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•A method is developed for day-ahead 96-step probabilistic wind speed forecasts.•This work analyzes error characteristic of Numerical Weather Prediction results.•Error correction ...models are established based on structured neural networks.•Mixture kernel density estimation is used for joint probabilistic information.•Case studies of wind farm in China certify the effectiveness of proposed method.
At present, wind forecast based on Numerical Weather Prediction is widely recognized and applied for a safer and more sufficient usage of wind sources. However, because of the unescapable inherent errors of numerical techniques, there are many negative cases of forecasts. Thus, aiming to quantize and evaluate the inherent errors of physical outcomes, this paper analyzes the characteristic of residuals between numerical results and actual measured data in statistical way, designs combined non-linear and non-parameter algorithms to correct original prediction values, and achieves probabilistic one-day-ahead 96-step wind speed forecasts. The concise process of the method can be described as followings. Firstly, this work utilizes autocorrelation analysis to verify the non-noise attribute of error sequences. Based on the characteristic, adaptive and structured error correction models of nonlinear autoregressive with exogenous inputs network are established to acquire deterministic optimized outcomes. Then, aiming to calculate conditional error boundaries of different confidence levels, mixture kernel density estimation is adopted step by step to estimate joint probability density of corrected values and revised errors. The results on test set show the correction considering inherent errors of numerical techniques can integrate the physical with statistical information effectively and enhance the forecast accuracy indeed.
Feature subset selection is viewed as an important preprocessing step for pattern recognition, machine learning and data mining. Most of researches are focused on dealing with homogeneous feature ...selection, namely, numerical or categorical features. In this paper, we introduce a neighborhood rough set model to deal with the problem of heterogeneous feature subset selection. As the classical rough set model can just be used to evaluate categorical features, we generalize this model with neighborhood relations and introduce a neighborhood rough set model. The proposed model will degrade to the classical one if we specify the size of neighborhood zero. The neighborhood model is used to reduce numerical and categorical features by assigning different thresholds for different kinds of attributes. In this model the sizes of the neighborhood lower and upper approximations of decisions reflect the discriminating capability of feature subsets. The size of lower approximation is computed as the dependency between decision and condition attributes. We use the neighborhood dependency to evaluate the significance of a subset of heterogeneous features and construct forward feature subset selection algorithms. The proposed algorithms are compared with some classical techniques. Experimental results show that the neighborhood model based method is more flexible to deal with heterogeneous data.
Solar flares originate from the release of the energy stored in the magnetic field of solar active regions, the triggering mechanism for these flares, however, remains unknown. For this reason, the ...conventional solar flare forecast is essentially based on the statistic relationship between solar flares and measures extracted from observational data. In the current work, the deep learning method is applied to set up the solar flare forecasting model, in which forecasting patterns can be learned from line-of-sight magnetograms of solar active regions. In order to obtain a large amount of observational data to train the forecasting model and test its performance, a data set is created from line-of-sight magnetogarms of active regions observed by SOHO/MDI and SDO/HMI from 1996 April to 2015 October and corresponding soft X-ray solar flares observed by GOES. The testing results of the forecasting model indicate that (1) the forecasting patterns can be automatically reached with the MDI data and they can also be applied to the HMI data; furthermore, these forecasting patterns are robust to the noise in the observational data; (2) the performance of the deep learning forecasting model is not sensitive to the given forecasting periods (6, 12, 24, or 48 hr); (3) the performance of the proposed forecasting model is comparable to that of the state-of-the-art flare forecasting models, even if the duration of the total magnetograms continuously spans 19.5 years. Case analyses demonstrate that the deep learning based solar flare forecasting model pays attention to areas with the magnetic polarity-inversion line or the strong magnetic field in magnetograms of active regions.
