• Journal of Electrical Engineering and Technology
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• 제10권6호
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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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• 제9권7호
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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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• 제18권4호
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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.

• 한국산학기술학회논문지
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• 제12권2호
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• pp.712-717
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• 2011

동적 델타 헤징(Dynamic Delta Hedging)이란 옵션 발행자가 옵션의 만기정산금액(payoff)을 지급하기 위해 주기적으로 델타에 근거한 헤지 포지션을 조절함으로써 옵션의 payoff를 복제하고 옵션 가치변화에 따른 위험을 회피하는 방법이다. 본 연구에서는 헤지에 있어서 주요 변수인 블랙-숄즈의 모형에 의해 산출된 델타의 대체 값을 찾기 위해 기계학습의 일종인 인공신경망 학습을 적용하여 옵션의 만기 시 헤지 비용의 최소화 및 차익 실현을 위한 방법론을 제시하고자 한다. 기초자산의 현재가격, 변동성, 무위험이자율, 만기 등의 시장 상황 변화에 따른 다양한 시나리오에 대한 실험을 통해 본 연구에서 제시하는 방법론의 성능을 분석하고 그 우수성을 보인다.

• Steel and Composite Structures
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• 제47권5호
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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.

• 반도체디스플레이기술학회지
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• 제22권4호
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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.

• 한국음향학회지
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• 제25권3호
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• pp.144-150
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• 2006

심장병이 있는 환자들을 진료할 때 의사들은 청진기를 이용하여 심음 (heart sound)을 듣고 이를 기준으로 환자의 병의 유무나 질환의 종류에 대한 기초적인 판단을 하게 된다. 하지만, 심음은 환자의 상태나 외부 잡음의 영향에 따라서 신호의 특성이 변하고 또한 정상적인 심음과 질병을 나타내는 심음과의 차이가 비교적 구분하기 어려울 정도로 작기 때문에 숙달된 전문의가 아니면, 진단의 정확도가 떨어질 가능성이 있다. 따라서 신호처리 기법을 이용하여 심음을 분석해서 심음이 정상적인지의 유무를 자동으로 판단할 수 있다면, 진단을 하는 의사들에게 유용한 정보가 될 것이라 생각된다. 본 연구에서는 심음의 질병유무와 질병종류를 자동으로 판단하기 위해서 기존에 많이 사용되었던 artificial neural network (ANN) 대신에 hidden Markov model (HMM)을 사용하는 방법을 제안하였으며, 기초적인 실험결과 상당히 우수한 성능을 보임을 알 수 있었다.

• 한국습지학회지
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• 제21권spc호
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• pp.61-68
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• 2019

최근 장기적인 기후 변동성을 고려하기 위하여 대기-해양 순환 패턴을 수치화한 기상인자가 수문 변수 예측에 널리 사용되고 있다. 또한 정확하고 안정적인 예측을 위해 인공신경망 기반의 예측 모형이 꾸준히 발전하고 있다. 기상인자를 활용하여 기후 변동성을 고려한 수문량 예측은 수자원 및 환경 보존의 장기적인 관리에 효율적으로 활용될 수 있으므로 수문 변수에 유의한 인자의 파악과 이를 활용한 예측 모형의 적용은 꾸준한 도전이 될 것이다. 본 연구에서는 우리나라 한강 유역 댐 유입량에 통계적으로 유의한 상관성이 있는 대표 기상인자를 선정하고, 이를 인공신경망 앙상블 모형에 적용하여 댐 유입량 예측을 수행하였다. 이를 위해 앙상블 경험적 모드분해법을 활용하여 댐 유입량과 기상인자간의 통계적 상관성을 확인하였으며, 기존 단일 인공신경망 모형의 한계를 보완한 인공신경망 앙상블 모형을 구축하였다. 예측 수행 결과, 5개 댐 상관계수 평균이 훈련 기간에서 0.88, 검증 기간에서 0.68의 예측력을 보이는 것을 확인하였으며, 본 연구에서의 절차를 토대로 우리나라의 다양한 수문 변수와 기후 변동성간의 관계를 활용한 다양한 적용 사례가 나오길 기대한다.

• 디지털산업정보학회논문지
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• 제15권4호
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• pp.149-157
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• 2019

In case of reading passport using a smart phone in contrast with a dedicated passport reading system, MRZ(Machine Readable Zone) character recognition can be hard when the character strokes were broken, touched or blurred according to the lighting condition, and the position and size of MRZ character lines were varied due to the camera distance and angle. In this paper, the effective recognition algorithm of the passport MRZ information using a combined neural network recognizer of CNN(Convolutional Neural Network) and ANN( Artificial Neural Network), is proposed under the various sized and skewed passport images. The MRZ line detection using connected component analysis algorithm and the skew correction using perspective transform algorithm are also designed in order to achieve effective character segmentation results. Each of the MRZ field recognition results is verified by using five check digits for deciding whether retrying the recognition process of passport MRZ information or not. After we implement the proposed recognition algorithm of passport MRZ information, the excellent recognition performance of the passport MRZ information was obtained in the experimental results for PC off-line mode and smart phone on-line mode.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2009년도 춘계 학술대회 제21권1호
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• pp.61-62
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• 2009

철근콘크리트 부재의 전단거동에 대한 다양한 이론모델들과 제안식이 존재한다. 하지만 전단거동에 대한 메커니즘이 매우 복잡하고 영향을 미치는 요소가 다양하기 때문에, 대다수의 제안식들은 경험식이며 그 예측 정확도도 매우 다르다. 이런 결점의 대안으로 인공신경망이 제안되어 여러 연구자들에 의해 연구되었지만 기존의 연구에서는 인공신경망 분석에 사용된 데이터베이스의 양이 충분하지 못한 문제점이 있었다. 그래서 본 논문에서는 방대한 전단실험 데이터베이스와 인공신경망을 이용하여 전단 보강근이 없는 철근콘크리트 보의 전단강도를 예측하고, 그 결과를 ACI 규준식과 비교 분석하였다. 분석 결과 인공신경망은 전단강도에 대한 주요 인자들의 영향을 적절히 반영하여 매우 우수한 예측 정확도를 보여 주었다.