Journal of the Society of Naval Architects of Korea
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v.53
no.6
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pp.482-493
/
2016
The identification of modal damping of a segmented hull model with torsional response is difficult task due to the coupling of modal response. This is because the 1st and 2nd torsional vibration modes are closely spaced in frequency domain leading to the situation that the modal decomposition is difficult to achieve by simple band-pass filter. Present study applied several different modal decomposition methods to derive the damping ratio of different modes. The modal decomposition methods considered in this study are simple band-pass filter, Hilbert vibration decomposition, Wavelet transform and proper orthogonal decomposition. Coupled free decay signal obtained from the torsional hammering test on a segmented hull model was processed with four different methods and the derived damping ratios were compared with each other. Discussions also have been made on the pros and cons of the different methodologies.
Ship-radiated noise received by passive sonar that can measure underwater noise can be identified and classified ship using Detection of Envelope Modulation on Noise (DEMON) analysis. However, in a low Signal-to-Noise Ratio (SNR) environment, it is difficult to analyze and identify the target frequency line containing ship information in the DEMONgram. In this paper, we conducted a study to extract target frequency lines using semantic segmentation among deep learning techniques for more accurate target identification in a low SNR environment. The semantic segmentation models U-Net, UNet++, and DeepLabv3+ were trained and evaluated using simulated DEMONgram data generated by changing SNR and fundamental frequency, and the DEMONgram prediction performance of DeepShip, a dataset of ship-radiated noise recordings on the strait of Georgia in Canada, was compared using the trained models. As a result of evaluating the trained model with the simulated DEMONgram, it was confirmed that U-Net had the highest performance and that it was possible to extract the target frequency line of the DEMONgram made by DeepShip to some extent.
Recent livestock people concern not only increase of production, but also superior quality of animal-breeding environment. So far, the optimization of the breeding and air environment has been focused on the production increase. In the very near future, the optimization will be emphasized on the environment for the animal welfare and health. Especially, cattle farming demands the precision livestock farming and special attention has to be given to the management of feeding, animal health and fertility. The management of individual animal is the first step for precision livestock farming and animal welfare, and recognizing each individual is important for that. Though electronic identification of a cattle such as RFID(Radio Frequency Identification) has many advantages, RFID implementations practically involve several problems such as the reading speed and distance. In that sense, computer vision might be more effective than RFID for the identification of an individual animal. The researches on the identification of cattle via image processing were mostly performed with the cows having black-white patterns of the Holstein. But, the native Korean and Japanese cattle do not have any definite pattern on the body. The purpose of this research is to identify the Japanese black cattle that does not have a body pattern using computer vision technology and neural network algorithm. Twelve heads of Japanese black cattle have been tested to verify the proposed scheme. The values of input parameters were specified and then computed using the face images of cattle. The images of cattle faces were trained using associate neural network algorithm, and the algorithm was verified by the face images that were transformed using brightness, distortion, and noise factors. As a result, there was difference due to transform ratio of the brightness, distortion, and noise. And, the proposed algorithm could identify 100% in the range from -3 to +3 degrees of the brightness, from -2 to +4 degrees of the distortion, and from 0% to 60% of the noise transformed images. It is concluded that our system can not be applied in real time recognition of the moving cows, but can be used for the cattle being at a standstill.
