Electric energy, as an economical and clean energy, plays a significant role in the development of science and technology and the economy. The motor is the core equipment of the power station; therefore, monitoring the motor vibration and predicting time series of the bearing vibration can effectively avoid hazards such as bearing heating and reduce energy consumption. Time series forecasting methods of motor bearing vibration based on sliding window forecasting, such as CNN, LSTM, etc., have the problem of error accumulation, and the longer the time-series forecasting, the larger the error. In order to solve the problem of error accumulation caused by the conventional methods of time series forecasting of motor bearing vibration, this paper innovatively introduces Informer into time series forecasting of motor bearing vibration. Based on Transformer, Informer introduces ProbSparse self-attention and self-attention distilling, and applies random search to optimize the model parameters to reduce the error accumulation in forecasting, achieve the optimization of time and space complexity and improve the model forecasting. Comparing the forecasting results of Informer and those of other forecasting models in three publicly available datasets, it is verified that Informer has excellent performance in time series forecasting of motor bearing vibration and the forecasting results reach 10−2∼10−6.
Due to the development needs of the intelligent manufacturing industry, the use of drones is expanding indefinitely, and at the same time, extremely high requirements are imposed on the autonomous navigation of drones to reduce human intervention. This paper presents an UAV/ UGV scheduling problem in which the UAV needs to be recharged by the UGV in order to working in persistent tasks, and meanwhile, the UGV need to visits UGV working depots. Two different problem are presented: fixed charging sets problem (FCSP) and discrete charging sets problem (DCSP). In FCSP, charging sets are fixed, and a two-stage travelling salesman problem method is proposed to solve the problem. DCSP is a modification of FCSP while charging segments are discrete into serval segments, an graph transformation approach was proposed to transform DCSP into GTSP, so DCSP can be resolved by using GTSP solvers. Simulation results shows that both DCSP and FCSP can ensure UAV/UGV work in persistent tasks, and the graph transformation algorithm can efficiently transform DCSP into GTSP.
Fussy c-mean cluster algorithm (FCM) is often used in image segmentation, but most FCM algorithm is time wasteful, for the purpose of improving segmentation efficiency, a fast segmentation algorithm based on histogram constraint is proposed. The new algorithm resample initial image to reduce data size, but reduction of data size space may cause distortion and make FCM converged to error threshold, in order to get best segmentation result, constraint based on distance deviation of histogram is incorporated. The initial histogram is smoothed to get its profile before calculating distance deviation, and its amplitude is transformed to a unified value. Correct resample ratio can be calculated from the changed histogram by golden section searching algorithm. Experiments are performed to validate the new fast FCM and the results shown that the segmentation result keeps in the same level with traditional FCM algorithm while the processing speed increases greatly. Compared with the other algorithms, the consumed time of our method is 1.0%-4.8% of traditional FCM algorithm, 3.4%-9.4% of 2D entropy algorithm and 5.1%-13.6% of Otsu algorithm, the average processing efficiency is 63, 22 and 15 times higher than above algorithms. The experiment results are consistent with theory and prove that distance deviation of histogram can reflect distortion degree of resample image, and the right segmentation can be deduced from reduced data.
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