Construction and optimization of non-parametric analysis model for meter coefficients via back propagation neural network.

This study addresses the drawbacks of traditional methods used in meter coefficient analysis, which are low accuracy and long processing time. A new method based on non-parametric analysis using the Back Propagation (BP) neural network is proposed to overcome these limitations. The study explores the classification and pattern recognition capabilities of the BP neural network by analyzing its non-parametric model and optimization methods. For model construction, the study uses the United Kingdom Domestic Appliance-Level Electricity dataset's meter readings and related data for training and testing the proposed model. The non-parametric analysis model is used for data pre-processing, feature extraction, and normalization to obtain the training and testing datasets. Experimental tests compare the proposed non-parametric analysis model based on the BP neural network with the traditional Least Squares Method (LSM). The results demonstrate that the proposed model significantly improves the accuracy indicators such as mean absolute error (MAE) and mean relative error (MRE) when compared with the LSM method. The proposed model achieves an MAE of 0.025 and an MRE of 1.32% in the testing dataset, while the LSM method has an MAE of 0.043 and an MRE of 2.56% in the same dataset. Therefore, the proposed non-parametric analysis model based on the BP neural network can achieve higher accuracy in meter coefficient analysis when compared with the traditional LSM method. This study provides a novel non-parametric analysis method with practical reference value for the electricity industry in energy metering and load forecasting.

Hypnotic and direct suggestions affect attitudes toward seeking professional psychological help.

This study investigated the impact of hypnotic suggestions on improving attitudes toward seeking professional psychological help (ATSPPH). The study administered the Chinese version of the ATSPPH scale on 303 college students, of which 61 with low levels of ATSPPH were recruited as the participants (male: 18; female: 43). All participants were tested with the Harvard Group Scale of Hypnotic Susceptibility, Form A, prior to the formal experiment and assigned with balancing hypnotic susceptibility in hypnotic suggestion, relaxation, or control groups. The main results were as follows: (1) counter-attitudinal information significantly improved explicit ATSPPH only for the hypnotic suggestion and relaxation groups, (2) the hypnotic suggestion group exhibited improvement in implicit ATSPPH and (3) a difference was observed between explicit and implicit attitudes in the process of providing counter-attitudinal information when changing ATSPPH.