farc

[AzetSar]

First, gravity is a force that causes objects to attract one another. The simplest way to understand gravity is through Isaac Newton's law of universal gravitation. This law states that every particle in the universe attracts every other particle. The more massive an object is, the more strongly it attracts other objects. The closer objects are, the more strongly they attract each other. An enormous object, like the Earth, easily attracts objects that are close to it, like apples hanging from trees. Scientists haven't decided exactly what causes this attraction, but they believe it exists everywhere in the universe. Second, air is a fluid that behaves essentially the same way liquids do. Like liquids, air is made of microscopic particles that move in relation to one another. Air also moves like water does -- in fact, some aerodynamic tests take place underwater instead of in the air. The particles in gasses, like the ones that make up air, are simply farther apart and move faster than the particles in liquids. The first step is to construct the salient feature extraction module. The convolutional block attention module (CBAM) based on SPCNN to perform saliency detection. Then a SPherical Convolutional Gated Recursive Unit (SP-ConvGRU) is used, which is a time-series model of FoV information from the FoV feedback from a small number of users. Finally, salient spatial-temporal video features and historical FoV information are combined for accurate FoV prediction. Experimental results show that the performance of the proposed method is better than other prediction methods. Presenting the special FoV prediction problem in a 360-degree video multicast scenario and designing a prediction method with video saliency and a limited number of real users’ FoV information. Using SPCNN to eliminate the projection distortion of 360-degree video, and proposing a saliency detection model based on SPCNN to extract spatial-temporal features from 360-degree video and introduce an attention mechanism in the network to improve the performance. Performing exhaustive experiments to show that the proposed method achieves better results than other methods on publicly available 360-degree video datasets.|Transient thermal analysis determines temperatures. Other thermal quantities which vary over time. In many instances, the temperatures from a transient thermal analysis are utilized as remarks for a structural analysis, which is used for thermal stress evaluations. The temperature distribution's variation over time is useful in many practical applications such as with a quenching analysis for heat treatment or with cooling of electronic packages. Several heat transfer applications such as electronic package design, heat treatment problems, nozzles, fluid-structure interaction problems, pressure vessels, engine blocks, etc. involve transient thermal analysis. A transient thermal assessment can be either linear or nonlinear. Temperature dependent material properties such as thermal conductivity, specific heat or density, or radiation effects or temperature dependent convection coefficients can produce in nonlinear analysis which requires a reiterative procedure to reach accurate solutions. Most materials' thermal properties vary with temperature. Hence the analysis generally is nonlinear. Specific heat, density and thermal conductivity need to be defined for transient thermal assessments. 2

















































2114095 5855404 6814368 1146265 4400278 755019 5899888 5803026 5233983 9642479 8851195 7668527 2903191 3703587 7800432 5990506 4101968 6192526 5444707 3823535 9444161 4377068 6599156 5164961 6017329 6672727 5775840 2218892 566914 2961503 628900 2001522 9413753 8543902 3612815 4016332 3698803 4713780 6702504 3209705 1027621 1399700 6507112 4927142 7141279 9865103 9630138 1094501 3685661 5927121 621597 9331692 670850 6649105 7351009 9236001 4730874 6003899 1174131 2528607 6766078 7450179 1696100 6304233 5060053 3907211 8516679 1100270 2471731 8085344 4774185 7984769 8792900 8654820 9834330 1414635 1870088 205213 987766 7043973 3567183 7473087 6466485 6243363 9191616 6817389 4663387 1509464 1452158 382971 6856858 9509610 2547249 947845 3319659 978012 495815 7914016 5130077 9157692 8275595 7406960 6548544 1051969 6890379 1951882 5288210 8149069 5933959 7702431 532131 5895358 7768302 9691632 1037229 3584645 3895415 8059058 8172673 8919614 4616522 740124 1233402 3948947 5583026 575401 4863662 1591196 7320091 7317500 960719 4483956 6325661 7496207 9866396 362190 8832959 7609985 8227154 683697 837633 8622821 3330921 1733897 469244 2226243 6293060 9912938 1322157 1833799 8923562 5437849 7817208 3788372 1615070 1161081 6205292 6736855 2098842 5745370 7291501 9649782 7422132 2901278 9490372 1453219 3975763 900121 3292556 7734387


site
site
site
site
site
site
site
site
site
site
site
site