• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.5
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• pp.617-626
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• 2019

This study analyzes the optimal location of gas detectors through the gas dispersion in a cargo compressor room of a 174K LNG carrier equipped with high-pressure cargo handling equipment; in addition, we propose a reasonable method for determining the safety regulations specified in the new International Code of the Construction and Equipment of Ships Carrying Liquefied Gases in Bulk (IGC). To conduct an LNG gas dispersion simulation in the cargo compressor room-equipped with an ME-GI engine-of a 174 K LNG carrier, the geometry of the room as well as the equipment and piping, are designed using the same 3D size at a 1-to-1 scale. Scenarios for a gas leak were examined under high pressure of 305 bar and low pressure of 1 bar. The pinhole sizes for high pressure are 4.5, 5.0, and 5.6mm, and for low pressure are 100 and 140 mm. The results demonstrate that the cargo compressor room will not pose a serious risk with respect to the flammable gas concentration as verified by a ventilation assessment for a 5.6 mm pinhole for a high-pressure leak under gas rupture conditions, and a low-pressure leak of 100 and 140 mm with different pinhole sizes. However, it was confirmed that the actual location of the gas detection sensors in a cargo compressor room, according to the new IGC code, should be moved to other points, and an analysis of the virtual monitor points through a computational fluid dynamics (CFD) simulation.

• Industrial Engineering and Management Systems
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• v.12 no.4
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• pp.380-388
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• 2013

Large variation in electrocardiogram (ECG) waveforms continues to present challenges in defining R-wave locations in ECG signals. This research presents a procedure to extract the R-wave locations by forward-backward (FB) algorithm and classify the arrhythmic beat conditions by using RR intervals. The FB algorithm shows forward and backward searching rules from QRS onset and eliminates lower-amplitude signals near the baseline using a statistical process control concept. The proposed algorithm was trained the optimal parameters by using MIT-BIH arrhythmia database (MITDB), and it was verified by actual Holter ECG signals from a local hospital. The signals are classified into normal (N) and three arrhythmia beat types including premature ventricular contraction (PVC), ventricular flutter/fibrillation (VF), and second-degree heart block (BII) beat. This work produces 98.54% accuracy in the detection of R-wave location; 98.68% for N beats; 91.17% for PVC beats; and 87.2% for VF beats in the collected Holter ECG signals, and the results are better than what are reported in literature.

• Journal of Korea Multimedia Society
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• v.23 no.10
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• pp.1236-1249
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• 2020

In a most recent object tracking research work, applying Convolutional Neural Network and Recurrent Neural Network-based strategies become relevant for resolving the noticeable challenges in it, like, occlusion, motion, object, and camera viewpoint variations, changing several targets, lighting variations. In this paper, the LSTM Network-based Tracking association method has proposed where the technique capable of real-time multi-object tracking by creating one of the useful LSTM networks that associated with tracking, which supports the long term tracking along with solving challenges. The LSTM network is a different neural network defined in Keras as a sequence of layers, where the Sequential classes would be a container for these layers. This purposing network structure builds with the integration of tracking association on Keras neural-network library. The tracking process has been associated with the LSTM Network feature learning output and obtained outstanding real-time detection and tracking performance. In this work, the main focus was learning trackable objects locations, appearance, and motion details, then predicting the feature location of objects on boxes according to their initial position. The performance of the joint object tracking system has shown that the LSTM network is more powerful and capable of working on a real-time multi-object tracking process.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.1
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• pp.1-8
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• 2018

This paper proposes a low complexity super resolution algorithm for frequency modulated continuous wave (FMCW) radar systems for foreign object debris (FOD) detection. FOD radar has a requirement to detect foreign object in small units in a large area. However, The fast Fourier transform (FFT) method, which is most widely used in FMCW radar, has a disadvantage in that it can not distinguish between adjacent targets. Super resolution algorithms have a significantly higher resolution compared with the detection algorithm based on FFT. However, in the case of the large number of samples, the computational complexity of the super resolution algorithms is drastically high and thus super resolution algorithms are difficult to apply to real time systems. In order to overcome this disadvantage of super resolution algorithm, first, the proposed algorithm coarsely obtains the frequency of the beat signal by employing FFT. Instead of using all the samples of the beat signal, the number of samples is adjusted according to the frequency of the beat signal. By doing so, the proposed algorithm significantly reduces the computational complexity of multiple signal classifier (MUSIC) algorithm. Simulation results show that the proposed method achieves accurate location even though it has considerably lower complexity than the conventional super resolution algorithms.

