• The Korean Journal of Financial Management
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• v.19 no.2
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• pp.211-231
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• 2002

본 연구의 목적은 VECM(Vector Error Correction Model)과 인공지능모형(Artificial Neural Networks)을 이용하여 우리나라 증권시장과 거시경제 변수들과의 장기적 관계에 대한 설명력을 비교해보고자 함에 있다. VECM이 APT(Arbitrage Pricing Theory)에 기초를 둔 선형동학모형이라고 한다면, 인공지능모형은 비모수적 비선형모형이라는 점에서, 두 방법론의 분석결과를 직접 비판하는 것은 의미있는 연구라고 할 수 있다. 인공지능모형을 주로 활용하는 선행연구들에 의하면, 증권시장은 시장의 특이패턴들로 인해 계량경제학적 접근인 선형 모형보다는 인공지능모형을 통해 증권시장의 움직임을 설명하고 예측하는 것이 더 바람직할 수도 있다는 것이다. 따라서, 본 연구에서는 VECM분석에서 자료의 안정성을 검증하고, 공적분 백터를 발견한 이후, 장기적 균형관계의 실증적 분석을 하였다. 그리고, 인공지능모형에서는 delta rule과 Sigmoid 함수를 이용한 GRNN(General Regression Neural Net)과 Back-Propagation등의 방법들을 활용하였다. 이러한 분석결과, Back-Propagation 모형이 다른 모든 모형들보다도 더 우수한 설명력을 보여주고 있었다. 이러한 결과들은 인공지능모형이 동태적인 선형 모형보다도 더 우수한 설명력을 제공할 수 있는 가능성을 보여주고 있었다.

• Neurospine
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• v.15 no.4
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• pp.329-337
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• 2018

Objective: Machine learning algorithms excel at leveraging big data to identify complex patterns that can be used to aid in clinical decision-making. The objective of this study is to demonstrate the performance of machine learning models in predicting postoperative complications following anterior cervical discectomy and fusion (ACDF). Methods: Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), and random forest decision tree (RF) models were trained on a multicenter data set of patients undergoing ACDF to predict surgical complications based on readily available patient data. Following training, these models were compared to the predictive capability of American Society of Anesthesiologists (ASA) physical status classification. Results: A total of 20,879 patients were identified as having undergone ACDF. Following exclusion criteria, patients were divided into 14,615 patients for training and 6,264 for testing data sets. ANN and LR consistently outperformed ASA physical status classification in predicting every complication (p < 0.05). The ANN outperformed LR in predicting venous thromboembolism, wound complication, and mortality (p < 0.05). The SVM and RF models were no better than random chance at predicting any of the postoperative complications (p < 0.05). Conclusion: ANN and LR algorithms outperform ASA physical status classification for predicting individual postoperative complications. Additionally, neural networks have greater sensitivity than LR when predicting mortality and wound complications. With the growing size of medical data, the training of machine learning on these large datasets promises to improve risk prognostication, with the ability of continuously learning making them excellent tools in complex clinical scenarios.

• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.940-945
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• 2009

Energy planning for hybrid energy system is important to increase the flexibility in the urban community and national energy systems. Expected maximum loads, load profiles and yearly energy demands are important input parameters to plan for the technical and environmental optimal energy system for a planning area. The method for energy demand prediction has been based on artificial neural networks(ANN). The advantage of ANN with respect to the other method is their ability of modeling a multivariable problem given by the complex relationships between the variables. This method can produce 10% of errors hourly load profile from individual building to urban community. As the results of this paper, energy demand prediction system has been developed based on simulink.

• Journal of Korea Game Society
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• v.16 no.2
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• pp.27-40
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• 2016

Predicting stealthy behaviors plays an important role in designing stealth games. It is, however, difficult to automate this task because human players interact with dynamic environments in real time. In this paper, we present a reinforcement learning (RL) method for simulating stealthy movements in dynamic environments, in which an integrated model of Q-learning with Artificial Neural Networks (ANN) is exploited as an action classifier. Experiment results show that our simulation agent responds sensitively to dynamic situations and thus is useful for game level designer to determine various parameters for game.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2277-2287
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• 2015

In recent times, multilevel inverters are given high priority in many large industrial drive applications. However, the reliability of multilevel inverters are mainly affected by the failure of power electronic switches. In this paper, open-switch and short-switch failure of multilevel inverters and its identification using a high performance diagnostic system is discussed. Experimental and simulation studies were carried out on five level cascaded H-Bridge multilevel inverter and its output voltage waveforms were analyzed at different switch fault cases and at different modulation index values. Salient frequency domain features of the output voltage signal were extracted using the discrete wavelet transform multi resolution signal decomposition technique. Real time application of the proposed fault diagnostic system was implemented through the LabVIEW software. Artificial neural network was trained offline using the Matlab software and the resultant network parameters were transferred to LabVIEW real time system. In the proposed system, it is possible to precisely identify the individual faulty switch (may be due to open-switch (or) short-switch failure) of multilevel inverters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2736-2754
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• 2015

