• Journal of Korean Tunnelling and Underground Space Association
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• v.5 no.2
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• pp.161-174
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• 2003

Applicability of the Decision Aids for Tunneling (DAT) technique is investigated in this study to better understand the efficiency of the decision making process during tunnel construction. For this, a traffic tunnel under construction is adopted and information on the construction procedure, i.e., overall geology, unit cost and construction time for each excavation process, is provided periodically. Various scattergrams in which cost-time simulation results are plotted are obtained according to the simulation methods and final prediction on the construction time/cost is made. It is found that the uncertainty in the cost distribution is greater than the uncertainty in the time distribution for each cycle simulation and the uncertainties in time and cost for the one time simulations are comparable. Future work will be concentrated on the updating scheme using the face mapping data and various parametric studies will also be performed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.1
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• pp.131-142
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• 1994

This paper analyzes the properties of such algorithm that corresponds to the nonlinear adaptive algorithm with additional update terns, parameterized by the scalar factors ${\alpha}1,\;and\;{\alpha}2$. The analysis of concergence leads to eigenvalues of the transition matrix for the mean filter coefficient vector. Regions in which the algorithm becomes stable are demonstrated. The time constant is derived and the computational complexity of the QFG algorithm is compared with those of the conventional LMS. sign, and LFG algorithm. The properties of convergence in the mean square error is derived and the neccessary condition for the CFG algorithm to be stable is attaned. In the computer simulation a channel equalization is utilized to demonstrate the performance feature of the QFG algorithm. The QFG algorithm has the more computational complexities but the faster convergence speed than LMS and LFG algorithm. Since the QFG algorithm has smoother convergence, it may be useful in case where error bursting is a problem.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.131-140
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• 2011

Proxy Mobile IPv6 (PMIPv6) is designed to provide a network-based localized mobility management protocol, but it does not handle the global mobility of hosts. In this paper, we propose a location management scheme based on Domain Name System (DNS) for PMIPv6. In this proposed scheme, DNS as a location manager provides PMIPv6 for global mobility. In addition, a paging extension scheme is introduced to PMIPv6 in order to support large numbers of mobile terminals and enhance network scalability. To evaluate the proposed location management scheme, we establish an analytical model, formulate the location update and the paging cost, and analyse the influence of the different factors on the total signalling cost. The performance results show how the total signal cost changes under various parameters.

• Journal of the Korean Geotechnical Society
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• v.27 no.11
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• pp.55-69
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• 2011

The importance of unsaturated state in various geo-engineering problems has led to the advance of mechanical constitutive model emulating behavior of unsaturated soils in response to thermo-hydro-mechanical loading. Elasto-plastic mechanical constitutive model for unsaturated soil is formulated based on Bishop's effective stress. Effective stress and temperature are main variables in constitutive equation, and incremental formulation of constitutive relationship is derived to compute stress update and stiffness tensor. Numerical simulations involving coupled THM processes are conducted to discuss numerical stability and applicability of developed constitutive model: one-dimensional test, tri-axial compression test, and clay-buffering at high level radioactive waste disposal. Numerical results demonstrated that developed model can predict very complex behavior of coupled THM phenomena and is applicable to geo-engineering problems under various environmental conditions, as well as interpret typical behavior of unsaturated soils.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.3
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• pp.190-197
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• 2004

This paper presents automated prediction of transient ischemic episodes using neural networks(NN) based pattern matching method. The learning algorithm used to train the multilayer networks is a modified backpropagation algorithm. The algorithm updates parameters of nonlinear function in a neuron as well as connecting weights between neurons to improve learning speed. The performance of the method was evaluated using ECG signals of the MIT/BIH long-term database. Experimental results for 15 records(237 ischemic episodes) show that the average sensitivity and specificity of ischemic episode prediction are 85.71% and 71.11%, respectively. It is also found that the proposed method predicts an average of 45.53［sec］ ahead real ischemia. These results indicate that the NN approach as the pattern matching classifier can be a useful tool for the prediction of transient ischemic episodes.

• Journal of Intelligence and Information Systems
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• v.11 no.2
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• pp.175-195
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• 2005

사례기반추론(case-based reasoning)은 사례간 유사도를 평가하여 유사한 이웃사례를 찾아내고, 이웃사례의 결과를 이용하여 새로운 사례에 대한 예측결과를 생성하는 전통적인 인공지능기법 중 하나다. 이러한 사례기반추론이 최근 적용이 쉽고 간단하다는 장점과 모형의 갱신이 실시간으로 이루어진다는 점 등으로 인해, 온라인 환경에서의 고객관계관리를 위한 도구로 학계와 실무에서 주목을 받고 있다 하지만, 전통적인 사례기반추론의 경우, 타 인공지능기법에 비해 정확도가 상대적으로 크게 떨어진다는 점이 종종 문제점으로 제기되어 왔다. 이에, 본 연구에서는 사례기반추론의 성과를 획기적으로 개선하기 위한 방법으로 유전자 알고리즘을 활용한 사례기반추론의 동시 최적화 모형을 제안하고자 한다. 본 연구가 제안하는 모형에서는 기존 연구에서 사례기반추론의 성과에 중대한 영향을 미치는 요소들로 제시된 바 있는 사례 특징변수의 상대적 가중치 선정(feature weighting)과 참조사례 선정(instance selection)을 유전자 알고리즘을 이용해 최적화함으로서, 사례간 유사도를 보다 정밀하게 도출하는 동시에 추론의 결과를 왜곡할 수 있는 오류사례의 영향을 최소화하고자 하였다. 제안모형의 유용성을 검증하기 위해, 본 연구에서는 국내 한 전문 인터넷 쇼핑몰의 구매예측모형 구축사례에 제안모형을 적용하여 그 성과를 살펴보았다. 그 결과, 제안모형이 지금까지 기존 연구에서 제안된 다른 사례기반추론 개선모형들은 물론, 로지스틱 회귀분석(LOGIT), 다중판별분석(MDA), 인공신경망(ANN), SVM 등 다른 인공지능 기법들에 비해서도 상대적으로 우수한 성과를 도출할 수 있음을 확인할 수 있었다.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.147-170
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• 2022

