Traditional Pattern Recognition Methods Research Articles

Design of Wearable Wireless Health Monitoring System and Status Recognition Algorithm

A wearable wireless health monitoring system for drug addicts in compulsory rehabilitation centers was proposed. The system can continuously monitor multiple physiological parameters of drug addicts in real time, and issue early warning information when abnormal physiological parameters occur, so as to play the role of timely medical practice. In addition, this study proposes a convolutional neural network (CNN)model, which can evaluate the health status of drug addicts based on multiple physiological parameters. Experiments show that the model can be applied to the task of body state recognition in the open physiological parameter data set, and the recognition accuracy can reach up to 100% in a single physiological parameter data set; when the whole physiological data set is used, the recognition accuracy can reach 99.1%. The recognition accuracy exceeds the performance of the traditional pattern recognition method on this task, which verifies the superiority of the model.

Urban expressway parallel pattern recognition based on intelligent IOT data processing for smart city

With the sustained and rapid development of the social economy and the rapid growth of urban vehicles, urban expressway has developed rapidly. As the roadmap of urban traffic, the urban expressway has a relatively high and stable driving speed, and also bears a large amount of urban traffic. However, in recent years, with the expansion of the city scale, the congestion of urban expressways has become increasingly severe. In various areas of the merged area, due to various factors such as mismatched traffic capacity and reduced driving speed, it is easy to cause deterioration of road conditions in a short period of time and cause more secondary accidents. In order to reduce the incidence of expressway traffic accidents and to avoid as much as possible the casualties and property losses caused by accidents, Intelligent Transportation Systems (ITS) supported by information technology, data communication transmission technology, control technology and traffic engineering were introduced. With the rise of artificial intelligence and the continuous development of ITS, the video acquisition methods of real-time traffic flow data and the image recognition ability of video sequences continue to improve, providing theoretical basis and technical support for the research of urban expressway parallel pattern recognition. Aiming at the shortcomings of traditional pattern recognition methods, such as weak anti-interference to complex traffic environment and low correct recognition rate, this paper studies the pattern recognition method based on image processing, and selects the fuzzy C-means clustering in the currently used clustering methods (FCM) algorithm Because the FCM algorithm cannot obtain the global optimal solution and need to determine the number of cluster categories in advance, this paper uses ReliefF algorithm and Particle Swarm Optimization (PSO) to compare the feature weight and number of clusters of traditional FCM algorithm. Make improvements. Through the experimental analysis of the acquired video images, the results show that the improved FCM algorithm based on the proposed method has better real-time and accuracy in the application of urban expressway parallel pattern recognition.

Deep Learning in Archaeological Remote Sensing: Automated Qanat Detection in the Kurdistan Region of Iraq

In this paper, we report the results of our work on automated detection of qanat shafts on the Cold War-era CORONA Satellite Imagery. The increasing quantity of air and space-borne imagery available to archaeologists and the advances in computational science have created an emerging interest in automated archaeological detection. Traditional pattern recognition methods proved to have limited applicability for archaeological prospection, for a variety of reasons, including a high rate of false positives. Since 2012, however, a breakthrough has been made in the field of image recognition through deep learning. We have tested the application of deep convolutional neural networks (CNNs) for automated remote sensing detection of archaeological features. Our case study is the qanat systems of the Erbil Plain in the Kurdistan Region of Iraq. The signature of the underground qanat systems on the remote sensing data are the semi-circular openings of their vertical shafts. We choose to focus on qanat shafts because they are promising targets for pattern recognition and because the richness and the extent of the qanat landscapes cannot be properly captured across vast territories without automated techniques. Our project is the first effort to use automated techniques on historic satellite imagery that takes advantage of neither the spectral imagery resolution nor very high (sub-meter) spatial resolution.

Study of damage identification for bridges based on deep belief network

To solve the problem of poor anti-noise ability faced by traditional pattern recognition methods in damage identification field, a bridge damage identification method based on deep belief network was proposed. Taken the modal curvature difference as the damage index, three restricted Boltzmann machines were constructed for pre-training. Then, the Softmax classifier and neural network were used to identify the damage location and degree under the environmental cases of no noise, weak noise, and strong noise, respectively. Subsequently, the influence of incomplete measurement modal data on the method was studied. Finally, damage identification based on deep belief network was implemented to a continuous beam bridge and compared with that of the back propagation neural network. The results showed that the proposed method could be highly effective not only on damage location but also on degree identification. Compared with back propagation neural network, deep belief network method may possess better identification ability and stronger anti-noise ability. It also demonstrates good identification effect under the condition of incomplete measurement modal data.

