• Earthquakes and Structures
• /
• v.24 no.1
• /
• pp.1-9
• /
• 2023

A model for calculating structure interacted mechanics is proposed. A structural interaction model and controller design based on tuned mass damping (TMD) was developed to control the induced vibration. A key point is to introduce a new analytical model to evaluate the properties of the TMD that recognizes the motion-dependent nonlinear response observed in the simulations. Aiming at the problem of increased current harmonics and low efficiency of permanent magnet synchronous motors for electric vehicles due to dead time effect, a dead time compensation method based on neural network filter and current polarity detection is proposed. Firstly, the DC components and the higher harmonic components of the motor currents are obtained by virtue of what the neural network filters and the extracted harmonic currents are adjusted to the required compensation voltages by virtue of what the neural network filters. Then, the extracted DC components are used for current polarity dead time compensation control to avert the false compensation when currents approach zero. The neural network filter method extracts the required compensation voltages from the speed component and the current polarity detection compensation method obtains the required compensation voltages by discriminating the current polarity. The combination of the two methods can more precisely compensate the dead time effect of the control system to improve the control performance. Furthermore, based on the relaxed method, the intelligent approach of stability criterion can be regulated appropriately and the artificial TMD was found to be effective in reducing cross-wind vibrations.

• Proceedings of the KSR Conference
• /
• 2007.11a
• /
• pp.913-916
• /
• 2007

In this paper, the possibility of time reversal phenomenon was investigated in damage detection of structure. In conventional lamb wave techniques, damage is identified by comparing the measured data (baseline signals) and the current data. But this method can lead to high false signal in the intact condition of structures due to environmental conditions of the structures. So in this studying, we investigate the possibility of damage detection in the aluminum plate using the time reversal phenomenon of lamb waves.

• The Bulletin of The Korean Astronomical Society
• /
• v.39 no.1
• /
• pp.77.1-77.1
• /
• 2014

Gravitational waves predicted by the general relativity almost 100 years ago have been implicated indirectly only by astrophysical observations such as the orbital evolution of binary pulsars. The advanced detectors of gravitational waves will become operational in a few years and they are expected to make direct detection of gravitational wave signal coming from merging of binaries composed of neutron stars or stellar mass black holes from external galaxies. Korean Gravitational Wave Group (KGWG) is contributing to the possible detection through the data analysis of LIGO and Virgo. We summarize the perspectives of the gravitational wave research and the impacts of the detection in the near future in astronomy and astrophysics.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.1074-1077
• /
• 1993

The objective of this paper is to develop a fuzzy logic based decision-making system to detect low current faults using multiple detection algorithms. This fuzzy system utilizes a fuzzy expert model which executes an operation without complicated mathematical models. This fuzzy system decides the performance weights of the detection algorithms. The weights and the turnouts of the detection algorithms discriminate faults from normal events. This system can also be a generic group decision-making tool for other areas of power system protection.

• Journal of Electrical Engineering and Technology
• /
• v.3 no.4
• /
• pp.484-490
• /
• 2008

In this paper, an integrated fault section detection and isolation strategy is proposed based on the application of the Distribution Automation System(DAS) utilizing advanced IT and communication technologies. The Feeder Remote Terminal Unit(FRTU) has been widely used to collect data in the Korean distribution system. The achieved data is adopted in this method for detecting multiple fault types. Especially in the case of single phase-to-ground fault, the fault section is detected by comparison of the zero-sequence current phase angle. The test results have verified the effectiveness of the proposed method in a radial distribution system through extensive simulations in Matlab/Simulink. Furthermore, a communication-based demo system identical to the simulation model has been developed, and it can be applied as an online monitoring and control program for fault section detection and isolation.

