An image fusion method based on fuzzy regional characteristics is proposed in this paper. After the multi-resolution decomposition of an image, k-mean clustering is firstly done for the low frequency components of the each layer to decompose the low frequency image into important region, sub important region and background region. Then, all areas of the image are fuzzificated and fusion strategies are determined according to their fuzzy membership degrees. Finally, fusion result is obtained by the reconstruction from the multiresolution image representation. Experimental results and fusion quality assessments show the effectiveness of the proposed fusion method.

No abstract

To solve the research–practice gap and take one step forward toward using big data with real-world evidence, the present study aims to adopt a novel method using machine learning to pool findings from meta-analyses and predict the change of countermovement jump. The data were collected through a total of 124 individual studies included in 16 recent meta-analyses. The performance of four selected machine learning algorithms including support vector machine, random forest (RF) ensemble, light gradient boosted machine, and the neural network using multi-layer perceptron was compared. The RF yielded the highest accuracy (mean absolute error: 0.071 cm; R2: 0.985). Based on the feature importance calculated by the RF regressor, the baseline CMJ (“Pre-CMJ”) was the most impactful predictor, followed by age (“Age”), the total number of training sessions received (“Total number of training_session”), controlled or non-controlled conditions (“Control (no training)”), whether the training program included squat, lunge, deadlift, or hip thrust exercises (“Squat_Lunge_Deadlift_Hipthrust_True”, “Squat_Lunge_Deadlift_Hipthrust_False”), or “Plyometric (mixed fast/slow SSC)”, and whether the athlete was from an Asian pacific region including Australia (“Race_Asian or Australian”). By using multiple simulated virtual cases, the successful predictions of the CMJ improvement are shown, whereas the perceived benefits and limitations of using machine learning in a meta-analysis are discussed.

With continuous long-range transmission of high voltage, routing inspection and fault diagnosis of power lines are becoming more and more difficult, and then the automatic routing inspection of power lines based on aerial images emerges. Therefore, this paper proposes a method for automatic extraction of power lines based on the improved morphological algorithm and aerial images based on Hough transform in a complex natural background, that is, to first use Wiener filter algorithm to restore high-altitude aerial images and Gaussian filter to de-noise images to complete the image preprocessing. On this basis, this paper proposes an improved morphological operator for the edge detection of power lines. Then, this paper implements automatic identification and extraction of power lines based on Hough transform.

The crucial point of the UHF detection is the UHF sensor, which affects the sensitivity and anti-interference ability of the UHF detection. In this paper. the introduction of 5 different kinds of internal sensors of the Ultra High frequency (UHF) technology used to monitor the PD in the GIS was made in detail. With comparion of the external sensors, the anti-interference ability of internal sensors is much better. In order to evaluate the property of different sensors more precisely, several indicators are introduced in this paper. Furthermore, some unsolved issues about the sensot inside the GIS are presented for researchers to solve.