• Fire Science and Engineering
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• v.13 no.4
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• pp.30-37
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• 1999

In this study, reduced-scale experiments as the alternative to a real-scale fire test were conducted to understand fire properties in atrium space. The scaling laws were derived from $\pi$-parameters which were deduced by dimensional analysis of governing equations (continuity, conservation of momentum and conservation energy). The 1/50 scale experiment simulated the real-scale fire test in SIVANS atrium at Japan were conducted under the scaling laws. And this results were compared with real-scale experiment results. Furthermore these results were visualized by video recording system using laser light sheet.

• The KSFM Journal of Fluid Machinery
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• v.13 no.3
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• pp.59-64
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• 2010

To analyze the three-dimensional flow in tunnel caused by operation of jet fan, both experimental and computational studies have been conducted. The experimental analysis of tunnel flow induced by jet fan is conducted on a real-scale apparatus with jet fan and tunnel, and air velocity at the monitoring points is measured for variation of fan's RPM. The three-dimensional numerical analysis including tunnel and jet fan is carried out for the same geometric configuration as the experimental analysis. The experimental and computational results are compared to examine the applicability of the numerical method.

• Earthquakes and Structures
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• v.18 no.3
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• pp.337-348
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• 2020

Experimental testing has been considered as one of the most straightforward approaches to realize the structural behavior for earthquake engineering studies. Recently, novel and advanced experimental techniques, which combine numerical simulation with experimental testing, have been developed and applied to structural testing practically. However, researchers have to take the risk of damaging specimens or facilities during the process of developing and validating new experimental methods. In view of this, a small-scale structural laboratory has been designed and constructed in order to verify the effectiveness of newly developed experimental technique before it is applied to large-scale testing for safety concerns in this paper. Two orthogonal steel reaction walls and one steel T-slotted reaction floor are designed and analyzed. Accordingly, a large variety of experimental setups can be completed by installing servo-hydraulic actuators and fixtures depending on different research purposes. Meanwhile, a state-of-the-art digital controller and multiple real-time computation machines are allocated. The integration of hardware and software interfaces provides the feasibility and flexibility of developing novel experimental methods that used to be difficult to complete in conventional structural laboratories. A simple experimental demonstration is presented which utilizes part of the hardware and software in the small-scale structural laboratory. Finally, experimental layouts of future potential development and application are addressed and discussed, providing the practitioners with valuable reference for experimental earthquake engineering.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.4
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• pp.231-238
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• 2017

Detecting small infrared targets from the low-SCR images at a long distance is very hard. The previous Local Contrast Method (LCM) algorithm based on the human visual system shows a superior performance of detecting small targets by a background suppression technique through local contrast measure. However, its slow processing speed due to the heavy multi-scale processing overhead is not suitable to a variety of real-time applications. This paper presents a lightweight real-time small target detection algorithm, called by the Improved Selective Local Contrast Method (ISLCM), to reduce the scale-invariant computational overhead. The proposed ISLCM applies the improved local contrast measure to the predicted selective region so that it may have a comparable detection performance as the previous LCM while guaranteeing low scale-invariant computational load by exploiting both adaptive scale estimation and small target feature feasibility. Experimental results show that the proposed algorithm can reduce its computational overhead considerably while maintaining its detection performance compared with the previous LCM.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.664-670
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• 2010

The study on mechanical behavior of the structure at the site includes experimental method and numerical analysis method. Experimental method is categorized into true-scale test and laboratory model test. A laboratory model test is to monitor the failure mechanism with a model simulated similar with a real ground so as to identify the quantitative result, while a true-scale model test is the approach which enables to identify the potential problems that may occur with a simulated construction situation similar with a real site circumstance. Thus this study was intended to carry out the experimental test of non open-cut excavation by pipe roof method which is mostly common in domestic sites. as well as was aimed at identifying the ground behavior occurred during pipe penetration using laboratory model test. Appropriate reduced-scale model was selected, taking into account of domestic geological characteristics and operation characteristics of traditional and high-speed rail trains and the qualitative evaluation of displacement was carried out based on a certain ground loss volume depending on excavation after categorizing trackbed settlement pattern by depth of top soil.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.1
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• pp.66-74
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• 2008

This paper presents a new small target detection method using scale invariant feature. Detecting small targets whose sizes are varying is very important to automatic target detection. Scale invariant feature using the Laplacian scale-space can detect different sizes of targets robustly compared to the conventional spatial filtering methods with fixed kernel size. Additionally, scale-reflected adaptive thresholding can reduce many false alarms. Experimental results with real IR images show the robustness of the proposed target detection in real world.

