• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.429-438
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• 1997

Mechanical valve is one of the most widely used implantable artificial organs of which the reliability is so important that its failure means the death of patient. Therefore early noninvasive detection is essentially required, though mechanical valve failure with thrombosis is the most common. The objective of this paper is to detect the thrombosis formation by spectral analysis and neural network. Using microphone and amplifier, we measured the sound from the mechanical valve which is attached to the pneumatic ventricular assist device. The sound was sampled by A/D converter(DaqBook 100) and the periodogram is the main algorithm for obtaining spectrum. We made the thrombosis models using pellethane and silicon and they are thrombosis model on the valvular disk, around the sewing ring and fibrous tissue growth across the orifice of valve. The performance of the measurment system was tested firstly using 1 KHz sinusoidal wave. The measurement system detected well 1KHz spectrum as expected. The spectrum of normal and 5 kinds of thrombotic valve were obtained and primary and secondary peak appeared in each spectrum waveform. We find that the secondary peak changes according to the thrombosis model. So to distinguish the secondary peak of normal and thrombotic valve quantatively, 3 layer back propagation neural network, which contains 7, 000 input node, 20 hidden layer and 1 output was employed The trained neural network can distinguish normal and valve with more than 90% probability. As a conclusion, the noninvasive monitoring of implanted mechanical valve is possible by analysing the acoustical spectrum using neural network algorithm and this method will be applied to the performance evaluation of other implantable artificial organs.

• Geophysics and Geophysical Exploration
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• v.18 no.1
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• pp.1-8
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• 2015

To estimate the shear-wave velocity (${\nu}_s$ beneath the OTVZ seismic station on Ngauruhoe volcano in New Zealand, we calculated receiver functions (RFs) using 127 teleseismic data ($Mw{\geq}5.5$) with high signal-to-noise ratios recorded during November 11, 2011 to September 11, 2013. The genetic inversion algorithms was applied to 21 RFs calculated by the iterative time-domain deconvolution method. In the 1-D ${\nu}_s$ model derived by the inversion, the Moho is observed at a 14 km depth, marked by a ${\nu}_s$ transition from 3.7 km/s to 4.7 km/s. The average ${\nu}_s$ of the overlying crust is 3.4 km/s, and the average ${\nu}_s$ of a greater than 9-km thick low-velocity layer (LVL) in the lower crust is 3.1 km/s. The LVL becomes thinner with increasing distance from the station. Another LVL thicker than 10 km with ${\nu}_s$ less than 4.3 km/s is found in the upper mantle. Those LVLs in the lower crust and the upper mantle and the relatively thin crust might be related to the magma activity caused by the subducting Pacific plate.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.2
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• pp.254-262
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• 2016

With the increased demand for automotive radar systems, mutual interference between vehicles has become a crucial issue that must be resolved to ensure better automotive safety. Mutual interference between frequency modulated continuous waveform (FMCW) radar system appears in the form of increased noise levels in the frequency domain and results in a failure to separate the target object from interferers. The traditional fast fourier transform (FFT) algorithm, which is used to estimate the beat frequency, is vulnerable in interference-limited automotive radar environments. In order to overcome this drawback, we propose a high-resolution frequency estimation technique for use in interference environments. To verify the performance of the proposed algorithms, a 77GHz FMCW radar system is considered. The proposed method employs a high-resolution algorithm, specially the multiple signal classification and estimation of signal parameters via rotational invariance techniques, which are able to estimate beat frequency accurately.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.7
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• pp.956-964
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• 2017

This study contributes to deepening understand of the characteristics of fishing vessel rolling motions to improve the development of capsizing alarm systems. A time domain rolling motion simulation was performed. In order to verify capsizing alarm systems, it is necessary to carry out experiments assuming a capsizing situation and perform actual fishing vessel measurements, but these tasks are impossible due to the danger of such a situation. However, in many capsizing accidents, a close connection with rolling motion was found. Accordingly, the rolling motion of a fishing boat, which is the core of a fishing vessel capsizing alarm system, has been accurately measured and a time domain based on a rolling motion simulation has been performed. This information was used to verify the algorithm for a capsizing alarm system. Firstly, the characteristics of rolling motion were measured through a motion experiment. For small vessels such as fishing vessels, it was difficult to interpret viscosity due to analytical methods including CFD and potential codes. Therefore, an experiment was carried out focusing on rolling motion and a rolling mode RAO was derived.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.27-36
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• 2019

