다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

/pdf/a-review-on-genetic-algorithm-past-present-and-future-2o5y8ru178.pdf

A review on genetic algorithm: past, present, and future

A review on the application of deep learning in system health management

A review of deep learning for renewable energy forecasting

https://ir.cwi.nl/pub/30568/1-s2.0-S096014811831231X-main.pdf

Machine learning methods for wind turbine condition monitoring: A review

The feasibility of monitoring the health of wind turbine (WT) gearboxes based on the lubricant pressure data in the supervisory control and data acquisition system is investigated in this paper. A deep neural network (DNN)-based framework is developed to monitor conditions of WT gearboxes and identify their impending failures. Six data-mining algorithms, the k- nearest neighbors, least absolute shrinkage and selection operator, ridge regression (Ridge), support vector machines, shallow neural network, as well as DNN, are applied to model the lubricant pressure. A comparative analysis of developed data-driven models is conducted and the DNN model is the most accurate. To prevent the overfitting of the DNN model, a dropout algorithm is applied into the DNN training process. Computational results show that the prediction error will shift before the occurrences of gearbox failures. An exponentially weighted moving average control chart is deployed to derive criteria for detecting the shifts. The effectiveness of the proposed monitoring approach is demonstrated by examining real cases from wind farms in China and benchmarked against the gearbox monitoring based on the oil temperature data.

Wind Turbine Gearbox Failure Identification With Deep Neural Networks

In this paper, a data-driven framework is proposed to automatically detect wind turbine blade surface cracks based on images taken by unmanned aerial vehicles (UAVs). Haar-like features are applied to depict crack regions and train a cascading classifier for detecting cracks. Two sets of Haar-like features, the original and extended Haar-like features, are utilized. Based on selected Haar-like features, an extended cascading classifier is developed to perform the crack detection through stage classifiers selected from a set of base models, the LogitBoost, Decision Tree, and Support Vector Machine. In the detection, a scalable scanning window is applied to locate crack regions based on developed cascading classifiers using the extended feature set. The effectiveness of the proposed data-driven crack detection framework is validated by both UAV-taken images collected from a commercial wind farm and artificially generated. The extended cascading classifier is compared with a cascading classifier developed by the LogitBoost only to show its advantages in the image-based crack detection. A computational study is performed to further demonstrate the success of the proposed framework in identifying the number of cracks and locating them in original images.

Automatic Detection of Wind Turbine Blade Surface Cracks Based on UAV-Taken Images

Recently, wind speed forecasting as an effective computing technique plays an important role in advancing industry informatics, while dealing with these issues of control and operation for renewable power systems. However, it is facing some increasing difficulties to handle the large-scale dataset generated in these forecasting applications, with the purpose of ensuring stable computing performance. In response to such limitation, this paper proposes a more practical approach through the combination of extreme-learning machine (ELM) method and deep-learning model. ELM is a novel computing paradigm that enables the neural network (NN) based learning to be achieved with fast training speed and good generalization performance. The stacked ELM (SELM) is an advanced ELM algorithm under deep-learning framework, which works efficiently on memory consumption decrease. In this paper, an enhanced SELM is accordingly developed via replacing the Euclidean norm of the mean square error (MSE) criterion in ELM with the generalized correntropy criterion to further improve the forecasting performance. The advantage of the enhanced SELM with generalized correntropy to achieve better forecasting performance mainly relies on the following aspect. Generalized correntropy is a stable and robust nonlinear similarity measure while employing machine learning method to forecast wind speed, where the outliers may exist in some industrially measured values. Specifically, the experimental results of short-term and ultra-short-term forecasting on real wind speed data show that the proposed approach can achieve better computing performance compared with other traditional and more recent methods.

/pdf/short-term-wind-speed-forecasting-via-stacked-extreme-4vzvty3bz2.pdf

Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy

This paper proposes a novel model-free solution algorithm, the natural cubic-spline-guided Jaya algorithm (S-Jaya), for efficiently solving the maximum power point tracking (MPPT) problem of PV systems under partial shading conditions. A photovoltaic (PV) system which controls the power generation with its operating voltage is considered. As the same as the generic Jaya algorithm, the S-Jaya is free of algorithm-specific parameters. A natural cubic-spline-based prediction model is incorporated into the iterative search process to guide the update of candidate solutions (operating voltage settings) in the S-Jaya and such extension is capable of improving the tracking performance. Simulation studies and experiments are conducted to validate the effectiveness of the proposed S-Jaya algorithm for better addressing PV MPPT problems considering a variety of partial-shading conditions. The performance of the proposed algorithm is benchmarked against the generic Jaya and the particle swarm optimization, which has been widely considered in the model-free MPPT, to demonstrate its advantages. Results of simulation studies and experiments demonstrate that the S-Jaya algorithm converges faster and provides a higher overall tracking efficiency.

A Prediction Model-Guided Jaya Algorithm for the PV System Maximum Power Point Tracking

A short-term forecasting of the electricity price with data-driven algorithms is studied in this research. A stacked denoising autoencoder (SDA) model, a class of deep neural networks, and its extended version are utilized to forecast the electricity price hourly. Data collected in Nebraska, Arkansas, Louisiana, Texas, and Indiana hubs in U.S. are utilized. Two types of forecasting, the online hourly forecasting and day-ahead hourly forecasting, are examined. In online forecasting, SDA models are compared with data-driven approaches including the classical neural networks, support vector machine, multivariate adaptive regression splines, and least absolute shrinkage and selection operator. In the day-ahead forecasting, the effectiveness of SDA models is further validated through comparing with industrial results and a recently reported method. Computational results demonstrate that SDA models are capable to accurately forecast electricity prices and the extended SDA model further improves the forecasting performance.

Long Wang

Papers

Wind Turbine Gearbox Failure Identification With Deep Neural Networks

Automatic Detection of Wind Turbine Blade Surface Cracks Based on UAV-Taken Images

Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy

A Prediction Model-Guided Jaya Algorithm for the PV System Maximum Power Point Tracking

Short-Term Electricity Price Forecasting With Stacked Denoising Autoencoders