• Geophysics and Geophysical Exploration
• /
• v.16 no.3
• /
• pp.139-144
• /
• 2013

A computer program has been developed to estimate time-domain electromagnetic (EM) responses for a onedimensional model with multiple source and receiver dipoles that are finite in length. The time-domain solution can be obtained by applying an inverse fast Fourier transform (FFT) to frequency-domain fields for efficiency. Frequency-domain responses are first obtained for 10 logarithmically equidistant frequencies per decade, and then cubic spline interpolated to get the FFT input. In the case of phases, the phase curve must be made to be continuous prior to the spline interpolation. The spline interpolated data are convolved with a source current waveform prior to FFT. In this paper, only a step-off waveform is considered. This time-domain code is verified with an analytic solution and EM responses for a marine hydrocarbon reservoir model. Through these comparisons, we can confirm that the accuracy of the developed program is fairly high.

• Journal of IKEEE
• /
• v.22 no.1
• /
• pp.156-161
• /
• 2018

Recently, as the population rapidly increases, food shortages and waste are emerging serious problem. In order to solve this problem, various countries and enterprises are trying research and product development such as a study of consumers' purchasing patterns of food and a development of smart refrigerator using IoT technology. However, the smart refrigerators which currently sold have high price issue and another waste due to malfunction and breakage by complicated configurations. In this paper, we proposed a low-cost smart refrigerator system based on IoT for solving the problem and efficient management of ingredients. The system recognizes and registers ingredients through QR code, image recognition, and speech recognition, and can provide various services of the smart refrigerator. In order to improve an accuracy of image recognition, we used a model using a deep learning algorithm and proved that it is possible to register ingredients accurately.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.42 no.3 s.303
• /
• pp.31-40
• /
• 2005

In this paper, we develop an approach toward JSC(Joint Source-Channel Coding) method for MPEG-4 based FGS(Fine Granular Scalability) video coding and transmission in fixed and mobile receiving environment(Digital Audio Broadcasting, DAB). The source coder used MPEG-4 FGS video codec, the channel coder used RCPC(Rate Compatible Punctured Convolution) code and the modulation method used QPSK modulation. We have considered channel environment of AWGN and mobile receiving environment. This study determined optimum Trade-off point between source bit rate and channel coding rate in variable channel states. We compared FGS-JSC method and general single layer CBR(Constant Bit Rate) transmission. In this results, FGS-JSC was appeared better performance than CBR transmission.

• IEIE Transactions on Smart Processing and Computing
• /
• v.6 no.1
• /
• pp.10-17
• /
• 2017

We propose classification algorithms for human and dog movement. The proposed algorithms use micro-Doppler signals obtained from humans and dogs moving in four different directions. A two-stage classifier based on a support vector machine (SVM) is proposed, which uses a radial-based function (RBF) kernel and $16^{th}$-order linear predictive code (LPC) coefficients as feature vectors. With the proposed algorithms, we obtain the best classification results when a first-level SVM classifies the type of movement, and then, a second-level SVM classifies the moving object. We obtain the correct classification probability 95.54% of the time, on average. Next, to deal with the difficult classification problem of human and dog running, we propose a two-layer convolutional neural network (CNN). The proposed CNN is composed of six ($6{\times}6$) convolution filters at the first and second layers, with ($5{\times}5$) max pooling for the first layer and ($2{\times}2$) max pooling for the second layer. The proposed CNN-based classifier adopts an auto regressive spectrogram as the feature image obtained from the $16^{th}$-order LPC vectors for a specific time duration. The proposed CNN exhibits 100% classification accuracy and outperforms the SVM-based classifier. These results show that the proposed classifiers can be used for human and dog classification systems and also for classification problems using data obtained from an ultra-wideband (UWB) sensor.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.6
• /
• pp.1-8
• /
• 2021

In this paper, we propose reinforced VGG style network structure for low performance embedded system to classify low resolution infrared image. The combination of reinforced VGG style network structure and global average pooling makes lower computational complexity and higher accuracy. The proposed method classify the synthesize image which have 9 class 3,723,328ea images made from OKTAL-SE tool. The reinforced VGG style network structure composed of 4 filters on input and 16 filters on output from max pooling layer shows about 34% lower computational complexity and about 2.4% higher accuracy then the first parameter minimized network structure made for embedded system composed of 8 filters on input and 8 filters on output from max pooling layer. Finally we get 96.1% accuracy model. Additionally we confirmed the about 31% lower inference lead time in ported C code.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.21 no.5
• /
• pp.46-52
• /
• 2022

Although pipe performs various functions in industrial sites and residential spaces, if it is damaged due to corrosion caused by the external environment, it may cause equipment failure or a major accident. For this reason, various studies for safety management are being conducted, but studies on detecting corrosion or cracks on the pipe surface using a laser displacement sensor have hardly been conducted. Therefore, in this study, the corrosion degree of the pipe surface was compared and classified into 4 corrosion conditions, and inspection equipment using a laser scanner was manufactured. The corrosion height was calculated from the four surface data obtained from the measuring equipment and applied to various CNN algorithms, and 91% accuracy was obtained during training using the Modified VGGNet16 code with reduced number of parameters.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.38 no.6
• /
• pp.859-865
• /
• 2018

A construction of infrastructures and base station on the moon could be undertaken by linking with the regions where construction materials and energy could be supplied on site. It is necessary to detect craters on the lunar surface and gather their topological information in advance, which forms permanent shaded regions (PSR) in which rich ice deposits might be available. In this study, an effective method for automatic detection of lunar craters on the moon surface is taken into consideration by employing a latest version of deep-learning algorithm. A training of a deep-learning algorithm is performed by involving the still images of 90000 taken from the LRO orbiter on operation by NASA and the label data involving position and size of partly craters shown in each image. the Faster RCNN algorithm, which is a latest version of deep-learning algorithms, is applied for a deep-learning training. The trained deep-learning code was used for automatic detection of craters which had not been trained. As results, it is shown that a lot of erroneous information for crater's positions and sizes labelled by NASA has been automatically revised and many other craters not labelled has been detected. Therefore, it could be possible to automatically produce regional maps of crater density and topological information on the moon which could be changed through time and should be highly valuable in engineering consideration for lunar construction.