Efficient solar power heating system based on lenticular condensation

Abstract

The utilization of solar power is one of the most effective approaches to achieve the goal of energy conservation and emission reduction for industrial areas. However, the temperature of the collected heat in a typical non-concentrating solar power collector is normally lower than 90 degrees Celsius, which indeed dissatisfies the demand of the heat required in this industry. Thus, in order to obtain an efficient source of heat, the condensed solar power heating technology will be put into practice in accordance with the features of low energy density and intermittence corresponding to solar power. In this paper, we firstly validate the correctness of the mathematical model of the efficient solar power heating system based on lenticular condensation. Thereafter, the practical design based on the verified mathematical model can be produced. Finally, the simulated results have been analyzed in detail and the assets of this design can thus be revealed.

The Cloud Computing Center Performance Analysis Model Based on Queuing Theory

“…(4), shows that with the increase of the input queue buffer length, when service node number m = 100, the growth of the total system requests is quite gentle, when m = 500, then the influence of queue buffer length on the total system requests cannot be observed [17,18]. (2) Blocking Probability Blocking probability curve is shown in Fig.…”

Performance Evaluation Model of Logistics Enterprises Considering Mass Data in the Internet of Things

“…GE can be obtained through an analogy way, and the tabular form thereof is as shown in Table 3. is calculated according to formulae (11) and (12), as shown in Table 4.…”

“…According to definition 2, the parameters of ( , ) GE are obtained from the combination of the evaluation index sets 12 …”

Image Denoising Algorithm Based on Non Related Dictionary Learning

“…[1,2], wherein the basic thought thereof is to adopt the local self-similarity or nonlocal self-similarity of the image for denoising, and such algorithm usually has the advantages of high computation efficiency, but the denoised images are usually too smooth; the denoising algorithm based on transform domain filtering involves Fourier transform, wavelet transform, BM3D algorithm, etc. [3,4], wherein the basic thought thereof is to adopt the threshold value method and the different energy distributions of the transformed noise system and the image system to filter noises, and BM3D algorithm is adopted for image block matching in order to convert the similarly structured two-dimension image blocks into three-dimension data through 3D transformation before implementing Wiener filtering; the denoising algorithm based on learning includes K-SVD algorithm [5][6][7], LSSC algorithm [8] and CSR algorithm [9][10][11][12][13], wherein the basic thought thereof is to adopt the local sparsity of the image for denoising. In allusion to the defects of the existing image denoising algorithms, the irrelevance of the redundant dictionary atoms shall be increased to enable the redundant dictionary obtained by learning to comprehensively describe the image texture information, so an image denoising algorithm based on non related dictionary learning is proposed in this article, wherein the basic thought of this algorithm is to adopt the non related dictionary learning technology to reduce the relevance of the redundant dictionary atoms and improve the image texture information expression ability of the redundant dictionary, thus to remove noises and keep the image detail information.…”