• Journal of the Korean Society for Precision Engineering
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• v.17 no.5
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• pp.70-75
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• 2000

After the patterning and probe process of wafer have been achieved, the dicing process is necessary to separate chips from a wafer. The dicing process cuts a wafer to lengthwise and crosswise direction to make many chips by using narrow circular rotating diamond blade. But inferior goods are made under the influence of complex dicing environment such as blade, wafer, cutting water and cutting conditions. This paper describes a monitoring algorithm using feature extraction in order to find out an instant of vibration signal change when bad dicing appears. The algorithm is composed of two steps: feature extraction and decision. In the feature extraction, two features processed from vibration signal which is acquired by accelerometer attached on blade head are proposed. In the decision. a threshold method is adopted to classify the dicing process into normal and abnormal dicing. Experiment have been performed for GaAs semiconductor wafer. Based upon observation of the experimental results, the proposed scheme shown a good accuracy of classification performance by which the inferior goods decreased from 35.2% to 12.8%.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.694-699
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• 2004

The purpose of this study is to estimate the availability of acoustic emission method to the internal leak of the valves at nuclear power plants. The acoustic emission method was applied to the valves at the site, and the background noise was measured for the abnormal plant condition. From the comparison of the background noise data with the experimental results as to relation between leak flow and acoustic signal, the minimum leak flow rates that can be detected by acoustic signal was suggested. When the background levels are higher than the acoustic signal, the method described below was considered that the analysis the remainder among the background noise frequency spectrum and the acoustic signal spectrum become a very useful leak detection method. A few experimental examples of the spectrum analysis that varied the background noise characteristic were given.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1045-1048
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• 2000

This paper presents the application of the wavelet transform analysis and the neural network method to the phonocardiogram (PCG) signal. Heart sound is a acoustic signal generated by cardiac valves, myocardium and blood flow and is a very complex and nonstationary signal composed of many source. Heart sound can be discriminated normal heart sound and heart murmur. Murmurs have broader frequency bandwidth than the normal ones and can occur at random position of cardiac cycle. In this paper, we classified the group of heart sound as normal heart sound(NO), pre-systolic murmur(PS), early systolic murmur(ES), late systolic murmur(LS), early diastolic murmur(ED). And we used the wavelet transform to shorten artifacts and strengthen the low level signal. The ANN system was trained and tested with the back- propagation algorithm from a large data set of examples-normal and abnormal signals classified by expert. The best ANN configuration occurred with 15 hidden layer neurons. We can get the accuracy of 85.6% by using the proposed algorithm.

• International Journal of Korean Welding Society
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• v.1 no.2
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• pp.1-6
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• 2001

A weld quality assessment method is proposed in this work, which can be applied to the short-circuit mode in GMAW. Information about the welding signal trajectory, distribution of the signal duration at each sub-regions and short-circuit frequency is used to evaluate the weld quality. The weighted penalty, which is determined experimentally, is imposed for each abnormal signal. Performance of the proposed method is compared with the Simpson's method under the conditions of shielding gas reduction, workpiece surface contamination and joint gap in the butt and fillet welds. Although the proposed method predicts the weld quality with reasonable accuracy, further modification and extension to other metal transfer modes are needed as a further study.

• Smart Structures and Systems
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• v.9 no.3
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• pp.287-301
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• 2012

It is well known that overloaded vehicles may cause severe damages to bridges, and how to estimate and evaluate the status of the overloaded vehicles passing through bridges become a challenging problem. Therefore, based on the monitored strain data from a structural health monitoring system (SHM) installed on a bridge, a method is recommended to identify and analyze the probability of overloaded vehicles. Overloaded vehicle loads can cause abnormity in the monitored strains, though the abnormal strains may be small in a concrete continuous rigid frame bridge. Firstly, the abnormal strains are identified from the abundant strains in time sequence by taking the advantage of wavelet transform in abnormal signal identification; secondly, the abnormal strains induced by heavy vehicles are picked up by the comparison between the identified abnormal strains and the strain threshold gotten by finite element analysis of the normal heavy vehicle; finally, according to the determined abnormal strains induced by overloaded vehicles, the statistics of the overloaded vehicles passing through the bridge are summarized and the whole probability of the overloaded vehicles is analyzed. The research shows the feasibility of using the monitored strains from a long-term SHM to identify the information of overloaded vehicles passing through a bridge, which can help the traffic department to master the heavy truck information and do the damage analysis of bridges further.

• Journal of Biomedical Engineering Research
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• v.25 no.2
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• pp.129-135
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• 2004

In the home health management system, we often face the situation to handle biological signals that are frequently measured from normal subjects. In such a case, it is necessary to decide whether the signal at a certain moment is normal or abnormal. Since ECC is one of the most frequently measured biological signals, we describe algorithms that detect QRS-complex and decide whether it is normal or abnormal. The developed QRS detection algorithm is a simplified version of the conventional algorithm providing enough performance for the proposed application. The developed classification algorithm that detects abnormal from mostly normal beats is based on QRS width, R-R interval and QRS shape parameter using Karhunen-Loeve transformation. The simplified QRS detector correctly detected about 99% of all beats in the MTT/BIH ECG database. The classification algorithm correctly classified about 96% of beats as normal or abnormal. The QRS detection and classification algorithm described in this paper could be used in home health management system.

