In this paper, cross-linked polyethylene (XLPE) cables of the same batch from Factory A, which ran from 1 to 8 years in Jiangsu Province, are sampled. Some widely accepted aging characterization methods of XLPE cables such as the gel content test, differential scanning calorimetry (DSC) test, tensile test and hardness test are employed to obtain the physicochemical, mechanical and electrical properties of the samples. Then, some lifespan prediction parameters significantly correlated with operating time are obtained through correlation calculations. Finally, a prediction method is proposed to predict the operating time of XLPE cables from Factory A. The test results indicate that parameters including the gel content Cge, the crystallinity XC, tensile strength σ, ultimate elongation δ, the dielectric permittivity e, and the dielectric loss Jtan are significantly correlated with operating time, which can be used in evaluating the aging degree of XLPE cables. Moreover, due to the high accuracy of the experimental verification, it turns out that the lifespan prediction method proposed in this paper can be used to determine the operating time of XLPE cables from Factory A in future research.

https://www.mdpi.com/2076-3417/10/15/5381/pdf

Research on Lifespan Prediction of Cross-Linked Polyethylene Material for XLPE Cables

The output of the micromachined silicon resonant accelerometer (MSRA) is prone to drift in a temperature-changing environment. Therefore, it is crucial to adopt an appropriate suppression method for temperature error to improve the performance of the accelerometer. In this study, an improved firefly algorithm-backpropagation (IFA-BP) neural network is proposed in order to realize temperature compensation. IFA can improve a BP neural network's convergence accuracy and robustness in the training process by optimizing the initial weights and thresholds of the BP neural network. Additionally, zero-bias experiments at room temperature and full-temperature experiments were conducted on the MSRA, and the reproducible experimental data were used to train and evaluate the temperature compensation model. Compared with the firefly algorithm-backpropagation (FA-BP) neural network, it was proven that the IFA-BP neural network model has a better temperature compensation performance. The experimental results of the zero-bias experiment at room temperature indicated that the stability of the zero-bias was improved by more than an order of magnitude after compensation by the IFA-BP neural network temperature compensation model. The results of the full-temperature experiment indicated that in the temperature range of -40 °C~60 °C, the variation of the scale factor at full temperature improved by more than 70 times, and the variation of the bias at full temperature improved by around three orders of magnitude. 

/pdf/temperature-compensation-method-based-on-an-improved-firefly-3ft1gmc3.pdf

Temperature Compensation Method Based on an Improved Firefly Algorithm Optimized Backpropagation Neural Network for Micromachined Silicon Resonant Accelerometers

In this paper, composite insulators of the same batch from Factory A, aged for 1–12 years in a high altitude area experimental station of Hunan province, were sampled. In order to investigate the changing law of lifespan prediction parameters with aging time, widely accepted testing methods, such as the static contact angle (CA) and hardness, were employed for composite insulators in accordance with previous research. Based on test results, lifespan prediction parameters were concluded and some parameters significantly correlated with aging time were filtered by means of correlation calculation. On this basis, a prediction method which can be used to determine the aging time of composite insulators was proposed based on a back propagation (BP) neural network. Test results indicate that parameters, including the static contact angle (θav), the relative content of Si (XSi) and O (XO) elements, and salt-fog flashover voltage (Uf), have significant correlation with aging time, and that these parameters can be used to evaluate the aging degree of composite insulators. In addition, due to the high accuracy in experimental verification, the method proposed in this paper can be used to predict the aging time of composite insulators from Factory A in high altitude areas in future research.

Research on Lifespan Prediction of Composite Insulators in a High Altitude Area Experimental Station

There are many rice fields and rice milling factories. Rice Milling Units (RMU) are still many who have not applied the prediction method for the sale of rice so that it can affect the availability of raw materials. The purpose of this research is to forecast product sales (rice) at RMU so that it can know the number of raw materials needed so that they avoid idle time. Obtain optimal forecasting; it compared to 2 (two) forecasting methods, Linear Regression, and Artificial Neural network with a Backpropagation algorithm. The results showed that the value of MSE on a linear regression method of 0.214, while at the time using Artificial Neural Network obtained an MSE value of 0.00099713. Based on the value of the MSE, the smallest MSE is forecasting by the Backpropagation Neural Network method.

An Application of Backpropagation Neural Network for Sales Forecasting Rice Miling Unit

Nanowires (NWs) with a unique one-dimensional structure can monolithically integrate high-quality III-V semiconductors onto Si platform, which is highly promising to build lasers for Si photonics. However, the lasing from vertically-standing NWs on silicon is much more difficult to achieve compared with NWs broken off from substrates, causing significant challenges in the integration. Here, the challenge of achieving vertically-standing NW lasers is systematically analysed with III-V materials, e.g. GaAs(P) and InAs(P). The poor optical reflectivity at the NW/Si interface results severe optical field leakage to the substrate, and the commonly used SiO2or Si2N3dielectric mask at the interface can only improve it to ∼10%, which is the major obstacle for achieving low-threshold lasing. A NW super lattice distributed Bragg reflector is therefore proposed, which is able to greatly improve the reflectivity to >97%. This study provides a highly-feasible method to greatly improve the performance of vertically-standing NW lasers, which can boost the rapid development of Si photonics.

Design of high-quality reflectors for vertical III-V nanowire lasers on Si.

Prediction of congestion degree is critical for optical networks to cooperate with the network control strategies. Proposed in this paper is a method of congestion degree prediction for optical networks based on BP artificial neural network. The principles are presented, and the simulation results show that our proposed is competent for network congestion degree prediction.

Prediction of congestion degree for optical networks based on bp artificial neural network

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Prediction of congestion degree for optical networks based on bp artificial neural network