Android dominates the mobile operating system market, which stimulates the rapid spread of mobile malware. It is quite challenging to detect mobile malware. System call sequence analysis is widely used to identify malware. However, the malware detection accuracy of existing approaches is not satisfactory since they do not consider correlation of system calls in the sequence. In this paper, we propose a new scheme called Artificial Neural Networks (ANNs) on Co-occurrence Matrices Droid (ANNCMDroid), using co-occurrence matrices to mine correlation of system calls. Our key observation is that correlation of system calls is significantly different between malware and benign software, which can be accurately expressed by co-occurrence matrices, and ANNs can effectively identify anomaly in the co-occurrence matrices. Thus at first we calculate co-occurrence matrices from the system call sequences and then convert them into vectors. Finally, these vectors are fed into ANN to detect malware. We demonstrate the effectiveness of ANNCMDroid by real experiments. Experimental results show that only 4 applications among 594 evaluated benign applications are falsely detected as malware, and only 18 applications among 614 evaluated malicious applications are not detected. As a result, ANNCMDroid achieved an F-Score of 0.981878, which is much higher than other methods.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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• pp.651-661
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• 2017

A truss-braced wing (TBW) aircraft has recently received increasing attention due to higher aerodynamic efficiency compared to conventional cantilever wing aircraft. For conceptual TBW aircraft design, we developed a propulsion-and-airframe integrated design environment by replacing a semi-empirical turbofan engine model with a thermodynamic cycle-based one built upon the numerical propulsion system simulation (NPSS). The constructed NPSS model benefitted TBW aircraft design study, as it could handle engine installation effects influencing engine fuel efficiency. The NPSS model also contributed to broadening TBW aircraft design space, for it provided turbofan engine design variables involving a technology factor reflecting progress in propulsion technology. To effectively consolidate the NPSS propulsion model with the TBW airframe model, we devised a rapid, approximate substitute of the NPSS model by reduced-order modeling (ROM) to resolve difficulties in model integration. In addition, we formed an artificial neural network (ANN) that associates engine component attributes evaluated by object-oriented weight analysis of turbine engine (WATE++) with engine design variables to determine engine weight and size, both of which bring together the propulsion and airframe system models. Through propulsion-andairframe design space exploration, we optimized TBW aircraft design for fuel saving and revealed that a simple engine model neglecting engine installation effects may overestimate TBW aircraft performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.712-717
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• 2011

Option issuers generally utilize Dynamic Delta Hedging(DDH) technique to avoid the risk resulting from continuously changing option value. DDH duplicates payoff of option position by adjusting hedge position according to the delta value from Black-Scholes(BS) model in order to maintain risk neutral state. DDH, however, is not able to guarantee optimal hedging performance because of the weaknesses caused by impractical assumptions inherent in BS model. Therefore, this study presents a methodology for dynamic option hedge using artificial neural network(ANN) to enhance hedging performance and show the superiority of the proposed method using various computational experiments.

• Steel and Composite Structures
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• v.47 no.5
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• pp.601-613
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• 2023

Numerous methods have been proposed in predicting formability of sheet metals based on microstructural and macro-scale properties of sheets. However, there are limited number of papers on the optimization problem to increase formability of sheet metals. In the present study, we aim to use novel optimization algorithms in neural networks to maximize the formability of sheet metals based on tensile curve and texture of aluminum sheet metals. In this regard, experimental and numerical evaluations of effects of texture and tensile properties are conducted. The texture effects evaluation is performed using Taylor homogenization method. The data obtained from these evaluations are gathered and utilized to train and validate an artificial neural network (ANN) with different optimization methods. Several optimization method including grey wolf algorithm (GWA), chimp optimization algorithm (ChOA) and whale optimization algorithm (WOA) are engaged in the optimization problems. The results demonstrated that in aluminum alloys the most preferable texture is cube texture for the most formable sheets. On the other hand, slight differences in the tensile behavior of the aluminum sheets in other similar conditions impose no significant decreases in the forming limit diagram under stretch loading conditions.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.179-184
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• 2023

We propose the modeling methodology of CMOS inverter made of LTPO TFT using a machine learning. LTPO can achieve advantages of LTPS TFT with high electron mobility as a driving TFT and IGZO TFT with low off-current as a switching TFT. However, since the unified model of both LTPS and IGZO TFTs is still lacking, it is necessary to develop a SPICE-compatible compact model to simulate the LTPO current-voltage characteristics. In this work, a generic framework for combining the existing formula of I-V characteristics with artificial neural network is presented. The weight and bias values of ANN for LTPS and IGZO TFTs is obtained and implemented into PSPICE circuit simulator to predict CMOS inverter. This methodology enables efficient modeling for predicting LTPO TFT circuit characteristics.