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Power System Harmonic Estimation Based on Park Transform
Ya Chen,Tianyao Ji,Mengshi Li,Qinghua Wu,Xuejian Wang 대한전기학회 2016 Journal of Electrical Engineering & Technology Vol.11 No.3
This paper presents a novel method for power system harmonic estimation based on the Park transform. The proposed method firstly extends the signal to a group of three-phase signals in a-b-c coordinate. Then, a linear fitting based method is adopted to estimate the fundamental frequency. Afterwards, the Park transform is utilized to convert the three-phase signals from a-b-c coordinate to d-q- 0 coordinate. Finally, the amplitude and phase of a harmonic component of interest can be calculated using the d-axis and q-axis components obtained. Simulation studies have been conducted using matrix laboratory (MATLAB) and power system computer aided design/electromagnetic transients including direct current (PSCAD/EMTDC). Simulation studies in MATLAB have considered three scenarios, i.e., no-frequency-deviation scenario, frequency-deviation scenario and the scenario in the presence of inter-harminics. The results have demonstrated that the proposed method achieves very high accuracy in frequency, phase and amplitude estimation under noisy conditions, and suffers little influence of the inter-harmonics. Moreover, comparison studies have proved that the proposed method is superior to FFT and Interpolated FFT with the Hanning Window (IpFFTHW). Finally, a popular case in PSCAD/EMTDC has been employed to further verify the effectiveness of the proposed method.
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Text Classification on Social Network Platforms Based on Deep Learning Models
Ya Chen,Tan Juan,정회경 한국정보통신학회 2023 Journal of information and communication convergen Vol.21 No.1
The natural language on social network platforms has a certain front-to-back dependency in structure, and the direct conversion of Chinese text into a vector makes the dimensionality very high, thereby resulting in the low accuracy of existing text classification methods. To this end, this study establishes a deep learning model that combines a big data ultra-deep convolutional neural network (UDCNN) and long short-term memory network (LSTM). The deep structure of UDCNN is used to extract the features of text vector classification. The LSTM stores historical information to extract the context dependency of long texts, and word embedding is introduced to convert the text into low-dimensional vectors. Experiments are conducted on the social network platforms Sogou corpus and the University HowNet Chinese corpus. The research results show that compared with CNN + rand, LSTM, and other models, the neural network deep learning hybrid model can effectively improve the accuracy of text classification.
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Influence of Fundamental Parameters on the Intrinsic Voltage Gain of Organic Thin Film Transistors
Yachen Li,Luis Portilla,Chaewon Kim 대한금속·재료학회 2021 ELECTRONIC MATERIALS LETTERS Vol.17 No.3
The intrinsic gain is a key metric in analog electronics, it is the highest gain that can be obtained for an amplifi er in the transistorconfi guration. However, there lack the demonstration of the intrinsic gain with diff erent parameters comprehensively,they often focus on one or two features. In this work, we fabricated organic thin fi lm transistors (OTFTs) with two types ofsemiconducting material to compare the eff ect of mobility on intrinsic gain and varied structural parameters such as activelayer thickness and channel length to explore the impacts of those factors. We found that the intrinsic gain does not havemuch correlation with the mobility and the contact resistance. In addition, the intrinsic gain decreases as the channel lengthdecreases, the increment of the gate voltage, and the decrease of the thickness of the active layer. The better understandingof diff erent impacts on the intrinsic gain on OTFTs could provide indication for its real application design of organic circuitto obtain higher gain value, which is needed in the future amplifi er processing.
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The nonlinear absorption of graphene oxide water solution in femtosecond regime
Lingling Ran,Zhijun Chai,Yachen Gao,Wenzhi Wu,Qing Chang,Degui Kong 한국물리학회 2016 Current Applied Physics Vol.16 No.9
The nonlinear absorption properties of graphene oxide water solution were investigated with femtosecond pulses using Z-scan and pump-probe techniques at 800 nm wavelength. The researching results indicated that the nonlinear absorption of graphene oxide water solution include three parts: twophoton absorption of bound electrons from valence band, excited state absorption of electrons from the low energy state in conduction band and the excited state absorption of electrons from the bottom of conduction band. By theoretically fitting the experimental results, we got the two-photon absorption coefficient about b ¼ 3 1014 m/W, and the two excited state absorption cross section in the order of 1020 m2 and 1021 m2 respectively. In addition, the excited state lifetime of electron on the low energy state of conduction band was obtained. The investigation indicated that graphene oxide water solution is a good nonlinear optical material.
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Xue Zhang,Yamei Ge,Ashfaq-Ahmad-Shah Bukhari,Qian Zhu,Yachen Shen,Min Li,Hui Sun,Dongming Su,Xiubin Liang 생화학분자생물학회 2019 Experimental and molecular medicine Vol.51 No.-
The main functions of the epithelial sodium channel (ENaC) in the kidney distal nephron are mediation of sodium and water balance and stabilization of blood pressure. Estrogen has important effects on sodium and water balance and on premenopausal blood pressure, but its role in the regulation of ENaC function is not fully understood. Female Sprague–Dawley rats were treated with 17β-estradiol for 6 weeks following bilateral ovariectomy. Plasma estrogen, aldosterone, creatinine, and electrolytes were analyzed, and α-ENaC and derlin-1 protein expression in the kidney was determined by immunohistochemistry and western blotting. The expression levels of α-ENaC, derlin-1, AMPK, and related molecules were also examined by western blotting and real-time PCR in cultured mouse renal collecting duct (mpkCCDc14) epithelial cells following estrogen treatment. Immunofluorescence and coimmunoprecipitation were performed to detect α-ENaC binding with derlin-1 and α-ENaC ubiquitination. The results demonstrated that the loss of estrogen elevated systolic blood pressure in ovariectomized (OVX) rats. OVX rat kidneys showed increased α-ENaC expression but decreased derlin-1 expression. In contrast, estrogen treatment decreased α-ENaC expression but increased derlin-1 expression in mpkCCDc14 cells. Moreover, estrogen induced α-ENaC ubiquitination by promoting the interaction of α-ENaC with derlin-1 and evoked phosphorylation of AMPK in mpkCCDc14 cells. Our study indicates that estrogen reduces ENaC expression and blood pressure in OVX rats through derlin-1 upregulation and AMPK activation.
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