• Journal of Korea Society of Industrial Information Systems
• /
• v.8 no.1
• /
• pp.92-96
• /
• 2003

본 논문에서는 비대칭 터보부호를 사용 무선 ATM환경에서 ATM셀을 전송할 때 비트 오류확률, 셀 손실확률을 대칭 터보부호와 비교하였다. 일반적으로 터보부호기는 동일한 재귀 컨벌루션코드(Resursive Systematic Convolution Code)로 구성된다. 비대칭 터보부호와 대칭적 터보부호를 비교하기 위한 생성다항식을 Primitive Polynomial과 Non-Primitive Polynomial 의 4가지 조합을 고려하여 시뮬레이션을 수행하였다. Feedback 다항식이 Primitive 다항식인 사용된 터보부호기가 Non-Primitive다항식이 사용된 터보부호기와 비교하여 1.2dB이상 성능 향상을 보였고, Primitive Polynomial과 Non-Primitive Polynomial이 같이 사용된 비대칭 터보부호기는 Non-Primitive Polynomial이 사용된 대칭 터보부호기와 비교하여 0.7dB 성능 향상을 보였다.

• Progress in Medical Physics
• /
• v.30 no.4
• /
• pp.120-127
• /
• 2019

Purpose: This study was designed to evaluate the dosimetric performance of Mobius3D by comparison with an aSi-based electronic portal imaging device (EPID) and Octavius 4D, which are conventionally used for patient-specific prescription dose verification. Methods: The study was conducted using nine patients who were treated by volumetric modulated arc therapy. To evaluate the feasibility of Mobius3D for prescription dose verification, we compared the QA results of Mobius3D to an aSi-based EPID and the Octavius 4D dose verification methods. The first was the comparison of the Mobius3D verification phantom dose, and the second was to gamma index analysis. Results: The percentage differences between the calculated point dose and measurements from a PTW31010 ion chamber were 1.6%±1.3%, 2.0%±0.8%, and 1.2%±1.2%, using collapsed cone convolution, an analytical anisotropic algorithm, and the AcurosXB algorithm respectively. The average difference was found to be 1.6%±0.3%. Additionally, in the case of using the PTW31014 ion chamber, the corresponding results were 2.0%±1.4%, 2.4%±2.1%, and 1.6%±2.5%, showing an average agreement within 2.0%±0.3%. Considering all the criteria, the Mobius3D result showed that the percentage dose difference from the EPID was within 0.46%±0.34% on average, and the percentage dose difference from Octavius 4D was within 3.14%±2.85% on average. Conclusions: We conclude that Mobius3D can be used interchangeably with phantom-based dosimetry systems, which are commonly used as patient-specific prescription dose verification tools, especially under the conditions of 3%/3 mm and 95% pass rate.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.6 no.2
• /
• pp.175-180
• /
• 2002

In this paper, the system performace with the convolution rode using a Viterbi decoding and the one tap LMS(Least Meam Square) equalizer applied to the OFDM(Orthogonal Frequency Division Multiplexing) system, is analyzed through computer simulation. DMRS(Digital Microwave Radio System)is modeled as Rummler fading channel. In Simulation result, we known that the coding system improved about 3.6dB~10.5dB when BER is 10 $^3$and b is 0.1~0.2 in case of 16QAM(Qurdrature Amplitude Modulation). Also, we known that was improved about 19.7dB when the b is 0.1 and was demanded about 10.5dB when the b is 0.2 in case of 64QAM. we known that the soft decision improved about 2~0.9dB when the b is 0.1~0.2 in case of 16QAM and about 3.3~7.8dB in case of 64QAM. In the equalizer system, efficiency improved from the case of that Eb/No is more than 13dB.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
• /
• v.28 no.2
• /
• pp.387-413
• /
• 1998

Image resampling is of particular interest in digital radiology. When resampling an image to a new set of coordinate, there appears blocking artifacts and image changes. To enhance image quality, interpolation algorithms have been used. Resampling is used to increase the number of points in an image to improve its appearance for display. The process of interpolation is fitting a continuous function to the discrete points in the digital image. The purpose of this study was to determine the effects of the seven interpolation functions when image resampling in digital periapical images. The images were obtained by Digora, CDR and scanning of Ektaspeed plus periapical radiograms on the dry skull and human subject. The subjects were exposed to intraoral X-ray machine at 60kVp and 70 kVp with exposure time varying between 0.01 and 0.50 second. To determine which interpolation method would provide the better image, seven functions were compared; (1) nearest neighbor (2) linear (3) non-linear (4) facet model (5) cubic convolution (6) cubic spline (7) gray segment expansion. And resampled images were compared in terms of SNR(Signal to Noise Ratio) and MTF(Modulation Transfer Function) coefficient value. The obtained results were as follows ; 1. The highest SNR value(75.96dB) was obtained with cubic convolution method and the lowest SNR value(72.44dB) was obtained with facet model method among seven interpolation methods. 2. There were significant differences of SNR values among CDR, Digora and film scan(P<0.05). 3. There were significant differences of SNR values between 60kVp and 70kVp in seven interpolation methods. There were significant differences of SNR values between facet model method and those of the other methods at 60kVp(P<0.05), but there were not significant differences of SNR values among seven interpolation methods at 70kVp(P>0.05). 4. There were significant differences of MTF coefficient values between linear interpolation method and the other six interpolation methods (P< 0.05). 5. The speed of computation time was the fastest with nearest -neighbor method and the slowest with non-linear method. 6. The better image was obtained with cubic convolution, cubic spline and gray segment method in ROC analysis. 7. The better sharpness of edge was obtained with gray segment expansion method among seven interpolation methods.

