基于模糊神经网络味觉信号识别的研究

文中提出了一种基于模糊神经网络方法的味觉信号识别模型,利用小波变换实现了对传感器所采集的味觉信号进行数据压缩及特征抽取,以模糊神经网络作为味觉信号的识别工具。     

A new feature extraction method for odour classification

A new method for real time classification of volatile chemical substance traces is presented. The method is based on electrochemical signals of an array of semiconductor gas sensors. In these sensor signals characteristic patterns of different substances are hidden. There are non-linear correlative relationships between the measured sensor signals and the chemical substances which are treated using two methods derived from statistical learning theory (Support Vector Machine - SVM, Maximum Likelihood Estimation - MLE) for the detection of the substance characteristics in the sensor signals. A key criterion for the presented pattern recognition is a newly developed type of features, which is specially adapted to the low frequency signals of semiconductor sensors. The presented features are based on the evaluation of the range of the transient response in the sensor signals in the frequency domain.To derive the new features, both real measurement data and synthetic generated signals were used. In the experiments the focus was set on the creation of reproducible sensor signals to get characteristic signal patterns. Synthetic signals were derived from a Gaussian Plume Model. With the new features, training data sets were calculated using the classification methods SVM and MLE. With these training data sets new sensor measurements may be assigned to the substances which are to be sought. The advantage of the presented method is that no feature reduction is needed and no loss of information occurs in the learning process.The classification results based on the new features have been compared with the classification based on a conventional method for feature extraction. It was proved that the recognition rate of the substances used with the new feature type is higher.The substance classification is primarily limited by the sensitivity of the semiconductor sensors, because sufficiently large sensor signals must have been provided to obtain appropriate substance patterns. At the present stage of development the method presented is suitable for the classification of substance groups, such as nitro aromatics or alcohols, but not for specific substances.     

基于小波与GBDT的无人机传感器故障诊断

相对于有人飞行器,确保无人机传感器的正常工作更为重要.针对无人机传感器的故障诊断,提出了一种将小波特征提取与梯度提升决策树(GBDT)算法相结合的故障诊断方法.采用基于多层小波包分解的特征提取方法,将小波包分解系数与频带能量熵组合构成特征向量,相比单一的能量特征提取方法,有效提升了故障的可分性.采用梯度提升的策略对弱分类器进行迭代优化和线性组合,构成强分类器,使故障分类精度得到显著提高.仿真结果表明,该方法能有效进行特征提取和故障类型识别,且有较高的诊断精度和较强的泛化能力.     

Human emotion recognition using deep belief network architecture

Recently, deep learning methodologies have become popular to analyse physiological signals in multiple modalities via hierarchical architectures for human emotion recognition. In most of the state-of-the-arts of human emotion recognition, deep learning for emotion classification was used. However, deep learning is mostly effective for deep feature extraction. Therefore, in this research, we applied unsupervised deep belief network (DBN) for depth level feature extraction from fused observations of Electro-Dermal Activity (EDA), Photoplethysmogram (PPG) and Zygomaticus Electromyography (zEMG) sensors signals. Afterwards, the DBN produced features are combined with statistical features of EDA, PPG and zEMG to prepare a feature-fusion vector. The prepared feature vector is then used to classify five basic emotions namely Happy, Relaxed, Disgust, Sad and Neutral. As the emotion classes are not linearly separable from the feature-fusion vector, the Fine Gaussian Support Vector Machine (FGSVM) is used with radial basis function kernel for non-linear classification of human emotions. Our experiments on a public multimodal physiological signal dataset show that the DBN, and FGSVM based model significantly increases the accuracy of emotion recognition rate as compared to the existing state-of-the-art emotion classification techniques.     

Feature extraction by learning Lorentzian metric tensor and its extensions

We develop a supervised dimensionality reduction method, called Lorentzian discriminant projection (LDP), for feature extraction and classification. Our method represents the structures of sample data by a manifold, which is furnished with a Lorentzian metric tensor. Different from classic discriminant analysis techniques, LDP uses distances from points to their within-class neighbors and global geometric centroid to model a new manifold to detect the intrinsic local and global geometric structures of data set. In this way, both the geometry of a group of classes and global data structures can be learnt from the Lorentzian metric tensor. Thus discriminant analysis in the original sample space reduces to metric learning on a Lorentzian manifold. We also establish the kernel, tensor and regularization extensions of LDP in this paper. The experimental results on benchmark databases demonstrate the effectiveness of our proposed method and the corresponding extensions.     

