• The Journal of the Acoustical Society of Korea
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• v.36 no.3
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• pp.158-164
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• 2017

Typical hydrophones in line array sensors for early detection of covert underwater targets can measure only sound-pressure-magnitude with the limitation of being unable to identify the direction of an incoming wave. In this study, a thickness shear mode vibrator was proposed as the main component of an inertia type vector hydrophone to measure both magnitude and direction of acoustic signals from targets. The equation to analyze the output voltage of the vibrator to an external force was derived, and the validity of the equation was verified through finite element analysis of a PMN-PT single crystal vibrator. The analysis results from this study will be utilized in the future for the design of inertia type vector hydrophones made of thickness shear vibrators.

• Korean Chemical Engineering Research
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• v.60 no.2
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• pp.223-228
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• 2022

In chemical processes, unintended faults can make serious accidents. To tackle them, proper fault diagnosis models should be designed to identify the root cause of faults. To design a fault diagnosis model, a process and its data should be analyzed. However, most previous researches in the field of fault diagnosis just handle the data set of benchmark processes simulated on commercial programs. It indicates that it is really hard to get fresh data sets on real processes. In this study, real faulty conditions of an industrial polystyrene process are tested. In this process, a runaway reaction occurred and this caused a large loss since operators were late aware of the occurrence of this accident. To design a proper fault diagnosis model, we analyzed this process and a real accident data set. At first, a mode classification model based on support vector machine (SVM) was trained and principal component analysis (PCA) model for each mode was constructed under normal operation conditions. The results show that a proposed model can quickly diagnose the occurrence of a fault and they indicate that this model is able to reduce the potential loss.

• Nuclear Engineering and Technology
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• v.44 no.3
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• pp.323-330
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• 2012

The development of data-based models requires uncertainty analysis to explain the accuracy of their predictions. In this paper, an uncertainty analysis of the support vector regression (SVR) model, which is a data-based model, was performed because previous research showed that the SVR method accurately estimates the collapse moments of wall-thinned pipe bends and elbows. The uncertainty analysis method used in this study was an analytic uncertainty analysis method, and estimates with a 95% confidence interval were obtained for 370 test data points. From the results, the prediction interval (PI) was very narrow, which means that the predicted values are quite accurate. Therefore, the proposed SVR method can be used effectively to assess and validate the integrity of the wall-thinned pipe bends and elbows.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.320-328
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• 2016

The introduction of renewable energy sources into the AC grid can change and weaken the strength of the grid, which will in turn affect the stability and robustness of the doubly-fed induction generator (DFIG) wind farm. When integrated with weak grids, the DFIG wind turbine with vector power control often suffers from poor performance and robustness, while the DFIG wind turbine with synchronized control provides better stability. This paper investigates the critical short circuit ratios of DFIG wind turbine with vector power control and synchronized control, to analyze the stability boundary of the DFIG wind turbine. Frequency domain methods based on sensitivity and complementary sensitivity of transfer matrix are used to investigate the stability boundary conditions. The critical capacity of DFIG wind farm with conventional vector power control at a certain point of common coupling (PCC) is obtained and is further increased by employing synchronized control properly. The stability boundary is validated by electromagnetic transient simulation of an offshore wind farm connected to a real regional grid.

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.731-736
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• 2012

Latest digital cinema is getting more interest on recent days. The combination of visually immersive 3D movie with chair movements and other physical effects has added more enjoyment. The movement of the chair is controlled manually in these digital cinemas. By the analysis of the digital cinema's video sequences, movement of the chair can be controlled automatically. In the proposed method first of all the motion of focused object and the background is identified and then the motion vector information is extracted by using the 9-search range. The motion vector is determined only for the movement of background while the object is stationary. The extracted Motion information from the digital cinemas is used for the movement control of the chair. The experimental results show that the proposed method outperforms the existing methods in terms of accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.708-713
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• 1994

