- 标题
- 摘要
- 关键词
- 实验方案
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Adaptive Solar Power Forecasting based on Machine Learning Methods
摘要： Due to the existence of predicting errors in the power systems, such as solar power, wind power and load demand, the economic performance of power systems can be weakened accordingly. In this paper, we propose an adaptive solar power forecasting (ASPF) method for precise solar power forecasting, which captures the characteristics of forecasting errors and revises the predictions accordingly by combining data clustering, variable selection, and neural network. The proposed ASPF is thus quite general, and does not require any specific original forecasting method. We first propose the framework of ASPF, featuring the data identification and data updating. We then present the applied improved k-means clustering, the least angular regression algorithm, and BPNN, followed by the realization of ASPF, which is shown to improve as more data collected. Simulation results show the effectiveness of the proposed ASPF based on the trace-driven data.
关键词： machine learning,k-means,BPNN,adaptive solar power forecasting,LARS
更新于2025-09-23 15:23:52
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Potential of Near-infrared Spectroscopy to Detect Defects on the Surface of Solid Wood Boards
摘要： Defects on the surface of solid wood boards directly affect their mechanical properties and product grades. This study investigated the use of near-infrared spectroscopy (NIRS) to detect and classify defects on the surface of solid wood boards. Pinus koraiensis was selected as the raw material. The experiments focused on the ability to use the model to sort defects on the surface of wood into four types, namely live knots, dead knots, cracks, and defect-free. The test data consisted of 360 NIR absorption spectra of the defect samples using a portable NIR spectrometer, with the wavelength range of 900 to 1900 nm. Three pre-processing methods were used to compare the effects of noise elimination in the original absorption spectra. The NIR discrimination models were developed based on partial least squares and discriminant analysis (PLS-DA), least squares support vector machine (LS-SVM), and back-propagation neural network (BPNN) from 900 to approximately 1900 nm. The results demonstrated that the BPNN model exhibited the highest classification accuracy of 97.92% for the model calibration and 97.50% for the prediction set. These results suggest that there is potential for the NIR method to detect defects and differentiate between types of defects on the surface of solid wood boards.
关键词： Surface defects,BPNN,PLS - DA,LS-SVM,Near-infrared spectroscopy,Solid wood boards
更新于2025-09-23 15:22:29
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[Laser Institute of America ICALEO? 2015: 34th International Congress on Laser Materials Processing, Laser Microprocessing and Nanomanufacturing - Atlanta, Georgia, USA (October 18–22, 2015)] International Congress on Applications of Lasers & Electro-Optics - Prediction of weld bead for fiber laser keyhole welding based on FEA
摘要： Fiber laser keyhole welding as a popular metal joining process has been widely used in a variety of applications especially automotive, shipbuilding and aerospace industries. Although process parameters determination based on experiments is the frequently used in the practical welding, it is often a very costly and time consuming. Accurately predicting the weld bead without expensive trial experiments has great theoretical significance and engineering value for welding process parameters pre-selection. An innovative volume heat source model was proposed for weld bead geometry prediction through finite element analysis (FEA) in fiber laser keyhole welding. The hybrid heat source model consists of a double ellipsoid heat source and a 3D Gaussian heat distribution model. To validate the effectiveness of the proposed heat source model, the fiber laser keyhole welding of the stainless steel SUS301L-HT has been carried out in this paper. The main three parameters, laser power (LP), welding speed (WS) and focal position (FP) have been taken into consideration as the design variables. Both of the predicted values from the FEA and back propagation neural network (BPNN) are compared with the experimental results. The FEA predicted results achieve good agreement with experimental results of weld bead shape and dimension and are better than BPNN predicted results. The objective variation trend is also analyzed by two prediction methods. From the discussion, it is revealed that the proposed prediction method of weld bead is effective for fiber laser keyhole welding process and replacing the expensive experiments.
关键词： Weld bead prediction,Keyhole welding,Fiber laser,FEA,BPNN
更新于2025-09-12 10:27:22
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - A New Strategy to Detect Lung Cancer on CT Images
摘要： Lung cancer has a very low cure rate in the advanced stages, with effective early detection, the survival rate of lung cancer could be highly raised. Detection of lung cancer in the early stages plays a vital role for human health. Computed tomography (CT) images, which provide electronic densities of tissues, are widely applied in radiotherapy planning. The proposed system based on CT technology consists of image acquisition, preprocessing, feature extraction, and classification. In the preprocessing stage, RGB images are converted to grayscale images, the median filter and the Wiener filter are used to uproot noises, Otsu thresholding method is applied to convert CT images, and REGIONPROPS function is used to exact body region from binary images. In the feature extraction stage, features, like Contrast, Correlation, Energy, Homogeneity, are extracted through statistic method Gray Level Co-occurrence Matrix (GLCM). In the final stage, extracted features, together with Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN), are used to identify lung cancer from CT images. The performance of the proposed system shows satisfactory results of 96.32% accuracy on SVM and 83.07% accuracy on BPNN respectively.
关键词： BPNN,SVM,image processing,lung cancer detection,GLCM
更新于2025-09-10 09:29:36