改进MFCC算法在朱鹮鸣声个体识别中的应用

朱鹮鸣声信号具有非平稳性,针对FFT不能反映信号的瞬时性以及无法摆脱基函数的问题,提出了一种基于HHT变换的MFCC参数特征提取算法,通过对信号进行EMD分解,得到每一帧固有模态函数后进行HT变换,频率合成后的边际谱通过Mel滤波器;然后取对数能量,经过DCT变换后得到改进的MFCC系数,采用高斯混合模型分别在纯净和加噪两种环境以及不同信噪比下进行朱鹮鸣声的个体识别。实验结果表明,改进算法不仅能更好地体现鸣声信号的瞬时性,朱鹮个体的平均识别率也提高了4%。     

Estimation of neurological status from non-electroencephalography bio-signals by motif patterns

In this paper, a novel feature extraction approach, which was called motif patterns, was proposed and it was employed to estimate the neurological status from non-electroencephalography (non-EEG) bio-signals. It was found from the literature that successful results were obtained by using the feature extraction methods that are sensitive to local changes such as one-dimensional local binary patterns (1D-LBP). In 1D-LBP, the local changes in a signal were determined based on the comparisons between each “central value” with its neighbors. In order to increase the sensitivity of extracted features from the local changes in a signal, each “value” in the signal was compared with its neighbor, and by this way, a motif was obtained in the result of the comparisons in a specified window. To evaluate and validate the proposed approach, the non-EEG bio-signals, which were recorded by electrodermal activity, temperature, accelerometer, heart rate, and arterial oxygen level sensors, were employed. The features that were extracted from these signals by the proposed motif patterns were classified by machine learning methods. The neurological status of each of the samples was classified accurately by the proposed approach. Furthermore, the optimal sensor types were investigated and it was found that heart rate signals are enough to estimate the neurological status.     

基于指纹识别的通信电台识别仿真算法研究

研究电台准确识别的问题。在准确跟踪敌台活动、检测有效信息的过程中,由于信号受到哭声影响,实现识别较难。当待识别电台是相同调制模式和型号的不同电台个体,发射信号的差别非常细微。传统的关于暂态信号的识别方法是利用瞬间的暂态信号提取细微特征信息,造成信号的信噪比不高,不能正确识别电台信号。为了解决上述难题,提出了应用电台指纹的电台识别技术,通过对电台的稳态信号进行分析,计算信号的双谱特性,采用方形双谱和核主元分析算法,提取出信号中细微的指纹信息,通过分析电台的指纹信息完成电台的识别。实验表明,这种方法能够准确将差别细微的电台识别出来,避免传统方法信噪比不高的问题,保证了电台识别的准确率,取得了满意的结果。     

基于变换步长的车辆压线声信号包络提取算法

车辆经过减速带时与其在路面正常行驶时的声信号波形明显不同,其特征参数的提取对车辆数量、速度、类型等的自动判断至关重要,声信号包络曲线对其特征参数的提取相比原始信号有诸多优势,但传统包络提取算法在此类交通领域声信号包络提取方面存在毛刺多、特征参数难以真正体现信号性质和特征的问题。为解决此问题,结合车辆经过减速带时的声信号特点,提出一种基于变换步长的车辆压线声信号包络提取算法。该算法通过设置不同步长遍历信号,以每个步长内的最大值点绘制曲线并与原信号波形对比,以轮廓清晰度和特征点提取误差值为判断依据实现声信号包络的有效提取。实验结果表明,在相同采样点数条件下,所提算法比传统包络提取算法提取的包络曲线轮廓更清晰、毛刺少,且特征参数提取误差小。     

Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition

Epilepsy is one of the most common neurological disorders characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is an invaluable measurement for the purpose of assessing brain activities, containing information relating to the different physiological states of the brain. It is a very effective tool for understanding the complex dynamical behavior of the brain. This paper presents the application of empirical mode decomposition (EMD) for analysis of EEG signals. The EMD decomposes a EEG signal into a finite set of bandlimited signals termed intrinsic mode functions (IMFs). The Hilbert transformation of IMFs provides analytic signal representation of IMFs. The area measured from the trace of the analytic IMFs, which have circular form in the complex plane, has been used as a feature in order to discriminate normal EEG signals from the epileptic seizure EEG signals. It has been shown that the area measure of the IMFs has given good discrimination performance. Simulation results illustrate the effectiveness of the proposed method.     

呼吸音信号的包络特征提取方法

针对时变宽带的呼吸音信号,在分析传统Hilbert变换方法提取包络的缺点基础上,提出基于复小波变换的呼吸音信号包络特征提取方法。选取Morlet复小波,以适当的尺度对预处理后的呼吸音数据进行变换得到包络,提取包络的统计量和能量作为特征,构造BP分类神经网络的输入矢量,经训练识别取得较好分类效果。研究表明该文的特征提取方法是行之有效的。     

基于KIV模型的脑电识别方法

脑电信号具有动态、非线性和数值高度随机的特点, 针对传统的人工神经网络模型识别脑电信号时在特征提取和识别精度方面表现出的局限性, 本研究提出了一种新的识别方法, 使用KIV模型对脑电信号进行识别. 首先, 通过仿真实验, 分析了KIV模型不同的刺激下表现出的动力学特性. 接着, 使用KIV模型分别对癫痫脑电信号和情感脑电信号进行识别, 在实验过程中不进行特征提取, 直接将多通道原始脑电信号输入到KIV模型中, 在BONN和GAMEEMO数据集上分别获得了99.50%和90.83%的识别准确率. 研究结果表明, 与现有的模型相比, KIV模型具有较好的识别脑电信号的能力, 可为脑电识别提供帮助.     

