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As an important basic industry of national economy, the iron and steel industry has provided an important raw material guarantee for a long time. However there are a large number of hazard sources which are prone to safety accidents in the production process. Then safety evaluation in the production system is highly needed to effectively prevent the occurrence of accidents in iron and steel enterprises. Hence we introduce a method based on deep learning model to evaluate safety of the enterprises. Firstly, the risk factors and casualties in production process are investigated, and a set of safety evaluation index system is constructed.Secondly, since deep neural network model has the characteristics of strong feature extraction ability and simple model structure, we design a safety evaluation model based on deep neural network. The 25-dimensional evaluation index value is the input of the network, and the network output corresponds to five risk levels. On this basis, the optimization algorithm of deep neural network model is designed to explore the mapping relationship between risk characteristics and safety level. Tensorflow deep learning framework is used to build the evaluation model, classification loss function and network optimization method are designed to train the model. Finally, through experiments, the optimal model structure is determined by comparing the influence of different parameter optimization strategies, different hidden layer structures, and different activation functions on the safety evaluation performance. A three hidden layer structure with the Adam back propagation algorithm and LeakyRelu activation function is adopted to obtain higher accuracy and faster convergence rate. The experiments show that our evaluation model provides an operational method for evaluating the safety management status of iron and steel enterprises.
As an important basic industry of national economy, the iron and steel industry has provided an important raw material guarantee for a long time. However there are a large number of hazard sources which are prone to safety accidents in the production process. Then safety evaluation in the production system is highly needed to effectively prevent the occurrence of accidents in iron and steel enterprises. Hence we introduce a method based on deep learning model to evaluate safety of the enterprises. Firstly, the risk factors and casualties in production process are investigated, and a set of safety evaluation index system is constructed.Secondly, since deep neural network model has the characteristics of strong feature extraction ability and simple model structure, we design a safety evaluation model based on deep neural network. The 25-dimensional evaluation index value is the input of the network, and the network output corresponds to five risk levels. On this basis, the optimization algorithm of deep neural network model is designed to explore the mapping relationship between risk characteristics and safety level. Tensorflow deep learning framework is used to build the evaluation model, classification loss function and network optimization method are designed to train the model. Finally, through experiments, the optimal model structure is determined by comparing the influence of different parameter optimization strategies, different hidden layer structures, and different activation functions on the safety evaluation performance. A three hidden layer structure with the Adam back propagation algorithm and LeakyRelu activation function is adopted to obtain higher accuracy and faster convergence rate. The experiments show that our evaluation model provides an operational method for evaluating the safety management status of iron and steel enterprises.
In this paper, we consider the problem of approximating the safety margin of a single instance of a technical system based on inaccurate observations at specified time points. The solution to this problem is based on the selection of a certain set of reference points in time, in a small neighbourhood of which a sufficiently large number of inaccurate measurements are carried out. Analogously with the failure rate, it is assumed that the rate of decrease in the safety margin over time is represented by a polynomial of the fourth degree, and the safety margin itself is a polynomial of the fifth degree. A system of linear algebraic equations is constructed that determine the coefficients of this polynomial through its values and the values of its derivative at reference points in time. These values themselves are estimated by the method of linear regression analysis based on numerous observations in a small neighbourhood of reference points in time. A detailed computational experiment is carried out to verify the accuracy of the approximation of a fifth-degree polynomial and alternative ways of estimating it are constructed in the vicinity of points where the quality of approximation is insufficient.
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