• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.325-327
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• 2004

ECG limb lead II signal widely used to diagnosis heart diseases and it is essential to detect ECG events (onsets, offsets and peaks of the QRS complex P wave and T wave) and extract them from ECG signal for heart diseases diagnoses. However, it is very difficult to develop standardized feature extraction formulas since ECG signals are varying on patients and disease types. In this paper, simple feature extraction method from normal and abnormal types of ECG signals is proposed. As a signal features, heart rate, PR interval, QRS interval, QT interval, interval between S wave and baseline, and T wave types are extracted. To show the validity of proposed method, Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Sinus Bradycardia, and Sinus Tachycardia data from MIT-BIH arrhythmia database are used for feature extraction and the extraction results showed higher extraction capability compare to conventional formula based extraction method.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.245-250
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• 2002

A successful on-line monitoring system for conventional machining operations has the potential to reduce cost, guarantee consistency of product quality, improve productivity and provide a safer environment for the operator. In fee-shape machining, typical signs of tool problems such as vibration, noise, chip flow characteristics and visual signs are almost unnoticeable without the use of special equipment. These characteristics increase the importance of automatic monitoring in fine-shape machining; however, sensing and interpretation of signals are more complex. In addition, the shafts of the micro-tools break before the typical extensive cutting edge of the tool gets damaged. In this study, the existence of a relationship between the characteristics of the cutting force and tool usage was investigated, and tool breakage detection algorithm was developed and the fellowing results are obtained. In data analysis, didn't use a relative error compare which mainly used in established experiment and investigated tool breakage detection algorithm in time domain which can detect AE and cutting force signals more effective and accurate.

• Journal of Power System Engineering
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• v.4 no.2
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• pp.25-30
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• 2000

The grinding process is very complex and relates many parameters to control the process. As this reason, a theoretical analysis and a quantitative estimation of the grinding process has not been well established. In this study, the in-process monitoring system was suggested by applying the neural network for monitoring and shooting the malfunction of cylindrical plunge grinding process. This system used the power signals from the electric power meter. This neural network was composed of processing elements [4-(5-5)-3] with 4 identified power parameters. Because sensitivity is blunted some minute vibration components, the simulation result of this system has appeared about 10% erroneous recognition in the uncertain pattern and the average success rate of the trouble recognition was about 90%. Consequently, the developed system, which applied to the power signals, can be recognize enough to monitor the grinding process as in-process.

• Journal of the Korea Institute of Military Science and Technology
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• v.5 no.4
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• pp.38-48
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• 2002

Some signals, such as in radar system, are transmitted as periodic pulse trains. If more than one pulse train are transmitted over the same communication channel, a problem is to separate them for source identification and extract each pulse train at the receiver. This is known as pulse train deinterleaving. In this paper, we present an approach for deinterleaving the pulse trains and extracting their periods combining the estimation of the frequency of each pulse train by DFT, CDIF/SDIF histogram and Sequence Search technique. Also, we present the result of deinterleaving pulse trains and extracting PRI in the complex environment which multiple signals are interleaved.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.39-42
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• 1995

A new methodology for representing the interaction between machines and the interlock signals required in FMCs has been developed. Object-Oriented Philosophies (OOPs) lend themselves to the development of such a scheme. A methodology developed here regards the tasks to be performed by the cell or any of its constituent machines for being primal. Sensory signals indicating the changes of state pf machines are used to trigger or initiate tasks. A task may be simple and require a relatively short time to execute, or may be complex and lengthy. This methodology may be depicated by a set of diagrams called Task Initiation Diagram (TID) and their accompanying rules.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.599-607
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• 2006

The ship radiated noise appear the various characteristic signals due to the mechanic system in the ship, the propeller and the interaction between ship body and sea water. Generally, it is classified two main components: the speed dependent signal and the speed independent signal. It is required that very complex procedure to classify the signal origin from the ship-radiated noise. This paper presents techniques to automatically detect and classify the tonal signals ken the ship-radiated noise, using the Q factor and the neural network.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.7 no.3
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• pp.125-129
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• 1982

Most A/D converters which encode baseband signals of several magahertz band width to accuracies such as 8 bits are complez and therefore expensive. This letter suggests that a simple fast digital encoder can be formed the combination of V.C.O. and digital discriminator, automatically elimnating the complex logic process of conventional fast baseband A/D converters. The techique is suitable for encoding video signals to 8bits.

• Electrical & Electronic Materials
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• v.7 no.1
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• pp.42-48
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• 1994

The preventive diagnosis technique for power system is being highlighted as a research area for deterioration of insulation in machinery because of high-voltage power system. We make efforts to develop not only diagnosis of aging state but also detection of defects in the initial stage from preventive diagnosis technique. Especially, partial discharge is actively studied as a non-destructive diagnosis technique and very useful because partial discharge measurement reduces damage than conventional diagnosis technique. The loaded stress during this test is smaller than that of other diagnosis techniques. But the continuous research for various complicated analysis method is required because partial discharge has very small signals and its signals have complex forms. In this paper, the measurement of partial discharge was investigated and studied on many specimens with void. We made samples having artificial voids and measured partial discharge. In order to use as a practical diagnosis technique, we studied ways of measurement, measured illustrations and types of partial discharge which could be used in order to diagnose defects of power machinery.

• Clinical and Experimental Pediatrics
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• v.48 no.2
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• pp.126-137
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• 2005

New insights in the complex metabolic pathways and its control mechanism for energy homeostasis have refined our understanding of the pathophysiology of obesity. It is now recognized that there are several additional regulatory mechanism such as peripheral signals including leptin, ghrelin, GLP-1 and PYY and cellular signals including uncoupling proteins and ${\beta}$ Adrenergic receptors, which contribute to the regulation of food intake and energy expenditure, respectively. In addition, the function of adipocyte as an endocrine organ in energy homeostasis has been recently emphasized. Recent findings suggest that elevated levels of adipokines, such as leptin, adiponectin, resistin and TNF-${\alpha}$, in addition to increased free fatty acid level could be related to the pathophysiology of insulin resistance in obesity. For effective treatments and prevention of obesity, further studies on the circuits of neural and endocrine interactions in the regulation of energy homeostasis are needed.

• Journal of the Korean Statistical Society
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• v.36 no.1
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• pp.1-29
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• 2007

Suppose one is trying to estimate a high dimensional vector of parameters from a series of one observation per parameter. Often, it is possible to take advantage of sparsity in the parameters by thresholding the data in an appropriate way. A marginal maximum likelihood approach, within a suitable Bayesian structure, has excellent properties. For very sparse signals, the procedure chooses a large threshold and takes advantage of the sparsity, while for signals where there are many non-zero values, the method does not perform excessive smoothing. The scope of the method is reviewed and demonstrated, and various theoretical, practical and computational issues are discussed, in particularly exploring the wide potential and applicability of the general approach, and the way it can be used within more complex thresholding problems such as curve estimation using wavelets.