• Structural Engineering and Mechanics
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• v.77 no.3
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• pp.305-314
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• 2021

In order to reduce enormous cost of real-scale underwater explosion experiments on ships, the mechanical response of the ships have been analyzed by combining scaled-down experiments and Hopkinson's scaling law. However, the Hopkinson's scaling law is applicable only if all variables vary in an identical ratio; for example, thickness of ship, size of explosive, and distance between the explosive and the ship should vary with same ratio. Unfortunately, it is infeasible to meet such uniform scaling requirement because of environmental conditions and limitations in manufacturing scaled model systems. For the facile application of the scaling analysis, we propose a generalized scaling law that is applicable for non-uniform scaling cases in which different parts of the experiments are scaled in different ratios compared to the real-scale experiments. In order to establish such a generalized scaling law, we conducted a parametric study based on numerical simulations, and validated it with experiments and simulations. This study confirms that the initial peak value of response variables in a real-scale experiment can be predicted even when we perform a scaled experiment composed of different scaling ratios for each experimental variable.

• Korean Journal of Artificial Intelligence
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• v.11 no.3
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• pp.17-22
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• 2023

In this paper, we propose a black ice detection system using Convolutional Neural Networks (CNNs). Black ice poses a serious threat to road safety, particularly during winter conditions. To overcome this problem, we introduce a CNN-based architecture for real-time black ice detection with an encoder-decoder network, specifically designed for real-time black ice detection using thermal images. To train the network, we establish a specialized experimental platform to capture thermal images of various black ice formations on diverse road surfaces, including cement and asphalt. This enables us to curate a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Additionally, in order to enhance the accuracy of black ice detection, we propose a multi-scale dilation convolution feature fusion (MsDC-FF) technique. This proposed technique dynamically adjusts the dilation ratios based on the input image's resolution, improving the network's ability to capture fine-grained details. Experimental results demonstrate the superior performance of our proposed network model compared to conventional image segmentation models. Our model achieved an mIoU of 95.93%, while LinkNet achieved an mIoU of 95.39%. Therefore, it is concluded that the proposed model in this paper could offer a promising solution for real-time black ice detection, thereby enhancing road safety during winter conditions.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.1-8
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• 2023

In this paper, we propose a black ice detection platform framework using Convolutional Neural Networks (CNNs). To overcome black ice problem, we introduce a real-time based early warning platform using CNN-based architecture, and furthermore, in order to enhance the accuracy of black ice detection, we apply a multi-scale dilation convolution feature fusion (MsDC-FF) technique. Then, we establish a specialized experimental platform by using a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Experimental results of a real-time black ice detection platform show the better performance of our proposed network model compared to conventional image segmentation models. Our proposed platform have achieved real-time segmentation of road black ice areas by deploying a road black ice area segmentation network on the edge device Jetson Nano devices. This approach in parallel using multi-scale dilated convolutions with different dilation rates had faster segmentation speeds due to its smaller model parameters. The proposed MsCD-FF Net(2) model had the fastest segmentation speed at 5.53 frame per second (FPS). Thereby encouraging safe driving for motorists and providing decision support for road surface management in the road traffic monitoring department.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1594-1599
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• 2008

Haptic rendering is a process that provides force feedback during interactions between a user and an object. This paper presents a haptic rendering technique for a telemanipulation system of deformable objects using image processing and physically based modeling techniques. The interaction forces between an instrument driven by a haptic device and a deformable object are inferred in real time based on a continuum mechanics model of the object, which consists of a boundary element model and ${\alpha}$ priori knowledge of the object's mechanical properties. Macro- and micro-scale experimental systems, equipped with a telemanipulation system and a commercial haptic display, were developed and tested using silicone (macro-scale) and zebrafish embryos (micro-scale). The experimental results showed the effectiveness of the algorithm in different scales: two experimental systems applied the same algorithm provided haptic feedback regardless of the system scale.