LSTM-CNN Architecture for Human Activity Recognition

In the past years, traditional pattern recognition methods have made great progress. However, these methods rely heavily on manual feature extraction, which may hinder the generalization model performance. With the increasing popularity and success of deep learning methods, using these techniques to recognize human actions in mobile and wearable computing scenarios has attracted widespread attention. In this paper, a deep neural network that combines convolutional layers with long short-term memory (LSTM) was proposed. This model could extract activity features automatically and classify them with a few model parameters. LSTM is a variant of the recurrent neural network (RNN), which is more suitable for processing temporal sequences. In the proposed architecture, the raw data collected by mobile sensors was fed into a two-layer LSTM followed by convolutional layers. In addition, a global average pooling layer (GAP) was applied to replace the fully connected layer after convolution for reducing model parameters. Moreover, a batch normalization layer (BN) was added after the GAP layer to speed up the convergence, and obvious results were achieved. The model performance was evaluated on three public datasets (UCI, WISDM, and OPPORTUNITY). Finally, the overall accuracy of the model in the UCI-HAR dataset is 95.78%, in the WISDM dataset is 95.85%, and in the OPPORTUNITY dataset is 92.63%. The results show that the proposed model has higher robustness and better activity detection capability than some of the reported results. It can not only adaptively extract activity features, but also has fewer parameters and higher accuracy.

Cable Incipient Fault Identification with a Sparse Autoencoder and a Deep Belief Network

Incipient faults in power cables are a serious threat to power safety and are difficult to accurately identify. The traditional pattern recognition method based on feature extraction and feature selection has strong subjectivity. If the key feature information cannot be extracted accurately, the recognition accuracy will directly decrease. To accurately identify incipient faults in power cables, this paper combines a sparse autoencoder and a deep belief network to form a deep neural network, which relies on the powerful learning ability of the neural network to classify and identify various cable fault signals, without requiring preprocessing operations for the fault signals. The experimental results demonstrate that the proposed approach can effectively identify cable incipient faults from other disturbances with a similar overcurrent phenomenon and has a higher recognition accuracy and reliability than the traditional pattern recognition method.

Exploiting the Intertemporal Structure of the Upper-Limb sEMG: Comparisons between an LSTM Network and Cross-Sectional Myoelectric Pattern Recognition Methods.

The use of natural myoelectric interfaces promises great value for a variety of potential applications, clinical and otherwise, provided a computational mapping between measured neuromuscular activity and executed motion can be approximated to a satisfactory degree. However, prevalent methods intended for such decoding of movement intent from the surface electromyogram (sEMG) based on pattern recognition typically do not capitalize on the inherently time series-like nature of the acquired signals. In this paper, we present the results from a comparative study in which the performances of traditional cross-sectional pattern recognition methods were compared with that of a classifier built on the natural assumption of temporal ordering by utilizing a long short-term memory (LSTM) neural network. The resulting evaluation indicate that the LSTM approach outperforms traditional gesture recognition techniques which are based on cross-sectional inference. These findings held both when the LSTM classifier operated on conventional features and on raw sEMG and for both healthy subjects and transradial amputees.

Rolling Bearing Fault Diagnosis Algorithm Based on FMCNN-Sparse Representation

The time-frequency analysis of vibration signals is an effective means to analyze the fault characteristics of rolling bearings. The traditional pattern recognition method is difficult to adapt to the complex mapping relationship between the high-dimensional feature space and the state space. The deep learning method has high-dimensional feature adaptive analysis ability, which is suitable for the intelligent analysis of the high-dimensional feature space in fault states. The feedforward deep convolutional neural network (CNN) has achieved some success in mechanical fault diagnosis. However, the rolling bearing fault signal is complex, and there are many interference factors. The CNN relying on the simple feedforward method cannot effectively meet the actual needs in the field of fault diagnosis. Although there are some CNNs with feedback methods, the CNNs of these feedback methods cannot systematically obtain the characteristic information of rolling bearing faults. Therefore, they do not solve the feature extraction problem of rolling bearing faults well. In view of this, this paper provides a specific mathematical definition of the feedback mechanism for constructing the feedback mechanism in the deep CNN, models the feedback mechanism into an optimization problem, determines the basic framework of the feedback mechanism, and an effective feedback mechanism calculation model is proposed. Based on this, a solution algorithm based on the gradient descent method is proposed. Then, an effective supervised feature extraction method based on sparse expression is proposed. It maps the sample features to the feature domain through the effective transform method. In the process, the wavelet packet transform (WPT) transform is used as the basis function to construct a dictionary with structural effects, and mixed penalty terms are introduced to further optimize the performance of structural sparse expression. Finally, the sparse expression is combined with the feedback mechanism CNN (FCNN) to establish a sub-module fault diagnosis network so that a diagnosis can determine the fault severity while assessing the bearing fault location. The example shows that the method proposed in this paper has high accuracy in determining the state of rolling bearings and has great application potential in engineering.

