Hydraulic ram pump is an automatic water-pumping equipment generally used to pump drinking and irrigation water in mountainous and rural areas having short of power. In the past, it has been analyzed and optimized by fabricating various prototypes and conducting experiments and comparisons. This process is time and labor consuming and detailed flow features cannot be determined except efficiency, discharge, and period. In this paper, a method for the optimal design and performance analysis of hydraulic ram pump system with numerical simulation and physical experiment is presented to shorten the number of prototypes and develop high-performance product. The proposed evaluation indexes include head loss coefficient, drag coefficient, eccentric distance of pressure, and velocity distribution uniformity. Two types of structures, named front-enlargement and back-enlargement, were initially designed. According to the numerical simulation, the latter one has lower head loss coefficient and drag coefficient, larger eccentric distance of pressure and higher velocity distribution uniformity and was adopted in the novel hydraulic ram pump. Then, the design theory and method on adjustable and high-head experimental platform have been developed, so that the delivery head can be easily controlled and regulated in laboratory. Experiments were carried out for the delivery heads of 2.0 m and 2.7 m and comparisons were conducted with other products. The results show that, when the delivery head is less than 50 m, the efficiency of the new product ranges from 50% to 70% while the delivery flow is the largest. Its application in Liangshui River, Beijing, indicates that the novel hydraulic ram pump is a practical tool in fountain sight and for irrigation purpose without external power input.
Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.
The occurrence of fog, mist, smog, or haze significantly reduces the visibility of the scenes and images, resulting in limited recognition of computer vision and computer graphics. So, removing haze from images is a must. In this paper, we regard image dehazing as a mathematical inversion process and image restoration based on atmospheric scattering models. The atmospheric light can be accurately estimated by combining the gray threshold segmentation and the skyline method. The improved least squares filtering method is used to optimize the transmittance map so that the edge details can be highlighted and the halo effect can be alleviated. A large number of test images show that our algorithm can achieve better experimental results than the other seven most advanced dehazing strategies. INDEX TERMS Grey-level threshold segmentation, skyline, least-square filter, halo artifact, dehazing.
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