• The Journal of the Acoustical Society of Korea
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• v.43 no.3
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• pp.305-313
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• 2024

For effective bi-static sonar operation, detection performance analysis must be performed reflecting the characteristics of sound propagation due to the ocean environment and target information. However, previous studies analyzing bistatic sonar detection performance have either not considered the ocean environment and target characteristics or have been conducted using simplified approaches. Therefore, in this study, we compared and analyzed the bistatic detection performance in Yellow sea and Ulleung basin both with and without considering target characteristics. A numerical analysis model was used to derive an accurate bistatic target strength for the submarine-shaped target, and signal excess was calculated by reflecting the simulated target strength. As a result, significant changes in detection performance were observed depending on the source and receiver locations as well as the target strength.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1654-1659
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• 2003

In this paper, the performance evaluation of the JADE-MUSIC estimation based on the indoor channel is presented. By means of the JADE-MUSIC algorithm, DOA and time delay can be obtained simultaneously. In the JADE-MUSIC method, the channel impulse response is first estimated from the received samples and then this impulse response is employed to estimate DOAs and time delays of multipath waves. Moreover, according to the JADE-MUSIC characteristics, it can work in cases when the number of impinging waves is more than the number of antenna elements, unlike the traditional parametric subspace-based method, such a case is not true. Therefore, we employ the JADE-MUSIC algorithm applying for the real indoor environment where is rich of the multipath propagation waves and can imply that the number of waves is very possibly higher than that of the array element. The experiment is carried out in our laboratory considered to be the real indoor environment. The performance of the JADE-MUSIC algorithm is evaluated in terms of the comparison between the simulation and experiment results by using the simulated channel model and the real indoor channel model, respectively. It is clear that the joint angle and delay estimation using the simulated channel model are in good agreement with the estimation using the real indoor channel model. Therefore, we can say that the JADE-MUSIC algorithm accomplishes the high performance to jointly estimate the angle and delay of the arriving signal for the indoor environment.

• Journal of Korean Society on Water Environment
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• v.24 no.1
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• pp.91-98
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• 2008

This work was performed to develop a model possible to predict the influent flow and influent components, which are one of main disturbances causing process problems at the operation of municipal wastewater treatment plant. In this study, artificial neural network (ANN) was used in order to develop a model that was able to predict the influent flow, $COD_{Mn}$, SS, TN 1 day-ahead, 2day-ahead and 3 day ahead. Multi-layer feed-forward back-propagation network was chosen as neural network type, and tanh-sigmoid function was used as activation function to transport signal at the neural network. And Levenberg-Marquart (LM) algorithm was used as learning algorithm to train neural network. Among 420 data sets except missing data, which were collected between 2005 and 2006 at field plant, 210 data sets were used for training, and other 210 data sets were used for validation. As result of it, ANN model for predicting the influent flow and components 1-3day ahead could be developed successfully. It is expected that this developed model can be practically used as follows: Detecting the fault related to effluent concentration that can be happened in the future by combining with other models to predict process performance in advance, and minimization of the process fault through the establishment of various control strategies based on the detection result.

• 한국정보디스플레이학회:학술대회논문집
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• 2006.08a
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• pp.733-738
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• 2006

As the film transistor-liquid crystal display (TFTLCD) panels become larger and provide higher resolution, the propagation delay of row and column lines, the voltage modulation of Vcom, and the response time of liquid crystal affect the display images now more than in the past. It is more important to understand the electrical characteristic of TFT-LCD panels these days. This paper describes the electrical model of a 15-inch XGA ($1024{\times}768$) TFT-LCD panel. The parasitic resistance and capacitance of its panel are obtained by 3D simulation of a sub pixel. The accuracy of these data is verified by the measured values in an actual panel [1]. The developed panel simulation platform, the equivalent circuit of a 15-inch XGA panel, is simulated by HSPICE. The results of simulation are compared with those of experiment, according to changing the width of signal. Especially, the proposed simulation platform for modeling TFTLCD panels can be applied to large size LCD TVs. It can help panel and circuit designers to verify their ideas without making actual panels and circuits.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.1528-1532
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• 2000

The conventional optics and near field optics are compared numerically in the view points of the spot size and propagation characteristics. The decaying characteristics of near field light require the optics to access the object within several tens of nanometers. Therefore the gap control is one of the main issues in the near field optics area. In this paper the gap control is done by using the shear force of the NF(Near Field) probe and the characteristics are examined. The probe is modeled as a 2'nd order mass-spring-damper system driven by a harmonic force. The primary cause of the decrease in vibration amplitude is due to the damping force - shear force - between the surface and the probe. Using the model, damping constant and resonance frequency of the probe is calculated as a function of probe-sample distance. Detecting the amplitude and phase shift of the NF probe attached to the high Q-factor piezoelectric tuning fork, we can control the position of the NF probe about 0 to 50nm above the sample. The feedback signal to regulate the probe-sample distance can be used independently for surface topography imaging. 3-D view of the shear force image of a testing sample with the period of $1{\mu}m$ will be shown.