PRPD data analysis with Auto-Encoder Network

Gas Insulated Switchgear (GIS) is related to the stable operation of power equipment. The traditional partial discharge pattern recognition method relies on expert experience to carry out feature engineering design artificial features, which has strong subjectivity and large blindness. To address the problem, we introduce an encoding-decoding network to reconstruct the input data and then treat the encoded network output as a partial discharge signal feature. The adaptive feature mining ability of the Auto-Encoder Network is effectively utilized, and the traditional classifier is connected to realize the effective combination of the deep learning method and the traditional machine learning method. The results show that the features extracted based on this method have better recognition than artificial features, which can effectively improve the recognition accuracy of partial discharge.

Research on regional clustering and two-stage SVM method for container truck recognition

With large-scale, integrated, intelligence for ports, many ports begin to use intelligent detection systems to make their operations more efficient. The container truck recognition and positioning system is also beginning to apply into container quayside to assist the joint operations between quay cranes and container trucks. However, the traditional vehicle detection by using motion region detection cannot recognize the type of moving object, and the traditional pattern recognition method cannot meet the requirements in real-time operation. In order to solve these problems, an algorithm fused by regional clustering and two-stage SVM classifier is proposed in this paper. The method consists of two phases, which are independently executed in two camera systems on quay cranes. In the first stage, a fast motion regional clustering algorithm is used to detect moving image patches as the truck candidate sub-windows. In the second stage, the container trucks will be recognized in these sub-windows by an optimized two-stage SVM classifier. Compared with existing traditional algorithm, experimental results in container terminal show that the fusion algorithm with regional clustering and two-stage SVM has higher efficiency and better truck recognition performance.

Semi-supervised spatio-temporal CNN for recognition of surgical workflow

Robust and automated surgical workflow detection in real time is a core component of the future intelligent operating room. Based on this technology, it can help medical staff to automate and intelligently complete many routine activities during surgery. Recognition of surgical workflow based on traditional pattern recognition methods requires a large number of labeled surgical video data. However, the labeled surgical video data requires expert knowledge and it is difficult and time consuming to collect a sufficient number of labeled surgical video data in the medical field. Therefore, this paper proposes a semi-supervised spatio-temporal convolutional network for the recognition of surgical workflow based on convolutional neural networks and temporal-recursive networks. Firstly, we build a spatial convolutional extraction feature network based on unsupervised generative adversarial learning. Then, we build a bridge between low-level surgical video features and high-level surgical workflow semantics based on an unsupervised temporal-ordered network learning approach. Finally, we use the semi-supervised learning method to integrate the spatial model and the temporal model to fine-tune the network, and realize the intelligent recognition of the surgical workflow at a low cost to efficiently determine the progress of the surgical workflow. We performed some experiments for validating the mode based on m2cai16-workflow dataset. It shows that the proposed model can effectively extract the surgical feature and determine the surgical workflow. The Jaccard score of the model reaches 71.3%, and the accuracy of the model reaches 85.8%.

Robust low-rank decomposition of multi-channel feature matrices for fabric defect detection

Fabric defect detection plays an important role in the quality control of textile products. Most existing defect detection techniques adopted traditional pattern recognition methods, which were lacking adaptability and presented the undesirable detection accuracy. In this paper, a fabric defect detection algorithm based on multi-channel feature matrixes extraction and joint low-rank decomposition was proposed by simulating biological visual perception mechanism. Based on the fact that the second-order gradient information is more suitable for characterizing the fabric texture, we developed a novel second-order multi-channel feature extraction method by modeling the response and distribution properties of the P-type ganglion cells in the primate retina. Upon devising a powerful descriptor, a joint low-rank decomposition method is utilized to model biological visual saliency, and decomposes the fabric images into backgrounds and salient defect objects. Experimental results demonstrate that our proposed algorithm has good self-adaptability and detection performance for plain and twill fabrics or complex patterned fabrics, and is superior to the state-of-the-art methods.

