Power Balance for Voltage-controlled PV Generator without Robust Voltage Source

Wang Zhenxiong,Zhuo Fang,Lv Nian,Yi Hao,Wu Jiaqi,Yan Hui,Ma Zekun 전력전자학회 2019 ICPE(ISPE)논문집 Vol.2019 No.5

Voltage control mode is preferable because of better bus voltage support and low reliance on communication. However, that is difficult to implement on photovoltaic inverter without robust voltage source. Meanwhile, photovoltaic arrays have some power management ability that usually neglected due to the use of MPPT, which in fact damages balanced power flow and a robust and stable grid. As a result, control of dc and ac side of photovoltaic inverter is usually separated and broken, where the dc side is usually supposed as an ideal voltage source and the corresponding relationships are neglected. This paper proposes a control scheme called photovoltaic generator for photovoltaic microgrid without robust voltage source, which imitates the principle of power conversion from mature synchronous generator theory. The scheme focuses on the active participation of solar energy in power management and voltage support. The effectiveness is verified in simulation and experiment.

A Frequency Stable and Tunable Optoelectronic Oscillator Using an Optical Phase Shifter and a Phase-shifted Fiber Bragg Grating

Zekun Wu,Jiahong Zhang,Yao Wang 한국광학회 2022 Current Optics and Photonics Vol.6 No.6

A frequency stable and tunable optoelectronic oscillator (OEO) incorporating an optical phase shifter and a phase-shifted fiber Bragg grating (PS-FBG) is designed and analyzed. The frequency tunability of the OEO can be realized by using a tunable microwave photonic bandpass filter consisting of a PSFBG, a phase modulator. The optical phase compensation loop is used to compensate for the phase variations of the RF signal from the OEO by adjusting an optical phase shifter. Simulation results demonstrate that the output RF signals of the OEO can be tuned in a frequency range of 118 MHz to 24.092GHz. When the ambient temperature fluctuates within ±3.9 ℃, the frequency drifts of the output RFsignals are less than 68 Hz, the side-mode suppression ratios are more than 69.39 dB, and the phasenoise is less than −92.49 dBc/Hz at a 10 kHz offset frequency.

Yeast Extract: Characteristics, Production, Applications and Future Perspectives

Tao Zekun,Yuan Haibo,Liu Meng,Liu Qian,Zhang Siyi,Liu Hongling,Jiang Yi,Huang Di,Wang Tengfei 한국미생물·생명공학회 2023 Journal of microbiology and biotechnology Vol.33 No.2

Yeast extract is a product prepared mainly from waste brewer’s yeast, which is rich in nucleotides, proteins, amino acids, sugars and a variety of trace elements, and has the advantages of low production cost and abundant supply of raw material. Consequently, yeast extracts are widely used in various fields as animal feed additives, food flavoring agents and additives, cosmetic supplements, and microbial fermentation media; however, their full potential has not yet been realized. To improve understanding of current research knowledge, this review summarizes the ingredients, production technology, and applications of yeast extracts, and discusses the relationship between their properties and applications. Developmental trends and future prospects of yeast extract are also previewed, with the aim of providing a theoretical basis for the development and expansion of future applications.

An Improved RF Detection Algorithm Using EMD-based WT

( Xue Lv ),( Zekun Wang ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.8

More and more problems for public security have occurred due to the limited solutions for drone detection especially for micro-drone in long range conditions. This paper aims at dealing with drones detection using a radar system. The radio frequency (RF) signals emitted by a controller can be acquired using the radar, which are usually too weak to extract. To detect the drone successfully, the static clutters and linear trend terms are suppressed based on the background estimation algorithm and linear trend suppression. The principal component analysis technique is used to classify the noises and effective RF signals. The automatic gain control technique is used to enhance the signal to noise ratios (SNR) of RF signals. Meanwhile, the empirical mode decomposition (EMD) based wavelet transform (WT) is developed to decrease the influences of the Gaussian white noises. Then, both the azimuth information between the drone and radar and the bandwidth of the RF signals are acquired based on the statistical analysis algorithm developed in this paper. Meanwhile, the proposed accumulation algorithm can also provide the bandwidth estimation, which can be used to make a decision accurately whether there are drones or not in the detection environments based on the probability theory. The detection performance is validated with several experiments conducted outdoors with strong interferences.