• Journal of Navigation and Port Research
• /
• v.46 no.3
• /
• pp.280-288
• /
• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.1
• /
• pp.89-101
• /
• 2015

This paper proposes a statistical model of 3-D positions when tracking moving targets using the uncooled infrared (IR) stereo camera system. The proposed model is derived from two errors. One is the position error which is caused by the sampling pixels in the digital image. The other is the timing jitter which results from the irregular capture-timing in the infrared cameras. The capture-timing in the IR camera is measured using the jitter meter designed in this paper, and the observed jitters are statistically modeled as Gaussian distribution. This paper derives an integrated probability distribution by combining jitter error with pixel position error. The combined error is modeled as the convolution of two error distributions. To verify the proposed statistical position error model, this paper has some experiments in tracking moving objects with IR stereo camera. The 3-D positions of object are accurately measured by the trajectory scanner, and 3-D positions are also estimated by stereo matching from IR stereo camera system. According to the experiments, the positions of moving object are estimated within the statistically reliable range which is derived by convolution of two probability models of pixel position error and timing jitter respectively. It is expected that the proposed statistical model can be applied to estimate the uncertain 3-D positions of moving objects in the diverse fields.

• Smart Media Journal
• /
• v.11 no.1
• /
• pp.31-37
• /
• 2022

This paper proposes a study on applying signal processing and deep learning for sound recognition that detects sounds commonly heard in daily life (Screaming, Clapping, Crowd_clapping, Car_passing_by and Back_ground, etc.). In the proposed sound recognition, several techniques related to the spectrum of sound waves, augmentation of sound data, ensemble learning for various predictions, convolutional neural networks (CNN) deep learning, and two-dimensional (2-D) data are used for improving the recognition accuracy. The proposed sound recognition technology shows that it can accurately recognize various sounds through experiments.

• Journal of The Korean Astronomical Society
• /
• v.56 no.2
• /
• pp.213-224
• /
• 2023

Type II solar radio bursts show frequency drifts from high to low over time. They have been known as a signature of coronal shock associated with Coronal Mass Ejections (CMEs) and/or flares, which cause an abrupt change in the space environment near the Earth (space weather). Therefore, early detection of type II bursts is important for forecasting of space weather. In this study, we develop a deep-learning (DL) model for the automatic detection of type II bursts. For this purpose, we adopted a 1-D Convolution Neutral Network (CNN) as it is well-suited for processing spatiotemporal information within the applied data set. We utilized a total of 286 radio burst spectrum images obtained by Hiraiso Radio Spectrograph (HiRAS) from 1991 and 2012, along with 231 spectrum images without the bursts from 2009 to 2015, to recognizes type II bursts. The burst types were labeled manually according to their spectra features in an answer table. Subsequently, we applied the 1-D CNN technique to the spectrum images using two filter windows with different size along time axis. To develop the DL model, we randomly selected 412 spectrum images (80%) for training and validation. The train history shows that both train and validation losses drop rapidly, while train and validation accuracies increased within approximately 100 epoches. For evaluation of the model's performance, we used 105 test images (20%) and employed a contingence table. It is found that false alarm ratio (FAR) and critical success index (CSI) were 0.14 and 0.83, respectively. Furthermore, we confirmed above result by adopting five-fold cross-validation method, in which we re-sampled five groups randomly. The estimated mean FAR and CSI of the five groups were 0.05 and 0.87, respectively. For experimental purposes, we applied our proposed model to 85 HiRAS type II radio bursts listed in the NGDC catalogue from 2009 to 2016 and 184 quiet (no bursts) spectrum images before and after the type II bursts. As a result, our model successfully detected 79 events (93%) of type II events. This results demonstrates, for the first time, that the 1-D CNN algorithm is useful for detecting type II bursts.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.1
• /
• pp.55-64
• /
• 2004

Conventional bipolar surface electromyography(EMG) technique detects only the superimposed electromyographic activity of a large number of motor units due to its low spatial resolution. For the diagnosis of neuromuscular disorder, the information of single MU is required. In this paper, 9 channel array surface electrode system was as designed and MLoG filter was proposed. Also the MCPT(modified convolution processing technique)method was proposed for the improvement of MUAP resolution. For performance evaluation, power spectrum analysis of random data and raw EMG signal comparison of MUAP shape and quantitative estimation of SNR were executed. As a result, the MUAP resolution improvement of 32％ was obtained from the standpoint of the signal-to-noise ratio(SNR).

• Journal of Ocean Engineering and Technology
• /
• v.19 no.4 s.65
• /
• pp.1-8
• /
• 2005

A nonlinear time-domain strip theory for vertical wave loads and ship responses is to be investigated. The hydrodynamic memory effect is approximated by a higher order differential equation without convolution. The ship is modeled as a non-uniform Timoshenko beam. Numerical calculations are presented for the S175 Containership translating with the forward speed in regular waves. The approach described in this paper can be used in evaluating ship motions and wave loads in extreme wave conditions and validating nonlinear phenomena in ship design.