In power grids and towers of tens of kilometers, it is impractical to use only one drone for inspection and cleaning operations. The joint operation of multiple UAVs complies with the effectiveness and efficiency arrangements. However, in the case of joint flight and operation of multiple UAVs, GOS becomes important, which is also the important core of this research. This study mainly applies the combined optimization of hyperbolic partial differential equations through the mathematical wave vibration model to achieve the GOS of multi-unit UAV collaborative power grids and towers detection and cleaning system. This study analyzed the flight position convergence, and found that the OCS of multi-unit UAV collaborative power grids and towers detection and cleaning system in this study is more capable of controlling the flight position than the previous method, and the data is similar to 90 m. Also, the time delay error is also compared. The result can also be seen that the OCS of this study is better than the traditional method by about 0.2 under the same iteration steps. To sum up the foregoing, This study believes that the OCS of multi-unit UAV collaborative power grids and towers detection and cleaning system in this study has better tracking and matching capabilities of position and flight speed of UAV, with less delay error and better convergence, so it can provide The robustness and reliability of OCS that meet our needs.
Risk Assessment contributes to optimizing the allocation of resources at the enterprise level, which achieves its goals, so the matter needs centralized management of risks on the enterprise level, not for each project. The risk assessment is carried out through several stages and by using various methods. This research provides an analytical view of risk assessment in concurrent multiple software projects environment. Research experiment has proven high levels of accuracy, reaching almost from 93
For years, the study on human-computer interaction in the mixed reality has been exploring in more natural and efficient interacting ways. As a common interacting component, the traditional menu on tangible user interface is not well suited to virtual interactive scenes in mixed reality and virtual menu represents a new category of efficient interacting methods in mixed reality. Recently, the combination of gesture recognition with the virtual menu has attracted little attention from researchers. To implement a natural, intuitive, and efficient interaction, in this work, a virtual menu using gesture recognition is proposed for the 3D object manipulation in mixed reality. In particular, several gesture states with respect to the different stages of menu interaction are first defined; then, the gestures on the screen are mapped to 3D virtual objects through coordinate transformation; and finally, the style and interaction logic of the menu is introduced to. In the experiment, the user’s interaction results using different menus are evaluated by comparing the time needed and the result’s accuracy with the same tasks. The experimental results show that our proposal can effectively reduce the number of interacting operations, and improve the efficiency of interacting process.
There are incredibly renewable outlets such as solar and wind intermittent, and machine reliability is hard to sustain with the intolerable balance of green energy resources. Because of population growth and technological development, global energy demand is increasing exponentially. However, civilization must address a stable energy source for two benefits, a cost-effective and sustainable basis for future renewable energy production. Solar Energy is a platform for green energy and is a potential means of solving problems that face a possible energy system. The power output is primarily determined by the properties of the incoming radiation and solar panels. Precise and accurate knowledge of these variables provides a consistent model for predicting the solar future. Solar power forecasts would have a significant effect on the viability of massive solar energy projects. This study presents a new approach to predict the expected amount of energy to be produced from Photovoltaics using a hybrid deep neural network based on a combination of extended short-term memory networks (LSTM) and convolutional neural networks (CNN), taking into account the energy of electrons and photons as well as elements and climatic conditions as input data.
With an improved algorithm based on the Otsu method, this paper proposes solving the shadow region problem caused by non-uniform illumination. The recent image processing algorithms for handling the shaded area’s issue for the massive difference in the image grey value will not be practical. It is not easy to directly set the shaded area’s content to “black” based on this problem. This paper proposes improved threshold image segmentation for Non-uniform illumination based on the Otsu optimization Approach. It uses the local area threshold and window image pixel by sliding to enhance the image recognition rate of a shaded area. Experimental results verify that the proposed segmentation method has more robust adaptability to images with shadows. Compared with the classical global OTSU algorithm and genetic algorithm optimization, the image recognition rate is 96%.
