• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.5
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• pp.525-534
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• 2016

In South Korea, paddy rice has been consumed over the entire region and it is the main source of income for farmers, thus mathematical model for the estimation of rice yield is required for such decision-making processes in agriculture. The objectives of our study are to: (1) develop rice yield estimation model using Convolutional Neural Networks(CNN), (2) choose hyper-parameters for the model which show the best performance and (3) investigate whether CNN model can effectively predict the rice yield by the comparison with the model using Artificial Neural Networks(ANN). Weather and MODIS(The MOderate Resolution Imaging Spectroradiometer) products from April to September in year 2000~2013 were used for the rice yield estimation models and cross-validation was implemented for the accuracy assessment. The CNN and ANN models showed Root Mean Square Error(RMSE) of 36.10kg/10a, 48.61kg/10a based on rice points, respectively and 31.30kg/10a, 39.31kg/10a based on 'Si-Gun-Gu' districts, respectively. The CNN models outperformed ANN models and its possibility of application for the field of rice yield estimation in South Korea was proved.

• Korean Journal of Construction Engineering and Management
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• v.15 no.4
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• pp.68-76
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• 2014

The accurate estimation of construction cost is important to a successful development in construction projects. In previous studies, the construction cost are estimated by statistical methods. Among the statistical methods, support vector regression (SVR) has attracted a lot of attentions because of the generalization ability in the field of cost estimation. However, despite the simplicity of the parameter to be adjusted, it is not easy to find optimal parameters. Therefore, to build an effective SVR model, SVR's parameters must be set properly without additional data handling loads. So this study proposes a novel approach, known as genetic algorithm (GA), which searches SVR's optimal parameters, then adopt the parameters to the SVR model for estimating cost in the early stage of apartment housing projects. The aim of this study is to propose a GA-SVR model and examine the feasibility in cost estimation by comparing with multiple regression analysis (MRA). The experimental results demonstrate the estimating performance based on the percentage of estimations within 25% and find it can effectively do the accurate estimation without through the trial and error process.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1C
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• pp.113-122
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• 2010

In this paper, we propose a CP (Cyclic Prefix) detection based SSS (Secondary Synchronization Signal) detection method for initial cell search in 3GPP LTE (3rd Generation Partnership Project Long Term Evolution) FDD/TDD (Frequency Division Duplex/Time Division Duplex) dual mode downlink receiver. In general, a blind coherent SSS detection method which can detect SSS without CP detection is applied. However, coherent detection method caused performance degradation by channel compensation error at high speed environment because it uses estimated CFR (Channel Frequency Response) at PSS (Primary Synchronization Signal), and it can be more serious problem in TDD mode due to increased distance between PSS and SSS. Also blind detectionhas the drawback of high computational complexity. Therefore, we proposed a CP type pre-decision structure with non-coherent SSS detection which has stable operation in high speed channel environments for 3GPP LTE TDD mode as well as FDD mode, and can reduce computational complexity by applying CP detection before SSS detection. Simulation results show that the proposed method has stable operation for 3GPP LTE TDD/FDD dual mode downlink receiver in various channel environments.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.479-487
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• 2004

Biopotential signals have been used as command in systems using electrical stimulation of motor nerves to restore movement after an injury to the central nervous system (CNS). In order to use the voluntary EMG (electromyography) among the biopotentials as a control signal for the electrical stimulation of the same muscle for CNS injury patients, it is necessary to remove M-wave of having high magnitude from raw data. We designed an optimal filter for removing the M-wave and preserving the voluntary EMG and showed that the optimal filter is eigen filter. We also proved that the previous method using the prediction error filter(PEF) is a suboptimal filtering in the sense of preserving the voluntary EMG. On basis of the data obtained from a model for M-wave and voluntary EMG and from actual CNS injury patients, with false-positive rate analysis, the proposed adaptive filter showed a very promising performance in comparison with previous method.

• Journal of Biomedical Engineering Research
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• v.19 no.5
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• pp.469-476
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• 1998

In the case of acquisition and displaying medical Images such as ultrasonography and endoscopy on VGA monitor of PC system, image degradation of tear-drop appears through scan conversion. In this study, we compare several methods which can solve this degradation and implement the hardware system that resolves this problem in real-time with PC. It is possible to represent high quality image display and real-time processing and acquisition with specific de-interlacing device and PCI bridge on our hardware system. Image quality is improved remarkably on our hardware system. It is implemented as PC-based system, so acquiring, saving images and describing text comment on those images and PACS networking can be easily implemented.metabolism. All images were spatially normalized to MNI standard PET template and smoothed with 16mm FWHM Gaussian kernel using SPM96. Mean count in cerebral region was normalized. The VOls for 34 cerebral regions were previously defined on the standard template and 17 different counts of mirrored regions to hemispheric midline were extracted from spatially normalized images. A three-layer feed-forward error back-propagation neural network classifier with 7 input nodes and 3 output nodes was used. The network was trained to interpret metabolic patterns and produce identical diagnoses with those of expert viewers. The performance of the neural network was optimized by testing with 5~40 nodes in hidden layer. Randomly selected 40 images from each group were used to train the network and the remainders were used to test the learned network. The optimized neural network gave a maximum agreement rate of 80.3% with expert viewers. It used 20 hidden nodes and was trained for 1508 epochs. Also, neural network gave agreement rates of 75~80% with 10 or 30 nodes in hidden layer. We conclude that artificial neural network performed as well as human experts and could be potentially useful as clinical decision support tool for the localization of epileptogenic zones.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.53-59
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• 2020

