KEYWORDS: Clouds, Mathematics, Education and training, Integration, Electrical engineering, Control systems, Universities, Professional development courses, Analog electronics, Servomechanisms
Based on the teaching method of "cloud classroom+integration of theory and practice", the curriculum group system of electrical and electronics is constructed according to the cloud platform. According to the training goal of electromechanical talents in our school, the main courses of electrical and electronics are divided based on the cloud platform, and the practice links of electrical and electronics courses group are run through with the help of "skill competition+subject competition". By using cloud classroom, mathematical knowledge and professional knowledge can be integrated into the teaching process, and the mathematical knowledge points involved in the courses of electrical engineering and electronics can be strengthened to improve the theoretical level of students. It aims to cultivate high- quality and high-level skilled talents who can understand both theory and practice and improve teachers' professional level and teaching efficiency.
In view of the stability and reliability of power system operation, it is necessary to adopt accurate short-time load forecasting. In order to improve the forecasting accuracy, a very short time power load forecasting method based on EEMD-LSSVM- integrated BPNN is proposed. Firstly, EEMD (Ensemble Empirical Mode Decomposition) is used to decompose the initial power sequence into a plurality of components with different frequencies, calculate the SE (Sample Entropy) of each component, and recombine the similar components with entropy values. LSSVM (Least Squares Support Vector Machine) is used to predict the low-frequency trend components, and integrated BPNN (Back Propagation Neural Network) based on Bagging integrated learning algorithm is used to predict the remaining components. Finally, the distributed power series are comprehensively predicted. The example shows that this method is a feasible short-term power load forecasting method and can effectively improve the forecasting accuracy and stability.
KEYWORDS: Data processing, Control systems, Transponders, Signal processing, Video processing, Process control, Microelectromechanical systems, Transceivers, Video, Data storage
TCAS can provide drivers with two kinds of information: traffic warning and decision warning. Among them, the relative height, distance, azimuth ascending/descending state and threat level of adjacent aircraft can be displayed on the electronic flight instrument by symbols with different colors and shapes. When the adjacent aircraft poses or may pose a threat to the aircraft, the traffic monitor will give a warning to the pilot and provide the relative orientation, which is helpful for the pilot to observe the adjacent aircraft before responding to the decision warning. When the adjacent aircraft is at the TA/VSI (Traffic Warning/Vertical Speed Indicator) boundary, a decision warning command will be issued.
The combination of rule-based reasoning (RBR) and case-based reasoning (CBR) is used to build an expert system for the endoscopic detection of aero-engines. The aircraft maintenance manual is converted into knowledge rules, and a rule base is constructed. Typical damages and corresponding maintenance decisions adopted are taken as cases to construct a case base. The rule base and the case base together form the expert system knowledge base, which is stored in the database. At the same time, the corresponding learning mechanism is established to realize the maintenance of diagnostic rules and typical cases. When the expert system works, through the man-machine interface, the user inputs the image related information, extracts the image damage information, carries on the reasoning in the rule base, obtains the maintenance decision according to the matching rule, regarding is difficult to make the maintenance decision in the critical state damage, needs to carry on the case-based reasoning, retrieves the similar historical case from the case base, finally unifies RBR and CBR reasoning result to make the maintenance decision comprehensively.
There is serious coherent speckle noise in the synthetic aperture radar (SAR) image. Several gradient operators’ performance was studied without speckle reducing in SAR image, included the Canny, Ratio operator and instantaneous coefficient of variation (ICOV). We mainly studied the performance of the above operators on edge detection capability and positioning accuracy for SAR image. Simulation test of one-dimensional synthesis signal showed that only ICOV can get the optimal single-value width response. In order to further validation, synthetic image and real SAR image would also be respectively applied in segmentation experiments. According to the segmentation results, it can be easily known that the segmentation results based on ICOV operator always get the fastest efficiency and the highest accuracy.
Aiming at the shortcomings of endoscopic image processing and detection technology in aero-engine fault diagnosis and maintenance, this paper proposes an endoscopic image processing and diagnosis method based on BP neural network learning algorithm. In this method, the feature extraction technology of endoscopic image in aero-engine fault diagnosis is studied, and the effective feature parameters are extracted from the internal damage region of aero-engine. The BP neural network model is established to improve the endoscopic image processing effect and improve the level of fault diagnosis. Finally, the BP neural network endoscopic image processing and diagnosis mechanism is simulated and experimentally studied for a Boeing 787 engine, and the expected diagnosis effect is achieved.
Aiming at the shortcomings of endoscopic image processing and detection technology in aero-engine fault diagnosis and maintenance, this paper proposes an endoscopic image processing and diagnosis method based on BP neural network learning algorithm. In this method, the feature extraction technology of endoscopic image in aero-engine fault diagnosis is studied, and the effective feature parameters are extracted from the internal damage region of aero-engine. The BP neural network model is established to improve the endoscopic image processing effect and improve the level of fault diagnosis. Finally, the BP neural network endoscopic image processing and diagnosis mechanism is simulated and experimentally studied for a Boeing 787 engine, and the expected diagnosis effect is achieved.
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