脑电信号灰建模及特征提取新方法研究

脑电信号包含着大脑皮层活动的丰富信息，但同时也包含了大量的噪声。如何有效地从这些丰富的信息中提取有用特征．一直是该研究领域的热点问题。文中提出利用灰建模的方法进行脑电特征提取，具有一定的创新性。介绍了灰色建模机理及其在脑电特征提取中的应用，利用实测脑电信号建立了脑电GM（1，1）模型，并进行了模型参数估计和特征提取，用K近邻算法对所提取的特征参数进行了分类。分类结果表明，利用灰建模的方法进行脑电特征提取和分类的方法是可行、有效的，为脑电信号的特征提取提供了一种新的思路和方法。     

Real-time gait subphase detection using an EMG signal graph matching (ESGM) algorithm based on EMG signals

This study presents a gait subphase recognition method using an electromyogram (EMG) with a signal graph matching (ESGM) algorithm. Existing pattern recognition and machine learning using EMG signals has several innate problems in gait subphase detection. With respect to time domain features, their feature values may be analogous because two different gait steps may have similar muscle activation. In addition, the current gait subphase might not be recognized until the next gait subphase passes because the window size needed for feature extraction is larger than the period of the gait subphase. The ESGM algorithm is a new approach that compares reference EMG signals and input EMG signals according to time variance to solve these problems and considers variations of physiological muscle activity. We also determined all the elements of the ESGM algorithm using kinematic gait analysis and optimized the algorithm using experiments. Therefore, the ESGM algorithm reflects better timing characteristics of EMG signals than the time domain feature extraction algorithm. In addition, it can provide real-time and user-adaptive recognition of the gait subphase by using only EMG signals. Experimental results show that the average accuracy of the proposed method is 13% better than existing methods and the average detection latency of the proposed method was 5.5 times lower than existing methods.     

Failure prognostics of heavy vehicle hydro-pneumatic spring based on novel degradation feature and support vector regression

The hydro-pneumatic spring, as an important element of the suspension system for heavy vehicles, has attracted the attention of researchers for a long time because it plays such an important role in the steering stability, driving comfort, and driving safety of these vehicles. In this paper, we aim to solve the maintenance problems caused by gas leakage and oil leakage faults in hydro-pneumatic springs. The causes of hydro-pneumatic spring faults and their modes are investigated first. Then, we propose a novel time domain fault feature, called degraded pressure under the same displacement, and a novel feature extraction method based on linear interpolation and redefined time intervals. This feature extraction method is then combined with a data-driven prognostic method that is based on support vector regression to predict the failure trends. When compared with traditional prognostic methods for suspension systems based on vibration signals and vehicle dynamics models, the proposed method can evaluate the real-time spring condition without use of additional sensors or an accurate dynamic model. Therefore, the computational cost of the proposed method is very low and is also suitable for use in vehicles that are equipped with low-cost microprocessors. In addition, hydro-pneumatic spring performance degradation experiments and simulations based on AMEsim software are designed. The experimental data, real vehicle historical data, and simulation data are used to verify the feasibility of the proposed method.

     

High-speed train fault detection with unsupervised causality-based feature extraction methods

With the development of smart sensors, large amount of operating data collected from a complex system as a high-speed train providing opportunities in efficient and effective fault detection and diagnosis (FDD). The data brings also challenges in the FDD modelling process, since the various signals may be redundant, useless and noisy for the FDD modelling of a specific sub-system. The data-driven methods suffer also from the curse of dimensionality. Feature dimension reduction can reduce the dimension of the monitoring dataset and eliminate the useless information. Different from the classical methods based on the correlation among variables, recent studies have shown that causality-based methods can make the FDD model more explanatory and robust. From the adjacency matrix of the causal network diagram, three unsupervised causality-based feature extraction methods for FDD in the braking system of a high-speed train are proposed in this paper. By constructing the causal network diagram among the raw monitoring feature variables through the causal discovery algorithm, the proposed methods extract informative features based on the causal adjacency matrix or the full causal adjacency matrix proposed in this work. These methods are adopted for fault detection with real dataset collected from the braking system in a high-speed train to verify their effectiveness. The experimental results show that the proposed causality-based feature extraction methods are effective and have certain advantages in comparison with the classical correlation-based methods. Especially, the feature extraction method based on the correlation matrix constructed from full causal adjacency matrix achieves better and stable results than the benchmark methods in the experiment.     

