• Journal of Positioning, Navigation, and Timing
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• v.7 no.4
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• pp.189-203
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• 2018

In this paper, a Software Defined Radio (SDR) architecture was designed and implemented for rapid prototyping of GNSS receiver. The proposed SDR can receive various GNSS and direct sequence spread spectrum (DSSS) signals without software modification by expanded input parameters containing information of the desired signal. Input parameters include code information, center frequency, message format, etc. To receive various signal by parameter controlling, a correlator, a data bit extractor and a receiver channel were designed considering the expanded input parameters. In navigation signal processing, pseudorange was measured based on Coordinated Universal Time (UTC) and appropriate navigation message decoder was selected by message format of input parameter so that receiver position can be calculated even if SDR is set up various GNSS combination. To validate the proposed SDR, the software was implemented using C++, CUDA C based on GPU and USRP. Experimentation has confirmed that changing the input parameters allows GPS, GLONASS, and BDS satellite signals to be received. The precision of the position from implemented SDR were measured below 5 m (Circular Error Probability; CEP) for all scenarios. This means that the implemented SDR operated normally. The implemented SDR will be used in a variety of fields by allowing prototyping of various GNSS signal only by changing input parameters.

• Journal of Korea Trade
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• v.27 no.5
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• pp.113-136
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• 2023

Purpose - Global production chains and their impacts on economic growth have drawn extensive attention from researchers. Close relationships among global production chains, export and economic growth have been illuminated, as evidenced by the fast and stable economic growth of East Asian economies. These economies perform various roles within global production chains using offshoring, in which the impact of import on domestic gross output is as strong as that of export. The impact of import on economic growth would depend on whether imported inputs substitute or complement domestic inputs production, which is likely to vary according to individual countries' functions within global production chains. The economic growth of concerned countries would also be diverse. However, little attention has been paid to the impact brought by imports compared to its significance. Design/methodology - The principal methodology used in this paper is structural decomposition analysis (SDA), widely chosen to elucidate the impact of various factors on domestic gross output using input-output tables. This paper extracts trade data of six Asian economies from the World Input-Output Database (WIOD) 2016 release that covers 43 countries for the period 2000-2014. The extracted data is then categorised into 37 sectors. First, this paper calculates the Feenstra-Hanson Offshoring Index (OSI) of each country. It then applies SDA to measure the changes in each economy's gross output, export, import input coefficients, and domestic input coefficients. Finally, after taking the first difference from pooled time-series data, it estimates the correlations between imported input coefficients and OSI using the ordinary least square (OLS) method. Findings - The main findings of this paper can be summarised as follows. Firstly, all six countries have increasingly engaged in global production chains, as evidenced by the growing size of OSI. Secondly, there are negative correlations in five countries except Japan, with sectoral differences. Thirdly, changes in import input coefficients are not negative in all six countries, indicating that offshoring does not necessarily substitute for domestic inputs production but does complement it and, therefore, fosters their economic growth. This is observed in China, Indonesia, Korea and Taiwan. Offshoring has led to an increase in the use of imported inputs, which has, in turn, stimulated domestic inputs production in these countries. Originality/value - While existing studies focus on the role of export in evaluating the impact of participating global production chains, this paper explicitly examines the unexplored impact of import on domestic gross output by considering both the substitution and the complementary effect, using the WIOD. The findings of this paper suggest that Asian economies have achieved fast and stable economic growth not only through successful export management but also through effective import management within global production chains. This paper recommends that the Korean government and enterprises carefully choose offshoring strategies to minimise disruption to domestic production chains or foster them.

• Journal of the Korean Society of Marine Environment & Safety
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• v.5 no.1
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• pp.9-18
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• 1999

To predict the oil spill dispersion phenomena in the ocean, the oil spill response model, which can be used for strategic purpose on the oil spill site, based on Lagrangian particle-tracking method was formulated and applied to the neighboring area with Pusan port where the oil spill incident occurred when the tanker ship No.1 Youil struck on a small rock near the Namhyungjeto on September 21, 1995. The real-time tidal currents to be required as input data of the oil spill model were obtained by the two-dimensional hydrodynamic model and the tide prediction model. Evaluation of tidal currents using observation data was successful. For wind data, other input data of oil spill model, observed data on the spot were used. To verify the oil spill model, the oil spill modelling results were compared with the field data obtained from the spill site. Compared the modelling results with the observation data, there exist some discrepancies but the general pattern of modelling results was similar to that of field observation. The modelling results on 7 days after spill occurred showed that the 40% of spilled oil is in floating, 36% in evaporated, 23% at shore, and 1% in out of boundary, respectively. According to the evaluation of weighting curves of effective components to the dispersion of oil, the winds make a 37% of contribution to the dispersion of oil, turbulent diffusion 39.5%, and tidal currents 23.5%, respectively. Provided the more accurate wind data are supported, more favorable results might be obtained.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.395-400
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• 1999

At present, some evolutional system has been used to promote the efficiency of the DNC(Direct Numerical Control) Controller. However, these are many inconvenience to this operator because it lacks harmony in interaction between the computer and the NC(Numerical Control). Also, there are some controversial poults when data error occurs at the Data Input/output. According1y, this thesis explores a new Data Remote Control System. In this study, the NC Controller of the DNC network has to Bet full data by removing data error in this system. In this system, the main merits are easy manufacturing and the convenience of Data Input/output. That is, remote control of the NC machine tool is possible without mutual interaction between the computer and itself.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.6
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• pp.261-266
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• 2016

