• Transactions of the Korean Society of Machine Tool Engineers
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• 제11권2호
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• pp.103-111
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• 2002

Out of all metal-cutting process, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. One important aspect in controlling the drilling process is monitoring drill wear status. There are two systems, Basic system and Online system, to detect the drill wear. Basic system comprised of spindle rotational speed, feed rates, thrust torque and flank wear measured by tool microscope. Outline system comprised of spindle rotational speed feed rates, AE signal, flank wear area measured by computer vision, On-line monitoring system does not need to stop the process to inspect drill wear. Backpropagation neural networks (BPNs) were used for on-line detection of drill wear. The output was the drill wear state which was either usable or failure. This paper deals with an on-line drill wear monitoring system to fit the detection of the abnormal tool state.

• Transactions of the Korean Society of Automotive Engineers
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• 제8권1호
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• pp.23-31
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• 2000

Knock and misfire, kinds of abnormal combustion, are highly undesirable effect on the internal combustion engine. So, it is important to detect these avnormal combuition and control the ignition timing etc. to avoid these mal-effect factors in real engine system. In this study, the system which detects the knock and the misfire using by spark plug is presented. This system is based on the effect of modulation breakdown voltage(BDV) between the spark gaps. The voltage drop between spark plug electrodes, when an electrical breakdown is initiated, depends on the temperature and pressure in combustion chamber. So, we can detect knock and misfire that produce changes in gas temperature and pressure (consequently, its density) using by BDV signal change which carries information about the character of combustion.

• The Transactions of the Korean Institute of Electrical Engineers
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• 제35권4호
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• pp.127-135
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• 1986

This paper represents a real time algorithm which improves the some drawbacks in the past methods for detection of the QRS conplexes of ECG signals. In the conventional method we can't detect QRS complex and QRS duration more correctly in case of (1) the contaminated ECG with 60Hz noise, muscle noise. (2) the movement of the baseline for a QRS complex. (3) being abnormal QRS complex with prolonging QRS. Therefore, we have proposed a new algorithm which can detect accurate QRS complex detection in case of the contaminated ECG with 60Hz noise, muscle noise, and movement of baseline for QRS complex. Moreover, in case of prolonging QRS we accomplished to detect not only QRS complex but also a single pulse that has a width proportional to QRS duration. This algorithm which is proposed in our paper in our paper in programmed with 6502 assembly language for real time ECG signal processing.

• Journal of Sensor Science and Technology
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• 제28권1호
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• pp.13-16
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• 2019

The analysis of electrocardiogram (ECG) signals facilitates the detection of various abnormal conditions of the human heart. The QRS complex is the most critical part of the ECG waveform. Further, different diseases can be identified based on the QRS complex. In this paper, a new algorithm based on the well-known Pan-Tompkins algorithm has been proposed. In the proposed scheme, the QRS complex is initially extracted by removing the background noise. Subsequently, the R-R interval and heart rate are calculated to detect whether the ECG is normal or has some abnormalities such as tachycardia and bradycardia. The accuracy of the proposed algorithm is found to be almost the same as the Pan-Tompkins algorithm and increases the R peak detection processing speed. For this work, samples are used from the MIT-BIH Arrhythmia Database, and the simulation is carried out using MATLAB 2016a.

• Tribology and Lubricants
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• 제27권3호
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• pp.125-133
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• 2011

The purpose of this study is to develop an engine oil quality monitoring system to warn the abnormal condition of engine oil. To do this, first of all, it is needed a personal controller development to measure the capacitance of a pre-developed engine oil deterioration detection sensor integrated with an oil filter. To measure the capacitance of engine oil in the sensor, it is used the way measuring the electric charging time in a capacitor by impressing DC volt. This method has merits on cost and signal stability. The measured capacitance is compensated by comparing with the one measured by an impedance analyzer. Also, using the dielectric constant gained by an impedance analyzer, the calculating equation of the dielectric constant of engine oil related with the currently developed sensor is decided. Then, the deterioration degree of engine oil is estimated according to the change rate of dielectric constant between green oil and used oil. Finally, using this dielectric constant information together with engine oil temperature and pressure, the currently developed engine oil quality monitoring system is to tell the abnormal state of engine oil.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2009년도 학술대회
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• pp.368-372
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• 2009

The method to get the feature have been proposed to recognize the user activity by setting specific action for making the user independent result in previous research. However, it was only applied in specific environment and it was difficult to implement because it regarded only some specific feature as the recognized object. To improve this problem we detected the normality/abnormality of the activity based on the repetition and the continuity of the past activity pattern. We applied the unsupervised learning method, not supervised, and clustered the data which was collected within a certain period of time and we regarded it as the basis of the evaluation of the repetition. We demonstrated to be able to detect the abnormal activity based on wether the data was generated repeatedly.

