Lonnie J. King, dvm, ms, mpa, dacvpm; Larry R. Anderson, dvm, md; Carina G. Blackmore, dvm, phd; Michael J. Blackwell, dvm, mph; Elizabeth A. Lautner, dvm, ms; Leonard C. Marcus, vmd, md; Travis E. Meyer, bs; Thomas P. Monath, md; James E. Nave, dvm; Joerg Ohle; Marguerite Pappaioanou, dvm, mpvm, phd, dacvpm; Justin Sobota, ms, dvm; William S. Stokes, dvm, daclam; Ronald M. Davis, md; Jay H. Gl...

Since 2020, COVID-19 has repeatedly arisen around the world, which had a significant impact on global economy and culture. The prediction of epidemic will help to deal with current similar risks that may arise in future. So, this paper proposes hybrid model based particle swarm optimization variational mode decomposition (PSO-VMD), Long Short-Term Memory Network (LSTM) AdaBoost algorithm. To ad...

Arc fault diagnosis is necessary for the safety and efficiency of PV stations. This study proposed an arc algorithm formed by combining variational mode decomposition (VMD), improved multi-scale fuzzy entropy (IMFE), support vector machine (SVM). method first uses VMD to decompose current into intrinsic functions (IMFs) in time-frequency domain, then calculates IMFE according IMFs associated wi...

سنجش از دور علم دریافت اطلاعات سطح زمین، بدون تماس آشکار با اجزای مورد مطالعه است. تجاری‌‌سازی مجموعة فعالیت‌هایی است که نوآوری‌‌ها را به محصول یا خدماتی تبدیل می‌کند آن مزایای اقتصادی حاصل می‌شود. توجه کاربرد گستردة و اهمیت فراوان در کشاورزی، تجاری‌سازی این تکنولوژی کشاورزی دارای اولویت پژوهش، بررسی شده جامعة هدف شرکت‌های فعال غیرفعال زمینه‌‌اند؛ دلیل استفاده تجربیاتشان امکان فراهم‌آوردن زمینة...

In addition to its inherent evolution trend, landslide displacement contains strong fluctuation and randomness, omni-directional prediction is more scientific than single point or interval prediction. this study, a new hybrid approach composed of double exponential smoothing (DES), variational mode decomposition (VMD), long short-term memory network (LSTM), Gaussian process regression (GPR) pro...

The development of photovoltaic (PV) power forecast technology that is accurate utmost importance for ensuring the reliability and cost-effective functioning system. However, meteorological factors make solar energy have strong intermittent random fluctuation characteristics, which brings challenges to prediction. This work proposes, a new ultra-short-term PV prediction using an improved sparro...

In this paper, a deep learning-based prediction model VMD-GLRT is proposed to address the accuracy problem of service load prediction. The combines Variational Mode Decomposition (VMD) and GRU-LSTM. At same time, incorporates residual networks self-attentive mechanisms improve model. VMD part decomposes original time series into several intrinsic mode functions (IMFs) part. other uses GRU-LSTM ...