• Journal of Software Engineering Society
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• v.26 no.4
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• pp.93-103
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• 2013

Complexity and maintenance cost of software increase as much as software has been evolved, therefore importance of software maintenance recently arise. There are many signs that are difficulties to maintain software, called bad smell, in a large-scale software. The bad smell should be removed to improve maintainability. Recently, many software refactoring methods have researched to terminate the bad smell. In this paper, we propose how to identify long parameter list, which causes bad smell, and how to solve the problem for increasing software maintainability. In our approach, we classify the parameters for creating new objects by measuring semantic similarity among them. This is evaluated by experienced software developers, and the result is statistically verified.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.132-139
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• 2019

We address the problem about forecasting the direction of stock price movement in the Korea market. Recently, the deep neural network is popularly applied in this area of research. In deep neural network systems, proper parameter initialization reduces training time and improves the performance of the model. Therefore, in our study, we propose a novel parameter initialization technique and apply this technique for the stock price movement prediction model. Specifically, we design a framework which consists of two models: a base model and a main prediction model. The base model constructed with LSTM is trained by using the large data which is generated by a large amount of the stock data to achieve optimal parameters. The main prediction model with the same architecture as the base model uses the optimal parameter initialization. Thus, the main prediction model is trained by only using the data of the given stock. Moreover, the stock price movements can be affected by other related information in the stock market. For this reason, we conducted our research with two types of inputs. The first type is the stock features, and the second type is a combination of the stock features and the Korea Composite Stock Price Index (KOSPI) features. Empirical results conducted on the top five stocks in the KOSPI list in terms of market capitalization indicate that our approaches achieve better predictive accuracy and F1-score comparing to other baseline models.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.255-257
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• 2012

긴 파라미터 리스트(Long parameter list)는 소프트웨어 코드의 품질을 저해시키는 Bad Smell들 중 하나로써, 코드에 대한 이해도를 떨어뜨리고 코드의 변경을 어렵게 한다는 문제점이 있다. 이러한 문제를 해결하는 기법들 중 하나인 IPO(Introduce Parameter Object) 기법은 함께 사용되는 빈도가 높은 파라미터들을 하나의 클래스로 만든 후에 관련된 기능들을 해당 클래스의 메소드로 추출하여 사용하므로 코드의 중복을 막아 재사용성을 높이고 코드에 대한 이해도를 높일 수 있다. 하지만 IPO 기법의 주된 관심사는 파라미터 그룹의 발생 빈도에 초점을 두고 있으며, 커플링에 대한 고려가 충분히 이루어지지 않는다. 따라서 본 연구에서는 IPO 기법과 커플링의 관계를 분석하여 IPO 기법이 커플링까지 고려할 수 있도록 확장된 IPO기법을 제안한다. 제안된 기법은 기존의 기법에 비해 더 낮은 커플링을 달성하여 고품질의 코드를 얻을 수 있다.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.52.3-52.3
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• 2019

We investigate a relative contribution from short to long-term flaring rate to predicting M and X-class flare probabilities. In this study, we consider magnetic parameters summarizing distribution and non-potentiality by Solar Dynamics Observatory/Helioseimic and Magnetic Imager and flare list by Geostationary Operational Environmental Satellites. A short-term rate is the number of major flares that occurred in an given active region (AR) within one day before the prediction time. A mid-term rate is a mean flaring rate from the AR appearance day to one day before the prediction time. A long-term rate is a rate determined from a relationship between magnetic parameter values of ARs and their flaring rates from 2010 May to 2015 April. In our model, the predicted rate is given by the combination of weighted three rates satisfying that their sum of the weights is 1. We calculate Brier skill scores (BSSs) for investigating weights of three terms giving the best prediction performance using ARs from 2015 April to 2018 April. The BSS (0.22) of the model with only long-term is higher than that with only short-term or mid-term. When short or mid-term are considered additionally, the BSSs are improved. Our model has the best performance (BSS = 0.29) when all three terms are considered, and their relative contribution from short to long-term rate are 19%, 23%, and 58%, respectively. This model seems to be more effective when predicting active solar ARs having several major flares.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.2
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• pp.278-286
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• 2019

Conventional methods for selecting jamming techniques in electronic warfare are based on libraries in which a list of jamming techniques for radar signals is recorded. However, the choice of jamming techniques by the library is limited when modified signals are received. In this paper, we propose a method to predict the jamming technique for radar signals by using deep learning methods. Long short-term memory(LSTM) is a deep running method which is effective for learning the time dependent relationship in sequential data. In order to determine the optimal LSTM model structure for jamming technique prediction, we test the learning parameter values that should be selected, such as the number of LSTM layers, the number of fully-connected layers, optimization methods, the size of the mini batch, and dropout ratio. Experimental results demonstrate the competent performance of the LSTM model in predicting the jamming technique for radar signals.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.10
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• pp.869-877
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• 2013

