信阳师范学院学报（自然科学版）2024，Vol.37Issue(4)：460-469,10.DOI:10.3969/j.issn.1003-0972.2024.04.007
多特征反向传播-人工神经网络微钻阻力年轮识别方法
A Method for Identifying Annual Rings with Multiple Features in Micro Drilling Resistance Based on Back Propagation-Artificial Neural Network
摘要
Abstract
The peak-valley annual ring recognition algorithm only uses the feature of the difference between the peak value and valley value for annual ring recognition,so the algorithm has a high rate of false positives and false negatives.To enhance the accuracy of micro-drill resistance ring recognition,the Back Propagation-Artificial Neural Network(BP-ANN)algorithm was employed.Firstly,the peak-valley ring recognition algorithm was utilized to identify the effective peaks.Secondly,characteristics such as peak resistance value,resistance difference between the peak and adjacent valleys,distance between the peak and adjacent valleys,and distance between front and back valleys were employed to describe these peaks.Based on the analysis of resistance graphs and disk images,the effective peaks were classified accordingly.If the peak was an annual ring signal,it was marked as"1";otherwise,it was marked as"0".Finally,an effective peak classification model was constructed using BP-ANN algorithm.Compared to the peak valley annual ring recognition algorithm,the BP-ANN model improved the accuracy by 1.26 percentage points and reduced false positives and false negatives by 1.06 and 1.38 percentage points,respectively.The results indicated that the BP-ANN model based on multiple peak features was feasible for identifying annual rings.Compared with the traditional peak-valley annual ring recognition algorithms,the proposed method can effectively improve the accuracy of annual ring recognition and reduce the misjudgment and omission rates of annual rings.关键词
反向传播-人工神经网络(BP-ANN)/微钻阻力仪/峰谷年轮识别算法/年轮Key words
Back Propagation-Artificial Neural Network(BP-ANN)/micro-drill resistance instrument/peak-valley annual ring recognition algorithm/annual ring分类
信息技术与安全科学引用本文复制引用
姚建峰,吴振洋,胡雪凡,孙艳歌,田文静,路一曼,李晓..多特征反向传播-人工神经网络微钻阻力年轮识别方法[J].信阳师范学院学报（自然科学版）,2024,37(4):460-469,10.基金项目
国家自然科学基金项目(32071761) （32071761）
河南省自然科学基金项目(232300421167) （232300421167）
河南省高等学校重点科研项目(22A220002) （22A220002）
河南省研究生质量工程项目(YJS2023SZ23) （YJS2023SZ23）