Deep Neural Network Entrepreneurial Project Recommendation Model for the Integration of Industry, Education, and Entrepreneurship Needs of Students

Tao Long,ZeKun Wang 대한전자공학회 2024 IEIE Transactions on Smart Processing & Computing Vol.13 No.1

As the size of the entrepreneurship project information platform grows, it is becoming increasingly difficult for student users to find in-demand entrepreneurship projects that integrate industry and education comprehensively and rapidly. The severe information overload leads to poor accuracy of recommendation results. This study addressed these problems based on Deep Neural Networks (DNNs) and Matrix Decomposition Algorithms (MDAs) by combining a Convolutional Neural Network (CNN), word embedding, and one-hot coding techniques. The DNN-MF model was used to extract the entrepreneurial needs and implicit features of students. The DNN-MF model designed for the study was also improved and incorporated with student user features, i.e., the DNN-DNN2 model was constructed. The experiments showed that the Root Mean Square Error (RMSE) of the DNN-MF model was lower than that of the Convolution Matrix Factorization (ConvMF) and Probabilistic Matrix Factorization (PMF) by 0.1190 and 0.1677, respectively. The RMSE of the DNN-DNN2 model was lower than that of the DNN-MF model, and the recommendation accuracy of the study model was 2.35% higher than that of the DNN-DNN1 model, which did not incorporate the student user characteristics. These results showed that the proposed recommendation model for entrepreneurial projects was significantly better than the current popular ones. Moreover, the model could complete the task of recommending entrepreneurial projects faster and more accurately, effectively solving the cold start problem of users and projects, which has certain practical significance.

Association between Initial Chest CT or Clinical Features and Clinical Course in Patients with Coronavirus Disease 2019 Pneumonia

Liu Zhe,Jin Chao,Wu Carol C.,Liang Ting,Zhao Huifang,Wang Yan,Wang Zekun,Li Fen,Zhou Jie,Cai Shubo,Zeng Lingxia,Yang Jian 대한영상의학회 2020 Korean Journal of Radiology Vol.21 No.6

Objective: To identify the initial chest computed tomography (CT) findings and clinical characteristics associated with the course of coronavirus disease 2019 (COVID-19) pneumonia. Materials and Methods: Baseline CT scans and clinical and laboratory data of 72 patients admitted with COVID-19 pneumonia (39 men, 46.2 ± 15.9 years) were retrospectively analyzed. Baseline CT findings including lobar distribution, presence of ground glass opacities, consolidation, linear opacities, and lung severity score were evaluated. The outcome event was recovery with hospital discharge. The time from symptom onset to discharge or the end of follow-up (for those remained hospitalized) was recorded. Data were censored in events such as death or discharge without recovery. Multivariable Cox proportional hazard regression was used to explore the association between initial CT, clinical or laboratory findings, and discharge with recovery, whereby hazard ratio (HR) values < 1 indicated a lower rate of discharge at four weeks and longer time until discharge. Results: Thirty-two patients recovered and were discharged during the study period with a median length of admission of 16 days (range, 9 to 25 days), while the rest remained hospitalized at the end of this study (median, 17.5 days; range, 4 to 27 days). None died during the study period. After controlling for age, onset time, lesion characteristics, number of lung lobes affected, and bilateral involvement, the lung severity score on baseline CT (> 4 vs. ≤ 4 [reference]: adjusted HR = 0.41 [95% confidence interval, CI = 0.18–0.92], p = 0.031) and initial lymphocyte count (reduced vs. normal or elevated [reference]: adjusted HR = 0.14 [95% CI = 0.03–0.60], p = 0.008) were two significant independent factors that influenced recovery and discharge. Conclusion: Lung severity score > 4 and reduced lymphocyte count at initial evaluation were independently associated with a significantly lower rate of recovery and discharge and extended hospitalization in patients admitted for COVID-19 pneumonia.