ECG signal classification via combining hand-engineered features with deep neural network features

The electrocardiogram (ECG) has been proven to be the most common and effective approach to investigate cardiovascular diseases because that it is simple, noninvasive and inexpensive. However, the differences among ECG signals are difficult to be distinguished. In this paper, hand-engineered ECG features and automatic ECG features extracted with deep neural networks are combined to generate high dimensional features. First, rich hand-engineered features were extracted using some extraction methods for common ECG features. Second, a convolutional neural network model was designed to extract the ECG features automatically. High dimensional feature set is obtained through combing hand-engineered features and automatic features. To get the most informative ECG feature combination, a feature selection method based on mutual information was proposed. An ensemble learning method was then used to build the classification model for abnormal ECG types. Six atrial arrhythmia subtypes’ ECG signals from the Chinese cardiovascular disease database dataset were analyzed through the proposed method. The precision of the classification results reaches 98.41%, which is higher than the results based on other current methods.

     

Deep learning analysis of mobile physiological,environmental and location sensor data for emotion detection

The detection and monitoring of emotions are important in various applications, e.g., to enable naturalistic and personalised human-robot interaction. Emotion detection often require modelling of various data inputs from multiple modalities, including physiological signals (e.g., EEG and GSR), environmental data (e.g., audio and weather), videos (e.g., for capturing facial expressions and gestures) and more recently motion and location data. Many traditional machine learning algorithms have been utilised to capture the diversity of multimodal data at the sensors and features levels for human emotion classification. While the feature engineering processes often embedded in these algorithms are beneficial for emotion modelling, they inherit some critical limitations which may hinder the development of reliable and accurate models. In this work, we adopt a deep learning approach for emotion classification through an iterative process by adding and removing large number of sensor signals from different modalities. Our dataset was collected in a real-world study from smart-phones and wearable devices. It merges local interaction of three sensor modalities: on-body, environmental and location into global model that represents signal dynamics along with the temporal relationships of each modality. Our approach employs a series of learning algorithms including a hybrid approach using Convolutional Neural Network and Long Short-term Memory Recurrent Neural Network (CNN-LSTM) on the raw sensor data, eliminating the needs for manual feature extraction and engineering. The results show that the adoption of deep-learning approaches is effective in human emotion classification when large number of sensors input is utilised (average accuracy 95% and F-Measure=%95) and the hybrid models outperform traditional fully connected deep neural network (average accuracy 73% and F-Measure=73%). Furthermore, the hybrid models outperform previously developed Ensemble algorithms that utilise feature engineering to train the model average accuracy 83% and F-Measure=82%)     

基于分数阶傅里叶变换的运动脑电信号分类方法

运动想象脑电信号作为一种典型的非线性、非平稳信号,在传统基于单一特征提取的分类方法中难以取得理想的分类性能。针对该问题,将分数阶傅里叶变换(Fractional Fourier Transform, FrFT)引入到脑电信号特征提取过程中。首先利用FrFT对信号进行分析,在扩展特征域的同时从不同维度提取信号中的有用信息并构成特征向量,然后利用支持向量机(Support Vector Machine, SVM)分类器对所提取的特征向量进行分类,最后采用Graz数据开展实验。实验结果表明所提方法能够获得高达92.57%的正确分类结果,明显高于传统采用单一特征提取的分类方法。     

Machine learning model for mapping of music mood and human emotion based on physiological signals

Emotion is considered a physiological state that appears whenever a transformation is observed by an individual in their environment or body. While studying the literature, it has been observed that combining the electrical activity of the brain, along with other physiological signals for the accurate analysis of human emotions is yet to be explored in greater depth. On the basis of physiological signals, this work has proposed a model using machine learning approaches for the calibration of music mood and human emotion. The proposed model consists of three phases (a) prediction of the mood of the song based on audio signals, (b) prediction of the emotion of the human-based on physiological signals using EEG, GSR, ECG, Pulse Detector, and finally, (c) the mapping has been done between the music mood and the human emotion and classifies them in real-time. Extensive experimentations have been conducted on the different music mood datasets and human emotion for influential feature extraction, training, testing and performance evaluation. An effort has been made to observe and measure the human emotions up to a certain degree of accuracy and efficiency by recording a person’s bio- signals in response to music. Further, to test the applicability of the proposed work, playlists are generated based on the user’s real-time emotion determined using features generated from different physiological sensors and mood depicted by musical excerpts. This work could prove to be helpful for improving mental and physical health by scientifically analyzing the physiological signals.