A series of wind tunnel sectional model dynamic tests of a twin-deck bridge were conducted at the CLP Power Wind/Wave Tunnel Facility (WWTF) of The Hong Kong University of Science and Technology (HKUST) to investigate the effects of gap-width on the self-excited vibrations and the dynamic and aerodynamic characteristics of the bridge. Five 2.9 m long models with different gap-widths were fabricated and suspended in the wind tunnel to simulate a two-degrees-of-freedom (2DOF) bridge dynamic system, free to vibrate in both vertical and torsional directions. The mass, vertical frequency, and the torsional-to-vertical frequency ratio of the 2DOF systems were fixed to emphasize the effects of gap-width. A free-vibration test methodology was employed and the Eigensystem Realization Algorithm (ERA) was utilized to extract the eight flutter derivatives and the modal parameters from the coupled free-decay responses. The results of the zero gap-width configuration were in reasonable agreement with the theoretical values for an ideal thin flat plate in smooth flow and the published results of models with similar cross-sections, thus validating the experimental and analytical techniques utilized in this study. The methodology was further verified by the comparison between the measured and predicted free-decay responses. A comparison of results for different gap-widths revealed that variations of the gap-width mainly affect the torsional damping property, and that the configurations with greater gap-widths show a higher torsional damping ratio and hence stronger aerodynamic stability of the bridge.
Proceedings of the Korean Society of Computer Information Conference
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2021.07a
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pp.117-122
/
2021
In order to reveal the co-research relationship with key research institutes in the computer field in Korea, which have high competitiveness in the ICT field, this paper analyzes the co-author network of research institutes based on the papers of Korean journals. For this study, 15 computer journals published in Korea were selected and collected for the past 5 years. In addition, authors, affiliated institutions, and country information were extracted from the papers, and experimental data were produced using institutional identification data and SNA analysis techniques. Through this, statistical analysis was performed on researchers, research institutes, and countries, and the centrality of research institutes was measured through co-authored network analysis and visualized as a map. The results of the research that analyzed the co-author relationship based on the papers in computer science journals published in Korea are as follows. First, the status of co-author relations among researchers was statistically analyzed in detail. The number of authors, the number of co-authors, and statistics by co-authoring countries, as well as the ratio of co-authored research by institution and the ratio of co-authored by country were revealed. The co-authoring status of papers in English journals was analyzed separately to compare and analyze characteristics. Second, the centrality of each individual was measured through the co-authored network analysis of the identified researchers, institutions and countries affiliated with the institution, and the frequency of connection between data by level was identified. In addition, the co-authorization network between research institutes was visualized and presented as a map so that the overall view of the co-authoring network was presented, and the connection frequency between the institutes was checked. It is possible to recommend a research partner based on the co-authoring behavior of Korean computer researchers and the co-authoring relationship between major research institutes. For this, additional research is needed to analyze the subject areas of each institution.
KSCE Journal of Civil and Environmental Engineering Research
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v.32
no.2A
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pp.97-108
/
2012
Dynamic responses were measured using long-term monitoring system for Uldolmok tidal current pilot power plant which is one of jacket-type offshore structures. Among the dynamic quantities, the tilt angle was chosen because the low frequency response components can be precisely measured by dynamic tiltmeter, and the natural frequencies and modal damping ratio were successfully identified using proposed LS-FDD (least squared frequency domain decomposition) method. And the effects of tidal height and tidal current velocity on the variation of natural frequencies and modal damping ratios were investigated in time and frequency domain. Also the non-parametric models were tested to model the relationship between tidal conditions and modal properties such as natural frequencies and damping ratios.
RFID(Radio Frequency Identification) systems are attracting attention as a key component of Internet of Things technology due to the cost and energy efficiency of application services. In order to use RFID technology in the IoT application service field, it is necessary to be able to store and manage various information for a long period of time as well as simple recognition between the reader and tag of the RFID system. And in order to read and write information to tags, a performance improvement technology that is strong and reliable in poor wireless channels is needed. In particular, in the UHF(Ultra High Frequency) RFID system, since multiple tags communicate passively in a crowded environment, it is essential to improve the recognition rate and transmission speed of individual tags. In this paper, Middleton's Class A impulsive noise model was selected to analyze the performance of the RFID system in an impulsive noise environment, and FM0 encoding and Miller encoding were applied to the tag to analyze the error rate performance of the RFID system. As a result of analyzing the performance of the RFID system in Middleton's Class A impulsive noise channel, it was found that the larger the Gaussian noise to impulsive noise power ratio and the impulsive noise index, the more similar the characteristics to the Gaussian noise channel.