• Magazine of the Korea Concrete Institute
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• v.6 no.6
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• pp.180-189
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• 1994

A practical methodology to detect and localm da~nagc in rcinforced concrete structures by utilizing modal response parameters of as built and tiamaged states is presented. First, a damage detection algorithm which yields information on the, location of damage directly from changes in mode shapes of structures is outlined. Next, the algorithm is implemented to detec and localize damage in a real, 1 1/3 scale, reinforced concrete structure. A set of pre-damage and post damage modal parameters are used for I he damage detection exercise. The results of the damage prediction show that the proposed algorithm can correctly locate the damage inflicted in the test structure.

• Composites Research
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• v.13 no.1
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• pp.40-49
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• 2000

The objective of this research is to develop the impact monitoring techniques providing impact identification and damage diagnostics of smart composite laminates susceptible to impacts. This can be implemented simultaneously by using the acoustic waves by the impact loads and the acoustic emission waves from damage. In the previous research, we have discussed the impact location detection process in which impact generated acoustic waves are detected by PZT using the improved neural network paradigm. This paper describes the implementation of time-frequency analysis such as the Short-Time Fourier Transform (STFT) and the Wavelet Transform (WT) on the determination of the occurrence and the estimation of damage.

• Journal of Biomedical Engineering Research
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• v.24 no.2
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• pp.91-97
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• 2003

The Propose of this paper is hierarchical detection method for the optic disc in fundus image. We detected the optic disc boundary by using the Prior information. It is based on the anatomical knowledge of fundus which are the vessel information. the image complexity. and etc. The whole method can be divided into three stages . First, we selected the region of interest(ROI) which included optic disc region. This is used to calculate location and size of the optic disc which are prior knowledge to simplify image preprocessing. And then. we divided the fundus image into numberous regions with watershed algorithm and detected intial boundary of the optic disc by reducing the number of the separated regions in ROI. Finally, we have searching the defective parts of boundary as a result of serious vessel interference in order to detect the accurate boundary of optic disc and we have removing and interpolating them.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.8 no.2
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• pp.3-9
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• 2009

When washing machine is rotated in the laundry, it tends to lean toward one side. This tendency causes a serious vibration. The balance of washing machine plays an important role in order to reduce the vibration by injecting the sand or the salt water into the balance of washing machine. The hot plate welder is used to prevent from outflow of contents. The hot plate welder brings about many problems which is concerned with accidents. The direction recognition and location information of the balance are required in this system. In this paper, the recognition direction of balance in washing machine using machine vision system is studied. The template matching algorithm compares sub-image with original image acquired in real-time to obtain a center point of balance image. The mid points and the edges of balance are estimated by the edge detection and gauging algorithms. The data acquired by these results is used for recognition direction of balance. The automation software for image processing is developed by using LabVIEW.

• Journal of the Korean Society of Safety
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• v.23 no.2
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• pp.45-50
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• 2008

The purpose of the present study was to investigate the age difference in driving behavior(more specifically, left-turn). The participants were instructed to report whether they can turn left their car in the T-shape road(road and other vehicles' behavior relating to driver's tasks were recorded in advance and projected the simulation screen) after the leading vehicle passed them(i.e., before the target vehicle arrived). The participants' judgment accuracy and response bias were analyzed by using signal detection theory. The results showed that the old group tended to be less sensitive but more confident in their judgement of turning left their car. In particular, both age groups appeared to more depend on the distance from observation location to approaching vehicle rather than arrival times or driving speeds of the approaching vehicle.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.11
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• pp.30-36
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• 2010

Previous researches show that linearly integrated Hall sensor arrays (LIHaS) can detect cracks in the steel structure fast and effectively This paper proposes an algorithm that estimates the size and shape of cracks for the developed LIHaS. In most nondestructive testing (NDT), just crack existence and location are obtained by processing 1-dimensional data from the sensor that scans the object with relative speed in single direction. The proposed method is composed with two steps. The first step is constructing 2-dimensionally mapped data space by combining the converted position data from the time-based scan data with the position information of sensor arrays those are placed in the vertical direction to the scan direction. The second step is applying designed Laplacian filter and smoothing filter to estimate the size and shape of cracks. The experimental results of express train wheels show that the proposed algorithm is not only more reliable and accurate to detecting cracks but also effective to estimate the size and shape of cracks.