This paper analyzes and studies the relationship between line ice cover and its influencing factors by collecting and organizing transmission line ice cover-micro-meteorological monitoring data, meteorological automat...
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To advance understanding of the ceiling temperature characters in tunnel fires, a physical model-free ant colony optimization networkalgorithm is developed. Compared to the traditional physical model-based methods, t...
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To advance understanding of the ceiling temperature characters in tunnel fires, a physical model-free ant colony optimization networkalgorithm is developed. Compared to the traditional physical model-based methods, the algorithm is not limited to the specific fire conditions and the structure of tunnels. The main advantage and contribution of the algorithm is that a novel ant colony optimization (ACO) network is constructed and firstly used to predict the ceiling temperature distribution in the tunnel fire as well as the maximal ceiling temperature based on only some sensors data. In order to verify the effectiveness of the algorithm, full scale burning tests were investigated in the largest fire experiment platform of the utility tunnel at the Tianjin Fire Research Institute, China. In addition, the developed ACO networkalgorithm has excellent performance by contrast with the commonly used back propagation (bp) neuralnetworkalgorithm. By compared with the experimental results and the results obtained from the bp neural network algorithm, the ability and the effectiveness of the algorithm were supported. The algorithm can be used to predict the ceiling temperature in the tunnel fires for rapid and efficient fire disaster evaluation.
The seasonal distribution coefficients of traffic volume have not been yet taken into account in asphalt pavement design in China. Through comprehensive analysis on the assessed data and the factors affecting monthly ...
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The seasonal distribution coefficients of traffic volume have not been yet taken into account in asphalt pavement design in China. Through comprehensive analysis on the assessed data and the factors affecting monthly distribution coefficients of traffic volume in different areas, the monthly and seasonal distribution coefficients of traffic volume in Harbin city, China has been predicted and determined using a back propagation (bp) neuralnetworkalgorithm. The research contents involve data investigation and analysis of monthly distribution coefficients of traffic volume, analysing the factors affecting the mentioned coefficients, prediction and determination of monthly and seasonal distribution coefficients of traffic volume, and application of those coefficients, etc. Through the application of monthly and seasonal distribution coefficients, the seasonal traffic volumes in a year were calculated, and the application results demonstrated that the seasonal distribution of traffic volume has a scientific rationality. In addition, this study indicated that the seasonal traffic volume could accurately evaluate the actual conditions about axle loads on the pavement, resulting in the asphalt pavement design to be more rational. Eventually, the findings could appropriately meet the requirements of actual vehicle loads passing on the road surface.
To detect computer communication network failures, a computer communication network fault detection based on an improved neuralnetworkalgorithm is proposed. A network fault diagnosis example is used to verify the ef...
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To detect computer communication network failures, a computer communication network fault detection based on an improved neuralnetworkalgorithm is proposed. A network fault diagnosis example is used to verify the effectiveness of the method. There are many network failure phenomena. Here, the author selected 13 network fault information parameters for a comprehensive diagnosis of network failures. The author designed a three-layer backpropagation (bp) neuralnetwork. There are 13 nodes in the input layer, corresponding to the above 13 network fault parameter information. The output layer has three nodes, which output the fault code sequence. The training of the network uses the trainlm0 function. The performance function uses the mean square error performance function mse0 and set e = 0.001;the network learning rate is set to a = 0.05. The author selects 100 failure data as the training set for network training and selects 10 sets of samples as the test set. The experimental data shows that after the network has been trained 25 times, the output error reaches the set precision e. After training the bpnetwork using this algorithm 140 times, the output error reaches the set precision e. This method effectively improves the effectiveness and accuracy of S network fault diagnosis.
In order to reduce the problem of excessive capacity allocation of energy storage units and achieve comprehensive optimization of aircraft power supply system volume, weight, and price, this paper proposes a capacity ...
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Computers have entered thousands of households as a result of the ongoing development of high technology and the progressive rise in people's living standards. As a result, computers have become an essential compo...
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With the increase of demand for power energy in social life, more and more overhead transmission lines have been built. In order to ensure the safety and stability of the transmission network, lightning protection and...
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6s(Second Simulation of the Satellite Signal in the Solar Spectrum) radiative transfer model is one of the atmospheric correction algorithms based on the atmospheric radiative transmission model. It is widely used bec...
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ISBN:
(数字)9781510650145
ISBN:
(纸本)9781510650145;9781510650138
6s(Second Simulation of the Satellite Signal in the Solar Spectrum) radiative transfer model is one of the atmospheric correction algorithms based on the atmospheric radiative transmission model. It is widely used because of its high correction accuracy. Meanwhile, it is criticized for the complexity of the parameters and the efficiency of the correction process. 6S model needs to establish a look-up table based on the geometric conditions and aerosol conditions which directly determines the accuracy of the atmospheric correction. This paper analyzes the limitations of traditional look-up table method and uses artificial intelligence algorithms such as the support vector regression(SVR) algorithm and the back propagation (bp) algorithm to instead the traditional look-up table method. The experiments' results show that the output value and predictive value fit well. Both are better than the traditional linear interpolation performance results, and the bpalgorithms performs better, which verifies the feasibility of bp neural network algorithms prediction model instead of linear interpolation method for table lookup. Finally, this paper takes Landsat-8 data as an example, uses the method proposed in this article to perform atmospheric correction, and compares the FLAASH model correction results. The visual performance results of the two are roughly the same.
High-precision short-term wind generation prediction results are conducive to making a scientific generation plan and improving the wind power absorption capacity of the power grids. Based on the analysis of the relat...
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ISBN:
(纸本)9781665434980
High-precision short-term wind generation prediction results are conducive to making a scientific generation plan and improving the wind power absorption capacity of the power grids. Based on the analysis of the relationship between the numerical weather prediction and wind power, this paper proposes a short-term wind generation combined forecast model considering meteorological similarity to improve the prediction accuracy of short-term wind power. In this method, the meteorological similarity day model, the extreme gradient boosting algorithm and the back propagation neuralnetworkalgorithm are selected for achieving the short-term wind power prediction. Then, the particle swarm optimization algorithm is applied to determine the weight of each single forecasting model. Finally, the prediction results are obtained through the combination of the single model prediction results. With the realistic wind power data collected from a wind farm in Xinjiang province, the short-term wind forecasting task is achieved by the proposed method. The simulation results illustrate that the combined model proposed in this paper can effectively improve the forecasting performance of the benchmark models.
With the improvement in the integration of solar power generation, photovoltaic (PV) power forecasting plays a significant role in ensuring the operation security and stability of power grids. At present, the widely u...
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With the improvement in the integration of solar power generation, photovoltaic (PV) power forecasting plays a significant role in ensuring the operation security and stability of power grids. At present, the widely used backpropagation (bp) and improved bp neural network algorithm in short-term output prediction of PV power stations own the drawbacks of neglection of meteorological factors and weather conditions in inputs. Meanwhile, the existing traditional bp prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. Therefore, based on the PV power plant in Lijiang, considering the related factors that influence PV output such as solar irradiance, environmental temperature, atmospheric pressure, wind velocity, wind direction, and historical generation data of the PV power station, three neuralnetworkalgorithms (i.e., bp, GA-bp, and PSO-bp) are utilized respectively in this work to construct a short-term forecasting model of PV output. Simulation results show that GA-bp and PSO-bpnetwork forecasting models both obtain high prediction accuracy, which indicates GA and PSO methods can effectively reduce the prediction errors in contrast to the original bp model. In particular, PSO owns better applicability than GA, which can further reduce the errors of the PV power prediction model.
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