The assessment of wind resource is the basis of wind power utilization. An accurate and comprehensive characterization of wind resource is of vital importance to site planning, wind turbine ...selection, generation capacity estimation, back-up requirements estimation, and financial risks estimation, etc. The second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) dataset from 1998 to 2017 is used to construct the wind profiles at 80 m, 100 m and 150 m heights in China. To ensure the reliability of the assessment, the wind speed obtained from MERRA-2 is compared with the observations collected from two wind farms and 213 meteorological stations. Then a series of metrics are employed to comprehensively characterize wind resource in China, including the theoretical potential, variability, intermittency and complementarity. Meanwhile, the variation pattern of each metric with height is analyzed. The characterization results contribute to a better and more comprehensive understanding of wind resource in China.
•The variability and intermittency of wind resource across China are assessed.•The variation patterns of wind resource at different heights are analyzed.•The complementarity of wind resource at different sites is investigated.
Abstract
Microbes regulate the composition and turnover of organic matter. Here we developed a framework called Energy-Diversity-Trait integrative Analysis to quantify how dissolved organic matter ...and microbes interact along global change drivers of temperature and nutrient enrichment. Negative and positive interactions suggest decomposition and production processes of organic matter, respectively. We applied this framework to manipulative field experiments on mountainsides in subarctic and subtropical climates. In both climates, negative interactions of bipartite networks were more specialized than positive interactions, showing fewer interactions between chemical molecules and bacterial taxa. Nutrient enrichment promoted specialization of positive interactions, but decreased specialization of negative interactions, indicating that organic matter was more vulnerable to decomposition by a greater range of bacteria, particularly at warmer temperatures in the subtropical climate. These two global change drivers influenced specialization of negative interactions most strongly via molecular traits, while molecular traits and bacterial diversity similarly affected specialization of positive interactions.
Since climbing robots mainly rely on adhesion actuators to achieve adhesion, robust adhesion actuators have always been the challenge of climbing robot design. A novel under-actuated soft adhesion ...actuator (USAA) proposed in this paper for climbing robots can generate adhesion through robot’s load applied to the actuator. The actuator is composed of a soft film/substrate structure with an annular groove on the substrate and a cavity on the soft film. To fabricate the actuator, we first study the influence of the geometric parameters of the USAA on the maximum adhesion of the actuator by analysis and experiments, and then combine these parameters and the boundary conditions of the static meniscus in the mold to design the mold. Moreover, we fabricate a climbing robot equipped with USAAs and evaluate its performance on horizontal and inclined surfaces with a wide range of characteristics. The USAA can generate strong and controllable adhesion to various smooth and semi-smooth surfaces. Furthermore, the fabricated robot performs well on various surfaces under a certain load (at least 500 g) and speed (369 mm/min) through experiments. It’s adaptability to a variety of surfaces enables a wide range of applications and pushes the boundaries of soft adhesion actuators.
To investigate the hypoglycemic effect and potential mechanism of pumpkin polysaccharides and puerarin on type II diabetes mellitus (T2DM) mice, mice were fed a high-fat diet and injected ...intraperitoneally with streptozotacin to induce T2DM. After eight weeks of drug administration, blood samples were withdrawn from tail veins of mice that had been fasted overnight. The results showed that both pumpkin polysaccharides and puerarin, as well as a pumpkin polysaccharides and puerarin combination, could ameliorate T2DM. The pumpkin polysaccharides and puerarin combination had a synergetic hypoglycemic effect on T2DM mice that was greater than the pumpkin polysaccharides' or the puerarin's hypoglycemic effect. Both the pumpkin polysaccharides and the puerarin were found to ameliorate the blood glucose tolerance and insulin resistance of T2DM mice. They showed lipid-lowering activity by reducing the total cholesterol, triglycerides, and low-density lipoprotein levels, and improving the high-density lipoprotein level. They had beneficial effects on the oxidative stress by decreasing the reactive oxygen species and malondialdehyde levels, and increasing the glutathione level and the superoxide dismutase activity. Furthermore, the nuclear factor E2 related factor 2 (Nrf2), heme oxygenase-1, and phosphoinositide-3-kinase (PI3K) levels were upregulated, and the Nrf2 and PI3K signalling pathways might be involved in the hypoglycemic mechanism. The combined administration of pumpkin polysaccharides and puerarin could synergistically ameliorate T2DM.