Riemannian competitive learning for symmetric positive definite matrices clustering

Abstract Symmetric positive definite (SPD) matrices have achieved considerable success in numerous computer vision applications including activity recognition, texture classification, and diffusion tensor imaging. Traditional pattern recognition methods developed in Euclidean space are not suitable for direct processing of SPD matrices because they lie in a Riemannian manifold of negative curvature. In this paper, we draw inspiration from neural network based simple competitive learning, which updates the weight of a neural network in Euclidean space, and propose Riemannian competitive learning which updates the weight in the Riemannian weight space, for SPD manifold clustering. This framework is based on the Fisher-Rao metric which is theoretically very good. We further introduce a conscious competition mechanism to enhance the performance of the RCL algorithm and have developed a robust algorithm termed Riemannian Frequency Sensitive Competitive Learning (rFSCL). Compared with existing methods, the advantages are threefold: (1) rFSCL inherits the online nature of competitive learning, which makes it capable of handling very large data sets; (2) rFSCL inherits the advantage of conscious competitive learning, which indicates that it is less sensitive to the initial values of the cluster centers and that all clusters are fully utilized without the “dead unit” problem associated with many clustering algorithms; and (3) As an intrinsic Riemannian clustering method, rFSCL operates along the geodesic on the manifold in an closed-form analytic manner and is completely independent of the choice of local coordinate systems. Extensive experiments on both synthetic data sets and real data sets show its superior performance to that of other state-of-the-art SPD clustering methods.

A Novel Fine-Grained Method for Vehicle Type Recognition Based on the Locally Enhanced PCANet Neural Network

In this paper, we propose a locally enhanced PCANet neural network for fine-grained classification of vehicles. The proposed method adopts the PCANet unsupervised network with a smaller number of layers and simple parameters compared with the majority of state-of-the-art machine learning methods. It simplifies calculation steps and manual labeling, and enables vehicle types to be recognized without time-consuming training. Experimental results show that compared with the traditional pattern recognition methods and the multi-layer CNN methods, the proposed method achieves optimal balance in terms of varying scales of sample libraries, angle deviations, and training speed. It also indicates that introducing appropriate local features that have different scales from the general feature is very instrumental in improving recognition rate. The 7-angle in 180° (12-angle in 360°) classification modeling scheme is proven to be an effective approach, which can solve the problem of suffering decrease in recognition rate due to angle deviations, and add the recognition accuracy in practice.

Fabric Defect Detection Based on Biological Vision Modeling

Fabric defect detection plays a key role in the quality control of textiles. Existing fabric defect detection methods adopt traditional pattern recognition methods; however, these methods lack adaptability and present poor detection performance. Because biological vision system has the ability to quickly locate salient objects, we propose a novel fabric defect detection algorithm based on biological vision modeling by simulating the mechanism of biological visual perception. First, a distinct, efficient, and robust feature descriptor from the biological modeling of P ganglion cells, which was proposed in our previous work, is adopted to improve the representation of fabric images with complex textures. To account for the low-rank and sparsity characteristics of biological vision, the low-rank representation (LRR) technique is adopted to model biological visual saliency, and it can decompose the fabric image into backgrounds and salient defect objects. Meanwhile, dictionary learning and Laplacian regularization are integrated into the LRR model as follows: 1) dictionary learning is used to denoise the saliency map; and 2) Laplacian regularization enlarges the gaps between defective regions and the background. Finally, the linearized accelerated direction method with adaptive penalty is adopted to solve the proposed model. The experimental results emphasize that the proposed algorithm has good detection performance for plain or twill fabrics with simple textures as well as for patterned fabrics with complex textures. Moreover, the proposed method is superior to the state-of-the-art methods in terms of its adaptability and detection efficiency.

Unraveling traveler mobility patterns and predicting user behavior in the Shenzhen metro system

ABSTRACTOver the last few years, cities have made available large volumes of smart card data that shed light on the urban dynamics of transit users. This research uses metro card data from Shenzhen, China, to recognize individual mobility patterns and predict travelers' future movements. Joint entropy is proposed to measure the regularity of spatio-temporal patterns and travelers are divided into three groups, i.e. regular users, variable users and irregular users, based on the entropy value. Revised Markov chain model and hidden Markov model (HMM) are then introduced to predict individuals' future movement. We observe that the models predict with a high level of accuracy of 84.46%, 78.79% and 73.07% for three groups in the HMM. This study shows the potential to predict travel patterns and enriches traditional pattern recognition and prediction methods for modeling urban mobility. It also helps reveal structural properties of human behavior in urban metro systems.

