Summary form only given. Fiber grating devices have been intensively studied in filter application for their simple structure and flexible spectrum design.‾ 1 Along with development of wide band and flat gain optical amplifiers, cladding mode coupling fiber devices such as long period gratings (LPGs) and acousto-optic tunable filters (AOTFs) have been recently developed as gain equalization optical filters in erbium-doped fiber amplifier (EDFA) for wavelength-division multiplexing (WDM) systems2.
Prognostics and health management of general rotating machinery have been studied over time to improve system stability. Recently, the excellent abnormal diagnosis performance of artificial intelligence (AI) was demonstrated, and therefore, AI-based intelligent diagnosis is now being implemented in these systems. AI models are trained using large volumes of data. Therefore, we propose a transformer-based generative adversarial network (GAN) model with a multi-resolution short-time Fourier transform (multi-STFT) loss function to augment the vibration data of rotating machinery to facilitate the successful learning of deep learning models. We constructed a model with a conditional GAN structure, which is transformer based, for learning the feature points of vibration data in the time-series domain. In addition, we applied the multi-STFT loss function to capture the frequency features of the vibration data. The generated data, which adequately captured the frequency features, were used to augment the training data to improve the performance of a deep learning classifier. Furthermore, by visualizing the generated vibration data and comparing the visualizations to those of the vibration data obtained from real machinery, we demonstrated that the generated data were indistinguishable from the actual data.
We report a newly found terahertz generation, mediated via acoustic standing waves confined within GaN-based piezoelectric heterostructures and its spectral control by adapting the active layer thickness.
The degradation of clamping force in the core support barrel, which forms the internal structure of a nuclear power plant, has the potential to significantly impact the plant's safety and reliability. Previous studies have concentrated on the detection of clamping force degradation but have been constrained in their ability to identify the precise size and position. This study proposes a novel methodology for diagnosing the size and position of clamping force degradation in core support barrels, combining deep-learning techniques and dynamic time warping (DTW) algorithms. DTW is applied to the magnitude data of the ex-core neutron noise signal obtained in the frequency domain, thereby enabling the effective learning of changes in sensor data values. Moreover, autoencoder-based (AE-based) representation learning is utilized to extract features of the data, preventing overfitting and thus enhancing the robustness of the model. The experiment results demonstrate that the size and position of clamping force degradation can be accurately predicted. It is expected that this research will contribute to enhancing the precision and efficiency of internal structure monitoring in nuclear power plants.
We characterized polycrystalline silicon films produced by aluminum-induced layer exchange (ALILE) for the various thicknesses of an aluminum oxide layer. The pc-Si film is fabricated by the ALILE process with a structure of glass/Al/Al 2 O 3 /a-Si for application to a seed layer of polycrystalline silicon (pc-Si) solar cells using dc and RF sputtering, and PECVD methods, respectively. For investigation of the effects of oxide film thickness on the crystallinity in the ALILE process, the thickness of Al 2 O 3 was varied from 4 to 50 nm including native oxidation in the ambient atmosphere. For characterization OM, SEM, Raman spectroscopy analyses are carried out. As results, the crystallinity was exponentially decayed with increase of Al 2 O 3 thickness. Also, the grain size is decreased with increase of Al 2 O 3 layer thickness. The maximum pc-Si grain size of about 60 µm is obtained at the relatively thin oxide layer. The preferential crystal orientation was (111) and more dominant for the thinner Al 2 O 3 layers. In this work the effects of Al 2 O 3 film thickness on the crystallization properties of a-Si by ALILE process are closely demonstrated such as grain size, preferential crystal orientation, and crystallinity.
We report on the fabrication and characterization of a Schottky ultraviolet graphene/AlGaN/GaN photodetector (PD). The fabricated device clearly exhibits rectification behaviour, indicating that the Schottky barrier is formed between the AlGaN and the mechanically transferred graphene. The Schottky parameters are evaluated using an equivalent circuit with two diodes connected back-to-back in series. The PD shows a low dark current of 4.77 × 10−12 A at a bias voltage of −2.5 V. The room temperature current–voltage (I–V) measurements of the graphene/AlGaN/GaN Schottky PD exhibit a large photo-to-dark contrast ratio of more than four orders of magnitude. Furthermore, the device shows peak responsivity at a wavelength of 350 nm, corresponding to GaN band edge and a small hump at 300 nm associated to the AlGaN band edge. In addition, we examine the behaviour of Schottky PDs with responsivities of 0.56 and 0.079 A W−1 at 300 and 350 nm, respectively, at room temperature.
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