• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.12
• /
• pp.5085-5088
• /
• 2015

Background: The aim of this study was to assess the role of the presence of a choline peak in 3 Tesla 1H magnetic resonance spectroscopy (MRS) for differentiating benign from malignant adnexal masses. Materials and Methods: A total of 46 adnexal masses (23 malignant and 23 benign) underwent 1H MRS study prior to surgery to assess the presence of choline peak. Results: A choline peak was detected in 16 malignant masses (69.5%) and was absent in the other 7 (30.5%). A choline peak was only detected in 6 (26%) of the benign adnexal masses. The presence of an MRS choline peak had a sensitivity of 69.5%, a specificity of 74%, a positive predictive value (PPV) of 72.7%, and a negative predictive value (NPV) of 71% for diagnosing malignant adnexal masses. A significant difference between the frequency of mean choline peaks in benign and malignant adnexal masses was observed (P value < 0.01). Conclusions: A 1H MRS choline peak is seen in malignant adnexal masses more frequently than the benign masses, and may be helpful for diagnosing malignant adnexal masses.

• Journal of Advanced Navigation Technology
• /
• v.18 no.4
• /
• pp.304-311
• /
• 2014

In this paper, we propose a ultra sound inspection technique for automatic defect detection and classification in aircraft composite materials. Using local maximum values of ultra sound wave, we choose peak values for defect detection. Distance data among peak values are used to construct histogram and to determine surface and back-wall echo from the floor of composite materials. C-scan image is then composed through this method. A threshold value is determined by average and variance of the peak values, and defects are detected by the values. PCA(principal component analysis) and QDA(quadratic discriminant analysis) are carried out to classify the types of defects. In PCA, 512 dimensional data are converted into 30 PCs(Principal Components), which is 99% of total variances. Computational cost and misclassification rate are reduced by limiting the number of PCs. A decision boundary equation is obtained by QDA, and defects are classified by the equation. Experimental result shows that our proposed method is able to detect and classify the defects automatically.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.11 no.4
• /
• pp.154-159
• /
• 2012

Condition monitoring(CM) is a method based on Non-destructive test(NDT). Therefore, recently many kind of NDT were applied for CM. Acoustic emission(AE) is widely used for the early detection of faults in rotating machinery in these days also. Because its sensitivity is higher than normal accelerometers and it can detect low energy vibration signals. A machine tool consist of many parts such as the bearings, gears, process tools, shaft, hydro-system, and so on. Condition of Every part is connected with product quality finally. To increase the quality of products, condition monitoring of the components of machine tool is done completely. Therefore, in this paper, acoustic emission method is used to detect a machine fault seeded in a gearbox. The AE signals is saved, and power spectrums and feature values, peak value, mean value, RMS, skewness, kurtosis and shape factor, were determined through Matlab.

• Journal of information and communication convergence engineering
• /
• v.19 no.2
• /
• pp.93-101
• /
• 2021

Cardiac arrhythmias are common heart diseases and generally cause sudden cardiac death. Electrocardiogram (ECG) is an effective tool that can reveal the electrical activity of the heart and diagnose cardiac arrhythmias. We propose detection of P waves based on QRST cancellation zero-one substitution. After preprocessing, the QRST segment is determined by detecting the Q wave start point and T wave end point separately. The Q wave start point is detected by digital analyses of the QRS complex width, and the T wave end point is detected by computation of an indicator related to the area covered by the T wave curve. Then, we determine whether the sampled value of the signal is in the interval of the QRST segment and substitute zero or one for the value to cancel the QRST segment. Finally, the maximum amplitude is selected as the peak of the P wave in each RR interval of the residual signal. The average detection rate for the QT database was 97.67%.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.2
• /
• pp.233-242
• /
• 2018

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting feature point based on only R peak through optimal R wave. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 94.85% in PVC classification.

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.340_341
• /
• 2009

This paper suggests a detection scheme of a secondary arc extinction using the peak value of a fault voltage estimated by correlation algorithms. The system implemented in this paper is based on a Korean 765 kV system and the suggested proposed scheme is tested on the system. The performance of the method is analyzed by using Electro-Magnetic Transients Program (EMTP)/ATPDraw.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2000.05a
• /
• pp.352-355
• /
• 2000

In this paper, we propose an adaptive thresholding for edge detection. first, we got histograms for background image and image with moving object, respectively. Then we make difference histogram between histograms of background and object image. A thresholding value is decided using gradient of peak to peak in the difference histogram. The experimentation is processed using a moving car in the road. The result is that edge is detected well regardless of the brightness.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.04a
• /
• pp.881-888
• /
• 1997

A new algorithm for detection of tool fracture in milling process was developed. The variation of the peak-to-valley value of cutting load was used in this algorithm. Various kinds of vectors representing the condition of tool, such as tool condition vector, reference tool condition vector, tool condition variation vector were defined. Using these vectors, tool fracture index which represents the magnitude of tool fracture and is independent of tool run-outs is developed. Small and large tool fracture and chipping under various cutting condition could be detected using proposed tool fracture index, which was proved with cutting force model and experiments.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 1999.11a
• /
• pp.127-131
• /
• 1999

In this paper, using a wide-band AE sensor with the frequency range from 100[kHz], the frequency spectra of AE signals generated from the corona discharges of the needle-plane electrode was analyzed to determine the proper ultrasonic sensor. We also examined the relationship between the magnitude of corona discharge and the magnitude of AE signals in peak-to-peak value under the application of 60[Hz] AC high-voltage in oil. From these results, the main frequency spectra of AE signals emitted from the corona discharges of the needle-plane gap were found to be 130[kHz] by the fast fourier transform. The magnitude of AE signals was proportional to the magnitude of corona discharge and discharge current pulse with increasing the applied voltages. Also the detection of corona discharge point location by AE signals was found to be possible by using two sensors.

• Bulletin of the Korean Chemical Society
• /
• v.26 no.10
• /
• pp.1560-1564
• /
• 2005

The influence of cetyltrimethylammonium bromide (CTAB) on the electrochemical behavior of bisphenol A at the carbon paste electrode (CPE) was investigated. CTAB, with a hydrophobic C-H chain, can adsorb at the CPE surface via hydrophobic interaction and then change the electrode/solution interface, and finally affects the electrochemical response of bisphenol A, confirming from the remarkable oxidation peak current enhancement. The electrode process of bisphenol A was examined, and then all the experimental parameters which affects the electrochemical response of bisphenol A, such as pH value of the supporting electrolyte, accumulation potential and time, potential scan rate and the concentration of CTAB, were examined. Finally, a sensitive and simple voltammetric method was developed for the determination of bisphenol A. Under the optimum conditions, the oxidation peak current of bisphenol A varied linearly with its concentration over the range from $2.5\;{\times}\;10^{-8}\;to\;1\;{\times}\;10^{-6}$ mol/L, and the detection limit was found to be $7.5\;{\times}\;10^{-9}$ mol/L. This method was successfully employed to determine bisphenol A in some waste plastic samples.