary and block interior Those constraints have been used for defining convex sets for restoring differential images.

• Korean Chemical Engineering Research
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• v.52 no.6
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• pp.768-774
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• 2014

SI process is a thermochemical process producing hydrogen by decomposing water while recycling sulfur and iodine. Various technologies have been developed to improve the efficiency on Section III of SI process, where iodine is separated and recycled. EED(electro-electrodialysis) could increase the efficiency of Section III without additional chemical compounds but a substantial amount of $I_2$ from a process stream is loaded on EED. In order to reduce the load, a crystallization technology prior to EED is considered as an $I_2$ removal process. In this work, $I_2$ particle sinking behavior was modeled to secure basic data for designing an $I_2$ crystallizer applied to $I_2$-saturated $HI_x$ solutions. The composition of $HI_x$ solution was determined by thermodynamic UVa model and correlation equations and pure properties were used to evaluate the solution properties. A multiphysics computational tool was utilized to calculate particle sinking velocity changes with respect to $I_2$ particle radius and temperature. The terminal velocity of an $I_2$ particle was estimated around 0.5 m/s under considered radius (1.0 to 2.5 mm) and temperature (10 to $50^{\circ}C$) ranges and it was analyzed that the velocity is more dependent on the solution density than the solution viscosity.

• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.387-394
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• 2019

In this study, in order to evaluate the damage and deterioration of the track components of sleeper floating track (STEDEF), the field samples(specimens) were taken from the serviced line over 20 years old, and the track components were visually inspected, and investigated by laboratory tests and finite element analysis. As a result of visual inspection, the damage of the rail pad and fastener was slight, but the rubber boot was worn and torn at the edges of bottom. The resilience pads were clearly examined for thickness reduction and fatigue hardening layer. As a result of spring stiffness test of rail pad and resilience pad, the deterioration of rail pad was insignificant, but the deterioration of resilience pad exceeded design standard value. Therefore resilience pad was directly affected by train passing tonnage. As a result of comparing the deterioration state of the field sample and the numerical analysis result, the stress and displacement concentration position of the finite element model and the damage position of the field sample were coincident.