• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.22 no.2
• /
• pp.179-184
• /
• 2013

In this article, we introduce the study case of technology that can automatically compensate the errors of these factors of a machine during processing on the machine tool's CNC(Computerized Numerical Controller) in real time. The biggest factors that lower the machining accuracy are thermal deformation and chatter vibration. This study is related to the detection and compensation of thermal deformation and chatter vibration that can compensate for faster and produce processed goods with more precision by autonomous compensation. In addition, this study is related to the active control of vibration during machining, monitoring of cutting force and auto recognition of machining axes origin. Thus, we attempt to introduce the related contents of the development we have made in this article.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2000.11a
• /
• pp.934-938
• /
• 2000

This paper presents a laser cutting of 2D image. 2D image in pixel graphic format is converted into vector graphic image by image processing. Bitmap graphics are made easily, but can not being used in application works for geometry transition. The Sobel's Edge detection method is used to find boundary points on 2D image. The points are fitted into curves with sampling and filtering. Sampling can provide efficient computation and filtering reconstuct features in image. The NC code is generated on MURBS curve of the points. Also, the offset of contour and cutting conditions are considered.

• 한국지구물리탐사학회:학술대회논문집
• /
• 2006.06a
• /
• pp.113-116
• /
• 2006

Successful field test results for detection of buried undersea HVDC by magnetic vector measurements are discussed. Further study will be focused on practical utilization.

• International Journal of Precision Engineering and Manufacturing
• /
• v.2 no.4
• /
• pp.81-88
• /
• 2001

We present a new digital phase measuring method for heterodyne optical interferometry, which providers high measuring speed up to 6 m/s with a fine displacement resolution of 0.1 nanometer. The key idea is combining two distinctive digital phase measuring techniques with mutually complementary characteristics to earth other one is counting the Doppler shift frequency counting with 20 MHz beat frequency for high-velocity measurement and the other is the synchronous phase demodulation with 2.0 kHz beat frequency for extremely fine displacement resolution. The two techniques are operated in switching mode in accordance wish the object speed in a synchronized way. Experimental results prove that the proposed dual mode phase measuring scheme is realized with a set of relatively simple electronic circuits of beat frequency shifting, heterodyne phase detection. and low-pass filtering.

• Proceedings of the IEEK Conference
• /
• 2000.06e
• /
• pp.127-130
• /
• 2000

Classification of Electroencephalogram(EEG) makes one of key roles in the field of clinical diagnosis, such as detection for epilepsy. Spectrum analysis using the fourier transform(FT) uses the same window to signals, so classification rate decreases for nonstationary signals such as EEG's. In this paper, wavelet power spectrum method using wavelet transform which is excellent in detection of transient components of time-varying signals is applied to the classification of three types of Event Related Potential(EP) and compared with the result by fourier transform. In the experiments, two types of photic stimulation, which are caused by eye opening/closing and artificial light, are used to collect the data to be classified. After choosing a specific range of scales, scale-averaged wavelet spectrums extracted from the wavelet power spectrum is used to find features by Back-Propagation(13P) algorithm. As a result, wavelet analysis shows superiority to fourier transform for nonstationary EEG signal classification.

• Proceedings of the KIEE Conference
• /
• 1997.11a
• /
• pp.397-401
• /
• 1997

In this paper, we propose a neural-network that detects moving objects in an image using a diffusion neural network. The proposed neural network is improved by adding a self loop to diffusion layer to remove the noise in an image and to reduce the detection of phantom edge. Computer simulation with real images show that the proposed neural network can extract edges of moving object efficiently.

• Proceedings of the KIPE Conference
• /
• 2006.06a
• /
• pp.254-256
• /
• 2006

A Novel way to support typical Thermal Shut Down(TSD) circuit is proposed. In power ICs, on-time or on-duration is the key factor to anticipate an abrupt increase of internal temperature. Such an abrupt raise of the temperature can cause TSD circuit cannot protect on proper time due to the temperature detection delay come from the physical distance or the imperfect coupling between heat sources and detector. The proposed circuit checks the duty ratio touched their maximum or not in every cycle. Once duty ratio touches the maximum duty, new circuit generates the warning signal to the TSD circuit and lowers pre-determined temperature for shut down to compensate the detection delay. The novel circuit will be analyzed to the transistor level and checked the validity by simulation.

• The Korean Journal of Applied Statistics
• /
• v.7 no.2
• /
• pp.225-237
• /
• 1994

A generalized likelihood ratio test is developed to detect an outlier associated with monitoring nuclear proliferation. While the classical outlier detection methods consider continuous variables only, our approach allows both continuous and discrete variables or a mixture of continuous and discrete variables to be used. In addition, our method is free of the normality assumption, which is the key assumption in most of the classical methods. The proposed test is constructed by applying the bootstrap to a generalized likelihood ratio. We investigate the performance of the test by studying the power with simulations.

• Proceedings of the KIEE Conference
• /
• 1997.07c
• /
• pp.924-926
• /
• 1997

A high impedance fault(HIF) in a power system could be due to a downed conductor, and is a dangerous situation because the current may be too small to be detected by conventional means. In this paper, HIF(High impedance fault) and LIF(Low impedance fault) detection methods were reviewed. No single defection method can detect all electrical conditions resulting from downed conductor faults, because high impedance fault have arc phenomena, asymmetry and randomness. Neural network are well-suited for solving difficult signal processing and pattern recognition problem. This paper presents the application of artificial neural network(ANN) to detect the HIF and LIF. Test results show that the neural network was able to identify the high impedance fault by real-time operation. Furthermore, neural network was able to discriminate the HIF from the LIF.

• Journal of information and communication convergence engineering
• /
• v.9 no.1
• /
• pp.95-99
• /
• 2011

Digital forensics is the emerging research field for determining digital forgeries. Key issues of the tampered images are to solve the problems for detecting the interpolation factor and the tampered regions. This paper describes a method to detect the interpolation factors and the forged maps using the differential method and fast Fourier transform(FFT) along the horizontal, vertical, and diagonal direction, respectively from digital filtered tampered images. The detection map can be used to find out interpolated regions from the tempered image. Experimental results demonstrate the proposed algorithm proves effective on several filtering images by adobe $Photoshop^{TM}$ and show a ratio of detecting the interpolated regions and factors from digital filtered composite images.