• Journal of Korea Multimedia Society
• /
• v.17 no.4
• /
• pp.420-432
• /
• 2014

In detection of moving objects from video sequences, an essential process for intelligent visual surveillance, the cast shadows accompanying moving objects are different from background so that they may be easily extracted as foreground object blobs, which causes errors in localization, segmentation, tracking and classification of objects. Most of the previous research results about moving cast shadow detection and removal usually utilize color information about objects and scenes. In this paper, we proposes a novel cast shadow removal method of moving objects in gray level video data for visual surveillance application. The proposed method utilizes observations about edge patterns in the shadow region in the current frame and the corresponding region in the background scene, and applies Laplacian edge detector to the blob regions in the current frame and the corresponding regions in the background scene. Then, the product of the outcomes of application determines moving object blob pixels from the blob pixels in the foreground mask. The minimal rectangle regions containing all blob pixles classified as moving object pixels are extracted. The proposed method is simple but turns out practically very effective for Adative Gaussian Mixture Model-based object detection of intelligent visual surveillance applications, which is verified through experiments.

• Journal of Power Electronics
• /
• v.19 no.2
• /
• pp.509-518
• /
• 2019

A novel islanding detection method is proposed in this paper. It is based on a frequency drooping PLL, which was presented in a previous work. The cause of errors in the non-detection zone (NDZ) of conventional frequency disturbance islanding detection methods (IDM) is analyzed. A frequency drooping phase-locked-loop (FD-PLL) is introduced into a single-phase grid-connected inverter (SPGCI), which can guarantee that grid current is in phase with the grid voltage. A novel FD-PLL IDM is proposed by improving this PLL. In order to verify the performance of the proposed FD-PLL IDM, a full performance comparison between the proposed IDM and typical existing active frequency drift IDMs is carried out, which includes both dynamic performance and steady performance. With the same NDZ, the total harmonic distortion of the grid-current in the dynamic process and steady state is analyzed. The proposed FD-PLL IDM, regardless of the dynamic or steady process, has the best power quality. Experimental and simulation results verify that the proposed FD-PLL IDM has excellent performance.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.5
• /
• pp.1597-1610
• /
• 2022

At present, the main method of high-speed train chassis detection is using computer vision technology to extract keypoints from two related chassis images firstly, then matching these keypoints to find the pixel-level correspondence between these two images, finally, detection and other steps are performed. The quality and accuracy of image matching are very important for subsequent defect detection. Current traditional matching methods are difficult to meet the actual requirements for the generalization of complex scenes such as weather, illumination, and seasonal changes. Therefore, it is of great significance to study the high-speed train image matching method based on deep learning. This paper establishes a high-speed train chassis image matching dataset, including random perspective changes and optical distortion, to simulate the changes in the actual working environment of the high-speed rail system as much as possible. This work designs a convolutional neural network to intensively extract keypoints, so as to alleviate the problems of current methods. With multi-level features, on the one hand, the network restores low-level details, thereby improving the localization accuracy of keypoints, on the other hand, the network can generate robust keypoint descriptors. Detailed experiments show the huge improvement of the proposed network over traditional methods.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.58 no.2
• /
• pp.221-226
• /
• 2009

In this paper, we analyzed form of flowing leakage current in electrical installation. Leakage current ($I_g$) is consisted of resistive leakage current($I_{gr}$), capacitive leakage current($I_{gc}$), and inductive leakage current($I_{gl}$). Resistive leakage current($I_{gr}$) is big occasion than capacitive leakage current($I_{gc}$) in system, Residual Current Protective Device(RCD) detects correctly leakage current. But,$I_{gc}$ is big occasion than $I_{gr}$, RCD is malfunctioned It is resistance to lead to electric fire in electrical device. We manufactured outlet that resistive leakage current detecting circuit is had. Manufactured outlet displayed performance exactly in leakage current of 5 mA Therefore, this product estimates that contribute on electric fire courtesy call.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
• /
• v.5B no.2
• /
• pp.103-110
• /
• 2005

Broken rotor bars in induction motors can be detected by monitoring any abnormality of the spectrum amplitudes at certain frequencies in the motor current spectrum. Broken rotor bar fault detection schemes should rely on multiple signatures in order to overcome or reduce the effect of any misinterpretation of the signatures that are obscured by factors such as measurement noises and different load conditions. Multiple Discriminant Analysis (MDA) and Artificial Neural Networks (ANN) provide appropriate environments to develop such fault detection schemes because of their multi-input processing capabilities. This paper describes two fault detection schemes for broken rotor bar fault detection with multiple signature processing, and demonstrates that multiple signature processing is more efficient than single signature processing.