KEYWORDS: Education and training, Support vector machines, Neural networks, Feature extraction, Machine learning, Detection and tracking algorithms, Deep learning, Pattern recognition, Evolutionary algorithms, Data modeling
In recent years, the requirements for strip steel surface quality have also increased. However, traditional Non-destructive testing detection is not good enough. Deep learning-based algorithms are also unable to meet the requirements of small sample data and real-time performance for strip surface defect detection. The DAG-SVM has the advantage of solving small data, non-linear and high-dimensional pattern recognition. Therefore, this paper selects the Directed Acyclic Graph (DAG) to construct the SVM multi-class classifier. And representative shape features are selected as the feature vectors of the samples. At the same time, the kernel function and parameter search are used to further improve the recognition rate. The final results show that the average recognition rate using this method is 95.5%, which can meet the practical needs. In the comparison with BP neural network, the method of this paper is also better than BP neural network in general.
KEYWORDS: Data modeling, Error analysis, Evolutionary algorithms, Control systems, Neural networks, Chemical analysis, Analytical research, Mining, Industrial chemicals, Data analysis
As a basic source of energy in China, the rise in the price of coal has a bearing on the development of the Chinese economy. In view of the influencing factors of thermal coal price, the prophet algorithm and long term memory algorithm are proposed to predict the price of thermal coal. Based on the characteristics and principles of these two algorithms, and according to the characteristics of thermal coal, the parameters suitable for the experimental object are selected. Through the prediction of the historical data by using these two algorithms, analyzing and comparing the error of the prediction results with the actual value. Following this, a time series algorithm more suitable for thermal coal is obtained.
A method for optical generation and long-distance transmission of millimeter-wave signals is proposed in this paper. Two phase-dependent dual-wavelength laser beams are generated by a dual-wavelength laser. After long-distance transmission through the optical fiber, A beat signal can be generated. In this study, the frequency of signal can reach 24.640 GHz within 1km transmission distance. Although the beat signals above 30 GHz cannot be observed due to equipment limitations, an uncomplicated method has been shown in generating and long-distance transmission of millimeter-wave signals. Also, the method has its unique advantages of avoiding interference and electromagnetic pollution.
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