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.
In cartilage, chondrocytes are embedded within an abundant extracellular matrix (ECM). A typical chondron consists of a chondrocyte and the immediate surrounding pericellular matrix (PCM). The PCM ...has a patent structure, defined molecular composition, and unique physical properties that support the chondrocyte. Given this spatial position, the PCM is pivotal in mediating communication between chondrocytes and the ECM and, thus, plays a critical role in cartilage homeostasis. The biological function and mechanical properties of the PCM have been extensively studied, mostly in the form of chondrons. This review intends to summarize recent progress in chondron and chondrocyte PCM research, with emphasis on the re-establishment of the PCM by isolated chondrocytes or mesenchymal stem cells during chondrogenic differentiation, and the effects of the PCM on cartilage tissue formation.
Recent developments in wind energy research including wind speed prediction, wind turbine control, operations of hybrid power systems, as well as condition monitoring and fault detection are ...surveyed. Approaches based on statistics, physics, and data mining for wind speed prediction at different time scales are reviewed. Comparative analysis of prediction results reported in the literature is presented. Studies of classical and intelligent control of wind turbines involving different objectives and strategies are reported. Models for planning operations of different hybrid power systems including wind generation for various objectives are addressed. Methodologies for condition monitoring and fault detection are discussed. Future research directions in wind energy are proposed.
•Models and methods applied in wind energy are discussed.•Typology of research models is developed.•Control, condition, and performance monitoring of wind turbine components are highlighted.•Future research directions are outlined.
Alternative splicing is a tightly regulated biological process by which the number of gene products for any given gene can be greatly expanded. Genomic variants in splicing regulatory sequences can ...disrupt splicing and cause disease. Recent developments in sequencing technologies and computational biology have allowed researchers to investigate alternative splicing at an unprecedented scale and resolution. Population-scale transcriptome studies have revealed many naturally occurring genetic variants that modulate alternative splicing and consequently influence phenotypic variability and disease susceptibility in human populations. Innovations in experimental and computational tools such as massively parallel reporter assays and deep learning have enabled the rapid screening of genomic variants for their causal impacts on splicing. In this review, we describe technological advances that have greatly increased the speed and scale at which discoveries are made about the genetic variation of alternative splicing. We summarize major findings from population transcriptomic studies of alternative splicing and discuss the implications of these findings for human genetics and medicine.
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.
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.
This paper aims at studying the data-driven short-term provincial load forecasting (STLF) problem via an in-depth exploration of benefits brought by the feature engineering and model selection. Three ...core issues regarding model selections, feature selections, and feature encoding mechanism selections are deeply investigated. The candidate models are grouped into three types: the time series model, classical regression models, and the deep learning models. Three categories of features, historical loads, calendar effects, and weather factors, are considered and utilized in various encoding mechanisms. In experimental studies, an hourly provincial load dataset from Jiangsu Province in China and the corresponding weather records are utilized. The experiments are extensively performed in three parts according to model types. A time series model is conducted individually and the greedy forward wrapper-based feature selections (GFW-FS) are separately performed in six classical regression models to determine suitable encoded features. Deep learning approaches for developing STLF models are also considered. A deep neural network (DNN) model considering selected features of shallow neural networks (SNN) is developed. Meanwhile, a novel convolutional neural network (CNN) based model using GFW-FS is constructed. Through a comparative error analysis of the test set, the intrinsic linear nature among extracted features and the target in the 24-h-ahead provincial STLF problem is discovered. Feature effects are also evaluated. Data-driven models and their considered features, which are more effective to the STLF problem, are reported.
•A short-term provincial load forecasting problem is studied via an in-depth analytics of data-driven models and developed features.•Classical machine learning and recent deep learning methods are applied to develop short-term provincial load forecasting models.•A greedy forward wrapper-based feature selection framework is developed to construct effective features for forecasting models.•Extensive computational experiments are conducted to explore best performed modeling methods and valuable features in the forecasting.
