• Journal of the Korea Convergence Society
• /
• v.11 no.11
• /
• pp.257-267
• /
• 2020

In this study, we predicted the employment ratio by the export industry using various machine learning methods and verified whether the prediction performance is improved by applying the constrained forecast combination method to these predicted values. In particular, the constrained forecast combination method is known to improve the prediction accuracy and stability by imposing the sum of predicted values' weights up to one. In addition, this study considered various variables affecting the employment ratio of each industry, and so we adopted recursive feature elimination method that allows efficient use of machine learning methods. As a result, the constrained forecast combination showed more accurate prediction performance than the predicted values of the machine learning methods, and in particular, the stability of the prediction performance of the constrained forecast combination was higher than that of other machine learning methods.

• The Journal of Information Systems
• /
• v.26 no.4
• /
• pp.17-37
• /
• 2017

Purpose Customer Loyalty is the most important factor of customer relationship management (CRM). Especially in retailing industry, where customers have many options of where to spend their money. Classifying loyal customers through customers' data can help retailing companies build more efficient marketing strategies and gain competitive advantages. This study aims to construct classification models of distinguishing the loyal customers within a Korean retailing company using data mining techniques with R language. Design/methodology/approach In order to classify retailing customers, we used combination of support vector machines (SVMs) and other classification algorithms of machine learning (ML) with the support of recursive feature elimination (RFE). In particular, we first clean the dataset to remove outlier and impute the missing value. Then we used a RFE framework for electing most significant predictors. Finally, we construct models with classification algorithms, tune the best parameters and compare the performances among them. Findings The results reveal that ML classification techniques can work well with CRM data in Korean retailing industry. Moreover, customer loyalty is impacted by not only unique factor such as net promoter score but also other purchase habits such as expensive goods preferring or multi-branch visiting and so on. We also prove that with retailing customer's dataset the model constructed by SVMs algorithm has given better performance than others. We expect that the models in this study can be used by other retailing companies to classify their customers, then they can focus on giving services to these potential vip group. We also hope that the results of this ML algorithm using R language could be useful to other researchers for selecting appropriate ML algorithms.

• Proceedings of the KIEE Conference
• /
• 2005.10b
• /
• pp.104-106
• /
• 2005

In this work, a PWR reactor core dynamics is identified online by a recursive least squares method. Based on this identified reactor model consisting of the control rod position and the core average coolant temperature, the future average coolant temperature is predicted. A model predictive control method is applied to design an automatic controller for thermal power control in PWRs. The basic concept of the model predictive control is to solve an optimization problem for a finite future at current time and to implement as the current control input only the first optimal control input among the solutions of the finite time steps. At the next time step, the procedure to solve the optimization problem is then repeated. The objectives of the proposed model predictive controller are to minimize both the difference between the predicted core coolant temperature and the desired one, and the variation of the control rod positions. Also, the objectives are subject to maximum and minimum control rod positions and maximum control rod speed. Therefore, the genetic algorithm that is appropriate to accomplish multiple objectives is used to optimize the model predictive controller. A 3-dimensional nuclear reactor analysis code, MASTER that was developed by Korea Atomic Energy Research Institute (KAERI), is used to verify the proposed controller for a nuclear reactor. From results of numerical simulation to check the performance of the proposed controller at the 5%/min ramp increase or decrease of a desired load and its 10% step increase or decrease which are design requirements, it was found that the nuclear power level controlled by the proposed controller could track the desired power level very well.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.5
• /
• pp.87-97
• /
• 2004

In this paper, we propose an algorithm for interlaced-to-progressive conversion by the weighted summation of the information collected from spatial do-interlacing method, in which the weighted edge based line average is applied, and the temporal method in which the motion compensation is employed by using hi-directional BMA (block matching algorithm). We employed time-recursive and motion adaptive processing as motion detection is involved. Also, a median filter is used to deal with limitation of the linear summation in which only an intermediate of values being involved is determined. The main goal of the approach is to overcome the shortcomings of each of the do-interlacing techniques without significant increment of the computational complexity, and the proposed method is apt to implement in hardware for real-time processing.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.27 no.8
• /
• pp.665-675
• /
• 2016

With the development of active phased array radar technology in recent years, active phased array antennas, which digitally combine signals received from subarray units using dozens of digital receiver, have been developed. The beam characteristics are greatly affected by the shape of the subarray structure as well as the weight of subarray in digital beamforming. So in this paper, the method to generate subarray structures by using recursive element exchanging method and the method to optimize subarray structures that can minimize sidelobes of operating beams are proposed. Additionally it presents the result to find the optimized subarray structure to minimize the maximum sidelobe of monopulse beam and pencil multi-beam respectively or simultaneously which are commonly used for digital beamforming by applying the algorithm propsed in this paper.