Journal of the Institute of Electronics and Information Engineers
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v.53
no.3
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pp.114-123
/
2016
The effective feature extraction method for unmanned aerial vehicle (UAV) detection is proposed and verified in this paper. The UAV engine sound is harmonic complex tone whose frequency ratio is integer and its variation is continuous in time. Using these characteristic, we propose the feature vector composed of a mean and standard deviation of difference value between fundamental frequency with 1st overtone as well as mean variation of their frequency. It was revealed by simulation that the suggested feature vector has excellent discrimination in target signal identification from various interfering signals including frequency variation with time. By comparing Fisher scores, three features based on frequency show outstanding discrimination of measured UAV signals with low signal to noise ratio (SNR). Detection performance with simulated interference signal is compared by MFCC by using ELM classifier and the suggested feature vector shows 37.6% of performance improvement As the SNR increases with time, the proposed feature can detect the target signal ahead of MFCC that needs 4.5 dB higher signal power to detect the target.
Objectives : The aim of this study was to survey the present utilization of pattern identification(PI) by chronic disease and using PI system in the clinical field among Korean medicine doctors. Methods : This survey was conducted from Oct. 1 to Oct. 31, 2014 by face-to-face interview using structured questionnaire. The subjects were 400 Korean medicine doctors who worked in hospitals or clinics. The questionnaire consisted of whether use clinical experience by chronic disease and the utilization of PI, the utilization of PI systems, the PI utilization rate, and the correlation between utilization PI and so on. General elements consisted of sex, age, clinical experience, place of work, district and so on. Results and Conclusions : This study revealed that clinical experience by chronic disease used 24.0-90.8%. The most chronic diseases were chronic fatigue and chronic pain, and least disease was cancer. Experience of the utilization of PI among Korean medicine doctors who have clinical experience was 87.8-97.1%. Also, the most utilized disease was chronic gastritis & gastric ulcer, and least disease was dyslipidemia. In this case, the most common cases were using the one PI between the utilization of PI systems, and the utilization ratio of Visceral PI was the highest. Phi coefficient between the PI systems in chronic diseases divided into positive correlation and negative correlation. The correlation of dementia between Triple energizer PI and Six excesses PI was the highest at positive correlation, and the frequency of occurrence between Defense, Qi, Nutrient and Blood PI was the highest at positive correlation. The correlation of osteoarthropathy between Meridian and Collateral PI and Sasang Constitutional Medicine was the highest at negative correlation. Also the frequency of occurrence between Qi, Blood, Fluid and Humor PI and Sasang Constitutional Medicine was the highest at negative correlation. We hope that additional studies on systematic PI research of chronic disease which needed to be in korean oriental medicine and meet the needs of clinical consumer continue based on this study.
Kim, Jae-Cheon;Kwon, Soon-Jung;Yin, Jing-Lin;Lee, Hyeung-Jin;Kim, Man-Cheol;Shin, Soo-Bong
Journal of the Korean Society for Railway
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v.12
no.4
/
pp.506-511
/
2009
An algorithm for identifying track irregularities along the railway is presented. A baseline frequency-domain transfer function based on the equivalent SlSO(Single Input Single Output) model is defined at the intact condition between the measured track geometry of the ground displacement and the acceleration measured at a location in a train. The pre-defined transfer function at the intact condition is used inversely to predict track geometry in time with the currently measured acceleration at the same location in a train. The predicted track geometry is compared in time with that of the baseline values at the intact condition. The difference between them is calculated as an error in time and used to identify the track irregularities. An irregularity index is proposed as the ratio between the moving variance of the error at the current inspection and that at the intact condition. A 3D numerical simulation study has been carried out with a train model to verify the validity of the presented algorithm. In the analysis for the simulation, the track geometry has been considered as the displacement boundary condition varying in time.
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