In this paper, an improved Fast-R-CNN nuclear power cold source disaster biological image recognition algorithm is proposed to improve the safety operation of nuclear power plants. Firstly, the image data sets of the ...In this paper, an improved Fast-R-CNN nuclear power cold source disaster biological image recognition algorithm is proposed to improve the safety operation of nuclear power plants. Firstly, the image data sets of the disaster-causing creatures hairy shrimp and jellyfish were established. Then, in order to solve the problems of low recognition accuracy and unrecognizable small entities in disaster biometrics, Gamma correction algorithm was used to optimize the image of the data set, improve the image quality and reduce the noise interference. Transposed convolution is introduced into the convolution layer to increase the recognition accuracy of small targets. The experimental results show that the recognition rate of this algorithm is 6.75%, 7.5%, 9.8% and 9.03% higher than that of ResNet-50, MobileNetv1, GoogleNet and VGG16, respectively. The actual test results show that the accuracy of this algorithm is obviously better than other algorithms, and the recognition efficiency is higher, which basically meets the preset requirements of this paper.展开更多
The paper proposed a prediction method of combustion temperature field in a coal-fired boiler of a 350 MW unit through deep learning.The method utilizes operating parameters and multi-point temperature data as inputs ...The paper proposed a prediction method of combustion temperature field in a coal-fired boiler of a 350 MW unit through deep learning.The method utilizes operating parameters and multi-point temperature data as inputs for online predicting temperature field.Firstly,to establish the mapping relationship between temperature field and operating parameters as well as multi-point temperature data,a data set was constructed.In the data set,the temperature fields were obtained through the inversion of thermal radiation imaging model,while the operating parameters were collected from the distributed control system of the unit.Then,a transpose convolutional neural network(TCNN)model was developed to obtain the mapping relationship based on the data set.In the simulation study,multi-point temperature data were obtained through the forward calculation of the thermal radiation imaging model.The impact of the quantity and location of multi-point temperature data on generalization ability of the TCNN model was analyzed.In the experimental study,multi-point temperature data were measured by image probes.A comparative analysis was conducted to evaluate generalization ability of the TCNN model with and without the addition of multi-point temperature data,benchmarking against existing methods.With the addition of multi-point temperature data,the mean absolute percentage errors of predicted temperature fields are all less than 1.6%at four stable loads,while the maximum relative error of average value of predicted temperature field decreases from 7.24%to 2.77%during variable load process.The proposed prediction method has promising potential for online combustion monitoring in the furnace.展开更多
文摘In this paper, an improved Fast-R-CNN nuclear power cold source disaster biological image recognition algorithm is proposed to improve the safety operation of nuclear power plants. Firstly, the image data sets of the disaster-causing creatures hairy shrimp and jellyfish were established. Then, in order to solve the problems of low recognition accuracy and unrecognizable small entities in disaster biometrics, Gamma correction algorithm was used to optimize the image of the data set, improve the image quality and reduce the noise interference. Transposed convolution is introduced into the convolution layer to increase the recognition accuracy of small targets. The experimental results show that the recognition rate of this algorithm is 6.75%, 7.5%, 9.8% and 9.03% higher than that of ResNet-50, MobileNetv1, GoogleNet and VGG16, respectively. The actual test results show that the accuracy of this algorithm is obviously better than other algorithms, and the recognition efficiency is higher, which basically meets the preset requirements of this paper.
基金supported by the National Key R&D Program of China(2024YFB4104804).
文摘The paper proposed a prediction method of combustion temperature field in a coal-fired boiler of a 350 MW unit through deep learning.The method utilizes operating parameters and multi-point temperature data as inputs for online predicting temperature field.Firstly,to establish the mapping relationship between temperature field and operating parameters as well as multi-point temperature data,a data set was constructed.In the data set,the temperature fields were obtained through the inversion of thermal radiation imaging model,while the operating parameters were collected from the distributed control system of the unit.Then,a transpose convolutional neural network(TCNN)model was developed to obtain the mapping relationship based on the data set.In the simulation study,multi-point temperature data were obtained through the forward calculation of the thermal radiation imaging model.The impact of the quantity and location of multi-point temperature data on generalization ability of the TCNN model was analyzed.In the experimental study,multi-point temperature data were measured by image probes.A comparative analysis was conducted to evaluate generalization ability of the TCNN model with and without the addition of multi-point temperature data,benchmarking against existing methods.With the addition of multi-point temperature data,the mean absolute percentage errors of predicted temperature fields are all less than 1.6%at four stable loads,while the maximum relative error of average value of predicted temperature field decreases from 7.24%to 2.77%during variable load process.The proposed prediction method has promising potential for online combustion monitoring in the furnace.