Although the number of patent which is one of the core outputs of technological innovation continues to increase, the number of low-value patents also hugely increased. Therefore, efficient evaluation of patents has become important. Estimation of patent lifespan which represents private value of a patent, has been studied for a long time, but in most cases it relied on a linear model. Even if machine learning methods were used, interpretation or explanation of the relationship between explanatory variables and patent lifespan was insufficient. In this study, patent lifespan (number of renewals) is predicted based on the idea that patent lifespan represents the value of the patent. For the research, 4,033,414 patents applied between 1996 and 2017 and finally granted were collected from USPTO (US Patent and Trademark Office). To predict the patent lifespan, we use variables that can reflect the characteristics of the patent, the patent owner's characteristics, and the inventor's characteristics. We build four different models (Ridge Regression, Random Forest, Feed Forward Neural Network, Gradient Boosting Models) and perform hyperparameter tuning through 5-fold Cross Validation. Then, the performance of the generated models are evaluated, and the relative importance of predictors is also presented. In addition, based on the Gradient Boosting Model which have excellent performance, Accumulated Local Effects Plot is presented to visualize the relationship between predictors and patent lifespan. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the evaluation reason of individual patents, and discuss applicability to the patent evaluation system. This study has academic significance in that it cumulatively contributes to the existing patent life estimation research and supplements the limitations of existing patent life estimation studies based on linearity. It is academically meaningful that this study contributes cumulatively to the existing studies which estimate patent lifespan, and that it supplements the limitations of linear models. Also, it is practically meaningful to suggest a method for deriving the evaluation basis for individual patent value and examine the applicability to patent evaluation systems.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.361-381
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• 2023

Research on corporate bankruptcy prediction has been focused on financial information. Since the company's financial information is updated quarterly, there is a problem that timeliness is insufficient in predicting the possibility of a company's business closure in real time. Evaluated companies that want to improve this need a method of judging the soundness of a company that uses information other than financial information to judge the soundness of a target company. To this end, as information technology has made it easier to collect non-financial information about companies, research has been conducted to apply additional variables and various methodologies other than financial information to predict corporate bankruptcy. It has become an important research task to determine whether it has an effect. In this study, we examined the impact of electronic payment-related information, which constitutes non-financial information, when predicting the closure of business operators using electronic payment service and examined the difference in closure prediction accuracy according to the combination of financial and non-financial information. Specifically, three research models consisting of a financial information model, a non-financial information model, and a combined model were designed, and the closure prediction accuracy was confirmed with six algorithms including the Multi Layer Perceptron (MLP) algorithm. The model combining financial and non-financial information showed the highest prediction accuracy, followed by the non-financial information model and the financial information model in order. As for the prediction accuracy of business closure by algorithm, XGBoost showed the highest prediction accuracy among the six algorithms. As a result of examining the relative importance of a total of 87 variables used to predict business closure, it was confirmed that more than 70% of the top 20 variables that had a significant impact on the prediction of business closure were non-financial information. Through this, it was confirmed that electronic payment-related information of non-financial information is an important variable in predicting business closure, and the possibility of using non-financial information as an alternative to financial information was also examined. Based on this study, the importance of collecting and utilizing non-financial information as information that can predict business closure is recognized, and a plan to utilize it for corporate decision-making is also proposed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.458-465
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• 2020

The purpose of this study is to analyze the effect of policy in Jeju, using a macro-economic forecasting model of Jeju. First, the model's reality explanatory power improved by updating its statistics to 2017 and expanding new policy variables and modules. Also, the industrial structure of the model was further subdivided and extended to be considered simultaneously in the demand side of Keynesian theory. Second, it was determined that the predictive power for the model of this study was better than that of the existing model. However, with some endogenous variables, it was possible to identify implications that should be developed and considered when the model is improved with additional data in the future. Third, when the second airport construction was considered, it was observed that its effect was an increase of 1.25 times for GRDP, 1.2 times for employment, 1.48 times for private consumption, and 2.06 times for investment. Also, the economic growth rate was estimated to be 1.6% point higher than when the second airport was not constructed. Finally, the results of this study are expected to be used for policy decision making of the Jeju Government.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.1
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• pp.37-47
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• 2008

Exploitation of address generation units which are typically provided in DSPs plays an important role in DSP code generation since that perform fast address computation in parallel to the central data path. Offset assignment is optimization of memory layout for program variables by taking advantage of the capabilities of address generation units, consists of memory layout generation and address pointer assignment steps. In this paper, we propose an effective address pointer assignment method to minimize the number of address calculation instructions in DSP code generation. The proposed approach reduces the time complexity of a conventional address pointer assignment algorithm with fixed memory layouts by using minimum cost-nodes breaking. In order to contract memory size and processing time, we employ a powerful pruning technique. Moreover our proposed approach improves the initial solution iteratively by changing the memory layout for each iteration because the memory layout affects the result of the address pointer assignment algorithm. We applied the proposed approach to about 3,000 sequences of the OffsetStone benchmarks to demonstrate the effectiveness of the our approach. Experimental results with benchmarks show an average improvement of 25.9% in the address codes over previous works.