• Journal of Electrical Engineering and Technology
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• 제13권5호
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• pp.1945-1955
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• 2018

High frequency signal injection (HFI) is an alternative method for estimating rotor position of interior permanent magnet synchronous motor (IPMSM). The general method of frequency and amplitude selection is based on error tolerance and experiments, and is usually set with only one group of HF parameters, which is not efficient for different working modes. This paper proposes a novel rotor position estimation scheme by HFI with optimized frequency and amplitude, based on the mathematic model of IPMSM. The requirements for standstill and low-speed operational modes are met by applying this novel scheme. Additionally, the effects of the frequency and amplitude of the injected HF signal on the position estimation results under different operating conditions are analyzed. Furthermore, an optimization method for HF parameter selection is proposed to make the estimation process more efficient under different working conditions according to error tolerance. The effectiveness of the propose scheme is verified by the experiments on an IPMSM motor prototype.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2006년도 춘계학술대회 논문집
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• pp.81-85
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• 2006

In this study, we have been examined nano Injection Molding process which can improve transcription of 100nm-level pattern. We changed the various parameter (temperature of injection mold, clamp force, temperature of nozzle) which can be influence for improving transcription. And we measured and analyzed shapes of 100nm-level pattern by Automic Force Microscope for proving transcription. We made the Blu-ray Disc sample for proving transcription. And we measured HF-Signal and jitter. As a result, when the temperature of mold is more than $120^{\circ}C$ and the clamp force is more than 10 ton, We reached over 95 percent of transcription compared with stamper pattern. And we reached in-spec. value for HF-Signal and Jitter. Then we reached over 95 percent of transcription compared with stamper pattern.

• Transactions on Electrical and Electronic Materials
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• 제7권4호
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• pp.210-214
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• 2006

In this paper, two sampling methods which are Latin hypercube sampling (LHS) and simple random sampling were. compared to improve the modeling speed of neural network model. Sampling method was used to generate initial weights and bias set. Electrical characteristic data for $HfO_2$ thin film was used as modeling data. 10 initial parameter sets which are initial weights and bias sets were generated using LHS and simple random sampling, respectively. Modeling was performed with generated initial parameters and measured epoch number. The other network parameters were fixed. The iterative 20 minimum epoch numbers for LHS and simple random sampling were analyzed by nonparametric method because of their nonnormality.

• 센서학회지
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• 제24권1호
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• pp.10-14
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• 2015

This paper reports the results of a study on the optimization of the etching profile, which is an important factor in deep-reactive-ion etching (DRIE), i.e., dry etching. Dry etching is the key processing step necessary for the development of the Internet of Things (IoT) and various microelectromechanical sensors (MEMS). Large-area etching (open area > 20%) under a high-frequency (HF) condition with nonoptimized processing parameters results in damage to the etched sidewall. Therefore, in this study, optimization was performed under a low-frequency (LF) condition. The HF method, which is typically used for through-silicon via (TSV) technology, applies a high etch rate and cannot be easily adapted to processes sensitive to sidewall damage. The optimal etching profile was determined by controlling various parameters for the DRIE of a large Si wafer area (open area > 20%). The optimal processing condition was derived after establishing the correlations of etch rate, uniformity, and sidewall damage on a 6-in Si wafer to the parameters of coil power, run pressure, platen power for passivation etching, and $SF_6$ gas flow rate. The processing-parameter-dependent results of the experiments performed for optimization of the etching profile in terms of etch rate, uniformity, and sidewall damage in the case of large Si area etching can be summarized as follows. When LF is applied, the platen power, coil power, and $SF_6$ should be low, whereas the run pressure has little effect on the etching performance. Under the optimal LF condition of 380 Hz, the platen power, coil power, and $SF_6$ were set at 115W, 3500W, and 700 sccm, respectively. In addition, the aforementioned standard recipe was applied as follows: run pressure of 4 Pa, $C_4F_8$ content of 400 sccm, and a gas exchange interval of $SF_6/C_4F_8=2s/3s$.

• Nuclear Engineering and Technology
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• 제54권5호
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• pp.1825-1834
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• 2022

Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.