• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.31B no.6
• /
• pp.71-80
• /
• 1994

The purpose of this paper is to explore the use of fuzzy set theory for image processing and analysis. As an application example, the fuzzy method of edge detection is proposed to extract the edges of raised characters on tires.In general, Sobel, Prewitt, Robert and LoG filters are used to detect the edge, but it is difficult to detect the edge because of ambiguity of representations, noise and general problems in the interpretation of tire image. Therefore, in his paper, the fuzzy property plane has been extracted from the spatial domain using the ramp-mapping function. And then the ideas of fuzzy MIN and MAX are applied in removing noise and enhancement of the image simultaneously. From the result of MIN and MAX procedure a new fuzzy singleton is generated by extracting the difference between adjacent membership function values. And the edges are extracted by applying fuzzy $\alpha$-cut set to the fuzzy singletion, Finally, these ideas are applied to the tire images.

• The Transactions of the Korean Institute of Electrical Engineers C
• /
• v.49 no.11
• /
• pp.622-626
• /
• 2000

In this paper, an optical fiber sensor(OF sensor) utilizing the principal of Sagnac interferometer was proposed to detect the discharge signals. The needle-sphere electrode system in insulating oil generated the signals. The performance of OF sensor was checked by sinusoidal calibration signal generated by PZT actuator at 198KHz. The detected discharge signals consisted of acoustic signal and the electrical noise. The noise signal could be removed by digital low pass filter. It was demonstrated that the OF sensor in this research had a possibility to detect the discharge signals in power apparatus.

• Journal of Advanced Information Technology and Convergence
• /
• v.9 no.1
• /
• pp.127-134
• /
• 2019

In terms of deep Reinforcement Learning (RL), exploration can be worked stochastically in the action of a state space. On the other hands, exploitation can be done the proportion of well generalization behaviors. The balance of exploration and exploitation is extremely important for better results. The randomly selected action with ε-greedy for exploration has been regarded as a de facto method. There is an alternative method to add noise parameters into a neural network for richer exploration. However, it is not easy to predict or detect over-fitting with the stochastically exploration in the perturbed neural network. Moreover, the well-trained agents in RL do not necessarily prevent or detect over-fitting in the neural network. Therefore, we suggest a novel design of a deep RL by the balance of the exploration with drop-out to reduce over-fitting in the perturbed neural networks.

• Communications for Statistical Applications and Methods
• /
• v.19 no.2
• /
• pp.225-235
• /
• 2012

Noise reduction is an important issue in the field of image processing because image noise worsens the quality of the input image. The basic difficulty is that the noise and the signal are not easy to distinguish. Simple moothing is one of the most basic and important procedures to remove the noise, however, it does not consider the level of noise. This method effectively reduces the noise but the feature area is simultaneously blurred. This paper considers the block approach to detect noise and image features of the input image so that noise reduction could be adaptively applied. Simulation results show that the proposed algorithm improves the overall quality of the image by removing the noise according to the noise level.

• Journal of Biomedical Engineering Research
• /
• v.28 no.4
• /
• pp.471-476
• /
• 2007

Various active microwave imaging techniques have been developed for cancer detection for past several decades. Both the microwave tomography and the UWB radar techniques, constituting functional microwave imaging systems, use the electrical property contrast between normal tissues and malignancies to detect the latter in an early development stage. Even though promising simulation results have been reported, the understanding of the functional microwave imaging diagnostics has been relied heavily on the complicated numerical results. We present a computationally efficient and physically instructive analytical electromagnetic wave channel models developed for functional microwave imaging system in order to detect especially the breast tumors as early as possible. The channel model covers the propagation factors that have been examined in the previous 2-D models, such as the radial spreading, path loss, partial reflection and transmission of the backscattered electromagnetic waves from the tumor cell. The effects of the system noise and the noise from the inhomogeneity of the tissue to the reconstruction algorithm are modeled as well. The characteristics of the reconstructed images of the tumor using the proposed model are compared with those from the confocal microwave imaging.

• Proceedings of the KSME Conference
• /
• 2001.06a
• /
• pp.250-255
• /
• 2001

It is inevitable to evaluate the life of turbine rotor because the operating periods of power plants need to be extended. The magnetic methods utilizing Magnetic Barkhausen noise curve were applied to detect the degradation caused by thermal aging. The Magnetic property of material depends on the domain dynamics and it is affected by the microstructure of material. Therefore the magnetic property is very sensitive to the microstructure change of the material. It is, thus, very useful to detect the state of degradation of varying materials. The test specimen made of 1Cr-1Mo-0.25V steel was used widely for turbine rotor material, and seven kinds of specimens with different degradation levels were prepared by the isothermal heat treatment at $630^{\circ}C$. With the increase of degradation, BHN was decreased. The result was compared with coercive force and vickers hardness.