• Journal of Ocean Engineering and Technology
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• v.34 no.2
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• pp.147-154
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• 2020

Underwater acoustics that is the study of the phenomenon of underwater wave propagation and its interaction with boundaries, has mainly been applied to the fields of underwater communication, target detection, marine resources, marine environment, and underwater sound sources. Based on the scientific and engineering understanding of acoustic signals/data, recent studies combining traditional and data-driven machine learning methods have shown continuous progress. Machine learning, represented by deep learning, has shown unprecedented success in a variety of fields, owing to big data, graphical processor unit computing, and advances in algorithms. Although machine learning has not yet been implemented in every single field of underwater acoustics, it will be used more actively in the future in line with the ongoing development and overwhelming achievements of this method. To understand the research trends of machine learning applications in underwater acoustics, the general theoretical background of several related machine learning techniques is introduced in this paper.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.401-401
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• 2014

In this study, graphene, the most attractive material today, has been applied to the wavelength-modulated surface plasmon resonance (SPR) sensor. The optical fiber sensor technology is the most fascinating topic because of its several benefits. In addition to this, the SPR phenomenon enables the detection of biomaterials to be label-free, highly sensitive, and accurate. Therefore, the optical fiber SPR sensor has powerful advantages to detect biomaterials. Meanwhile, Graphene shows superior mechanical, electrical, and optical characteristics, so that it has tremendous potential to be applied to any applications. Especially, grapheme has tighter confinement plasmon and relatively long propagation distances, so that it can enhance the light-matter interactions (F. H. L. Koppens, et al., Nano Lett., 2011). Accordingly, we coated graphene on the optical fiber probe which we fabricated to compose the wavelength-modulated SPR sensor (Figure 1.). The graphene film was synthesized via thermal chemical vapor deposition (CVD) process. Synthesized graphene was transferred on the core exposed region of fiber optic by lift-off method. Detected analytes were biotinylated double cross-over DNA structure (DXB) and Streptavidin (SA) as the ligand-receptor binding model. The preliminary results showed the SPR signal shifts for the DXB and SA binding rather than the concentration change.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.33 no.11
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• pp.456-463
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• 1984

The fast-rising high-voltage pulse generation circuit system of a theta pinch is both theoretically and experimentally investigated. The idealized model of this circuit system is a hybrid circuit system composed of three parts: a lumped circuit part being consisted of a capacitor bank and a spark switch connected in series, another lumped circuit part being consisted of the Blumlein transmission line, whose end load is the pinch coil. the voltage difference between two ends of the pinch coil is formulated by analyzing this hybrid circuit system by means of the law of the signal propagation in the transmission line and Kirchhoff's laws. The expedient numerical method for computer calculation is developed to generate the pulse profile of the voltage difference across the pinch coil. The period of the experimentally measured main pulse is a fourth of the theoretical one neglecting the resistance of the pinch coil. We attribute this discrepancy to the modelling in the theoretical calculation that hte resistance and inductance of the spark switch and capacitor bank are assumed to be constant through discharge. Therefore, we can see that the rise time of the imploding magnetic-field pulse is mainly dependent on the spark switch and capacitor bank.

• The Journal of Korea Robotics Society
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• v.6 no.2
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• pp.97-107
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• 2011

This paper describes efficient flight control algorithms for building a reconfigurable ad-hoc wireless sensor networks between nodes on the ground and airborne nodes mounted on autonomous vehicles to increase the operational range of an aerial robot or the communication connectivity. Two autonomous flight control algorithms based on adaptive gradient climbing approach are developed to steer the aerial vehicles to reach optimal locations for the maximum communication throughputs in the airborne sensor networks. The first autonomous vehicle control algorithm is presented for seeking the source of a scalar signal by directly using the extremum-seeking based forward surge control approach with no position information of the aerial vehicle. The second flight control algorithm is developed with the angular rate command by integrating an adaptive gradient climbing technique which uses an on-line gradient estimator to identify the derivative of a performance cost function. They incorporate the network performance into the feedback path to mitigate interference and noise. A communication propagation model is used to predict the link quality of the communication connectivity between distributed nodes. Simulation study is conducted to evaluate the effectiveness of the proposed reconfigurable airborne wireless networking control algorithms.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.4
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• pp.22-35
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• 1992

In highly automated machining centers, intelligent sensor fddeback systems are indispensable on order to monitor their operations, to ensure efficient metal removal, and to initate remedial action in the event of accident. In this study, an on-line tool wear detection system for thrning operations is developed, and experimentally evaluated. The system employs multiple sensors and the signals from these sensors are processed using a multichannel autoegressive (AR) series model. The resulting output from the signal processing block is then fed to a previously tranied artificial neural network (multiayered perceptron) to make a final decision on the state of the cutting tool. To learn the necessary input/output mapping for tool wear detection, the weithts and thresholds of the network are adjusted according to the back propagation (BP) method during off-line training. The results of experimental evaluation show that the system works well over a wide range of cutting conditions, and the ability of the system to detect tool wear is improved due to the generalization, fault-tolearant and self-ofganizing properties of the neural network.