Wang Feilu, Hu Anyang, Song Yang, Zhang Wangyong, Zhu Jinggen, Liu Mengru
School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei 230601, China.
Key Laboratory of Building Information Acquisition and Measurement Control Technology, Anhui Jianzhu University, Hefei 230601, China.
Micromachines (Basel). 2024 Jun 30;15(7):864. doi: 10.3390/mi15070864.
Morse code recognition plays a very important role in the application of human-machine interaction. In this paper, based on the carbon nanotube (CNT) and polyurethane sponge (PUS) composite material, a flexible tactile CNT/PUS sensor with great piezoresistive characteristic is developed for detecting Morse code precisely. Thirty-six types of Morse code, including 26 letters (A-Z) and 10 numbers (0-9), are applied to the sensor. Each Morse code was repeated 60 times, and 2160 (36 × 60) groups of voltage time-sequential signals were collected to construct the dataset. Then, smoothing and normalization methods are used to preprocess and optimize the raw data. Based on that, the long short-term memory (LSTM) model with excellent feature extraction and self-adaptive ability is constructed to precisely recognize different types of Morse code detected by the sensor. The recognition accuracies of the 10-number Morse code, the 26-letter Morse code, and the whole 36-type Morse code are 99.17%, 95.37%, and 93.98%, respectively. Meanwhile, the Gated Recurrent Unit (GRU), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF) models are built to distinguish the 36-type Morse code (letters of A-Z and numbers of 0-9) based on the same dataset and achieve the accuracies of 91.37%, 88.88%, 87.04%, and 90.97%, respectively, which are all lower than the accuracy of 93.98% based on the LSTM model. All the experimental results show that the CNT/PUS sensor can detect the Morse code's tactile feature precisely, and the LSTM model has a very efficient property in recognizing Morse code detected by the CNT/PUS sensor.
莫尔斯电码识别在人机交互应用中发挥着非常重要的作用。本文基于碳纳米管（CNT）和聚氨酯海绵（PUS）复合材料，开发了一种具有优异压阻特性的柔性触觉CNT/PUS传感器，用于精确检测莫尔斯电码。将36种莫尔斯电码，包括26个字母（A-Z）和10个数字（0-9）应用于该传感器。每种莫尔斯电码重复60次，收集2160（36×60）组电压时序信号以构建数据集。然后，使用平滑和归一化方法对原始数据进行预处理和优化。在此基础上，构建具有优异特征提取和自适应能力的长短期记忆（LSTM）模型，以精确识别由该传感器检测到的不同类型的莫尔斯电码。10个数字的莫尔斯电码、26个字母的莫尔斯电码以及整个36种类型莫尔斯电码的识别准确率分别为99.17%、95.37%和93.98%。同时，基于相同数据集构建门控循环单元（GRU）、支持向量机（SVM）、多层感知器（MLP）和随机森林（RF）模型来区分36种类型的莫尔斯电码（A-Z字母和0-9数字），其准确率分别为91.37%、88.88%、87.04%和90.97%，均低于基于LSTM模型的93.98%的准确率。所有实验结果表明，CNT/PUS传感器能够精确检测莫尔斯电码的触觉特征，并且LSTM模型在识别由CNT/PUS传感器检测到的莫尔斯电码方面具有非常高效的性能。