• Journal of Korean Society of Transportation
• /
• v.33 no.1
• /
• pp.61-69
• /
• 2015

This study compares the forecasting accuracy of five econometric models on domestic total freight volume in South Korea. Applied five models are as follows: Ordinary Least Square model, Partial Adjustment model, Reduced Autoregressive Distributed Lag model, Vector Autoregressive model, Time Varying Parameter model. Estimating models and forecasting are carried out based on annual data of domestic freight volume and an index of industrial production during 1970~2011. 1-year, 3-year, and 5-year ahead forecasting performance of five models was compared using the recursive forecasting method. Additionally, two forecasting periods were set to compare forecasting accuracy according to the size of future volatility. As a result, the Time Varying Parameter model showed the best accuracy for forecasting periods having fluctuations, whereas the Vector Autoregressive model showed better performance for forecasting periods with gradual changes.

• Journal of KIISE:Software and Applications
• /
• v.36 no.7
• /
• pp.590-606
• /
• 2009

We present a static analyzer that detects memory leaks in C programs. It achieves relatively high accuracy at a relatively low cost on SPEC2000 benchmarks and several open-source software packages, demonstrating its practicality and competitive edge against other reported analyzers: for a set of benchmarks totaling 1,777 KLOCs, it found 332 bugs with 47 additional false positives (a 12.4% false-positive ratio), and the average analysis speed was 720 LOC/sec. We separately analyze each procedure's memory behavior into a summary that is used in analyzing its call sites. Each procedural summary is parameterized by the procedure's call context so that it can be instantiated at different call sites. What information to capture in each procedural summary has been carefully tuned so that the summary should not lose any common memory-leak-related behaviors in real-world C program. Because each procedure is summarized by conventional fixpoint iteration over the abstract semantics ('a la abstract interpretation), the analyzer naturally handles arbitrary call cycles from direct or indirect recursive calls.

• The Journal of the Acoustical Society of Korea
• /
• v.15 no.2
• /
• pp.79-84
• /
• 1996

In this paper, adaptive correlator model using backpropagation neural network based on complex multilayer perceptron is presented for suppressing interference of narrow-band of direct sequence spread spectrum receiver in CDMA mobile communication systems. Recursive least square backpropagation algorithm with backpropagation error is used for fast convergence and better performance in adaptive correlator scheme. According to signal noise ratio and transmission power ratio, computer simulation results show that bit error ratio of adaptive correlator uswing backpropagation neural network improved than that of adaptive transversal filter of direct sequence spread spectrum considering of co-channel and narrow-band interference. Bit error ratio of adaptive correlator using backpropagation neural network is reduced about $10^{-1}$ than that of adaptive transversal filter where interference versus signal ratio is 5 dB.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.10
• /
• pp.1923-1928
• /
• 2017

This paper is a study on error correction for three-dimensional shape reconstruction based on factorization method. The existing error correction method based on factorization has a limitation of correction because it is optimized globally. Thus in this paper, we propose our new method which can find and correct the only major error influence factor toward three-dimensional reconstructed shape instead of global approach. We define the error-influenced factor in two-dimensional re-projection deviation space and directly control the error components. In addition, it is possible to improve the error correcting performance by recursively applying the above process. This approach has an advantage under noise because it controls the major error components without depending on any geometric information. The performance evaluation of the proposed algorithm is verified by simulation with synthetic and real image sequence to demonstrate noise robustness.

• The KIPS Transactions:PartA
• /
• v.13A no.5 s.102
• /
• pp.405-412
• /
• 2006

Real-time operating systems have been used in various computing environments, where a job must be completed in its deadline, with various conditions, such as effective scheduling policies, the minimum of an interrupt delay, and the solutions of priority inversion problems, that should be perfectly satisfied to design and develop optimal real-time operating systems. Up to now, in order to solve priority inversion problems among several those conditions. There have been two representative protocols: basic priority inheritance protocol and priority ceiling emulation protocol. However, these protocols cannot solve complicated priority inversion problems. In this paper, we design a protocol, called recursive priority inheritance (RPI), protocol that effectively solves the complicated priority inversion problems. Our proposed protocol is also implemented in the Linux kernel and is compared with other existing protocols in the aspect of qualitative analysis.