• Journal of Power System Engineering
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• v.7 no.2
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• pp.29-35
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• 2003

The monitoring and diagnostics of the rotating machinery have been received considerable attention for many years. The objectives are to classify the machinery condition and to find out the cause of abnormal condition. This paper describes a classification method of diagnosing the small reciprocating compressor for refrigerators using the artificial neural network and the wavelet transform. In order to extract salient features, the wavelet transform are used from primary noise signals. Since the wavelet transform decomposes raw time-waveform signals into two respective parts in the time space and frequency domain, more and better features can be obtained easier than time-waveform analysis. In the training phase for classification, self-organizing feature map(SOFM) and learning vector quantization(LVQ) are applied, and the accuracies of them ate compared with each other. This paper is focused on the development of an advanced signal classifier to automatize the vibration signal pattern recognition. This method is verified by small reciprocating compressors, for refrigerator and normal and abnormal conditions are classified with high flexibility and reliability.

• BMB Reports
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• v.38 no.1
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• pp.9-16
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• 2005

Transforming Growth Factor (TGF)-$\beta$ family, including TGF-$\beta$, bone morphorgenic protein (BMP), and activn, plays an important role in essential cellular functions such as proliferation, differentiation, apoptosis, tissue remodeling, angiognesis, immune responses, and cell adhesions. TGF-$\beta$ predominantly transmits the signals through serine/threonine receptor kinases and cytoplasmic proteins called Smads. Since the discovery of TGF-$\beta$ in the early 1980s, the dysregulation of TGF-$\beta$/Smad signaling has been implicated in the pathogenesis of human diseases. Among signal transducers in TGF-$\beta$/Smad signaling, inhibitory Smads (I-Smads), Smad6 and Smad7, act as major negative regulators forming autoinhibitory feedback loops and mediate the cross-talking with other signaling pathways. Expressions of I-Smads are mainly regulated on the transcriptional levels and post-translational protein degradations and their intracellular levels are tightly controlled to maintain the homeostatic balances. However, abnormal levels of I-Smads in the pathological conditions elicit the altered TGF-$\beta$ signaling in cells, eventually causing TGF-$\beta$-related human diseases. Thus, exploring the molecular mechanisms about the regulations of I-Smads may provide the therapeutic clues for human diseases induced by the abnormal TGF-$\beta$ signaling.

• Clinical and Experimental Pediatrics
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• v.54 no.9
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• pp.389-393
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• 2011

We present the case of a patient with Epstein-Barr virus (EBV) encephalitis who developed abnormal white matter lesions during the chronic phases of the infection. A 2-year-old-boy was admitted for a 2 day history of decreased activity with ataxic gait. The results of the physical examination were unremarkable except for generalized lethargy and enlarged tonsils with exudates. Brain magnetic resonance imaging (MRI) at admission showed multiple high signal intensities in both basal ganglia and thalami. The result of EBV polymerase chain reaction (PCR) of the cerebral spinal fluid was positive, and a serological test showed acute EBV infection. The patient was diagnosed with EBV encephalitis and recovered fully without any residual neurologic complications. Subsequently, follow-up MRI at 5 weeks revealed extensive periventricular white matter lesions. Since the patient remained clinically stable and asymptomatic during the follow-up period, no additional studies were performed and no additional treatments were provided. At the 1-year follow-up, cranial MRI showed complete disappearance of the abnormal high signal intensities previously seen in the white matter. The patient continued to remain healthy with no focal neurologic deficits on examination. This is the first case of asymptomatic self-limited white matter lesions seen in serial MRI studies in a Korean boy with EBV encephalitis.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.4 s.97
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• pp.148-154
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• 1999

This study was carried out to verify grinding performance of dental diamond bur and investigate the possibility of AE application in dentistry field. Workpieces were made of acryl and bovine respectively for the experiments in this study. Grinding test was conducted to get the data of grinding resistance and specific grinding energy of four different types of diamond bur by using tool dynamometer. AE signal was acquired to verify grinding process in the AE measuring system. Tool wear was observed to find parameters about grinding characteristics of diamond bur by means of SEM picture. It was found that the wear of dental diamond bur could be detected with polishing of grinding material, removal of adhesive parts, wear of particles neighboring cutting nose, loss of material and elevation of temperature. The wear of B, C, D type diamond bur is due to wear and fracture of grain size. Abnormal state can be found through the behavior of AE signal in the grinding working. As a result, it is expected that forecast of abnormal state is possible using AE equipments under real time process.