A new flux observer based vector control system of an induction motor is constructed by using an observer in which the commanded stator currents are used to estimate the rotor flux. In this system, the flux observer is formulated by using a model of induction motor in a stationary coordinate system. By considering an observer of induction motor in a fixed co-ordinate system located on its secondary flux, a slip frequency controlled type of vector control system is also proposed. From these control schemes, the relation between the conventional slip frequency controlled type system and the observer based one is clarified. The steady-state error of the developed torque which is caused by the parameter change of induction motor is analyzed and discussed for the selection of observer gains. The poles of the observer error dynamics and those of the observer based vector control system are calculated analytically by neglecting the machine parameter change. In order to analyze the robust stability, a linear model of the observer based vector control system is proposed taking into account the machine parameter change. By using this model, the trajectories of the poles and zeros of the torque transfer function are computed and discussed. To test validity of the theoretical analysis, experiments are conducted by using a digital signal processor (TMS320C30) and a current controlled voltage source PWM inverter.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2009.05a
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• pp.123-127
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• 2009

The purpose of this research is to investigate the dynamic characteristics of fluidic thrust vector control using the co-flow injection. In previous research, both numerical and experimental approaches for steady state were conducted to investigate operation-parameters and detail flow structure of the fluidic thrust vector control system. Based upon the previous results, numerical unsteady calculation was conducted to analyze the dynamic characteristics of jet up- and down-ward vectoring so that the transition time and the pressure distribution along the wall, and so on were investigated.

• Horticultural Science & Technology
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• v.35 no.3
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• pp.323-332
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• 2017

Homology-specific transcriptional and post-transcriptional silencing, an intrinsic mechanism of gene regulation in most eukaryotes, can be induced by anti-sense, co-suppression, or hairpin-based double-stranded RNA. Hairpin-based RNA interference (RNAi) has been applied to analyze gene function and genetically modify crops. However, RNAi vector construction usually requires high-cost cloning steps and large amounts of time, or involves methods that are protected by intellectual property rights. We describe a more effective method for generating intron-spliced RNAi constructs. To produce intron-spliced hairpin RNA, an RNAi cassette was ligated with the first intron and splicing sequences of the Brassica rapa ssp. pekinensis histone deacetylase 1 gene. This method requires a single ligation of the PCR-amplified target gene to SpeI-NcoI and SacI-BglII enzyme sites to create a gene-specific silencing construct. We named the resulting binary vector system pKHi and verified its functionality by constructing a vector to silence DIHYDROFLAVONOL 4-REDUCTASE (DFR), transforming it into tobacco plants, and confirming DFR gene-silencing via PCR, RT-qPCR, and analysis of the accumulation of small interfering RNAs. Reduction of anthocyanin biosynthesis was also confirmed by analyzing flower color of the transgenic tobacco plants. This study demonstrates that small interfering RNAs generated through the pKHi vector system can efficiently silence target genes and could be used in developing genetically modified crops.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1135-1148
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• 2017

Speech recognition is one of the fascinating fields in the area of Computer science. Accuracy of speech recognition system may reduce due to the presence of noise present in speech signal. Therefore noise removal is an essential step in Automatic Speech Recognition (ASR) system and this paper proposes a new technique called combined thresholding for noise removal. Feature extraction is process of converting acoustic signal into most valuable set of parameters. This paper also concentrates on improving Mel Frequency Cepstral Coefficients (MFCC) features by introducing Discrete Wavelet Packet Transform (DWPT) in the place of Discrete Fourier Transformation (DFT) block to provide an efficient signal analysis. The feature vector is varied in size, for choosing the correct length of feature vector Self Organizing Map (SOM) is used. As a single classifier does not provide enough accuracy, so this research proposes an Ensemble Support Vector Machine (ESVM) classifier where the fixed length feature vector from SOM is given as input, termed as ESVM_SOM. The experimental results showed that the proposed methods provide better results than the existing methods.

• Smart Media Journal
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• v.7 no.4
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• pp.24-29
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• 2018

Text analysis technique for natural language processing in deep learning represents words in vector form through word embedding. In this paper, we propose a method of constructing a document vector and classifying it into spam and normal text message, using word embedding and deep learning method. Automatic spacing applied in the preprocessing process ensures that words with similar context are adjacently represented in vector space. Additionally, the intentional word formation errors with non-alphabetic or extraordinary characters are designed to avoid being blocked by spam message filter. Two embedding algorithms, CBOW and skip grams, are used to produce the sentence vector and the performance and the accuracy of deep learning based spam filter model are measured by comparing to those of SVM Light.