| 张 鑫,缪 楠,高继勇,李庆盛,王志强,孙 霞,李彩虹,袁文浩.基于电子舌和 WGAN-CNN 模型的小麦贮存
年限快速检测[J].电子测量与仪器学报,2021,35(6):176-183 |
| 基于电子舌和 WGAN-CNN 模型的小麦贮存
年限快速检测 |
| Rapid detection of wheat storage year based on electronic tongueand WGAN-CNN model |
| |
| DOI： |
| 中文关键词: 小麦 生成对抗网络 卷积神经网络 电子舌 模式识别 |
| 英文关键词:wheat generative adversarial networks convolutional neural network electronic tongue pattern recognition |
| 基金项目:山东省自然科学基金(ZR2019MF024）、国家自然科学基金(31772068)、教育部科技发展中心产学研创新基金(2018A02010)、赛尔网络下一代互联网技术创新项目(NGII20170314)资助 |
|
|
|
|
| 摘要点击次数: 544 |
| 全文下载次数: 3 |
| 中文摘要: |
| 为了实现对不同贮存年限陈化小麦的快速检测,提出一种伏安电子舌结合卷积神经网络( convolutional neural network,
CNN)和基于 Wasserstein 距离的生成对抗网络(wasserstein generative adversarial nets,WGAN)组合的模式识别模型。 使用伏安电
子舌对 6 种不同贮存时间小麦采集电子舌信号。 针对电子舌信号信息量大、特征提取困难等问题,设计了一种基于 CNN 结构
的电子舌信号特征自动提取和分类识别模型。 为解决 CNN 模型因训练样本不足而导致泛化能力差等问题,使用 WGAN 构建
电子舌信号样本集,通过对生成信号集的学习,提高了 CNN 模型对电子舌信号的识别能力。 实验结果表明,与 AlexNet、VGG16
等深度学习模型和随机森林(RM)、极限学习机(ELM)等传统机器学习模型相比, WGAN-CNN 模型对电子舌信号的分辨能力
更强,其测试集准确率、精确率、召回率和 F1-Score 分别达到 0. 98、0. 98、0. 977 和 0. 988。 研究表明电子舌结合 WGAN-CNN 模
型可实现对小麦贮存年限的快速检测,该研究为基于人工智能的感官识别技术提供了一种新的研究思路。 |
| 英文摘要: |
| To realize the rapid identification and analysis of aged wheat with different storage years, this paper puts forward one method to
identify the age of wheat by employing a voltammetric electronic tongue (VE-Tongue) combined convolutional neural networks (CNN)
and wasserstein generative adversarial networks (WGAN). VE-Tongue was used to detect six kinds of wheat with different storage time,
then the corresponding electronic tongue signals were acquired. An automatic feature extraction and classification recognition model of
electronic tongue signal based on CNN structure was designed to solve the problems of large amount of information and difficult feature
extraction of sensor array response signals. To solve the problem of insufficient training samples, WGAN was utilized to generate
electronic tongue signals to improve the generalization ability of CNN model and avoid over-fitting problem. The results showed that the
proposed method exhibited better classification performance compared with the deep learning models like AlexNet, VGG16 and the
traditional machine learning models like random forest (RM) and extreme learning machine (ELM). The test set accuracy, precision,
recall rate and F1-Score of the proposed model reached 0. 98, 0. 98, 0. 977 and 0. 988 respectively. This study found that the VETongue combined with CNN and WGAN could be a sensitive, reliable and effective detection method for identifying the amount of storage
year of wheat, it can provide a new research way of thinking for the sensory recognition technology based on artificial intelligence. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
