• Journal of Power Electronics
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• 제18권1호
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• pp.56-69
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• 2018

In this paper, an improved Space Vector Modulation (SVM) based fault tolerant operation on a nine-level Cascaded H-Bridge (CHB) inverter with an additional backup circuit is proposed. Any type of fault in a power converter may result in a power interruption and productivity loss. Three different faults on H-bridge modules in all three phases based on the SVM approach are investigated with diagrams. Any fault in an inverter phase creates an unbalanced output voltage, which can lead to instability in the system. An additional auxiliary unit is connected in series to the three phase cascaded H-bridge circuit. With the help of this and the redundant switching states in SVM, the CHB inverter produces a balanced output with low harmonic distortion. This ensures high DC bus utilization under numerous fault conditions in three phases, which improves the system reliability. Simulation results are presented on three phase nine-level inverter with the automatic fault detection algorithm in the MATLAB/SIMULINK software tool, and experimental results are presented with DSP on five-level inverter to validate the practicality of the proposed SVM fault tolerance strategy on a CHB inverter with an auxiliary circuit.

• Journal of Power Electronics
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• 제18권6호
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• pp.1670-1682
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• 2018

The further improvement of submarine propulsion is associated with the modularity of accumulator-fed inverters, such as cascaded inverters (CIs). CI technology guarantees smooth output voltages with reduced switch frequencies under linear loads. However, the output voltages of CIs are distorted under rectifier loads. This distortion requires harmonic suppression technology. One such technology is the repetitive controller (RC), which is commonly applied but suffers from poor performance in propulsion systems. In this study, the FFT spectrum of a CI under rectifier load is analyzed, and the harmonic contents are uneven in magnitude. For the purpose of harmonic suppression, the control gains at each harmonic frequency should be seriously considered. A RC with a specific harmonic gain compensation (SHGC) for CIs is proposed. This method provides additional control gains at low-order harmonic frequencies, which are difficult to achieve with conventional RCs. This SHGC consists of a band-pass filter (BPF) and proportional element and is easy to implement. These features make the proposed method suitable for submarine propulsion. Experimental results verify the feasibility of the improved RC.

• Journal of Power Electronics
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• 제17권4호
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• pp.1088-1096
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• 2017

Load variations on a distribution line result in voltage fluctuations at the point of common coupling (PCC). In order to keep the magnitude of the PCC voltage constant at its rated value and obtain zero voltage regulation (ZVR), a D-STATCOM is installed for voltage correction. Moreover, the ZVR mode of a D-STATCOM can also be used to balance the source current during unbalanced loading. For medium voltage and high power applications, a D-STATCOM is realized by the cascaded H-bridge topology. In the ZVR mode, the D-STATCOM may draw unbalanced current and in this process is required to handle different phase powers leading to deviations in the cluster voltages. Zero sequence voltage needs to be injected for ZVR mode, which creates circulating power among the phases of the D-STATCOM. The computed zero sequence voltage and the individual DC capacitor balancing controller help the DC cluster voltage follow the reference voltage. The effectiveness of the control scheme is verified by modeling the system in MATLAB/SIMULINK. The obtained simulations are further validated by the experimental results using a dSPACE DS1106 and five-level D-STATCOM experimental set up.

• Nuclear Engineering and Technology
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• 제49권3호
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• pp.495-503
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• 2017

Operators of nuclear power plants may not be equipped with sufficient information during a loss-of-coolant accident (LOCA), which can be fatal, or they may not have sufficient time to analyze the information they do have, even if this information is adequate. It is not easy to predict the progression of LOCAs in nuclear power plants. Therefore, accurate information on the LOCA break position and size should be provided to efficiently manage the accident. In this paper, the LOCA break size is predicted using a cascaded fuzzy neural network (CFNN) model. The input data of the CFNN model are the time-integrated values of each measurement signal for an initial short-time interval after a reactor scram. The training of the CFNN model is accomplished by a hybrid method combined with a genetic algorithm and a least squares method. As a result, LOCA break size is estimated exactly by the proposed CFNN model.

• Journal of Power Electronics
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• 제19권2호
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• pp.487-496
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• 2019

For a solid-state transformer (SST), some factors, such as signal delay, switching loss and differences in the system parameters, lead to unbalanced DC-link voltages among the cascaded H-bridges (CHB). With a control method implemented in the ${\alpha}{\beta}$ frame, the DC-link voltages are balanced, and the reactive power is equally distributed among all of the H-bridges. Based on the ${\alpha}{\beta}$ frame control, the system can achieve independent active current and reactive current control. In addition, the control method of the high-voltage stage is easy to implement without decoupling or a phase-locked loop. Furthermore, the method can eliminate additional current delays during transients and get the dynamic response rapidly without an imaginary current component. In order to carry out the controller design, the vector refactoring relations that are used to balance DC-link voltages are derived. Different strategies are discussed and simulated under the unbalanced load condition. Finally, a three-cell CHB rectifier is constructed to conduct further research, and the steady and transient experimental results verify the effectiveness and correctness of the proposed method.