• Journal of Biomedical Engineering Research
• /
• v.10 no.1
• /
• pp.17-24
• /
• 1989

This papaer presents a new algorithm for the P-wave detection in the ECG signal. Digital differentiation method (7-point derivative) is used for detecting P-waves exactly. This algorithm can detect various parameters of PR, PP, RR interval, which are important to diagnosis AV blocks and WPW syndrome. Especially, this algorithm can detect P-waves very efficiently not only in well-preprocessed waves but in pccr waves with noise and artifact. And it enables to develope more reliable automatic diagnosis algorithm.

• Journal of Multimedia Information System
• /
• v.4 no.4
• /
• pp.163-170
• /
• 2017

Internet of things (IoT) is a new paradigm for collecting, processing and analyzing various contents in order to detect anomalies and to monitor particular patterns in a specific environment. The collected data can be used to discover new patterns and to offer new insights. IoT-enabled data mashup is a new technology to combine various types of information from multiple sources into a single web service. Mashup services create a new horizon for different applications. Environmental monitoring is a serious tool for the state and private organizations, which are located in regions with environmental hazards and seek to gain insights to detect hazards and locate them clearly. These organizations may utilize IoT - enabled data mashup service to merge different types of datasets from different IoT sensor networks in order to leverage their data analytics performance and the accuracy of the predictions. This paper presents an IoT - enabled data mashup service, where the multimedia data is collected from the various IoT platforms, then fed into an environmental cognition service which executes different image processing techniques such as noise removal, segmentation, and feature extraction, in order to detect interesting patterns in hazardous areas. The noise present in the captured images is eliminated with the help of a noise removal and background subtraction processes. Markov based approach was utilized to segment the possible regions of interest. The viable features within each region were extracted using a multiresolution wavelet transform, then fed into a discriminative classifier to extract various patterns. Experimental results have shown an accurate detection performance and adequate processing time for the proposed approach. We also provide a data mashup scenario for an IoT-enabled environmental hazard detection service and experimentation results.

• Journal of Biomedical Engineering Research
• /
• v.17 no.4
• /
• pp.449-458
• /
• 1996

Most of the conventional electrocardiowaphs foil to detect signals other than P-QRS-T due to the limited SNR and bandwidth. High-resolution electrocardiography(HRECG) provides better SNR and wider bandwidth for the detection of micro-potentials with higher frequency components such as vontricular late potentials(LP). We have developed a HRECG using uncorrected XYZ lead for the detection of LPs. The overall gain of the amplifier is 4000 and the bandwidth is 0.5-300Hz without using 60Hz notch filter. Three 16-bit A/D converters sample X, Y, and Z signals simultaneously with a sampling frequency of 2000Hz. Sampled data are transmitted to a PC via a DMA-controlled, optically-coupled serial communication channel. In order to further reduce the noise, we implemented a signal averaging algorithm that averaged many instances of aligned beats. The beat alignment was carried out through the use of a template matching technique that finds a location maximizing cross-correlation with a given beat tem- plate. Beat alignment error was reduced to $\pm$0.25ms. FIR high-pass filter with cut-off frequency of 40Hz was applied to remove the low frequency components of the averaged X, Y, and Z signals. QRS onset and end point were determined from the vector magnitude of the sigrlaIL and some parameters needed to detect the existence of LP were estimated. The entire system was designed for the easy application of the future research topics including the optimal lead system, filter design, new parameter extraction, etc. In the developed HRECG, without signal averaging, the noise level was less than 5$\mu$V$_rms RTI$. With signal averaging of at least 100 beats, the noise level was reduced to 0.5$\mu$V$_rms RTI$, which is low enough to detect LPs. The developed HRECG will provide a new advanced functionality to interpretive ECG analyzers.

• The Journal of the Korea Contents Association
• /
• v.6 no.4
• /
• pp.11-18
• /
• 2006

A new algorithm for removing salt and pepper impulse noise in image using impulse noise detector and total variation optimization is presented. The proposed two types of noise detectors which are based on the adaptive median filter, can detect impulse noise with high accuracy while reducing the probability of detecting image details as impulses. And the detectors maintain its performance independent of noise density. For removing impulses, total variation optimization is applied only to those detected noise candidate to reduces unnecessary computation. The proposed approach successfully remove impulse noise while preserving image details.