In this study, experiments on the improvement of the emotion classification, analysis and accuracy of EEG data were proceeded, which applied DEAP (a Database for Emotion Analysis using Physiological signals) dataset. In the experiment, total 32 of EEG channel data measured from 32 of subjects were applied. In pre-processing step, 256Hz sampling tasks of the EEG data were conducted, each wave range of the frequency (Hz); Theta, Slow-alpha, Alpha, Beta and Gamma were then extracted by using Finite Impulse Response Filter. After the extracted data were classified through Time-frequency transform, the data were purified through Independent Component Analysis to delete artifacts. The purified data were converted into CSV file format in order to conduct experiments of Machine learning algorithm and Arousal-Valence plane was used in the criteria of the emotion classification. The emotions were categorized into three-sections; 'Positive', 'Negative' and 'Neutral' meaning the tranquil (neutral) emotional condition. Data of 'Neutral' condition were classified by using Cz(Central zero) channel configured as Reference channel. To enhance the accuracy ratio, the experiment was performed by applying the attributes selected by ASC(Attribute Selected Classifier). In "Arousal" sector, the accuracy of this study's experiments was higher at "32.48%" than Koelstra's results. And the result of ASC showed higher accuracy at "8.13%" compare to the Liu's results in "Valence". In the experiment of Random Forest Classifier adapting ASC to improve accuracy, the higher accuracy rate at "2.68%" was confirmed than Total mean as the criterion compare to the existing researches.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.3
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• pp.163-171
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• 2024

Structural health monitoring for ships and offshore structures is important in various aspects. Ships and offshore structures are continuously exposed to various environmental conditions, such as waves, wind, and currents. In the event of an accident, immense economic losses, environmental pollution, and safety problems can occur, so it is necessary to detect structural damage or defects early. In this study, structural response data of multi-linked floating offshore structures under various wave load conditions was calculated by performing fluid-structure coupled analysis. Furthermore, the order reduction method with distortion base mode was applied to the structures for predicting the structural response by using the results of numerical analysis. The distortion base mode order reduction method can predict the structural response of a desired area with high accuracy, but prediction performance is affected by sensor arrangement. Optimization based on a genetic algorithm was performed to search for optimal sensor arrangement and improve the prediction performance of the distortion base mode-based reduced-order model. Consequently, a sensor arrangement that predicted the structural response with an error of about 84.0% less than the initial sensor arrangement was derived based on the root mean squared error, which is a prediction performance evaluation index. The computational cost was reduced by about 8 times compared to evaluating the prediction performance of reduced-order models for a total of 43,758 sensor arrangement combinations. and the expected performance was overturned to approximately 84.0% based on sensor placement, including the largest square root error.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.725-730
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• 2013

This paper proposed a novel method for an analysis feature of an Electroencephalogram (EEG) at all channels simultaneously. In a BCI (Brain-Computer Interface) system, EEGs are used to control a machine or computer. The EEG signals were weak to noise and had low spatial resolution because they were acquired by a non-invasive method involving, attaching electrodes along with scalp. This made it difficult to analyze the whole channel of EEG signals. And the previous method could not analyze multiple stimuli, the result being that the BCI system could not react to multiple orders. The method proposed in this paper made it possible analyze multiple-stimuli by grouping the channels. We searched the groups making the largest correlation coefficient summation of every member of the group with a BHS (Binary Harmony Search) algorithm. Then we assumed the EEG signal could be written in linear summation of groups using concentration parameters. In order to verify this assumption, we performed a simulation of three subjects, 60 times per person. From the simulation, we could obtain the groups of EEG signals. We also established the types of stimulus from the concentration coefficient. Consequently, we concluded that the signal could be divided into several groups. Furthermore, we could analyze the EEG in a new way with concentration coefficients from the EEG channel grouping.