• Geomechanics and Engineering
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• 재33권6호
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• pp.567-581
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• 2023

The prediction and achievement of a proper rock fragmentation size is the main challenge of blasting operations in surface mines. This is because an optimum size distribution can optimize the overall mine/plant economics. To this end, this study attempts to develop four improved artificial intelligence models to predict rock fragmentation through cascaded forward neural network (CFNN) and radial basis function neural network (RBFNN) models. In this regards, the CFNN was trained by the Levenberg-Marquardt algorithm (LMA) and Conjugate gradient backpropagation (CGP). Further, the RBFNN was optimized by the Dragonfly Algorithm (DA) and teaching-learning-based optimization (TLBO). For developing the models, the database required was collected from the Midouk copper mine, Iran. After modeling, the statistical functions were computed to check the accuracy of the models, and the root mean square errors (RMSEs) of CFNN-LMA, CFNN-CGP, RBFNN-DA, and RBFNN-TLBO were obtained as 1.0656, 1.9698, 2.2235, and 1.6216, respectively. Accordingly, CFNN-LMA, with the lowest RMSE, was determined as the model with the best prediction results among the four examined in this study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1671-1686
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• 2022

Face manipulation tools represented by Deepfake have threatened the security of people's biological identity information. Particularly, manipulation tools with deep learning technology have brought great challenges to Deepfake detection. There are many solutions for Deepfake detection based on traditional machine learning and advanced deep learning. However, those solutions of detectors almost have problems of poor performance when evaluated on different quality datasets. In this paper, for the sake of making high-quality Deepfake datasets, we provide a preprocessing method based on the image pixel matrix feature to eliminate similar images and the residual channel attention network (RCAN) to resize the scale of images. Significantly, we also describe a Deepfake detector named Cascaded-Hop which is based on the PixelHop++ system and the successive subspace learning (SSL) model. By feeding the preprocessed datasets, Cascaded-Hop achieves a good classification result on different manipulation types and multiple quality datasets. According to the experiment on FaceForensics++ and Celeb-DF, the AUC (area under curve) results of our proposed methods are comparable to the state-of-the-art models.

• 대한전자공학회논문지SD
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• 제46권7호
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• pp.29-38
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• 2009

본 연구에서는 실시간 이산웨이블릿변환을 위한 컨볼루션기반 non-cascaded 구조를 구현하고자 병렬곱셈기-중간버퍼-병렬누적기의 고성능 병렬파이프라인 연산회로를 설계하였다. 이산웨이블릿변환의 컨볼루션 곱셈연산은 필터계수의 대칭성과 업/다운 샘플링이 고려된 최적화를 통해서 1/4정도로 감소시킬 수 있으며, 화상데이터와 다수 필터계수들 간의 곱셈과정을 LUT기반의 병렬계수 DA 곱셈기 구조로 구현하면 3$\sim$5배 고속연산처리가 가능하게 된다. 또한 컨볼루션의 곱셈결과를 중간버퍼에 저장하여 누적가산 과정에서 재사용하면 전체 곱셈연산량을 1/2로 감소시켜 연산전력을 절약시킬 수 있다. 중간버퍼는 화상데이터와 필터계수들의 곱셈결과값들을 컨볼루션의 누적가산 과정을 위해 정렬시켜 저장하게 되는데, 이때 병렬누적가산기의 고속 순차검색을 위해 정렬된 병렬저장이 이루어지도록 버퍼관리 구조를 설계한다. 컨볼루션의 병렬곱셈기와 병렬누적가산기는 중간버퍼를 이용한 파이프라인을 구성하게 되는데, 파이프라인 연산처리 효율을 높이기 위해 병렬곱셈기의 연산처리 성능에 맞추어 누적가산기 및 중간버퍼의 병렬화 구조가 결정된다. 설계된 고성능 이산웨이블릿변환기의 성능을 검증하기 위해서 0.18um 라이브러리를 이용한 후반부 설계를 하였으며, 90MHz에서 SVGA(800$\sim$600)영상을 30fps로 실시간 처리함을 확인하였다.

• 대한치과교정학회지
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• 제54권1호
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• pp.48-58
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• 2024

Objective: To quantify the effects of midline-related landmark identification on midline deviation measurements in posteroanterior (PA) cephalograms using a cascaded convolutional neural network (CNN). Methods: A total of 2,903 PA cephalogram images obtained from 9 university hospitals were divided into training, internal validation, and test sets (n = 2,150, 376, and 377). As the gold standard, 2 orthodontic professors marked the bilateral landmarks, including the frontozygomatic suture point and latero-orbitale (LO), and the midline landmarks, including the crista galli, anterior nasal spine (ANS), upper dental midpoint (UDM), lower dental midpoint (LDM), and menton (Me). For the test, Examiner-1 and Examiner-2 (3-year and 1-year orthodontic residents) and the Cascaded-CNN models marked the landmarks. After point-to-point errors of landmark identification, the successful detection rate (SDR) and distance and direction of the midline landmark deviation from the midsagittal line (ANS-mid, UDM-mid, LDM-mid, and Me-mid) were measured, and statistical analysis was performed. Results: The cascaded-CNN algorithm showed a clinically acceptable level of point-to-point error (1.26 mm vs. 1.57 mm in Examiner-1 and 1.75 mm in Examiner-2). The average SDR within the 2 mm range was 83.2%, with high accuracy at the LO (right, 96.9%; left, 97.1%), and UDM (96.9%). The absolute measurement errors were less than 1 mm for ANS-mid, UDM-mid, and LDM-mid compared with the gold standard. Conclusions: The cascaded-CNN model may be considered an effective tool for the auto-identification of midline landmarks and quantification of midline deviation in PA cephalograms of adult patients, regardless of variations in the image acquisition method.

• 한국광학회:학술대회논문집
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• 한국광학회 1998년도 제15회 광학 및 양자전자 학술발표회 논문집
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• pp.242-243
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• 1998