Considering that anxiety affects people's work and life, accurate detection of anxiety has important application value. Considering the interaction between different channels, a PLI and TD-2DPCA anxiety recognition method based on EEG signal is proposed. In this method, the collected original EEG signal is first preprocessed, the pre-processed signal is decomposed into five rhythmic waves by wavelet change, and the functional connection information of the brain is obtained by phase delay index coupling method for the five signals respectively, and then the anxiety features are extracted by bidirectional and two-dimensional principal component analysis method. Finally, the feature classification is implemented by support vector machine to realize the detection of anxiety. The results show that the proposed method has high recognition accuracy and can identify anxiety information well.
Nowadays, the world is facing the threat of energy exhaustion. In order to realize the maximum utilization of energy, this paper designs an organic Rankine cycle system for low-temperature waste heat generation. Firstly, the equipment and components in the organic Rankine cycle system are modeled. Secondly, combining specific analysis, designed the low temperature waste heat power generation organic Rankine's circulatory system, and analyses the relevant evaluation index, performance or independent variables is studied the effects of main parameters on the system performance, and finally, in finite time thermodynamic analysis method, analyzed the unit of each independent variable parameters on the heat transfer area of the influence of output power. It provides a powerful basis for the design of the system and the improvement and optimization of its performance. The simulation results show that the system designed in this paper can effectively recover waste heat from waste gas and realize the function of power generation, which has a certain practical value.
Single-image deraining is a classical problem in the field of low-level computer tasks. Most of the recent state-of-the-art image rain removal methods are trained on synthetic images, which have the problems of incomplete rain removal on real images and inability to process complex rain conditions. Based on these limitations, we propose a single-image deraining network based on multistage feature fusion (SIDNMFF). The network performs rain removal in four stages, with the first three stages using an improved encoder–decoder subnetwork for feature fusion to extract global and local information from the image, resulting in more detailed texture information of the obtained image. In the last stage, the network fuses the feature information extracted in the first three stages, performs feature extraction on the original resolution image, and finally outputs the final result of multistage deraining. The proposed method conducts a large number of comparative experiments on synthetic and real datasets as well as experiments on real datasets using no-reference metrics and the target detection method as the evaluation basis. Experimental results confirm that the proposed method achieves a satisfactory rain removal effect and outperforms the other methods.
KEYWORDS: Computer simulations, Signal to noise ratio, Navigation systems, Error analysis, Radar, Distance measurement, Time metrology, Mechanical engineering, Transmission electron microscopy, Data analysis
The matching function of terrain-aided navigation is not only related to the algorithm, also associated with the terrain characteristics of matching area. Aiming at terrain matching area selection and matching algorithm of the terrain height matching system, the method of terrain information entropy is put forward on the basis of statistical characteristics of the terrain roughness, signal-to-noise ratio, and then COR algorithm, MAD algorithm, MSD algorithm is adopted for real-time map and reference map matching, finally shows the simulation comparison of three kinds of matching algorithm. Result of simulation shows that among the index of matching accuracy and speed of three kinds of algorithm, COR algorithm possess fastest calculation speed and lowest precision, matching accuracy of MSD is slightly higher than MAD algorithm and calculation speed of MSD is placed in the middle, and the simulation results provide selection basis for terrain-aided inertial navigation.
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