Chart suggestion method provided by various existing data visualization tools makes chart recommendations without considering the user intention. Data visualization is not properly carried out and thus, unclear in some tools because they do not follow the segmented quantitative data classification policy. This paper provides a guideline that clearly classifies the quantitative input data and that effectively suggests charts based on user intention. The guideline is two-fold; the analysis guideline examines the quantitative data and the suggestion guideline recommends charts based on the input data type and the user intention. Following this guideline, we excluded charts in disagreement with the user intention and confirmed that the time user spends in the chart selection process has decreased.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.4
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• pp.362-370
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• 2004

In this work, the collapse moment due to wall-thinning defects is estimated by using fuzzy neural networks. The developed fuzzy neural networks have been applied to the numerical data obtained from the finite element analysis. Principal component analysis is used to preprocess the input signals into the fuzzy neural network to reduce the sensitivity to the input change and the fuzzy neural networks are trained by using the data set prepared for training (training data) and verified by using another data set different (independent) from the training data. Also, two fuzzy neural networks are trained for two data sets divided into the two classes of extrados and intrados defects, which is because they have different characteristics. The relative 2-sigma errors of the estimated collapse moment are 3.07% for the training data and 4.12% for the test data. It is known from this result that the fuzzy neural networks are sufficiently accurate to be used in the wall-thinning monitoring of elbows.

• Smart Structures and Systems
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• v.29 no.4
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• pp.599-616
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• 2022

Bridge displacement contains vital information for bridge condition and performance. Due to the limits of direct displacement measurement methods, the indirect displacement reconstruction methods based on the strain or acceleration data are also developed in engineering applications. There are still some deficiencies of the displacement reconstruction methods based on strain or acceleration in practice. This paper proposed a novel method based on long short-term memory (LSTM) networks to reconstruct the bridge dynamic displacements with the strain and acceleration data source. The LSTM networks with three hidden layers are utilized to map the relationships between the measured responses and the bridge displacement. To achieve the data fusion, the input strain and acceleration data need to be preprocessed by normalization and then the corresponding dynamic displacement responses can be reconstructed by the LSTM networks. In the numerical simulation, the errors of the displacement reconstruction are below 9% for different load cases, and the proposed method is robust when the input strain and acceleration data contains additive noise. The hyper-parameter effect is analyzed and the displacement reconstruction accuracies of different machine learning methods are compared. For experimental verification, the errors are below 6% for the simply supported beam and continuous beam cases. Both the numerical and experimental results indicate that the proposed data fusion method can accurately reconstruct the displacement.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.176-184
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• 2023

Points of interest (POIs) are widely used in tourism recommendations and to provide information about areas of interest. Currently, situation judgement using POI and GPS data is mainly rule-based. However, this approach has the limitation that inferences can only be made using predefined POI information. In this study, we propose an algorithm that uses POI data, GPS data, and schedule information to calculate the current speed, location, schedule matching, movement trajectory, and POI coverage, and uses machine learning to determine whether to stay or go. Based on the input data, the clustered information is labelled by k-means algorithm as unsupervised learning. This result is trained as the input vector of the SVM model to calculate the probability of moving and staying. Therefore, in this study, we implemented an algorithm that can adjust the schedule using the travel schedule, POI data, and GPS information. The results show that the algorithm does not rely on predefined information, but can make judgements using GPS data and POI data in real time, which is more flexible and reliable than traditional rule-based approaches. Therefore, this study can optimize tourism scheduling. Therefore, the stay detection algorithm using GPS movement trajectories and POIs developed in this study provides important information for tourism schedule planning and is expected to provide much value for tourism services.

• Journal of Information Technology Applications and Management
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• v.19 no.1
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• pp.1-12
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• 2012

Data mining is the process of searching and analyzing large quantities of data for finding out meaningful patterns and rules. Artificial Neural Network (ANN) is one of the tools of data mining which is becoming very popular in forecasting the future values. Some of the areas where it is used are banking, medicine, retailing and fraud detection. In finance, artificial neural network is used in various disciplines including stock market forecasting. In the stock market time series, due to high volatility, it is very important to choose a model which reads volatility and forecasts the future values considering volatility as one of the major attributes for forecasting. In this paper, an attempt is made to develop two models - one using feed forward back propagation Artificial Neural Network and the other using Autoregressive Conditional Heteroskedasticity (ARCH) technique for forecasting stock market returns. Various parameters which are considered for the design of optimal ANN model development are input and output data normalization, transfer function and neuron/s at input, hidden and output layers, number of hidden layers, values with respect to momentum, learning rate and error tolerance. Simulations have been done using prices of daily close of Sensex. Stock market returns are chosen as input data and output is the forecasted return. Simulations of the Model have been done using MATLAB$^{(R)}$ 6.1.0.450 and EViews 4.1. Convergence and performance of models have been evaluated on the basis of the simulation results. Performance evaluation is done on the basis of the errors calculated between the actual and predicted values.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.135-147
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• 2001

This study proposes bankruptcy prediction model using fuzzy neural networks. Neural networks offer preeminent learning ability but they are often confronted with the inconsistent and unpredictable performance for noisy financial data. The existence of continuous data and large amounts of records may pose a challenging task to explicit concepts extraction from the raw data due to the huge data space determined by continuous input variables. The attempt to solve this problem is to transform each input variable in a way which may make it easier fur neural network to develop a predictive relationship. One of the methods selected for this is to map each continuous input variable to a series of overlapping fuzzy sets. Appropriately transforming each of the inputs into overlapping fuzzy membership sets provides an isomorphic mapping of the data to properly constructed membership values, and as such, no information is lost. In addition, it is easier far neural network to identify and model high-order interactions when the data is transformed in this way. Experimental results show that fuzzy neural network outperforms conventional neural network for the prediction of corporate bankruptcy.