Tension force of hanger ropes has been recognized and utilized as an important parameter for health monitoring of suspension bridges. Conventional vibration method based on string theory has been utilized to estimate tension forces of relatively long hanger ropes without any problem, however it is convinced that the vibration method is not applicable for shorter hanger ropes in which the influence of flexural stiffness is not ignorable. Therefore, as an alternative of vibration method, a number of feasibility studies of system identification(SI) technique considering flexural stiffness of the hanger ropes are recently performed. In this study, the influence of support condition of the finite element model utilized for the SI method is investigated with numerical examples. The numerical examples are prepared with the specification of the Kwang-Ahn bridge hanger ropes, and it is revealed that the estimation result of the tension force can be varied from -21.6 % to +35.3 % of the exact value according to the consideration of the support condition of FE model. Therefore, it is concluded that the rotational stiffness of the support spring should be included to the list of the identification parameters of the FE model to improve the result of tension estimation.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.97.1-97.1
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• 2011

In this study we have examined the probability of solar proton events (SPEs) and their peak fluxes depending on flare (flux, longitude and impulsive time) and CME parameters (linear speed, longitude, and angular width). For this we used the NOAA SPE list and their associated flare data from 1976 to 2006 and CME data from 1997 to 2006. We find that about 3.5% (1.9% for M-class and 21.3% for X-class) of the flares are associated with SPEs. It is also found that this fraction strongly depends on longitude; for example, the fraction for $30W^{\circ}$ < L < $90W^{\circ}$ is about three times larger than that for $30^{\circ}E$ < L < $90^{\circ}E$. The SPE probability with long duration (${\geq}$ 0.3 hours) is about 2 (X-class flare) to 7 (M-class flare) times larger than that for flares with short duration (< 0.3 hours). In case of halo CMEs with V ${\geq}$ 1500km/s, 36.1% are associated with SPEs but in case of partial halo CME ($120^{\circ}$ ${\leq}$ AW < $360^{\circ}$) with 400 km/s ${\leq}$ V < 1000 km/s, only 0.9% are associated with SPEs. The relationships between X-ray flare peak flux and SPE peak flux are strongly dependent on longitude and impulsive time. The relationships between CME speed and SPE peak flux depend on longitude as well as direction parameter. From this study, we suggest a new SPE forecast method with three-steps: (1) SPE occurrence probability prediction according to the probability tables depending on flare and CME parameters, (2) SPE flux prediction from the relationship between SPE flux and flare (or CME) parameters, and (3) SPE peak time.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.236-242
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• 2008

In this paper, we propose an optimized fuzzy controller based on Hierarchical Fair Competition-based Genetic Algorithms (HFCGA) for rotary inverted pendulum system. We adopt fuzzy controller to control the rotary inverted pendulum and the fuzzy rules of the fuzzy controller are designed based on the design methodology of Linear Quadratic Regulator (LQR) controller. Simple Genetic Algorithms (SGAs) is well known as optimization algorithms supporting search of a global character. There is a long list of successful usages of GAs reported in different application domains. It should be stressed, however, that GAs could still get trapped in a sub-optimal regions of the search space due to premature convergence. Accordingly the parallel genetic algorithm was developed to eliminate an effect of premature convergence. In particular, as one of diverse types of the PGA, HFCGA has emerged as an effective optimization mechanism for dealing with very large search space. We use HFCGA to optimize the parameter of the fuzzy controller. A comparative analysis between the simulation and the practical experiment demonstrates that the proposed HFCGA based fuzzy controller leads to superb performance in comparison with the conventional LQR controller as well as SGAs based fuzzy controller.

• Journal of Gastric Cancer
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• v.13 no.3
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• pp.164-171
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• 2013

Purpose: Laparoscopy-assisted distal gastrectomy for early gastric cancer has gained acceptance and popularity worldwide. However, laparoscopy-assisted distal gastrectomy for advanced gastric cancer is still controversial. Therefore, we propose this prospective randomized controlled multi-center trial in order to evaluate the safety and feasibility of laparoscopy assisted D2-gastrectomy for advanced stage gastric cancer. Materials and Methods: Patients undergoing distal gastrectomy for advanced gastric cancer staged cT2/3/4 cN0/1/2/3a cM0 by endoscopy and computed tomography are eligible for enrollment after giving their informed consent. Patients will be randomized either to laparoscopyassisted distal gastrectomy or open distal gastrectomy. Sample size calculation revealed that 102 patients are to be included per treatment arm. The primary endpoint is the non-compliance rate of D2 dissection; relevant secondary endpoints are three-year disease free survival, surgical and postoperative complications, hospital stay and unanimity rate of D2 dissection evaluated by reviewing the intraoperative video documentation. Discussion: Oncologic safety is the major concern regarding laparoscopy-assisted distal gastrectomy for advanced gastric cancer. Therefore, the non-compliance rate of clearing the N2 area was chosen as the most important parameter for the technical feasibility of the laparoscopic procedure. Furthermore, surgical quality will be carefully reviewed, that is, three independent experts will review the video records and score with a check list. For a long-term result, disease free survival is considered a secondary endpoint for this trial. This study will offer promising evidence of the feasibility and safety of Laparoscopy-assisted distal gastrectomy for advanced gastric cancer. Trial Registration: NCT01088204 (international), NCCCTS-09-448 (Korea).