Visually inspecting integrated circuit (IC) wire bonding defects is important to ensuring the product quality after the packaging process. The availability of IC X-ray images offers an unprecedented ...opportunity of studying the image-based automatic IC wire inspection. In this paper, a data-driven method consists of data pre-processing, feature engineering, and classification is developed to address such problem. The data pre-processing is composed of a chip identification algorithm for locating and separating IC chip image patches from the raw images as well as a wire segmentation algorithm for obtaining the wire region. Next, geometric features extracted from the segmented wires are fed into classification models for identifying defects. Five data mining methods are utilised to develop classification models. The vision detection system (VDS) and convolutional neural networks (CNN) are considered as benchmarks. In computational studies, the effectiveness of the developed method is validated by using X-ray images collected from a semiconductor back-end factory in Mainland China. A comparative analysis is conducted to determine the most suitable classifier for the developed method in the chip classification and the SVM model is finally selected. Advantages of the developed method are verified by benchmarking against the VDS and CNN.
•We develop daily solar power prediction models with data-driven approaches.•We introduce a parameter selection procedure for reducing dimensions of prediction models.•We report a comparative ...analysis of data mining algorithms in daily solar power prediction.•Data mining algorithms can perform better than persistent methods.•None of data mining algorithms can dominate others in all prediction scenarios.
Daily solar power prediction using data-driven approaches is studied. Four famous data-driven approaches, the Artificial Neural Network (ANN), the Support Vector Machine (SVM), the k-nearest neighbor (kNN), and the multivariate linear regression (MLR), are applied to develop the prediction models. The persistent model is considered as a baseline for evaluating the effectiveness of data-driven approaches. A procedure of selecting input parameters for solar power prediction models is addressed. Two modeling scenarios, including and excluding meteorological parameters as inputs, are assessed in the model development. A comparative analysis of the data-driven algorithms is conducted. The capability of data-driven models in multi-step ahead prediction is examined. The computational results indicate that none of the algorithms can outperform others in all considered prediction scenarios.
Segmenting small-scale residential solar panels (RSPs) based on satellite images is an emerging data science problem in the renewable energy field. In this paper, we develop a cross learning driven ...U-Net (CrossNets) method and its extension, adaptive CrossNets, to automatically segment RSPs in satellite images. Proposed methods employ a group of generic U-Nets as a community and target to enhance the RSP segmentation performance. First, parameters of each generic U-Net in the community of CrossNets are initialized individually via the initialization with transfer learning and the classical initialization methods. Next, a novel training mechanism, cross learning, is developed to serve as a constraint for better optimizing CrossNets. Based on cross learning, each generic U-Net in the community first individually updates parameters at every epoch and next learns parameters from the best individual at specific epochs. Cross learning relieves the reliance of generic U-Nets on a careful initialization and better optimizes U-Nets. In testing, the result of the best performed generic U-Net in the community is selected as the final segmentation result of CrossNets. Adaptive CrossNets, a variant of CrossNets, is developed by applying an additional threshold to reduce the possibility of over-learning caused by cross learning. Satellite images collected from one city in U.S. are utilized to validate the performance of proposed methods. These images cover a large area of 135 km2 with 2794 RSPs. Compared with two generic U-Nets based benchmarks, our method can enhance the overall segmentation IoU by around 34% and 1.5%. Moreover, the segmentation robustness is improved from 1.191e−2 and 1.286e−4 to 2.481e−5. In addition, two new image datasets collected from other two cities in U.S. are applied to further examine the applicability of proposed methods.
•An automatic segmentation of residential solar panels from satellite images is studied.•A cross learning driven U-net method and its adaptive version are developed.•Effectiveness of developed cross learning U-nets on segmenting solar panels in satellite images is evaluated.•Three sets of satellite images are utilized in computational experiments.•The developed method offers a new option for surveying the solar energy utilization in residential regions.
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