Generalized interaction LASSO based on alternating direction method of multipliers for liver disease classification

Features and interaction between features of liver disease is of great significance for the classification of liver disease. Based on least absolute shrinkage and selection operator (LASSO) and interaction LASSO, the generalized interaction LASSO model is proposed in this paper for liver disease classification and compared with other methods. Firstly, the generalized interaction logistic classification model was constructed and the LASSO penalty constraints were added to the interactive model parameters. Then the model parameters were solved by an efficient alternating directions method of multipliers (ADMM) algorithm. The solutions of model parameters were sparse. Finally, the test samples were fed to the model and the classification results were obtained by the largest statistical probability. The experimental results of liver disorder dataset and India liver dataset obtained by the proposed methods showed that the coefficients of interaction features of the model were not zero, indicating that interaction features were contributive to classification. The accuracy of the generalized interaction LASSO method is better than that of the interaction LASSO method, and it is also better than that of traditional pattern recognition methods. The generalized interaction LASSO method can also be popularized to other disease classification areas.

Diesel engine fault diagnosis using intrinsic time-scale decomposition and multistage Adaboost relevance vector machine

Diesel engine is the most widely used power source of machines. However, faults occur frequently and often cause terrible accidents and serious economic losses. Therefore, diesel engine fault diagnosis is very important. Commonly, a single unitary pattern recognition method is used to diagnose the faults of diesel engine, but its performance decreases sharply when there are many fault types. Targeting this problem, a novel diesel engine fault diagnosis approach is proposed in this study. The approach is composed of four stages. Firstly, the nonstationary and nonlinear vibration signal of diesel engine is decomposed into a series of proper rotation components (PRCs) and a residual signal by the intrinsic time-scale decomposition (ITD) method. Secondly, six typical time-domain and four typical frequency-domain characteristics of the first several PRCs are extracted as fault features. Then, the modular and ensemble concepts are introduced to construct the multistage Adaboost relevance vector machine (RVM) model, in which the kernel fuzzy c-means clustering (KFCM) algorithm is used to decompose a complex classification task into several simple modules, and the Adaboost algorithm is used to improve the performance of each RVM based module. Finally, the fault diagnosis results can be obtained by inputting the fault features into the multistage Adaboost RVM model. The analysis results show that the fault diagnosis approach based on ITD and multistage Adaboost RVM performs effectively for the fault diagnosis of diesel engine, and it is better than the traditional pattern recognition methods.

Error-correcting output codes for multi-label emotion classification

Multi-modal affective data such as EEG and physiological signals is increasingly utilized to analyze of human emotional states. Due to the noise existed in collected affective data, however, the performance of emotion recognition is still not satisfied. In fact, the issue of emotion recognition can be regarded as channel coding, which focuses on reliable communication through noise channels. Using affective data and its label, the redundant codeword would be generated to correct signals noise and recover emotional label information. Therefore, we utilize multi-label output codes method to improve accuracy and robustness of multi-dimensional emotion recognition by training a redundant codeword model, which is the idea of error-correcting output codes. The experiment results on DEAP dataset show that the multi-label output codes method outperforms other traditional machine learning or pattern recognition methods for the prediction of emotional multi-labels.

The new method of flatness pattern recognition based on GA–RBF–ARX and comparative research

Radial basis function (RBF) network combines with AutoRegressive eXogenous (ARX) model to create RBF–ARX model. RBF–ARX model can describe the global nonlinear dynamic process of the object, and its function coefficients are approximated by data-driven method. Structured nonlinear parameters optimization method is generally used to optimize model parameters, but this method is very complicated and hard to be mastered by engineers. However, genetic algorithm (GA) is relatively simple and widely used. So thought of GA optimizing RBF–ARX is generated, called GA–ARX–RBF, and applied to nonlinear dynamic flatness recognition model. Flatness pattern recognition is the basis and core of flatness control. Considering disadvantages of traditional pattern recognition method, such as limitation of recognition accuracy and poor fault tolerance, a new intelligent method of flatness pattern recognition based on the GA–RBF–ARX model is proposed. Meanwhile, in order to examine the effectiveness of RBF–ARX, another intelligent flatness recognition model based on dynamic feedback Hopfield NN is set up. Simulation proves that RBF–ARX is more suitable for flatness pattern recognition than Hopfield NN. And optimization effect by GA can achieve the desired requirements and is more precise than structured nonlinear parameters optimization method, when RBF–ARX is applied to rolling mill. GA–RBF–ARX can be a new modeling method for complex nonlinear dynamic system.