• Proceedings of the Korean Society of Embryo Transfer Conference
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• 한국수정란이식학회 2002년도 국제심포지엄
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• pp.73-73
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• 2002

Nuclear transfer (NT) techniques have advanced in the last years, and cloned animals have been produced by using somatic cells in several species including pig. However, it is difficult that the nuclear transfer porcine embryos development to blastocyst stage overcoming the cell block in vitro. Abnormal segregation of chromosomes in nuclear transferred embryos on genome activation stage bring about embryo degeneration, abnormal blastocyst, delayed and low embryo development. Thus, we are evaluated that the correlations of the frequency of embryo developmental rates and chromosome aberration in NT and In viかo fertilization (IVF) derived embryo. We are used for ear-skin-fibroblast cell in NT. If only karyotyping of embryonic cells are chromosomally abnormal, they may difficultly remain undetected. Then, we evaluate the chromosome aberrations, fluorescent in situ hybridization (FISH) with porcine chromosome 1 submetacentric specific DNA probe were excuted. In normal diploid cell nucleus, two hybridization signal was detected. In contrast, abnormal cell figured one or three over signals. The developmental rates of NT and IVF embryos were 55% vs 63%, 32% vs 33% and 13% vs 17% in 2 cell, 8 cell and blastocyst, respectively. When looking at the types of chromosome aberration, the detection of aneuploidy at Day 3 on the embryo culture. The percentage of chromosome aneuploidy of NT and IVF at 4-cell stage 40.0%, 31.3%, respectively. This result indicate that chromosomal abnormalities are associated with low developmental rate in porcine NT embryo. It is also suggest that abnormal porcine embryos produced by NT associated with lower implantation rate, increase abortion rate and production of abnormal fetuses.

• Journal of the Institute of Electronics and Information Engineers
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• 제51권9호
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• pp.200-207
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• 2014

Arrhythmia electrocardiogram signal contains a specific unusual heartbeat with abnormal morphology. Because unusual heartbeat is useful for diagnosis and classification of various diseases, such as arrhythmia, detection of unusual heartbeat from the arrhythmic ECG signal is very important. Amplitude and kurtosis at R-peak point and RR interval are characteristics of ECG signal on R-wave. In this paper, we provide a method for detecting unusual heartbeat based on these. Through the value of the attribute deviates more from the average value if unusual heartbeat is more certainly, the proposed method detects unusual heartbeat in order using the mean and standard deviation. From 15 ECG signals of MIT-BIH arrhythmia database which has R-wave distortion, we compare the result of conventional method which uses the fixed threshold value and the result of proposed method. Throughout the experiment, the sensitivity is significantly increased to 97% from 50% using the proposed method.

• Smart Structures and Systems
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• 제5권2호
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• pp.153-171
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• 2009

In this paper, the analytical fault detection and diagnosis (FDD) is presented using model-based and signal-based methodology with wavelet analysis on signals obtained from sensors and sensor networks. In the model-based FDD, we present the modeling of contact interface found in soft materials, including the biomedical contacts. Fingerprint analysis and signal-based FDD are also presented with an experimental framework consisting of a mechanical pneumatic system typically found in manufacturing automation. This diagnosis system focuses on the signal-based approach which employs multi-resolution wavelet decomposition of various sensor signals such as pressure, flow rate, etc., to determine leak configuration. Pattern recognition technique and analytical vectorized maps are developed to diagnose an unknown leakage based on the established FDD information using the affine mapping. Experimental studies and analysis are presented to illustrate the FDD methodology. Both model-based and wavelet-based FDD applied in contact interface and manufacturing automation have implication towards better quality of life by applying theory and practice to understand how effective diagnosis can be made using intelligent FDD. As an illustration, a model-based contact surface technology an benefit the diabetes with the detection of abnormal contact patterns that may result in ulceration if not detected and treated in time, thus, improving the quality of life of the patients. Ultimately, effective diagnosis using FDD with wavelet analysis, whether it is employed in biomedical applications or manufacturing automation, can have impacts on improving our quality of life.

• Journal of the Korea Society of Computer and Information
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• 제26권7호
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• pp.29-36
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• 2021

In this paper, we classify ECG signal data for mobile devices using deep learning models. To classify abnormal heartbeats with high accuracy, three factors of the deep learning model are selected, and the classification accuracy is compared according to the changes in the conditions of the factors. We apply a CNN model that can self-extract features of ECG data and compare the performance of a total of 48 combinations by combining conditions of the depth of model, optimization method, and activation functions that compose the model. Deriving the combination of conditions with the highest accuracy, we obtained the highest classification accuracy of 97.88% when we applied 19 convolutional layers, an optimization method SGD, and an activation function Mish. In this experiment, we confirmed the suitability of feature extraction and abnormal beat detection of 1-channel ECG signals using CNN.