A PLS-Based Weighted Artificial Neural Network Approach for Alpha Radioactivity Prediction inside Contaminated Pipes

Xianguo Tuo,Mingzhe Liu,Jianbo Yang,Yi Cheng,Lei Wang

doi:10.1155/2014/517605

Abstract

Long-range alpha detection (LRAD) has been used to measure alpha particles emitting contamination inside decommissioned steel pipes. There exists a complex nonlinear relationship between input parameters and measuring results. The input parameters, for example, pipe diameter, pipe length, distance to radioactive source, radioactive source strength, wind speed, and flux, exhibit different contributions to the measuring results. To reflect these characteristics and estimate alpha radioactivity as exactly as possible, a hybrid partial least square back propagation (PLSBP) neural network approach is presented in this paper. In this model, each node in the input layer is weighted, which indicates that different input nodes have different contributions on the system and this finding has been little reported. The weights are determined by the PLS. After this modification, a variety of normal three-layered BP networks are developed. The comparison of computational results of the proposed approach with traditional BP model and experiments confirms its clear advantage for dealing with this complex nonlinear estimation. Thus, an integrated picture of alpha particle activity inside contaminated pipes can be obtained.

Highlights

With the rapid development of nuclear industry over the last 50 years, nuclear decommissioning has been paid more attention recently
The corresponding long-range alpha detection (LRAD) instrument has been developed, which consists of five units: (1) detection unit; (2) air drive unit; (3) power supply unit; (4) signal acquisition unit; (5) data processing control and display unit
Based on excellent artificial neural network (ANN) research works in the literature and our experimental results obtained from the LRAD, this paper presents a hybrid partial least square (PLS) and back propagation (BP) (PLSBP) model for alpha radioactivity assessment

Summary

Introduction

With the rapid development of nuclear industry over the last 50 years, nuclear decommissioning has been paid more attention recently. A long-range alpha detection (LRAD) technique has been presented to measure alpha particles emitting contamination inside pipes [1,2,3,4,5,6,7]. Based on excellent ANN research works in the literature and our experimental results obtained from the LRAD, this paper presents a hybrid PLS and BP (PLSBP) model for alpha radioactivity assessment. The weighted input nodes can be described by the PLS This is a novel of the paper and a difference from other ANN papers.

Hybrid PLSBP Model

Computational Results

Conclusion