Initial response is important in marine oil spills, such as the Hebei Spirit oil spill, but it is very difficult to predict the movement of oil out of the ocean, where there are many variables. In order to solve this problem, the forecasting of oil spill has been carried out by expanding the particle prediction, which is an existing study that studies the movement of floats on the sea using the data of the float. In the ocean data format HDF5, the current and wind velocity data at a specific location were extracted using bilinear interpolation, and then the movement of numerous points was predicted by particles and the results were visualized using polygons and heat maps. In addition, we propose a spill oil particle matching algorithm to compensate for the lack of data and the difference between the spilled oil and movement. The spilled oil particle matching algorithm is an algorithm that tracks the movement of particles by granulating the appearance of surface oil spilled oil. The problem was segmented using principal component analysis and matched using genetic algorithm to the point where the variance of travel distance of effluent oil is minimized. As a result of verifying the effluent oil visualization data, it was confirmed that the particle matching algorithm using principal component analysis and genetic algorithm showed the best performance, and the mean data error was 3.2%.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.204-208
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• 2018

In this paper, we proposed and evaluated the time series deep learning prediction model for learning fluctuation pattern of stock price. Recurrent neural networks, which can store previous information in the hidden layer, are suitable for the stock price prediction model, which is time series data. In order to maintain the long - term dependency by solving the gradient vanish problem in the recurrent neural network, we use LSTM with small memory inside the recurrent neural network. Furthermore, we proposed the stock price prediction model using bidirectional LSTM recurrent neural network in which the hidden layer is added in the reverse direction of the data flow for solving the limitation of the tendency of learning only based on the immediately preceding pattern of the recurrent neural network. In this experiment, we used the Tensorflow to learn the proposed stock price prediction model with stock price and trading volume input. In order to evaluate the performance of the stock price prediction, the mean square root error between the real stock price and the predicted stock price was obtained. As a result, the stock price prediction model using bidirectional LSTM recurrent neural network has improved prediction accuracy compared with unidirectional LSTM recurrent neural network.

• Journal of Navigation and Port Research
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• v.41 no.6
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• pp.373-380
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• 2017

Operational errors caused by human factors, which is the major cause of marine accidents, include lack of knowledge, misunderstanding knowledge, and inadequate procedures. Recently, the type of propulsion mounted on KCG cutters has been diversified. In particular, the water jet propulsion unit, which was mainly installed in small boats, have been gradually expanded to medium and large size Coast Guard cutters, reaching 50% of the total. Axes types are divided into 2 to 4, and the bucket types are divided into Double Reverse Bucket(DRB) and Single Reverse Bucket(SRB); in these, the backward and steering control methods are completely different. Diversification of these operating systems can increase factors causing human error by the ships' operators. However, there is a lack of research on the maneuvering methods, considering the inherent active characteristics of each type of water jet. In this paper, we analyze the sideway method suitable for the condition of Coast Guard Exclusive wharf without assistance, based on the astern performance of each type. Then, a ship handling simulator was used for the experiment; they compared and verified through interviews of captains.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.12
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• pp.1310-1317
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• 2012

The Global Navigation Satellite System (GNSS) like US Global Positioning System (GPS) and EU Galileo are based on providing precise time and frequency synchronized ranging signals. Because of the exploitation of very precise timing signals these GNSS are used to provide both navigation and time distribution services. Moreover, because the positioning accuracy will improve as more satellites become available, we should expect that a combination of Galileo and GPS will provide better performance than those of both systems separately. However, Galileo will not use the same time reference as GPS and thus, a time difference arises - the GPS-Galileo Time Offset (GGTO). The navigation solution calculated by receivers using signals from both navigation systems will consequently contain a supplementary error if the GGTO is not accounted for. In this paper, we compared GPS Time (GPST) with Galileo Sytem Time (GST) and analyzed the effects of GGTO on positioning accuracy by simulation test. And then we also analyzed the characteristics of two representative GGTO correction methods such as the navigation message based method at system level and the estimation method at user level and propose the conceptual design of the novel correction method being capable of preventing previous method's problems.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.504-506
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• 1993

The Joint Photographic Experts Group (JPEG) standard was proposed by the International Standardization Organization (ISO/SC 29/WG 10) and the CCITT SG VIII as an international standard for digital continuous-tone still image compression. The JPEG standard has been widely accepted in electronic imaging, computer graphics, and multi-media applications, however, due to the lossy character of the JPEG compression its application in the field of medical imaging has been limited. In this paper, the JPEG standard was applied to a series of head sections of magnetic resonance (MR) images (256 gray levels, $256{\times}256$ size) and its performance was investigated. For this purpose, DCT-based sequential mode of the JPEG standard was implemented using the CL550 compression chip and progressive and lossless coding was implemented by software without additional hardware. From the experiment, it appears that the compression ratio of about 10 to 20 was obtained for the MR images without noticeable distortion. It is also noted that the error signal between the reconstructed image by the JPEG and the original image was nearly random noise without causing any special-pattern-related artifact. Although the coding efficiency of the progressive and hierarchical coding is identical to that of the sequential coding in compression ratio and SNR, it has useful features In fast search of patient Image from huge image data base and in remote diagnosis through slow public communication channel.