• Transactions on Electrical and Electronic Materials
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• v.17 no.6
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• pp.375-379
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• 2016

Increasing fat tissue of obese people, increases the rate of cardiovascular disease, diabetes, metabolic syndromes and dyslipidemia. An increase in the focal tissue of pancreas is a known risk factor of these diseases. Although there exists sufficient research on the diagnosis and treatment of pancreatic cancer, studies have been done on fatty pancreas. In this study, based on ultrasound imaging and using a texture characteristic of GLCM, fatty pancreas was divided into three categories: mild, moderate and severe. We compared and analyzed the three groups was by Pancreatic ultrasonography and body characteristics, serological tests, pressure and the degree of arteriosclerosis, against normal control group. The following parameters of control and test groups were measured: WC (waist circumference),BMI (body mass index), TC (total cholesterol), TG (triglyceride), HDL-C (High-density lipoprotein cholesterol) and LDL-C (Low-density lipoprotein cholesterol), SBP (systolic blood pressure), BST (Blood Sugar Test) and aortic PWV (pulse wave velocity). We observed the values correspondingly increasing fat deposition. However, ABI (Ankle Brachial pressure index) stenosis and HDL-C levels decreased with increasing fat deposit (p <0.05); a drop in these parameters are known to be harmful to the human body. The difference in texture characteristics between normal control group and pancreatic fatty group (mild, moderate, and severe) was statistically confirmed. Ultrasound imaging of pancreatic steatosis categorized the disease as mild, moderate and severe based on the characteristic texture. In conclusion, we observed on increase in metabolic syndrome, dyslipidemia, and arteriosclerosis, proportional to the degree of pancreatic fat deposition. The escalation of these diseases was confirmed and was directly related with predictors of cardiovascular diseases.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.5
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• pp.242-248
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• 2002

It is important to develop new effective technologies to increase the interruption capacity and to reduce the size of a UB(Gas Circuit Breakers). Major design parameters such as nozzle geometries and interrupting chamber dimensions affect the cooling of the arc and the breaking performance. But it is not easy to test real GCB model in practice as in theory. Therefore, a simulation tool based on a computational fluid dynamics(CFD) algorithm has been developed to facilitate an optimization of the interrupter. Special attention has been paid to the supersonic flow phenomena between contacts and the observation of hat-gas flow for estimating the breaking performance. However, there are many difficult problems in calculating the flow characteristics in a GCB such as shock wave and complex geometries, which may be either static or in relative motion. Although a number of mesh generation techniques are now available, the generation of meshes around complicated, multi-component geometries like a GCB is still a tedious and difficult task for the computational fluid dynamics. This paper presents the CFD program using CFB-CAD integration technique based on Cartesian cut-cell method, which could reduce researcher's efforts to generate the mesh and achieve the accurate representation of the geometry designed by a CAD tools.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.6
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• pp.1092-1098
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• 2007

Since one decade, the detection of HFPD (High frequency Partial Discharge) has been proposed as one of the effective method for the diagnosis of the power component under service in power grids. As a tool for HFPD detection, Metal Foil sensor based on the embedded technology has been commercialized for mainly power cable due to its advantages. Recently, for the on-site noise discrimination, several PA (Pulse analysis) methods have been reported and the related software, such as Neural Network and Fuzzy, have been proposed to separate the PD (Partial Discharge) signals from the noises since their wave shapes are completely different from each other. On the other hand, the relevant fundamental investigation has not yet clearly made while it is reported that the effectiveness of the current methods based on PA is dependant on the types of sensors. Moreover, regarding the identification of the vital defects introducible into the Power Cable, the direct identification of the nature of defects from the PD signals through Metal Foil coupler has not yet been realized. As a trial for solving above shortcomings, different types of software have been proposed and employed without any convincing probability of identification. In this regards, our novel algorithm 'PA Map' based on the pulse analysis is suggested to identify directly the defects inside the power cable from the HFPD signals which is output of the HFCT and metal foil sensors. This method enables to discriminate the noise and then to make the data analysis related to the PD signals. For the purpose, the HFPD detection and PA (Pulse Analysis) system have been developed and then the effect of noise discrimination has been investigated by use of the artificial defects using real scale mockup. Throughout these works, our system is proved to be capable of separating the small void discharges among the very large noises such as big air corona and ground floating discharges at the on-site as well as of identifying the concerned defects.