Recognizing facial expression has attracted much more attention due to its broad range of applications in human-computer interaction systems. Although facial representation is crucial to final ...recognition accuracy, traditional handcrafted representations only reflect shallow characteristics and it is uncertain whether the convolutional layer can extract better ones. In addition, the policy that weights are shared across a whole image is improper for structured face images. To overcome such limitations, a novel method based on patches of interest, the Patch Attention Layer (PAL) of embedding handcrafted features, is proposed to learn the local shallow facial features of each patch on face images. Firstly, a handcrafted feature, Gabor surface feature (GSF), is extracted by convolving the input face image with a set of predefined Gabor filters. Secondly, the generated feature is segmented as nonoverlapped patches that can capture local shallow features by the strategy of using different local patches with different filters. Then, the weighted shallow features are fed into the remaining convolutional layers to capture high-level features. Our method can be carried out directly on a static image without facial landmark information, and the preprocessing step is very simple. Experiments on four databases show that our method achieved very competitive performance (Extended Cohn-Kanade database (CK+): 98.93%; Oulu-CASIA: 97.57%; Japanese Female Facial Expressions database (JAFFE): 93.38%; and RAF-DB: 86.8%) compared to other state-of-the-art methods.
Purpose
This paper aims to establish a multi-input equilibrium manifold expansion (EME) model for gas turbine (GT). It proposes that the extension of model input dimension is realized based on ...similarity theory and affine structure in the framework of single-input EME model. The study aims to expand the scope of application of the EME model so that it can be used for the control or fault diagnosis of GTs.
Design/methodology/approach
In this paper, the concepts of corrected equilibrium manifold expansion (CEME) model and multi-cell equilibrium manifold expansion (MEME) model are first proposed. This paper uses theoretical analysis and simulation experiments to demonstrate the effectiveness of the bilayer equilibrium manifold expansion (BEME) model, which is a combination of the CEME and the MEME models. Simulation experiments include confirmatory experiments and comparative experiments.
Findings
The paper provides a new sight into building a multiple-input EME (MI-EME) model for GTs. The proposed method can build an accurate and robust MI-EME model that has superior performance compared with widely used nonlinear models including Wiener model (WM), Hammerstein model (HM), Hammerstein–Wiener model (HWM) and nonlinear autoregressive with exogenous inputs (NARX) network model. In terms of accuracy, the maximum error percentage of the proposed model is just 1.309%, far less than WM, HM and HWM. In terms of the stability of model calculation, the range of the mean error percentage of the proposed model is just a quarter of that of NARX network model.
Originality/value
The paper fulfills the construction of a novel multi-input nonlinear model, which has laid a foundation for the follow-up research of model-based GT fault detection and isolation or GT control.
Fault detection and diagnosis can improve safety and reliability of gas turbines. Current studies on gas turbine fault detection and diagnosis mainly focus on the case of abundant fault samples. ...However, fault data are rare or even unavailable for gas turbines, especially newly-run gas turbines. Aiming to realize fault detection with only normal data, this paper proposes the concept of normal pattern group. A group of long-short term memory (LSTM) networks are first used for characterizing the mapping relationships among measurable parameters of healthy three-shaft gas turbines. Experiments show that the proposed method can detect all 13 common gas path faults of three-shaft gas turbines sensitively while remaining low false alarm rate. Comparison experiment with single normal pattern model verifies the necessaries and superiorities of using normal pattern group. Meanwhile, comparison between LSTM network and other methods including support vector regression, single-layer feedforward neural network, extreme learning machine and Elman recurrent neural network verifies the superiorities of LSTM network in fault detection. Furthermore, comparison experiment with four common one-class classifiers further verifies the superiorities of the proposed method. This also indicates the superiorities of data-driven methods and gas turbine principle fusion to some extent.
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