Traditional Recognition Methods Research Articles

Image Recognition of Sports Athletes' High-Intensity Sports Injuries Based on Binocular Stereo Vision.

Sports athletes are prone to certain injuries during high-intensity exercise training. In the process of treating an injury, images of the injury site need to be collected and identified. However, the traditional recognition method cannot effectively extract the features of the image. At the same time, it ignores the optimization of the damage image recognition results, resulting in low recognition accuracy and poor efficiency. Binocular stereo vision technology can quickly and accurately detect moving objects. Therefore, in order to more accurately identify high-intensity sports injury images, this study takes the high-intensity sports injury images as the basic research object. Several processes of image processing based on binocular stereo vision are analyzed, and the vulnerable parts of the body in high-intensity sports are also studied. Finally, the method in this study is verified. The experimental results show that the method proposed in this study reduces the average error rate by 0.19% compared with the traditional recognition method. It can effectively identify and detect injury images, thereby improving the accuracy and stability of sports injury image identification. The identification time is also shortened accordingly, which has certain practicability and feasibility. In addition, the binocular camera used in this study has high accuracy, and the obtained images of sports injuries are of good quality, which lays a foundation for image detection and recognition.

Identification and Early Warning of Financial Fraud Risk Based on Bidirectional Long-Short Term Memory Model

In modern market economy, corporate financial frauds emerge one after another, which have a huge impact on the stock market and triggered an unprecedented credit crisis. Therefore, it is particularly important to identify financial frauds. Improving the efficiency, accuracy, and coverage of fraud identification in financial reports through digital and intelligent means is one of the important links to improve credit risk control of securities companies and also for securities companies to accurately price related financial products of target companies. Traditional recognition methods based on artificial rules can cover relatively limited indicators; rule parameters are set randomly. Besides, it is difficult to make rules based on high-dimension indicators and to dig the hidden deep relationships between indicators. This paper summarizes the relevant indicators of financial fraud identification from the perspective of financial and nonfinancial characteristics. Then, the identification and early warning of financial fraud risk based on bidirectional long-short term memory model are proposed. This method uses the idea of ensemble learning, weights the probability of financial key indicators, and uses the optimal transfer probability to solve the financial fraud risk results. The results show that the industry-specific modeling can significantly improve the accuracy of the financial fraud identification model and it also can effectively help the government regulatory departments, investors, and audit departments to correctly identify the financial fraud of listed companies.

Deep Learning-Based Music Quality Analysis Model.

In order to build an efficient and effective deep much quality recognition model, a decision fusion method by leveraging the advantages of shallow learning and deep learning is formulated. In the literature, shallow learning is a traditional music-related quality recognition method, that is, artificial statistical feature extraction and recognition are designed. Meanwhile, our deep learning module leverages the so-called PCANET network to implement the feature extraction process, and subsequently takes the spectrogram describing the music-related information as the network input. First, a variety of task classifications for the music signal problem are divided. Afterward, the optimization and adoption of deep learning in the two major problems of music feature extraction and sequence modeling are introduced. Finally, a music application is presented to illustrate the practical application of deep learning in music quality evaluation. The shallow learning features and deep learning features are seamlessly combined into the SVM model for music quality modeling, based on which differential voting mechanisms are leveraged to realize the fusion of decision-making layers. Extensive experimental results have shown that the music quality recognition rate by this method can be significantly improved on our own compiled library and the Berlin database. Besides, it exhibits obvious advantages compared with the competitors.

Fault Diagnosis of a Solenoid Valve Based on Multi-Feature Fusion

This paper presents a non-intrusive solenoid valve fault diagnosis method, which adopts the eigenvalue extraction scheme and combines the time-frequency analysis and time-domain analysis of the current signal through the solenoid valve fault pattern recognition method. Specifically, the research group selected the solenoid valve driving end current value and its second-order change rate curve as the characteristic curve, and then it extracted the time-domain parameters and the corresponding frequency band energy of the second-order change rate as the characteristic value. Then, the research group conducted time-frequency analysis on the basis of the current signal, created a multi-feature fusion feature vector and then input it into a multi-class support vector machine (SVM) for solenoid valve fault diagnosis. To evaluate the method, the research group developed a working solenoid valve model that integrated a magnetic circuit, circuit and spool motion, and the research group examined four typical solenoid valve states (normal, spring broken, spool stuck and spool slightly stuck), for which the research group collected the current signals during solenoid valve energization and closing. The trials involved analyzing the current characteristics of the drive end, both in simulation and experimental setups, and the research group demonstrated that the proposed drive end current detection diagnosis method can improve the accuracy of recognition by 15.22% compared with traditional neural network recognition methods. The SVM strategy exploiting a multi-feature fusion strategy can improve recognition accuracy by 8.7% and verification accuracy by 42.11% compared with SVM on the basis of the energy feature values.

Identification Method of Citrus Aurantium Diseases and Pests Based on Deep Convolutional Neural Network.

The traditional identification methods of Citrus aurantium diseases and pests are prone to convergence during the running process, resulting in low accuracy of identification. To this end, this study reviews the newest methods for the identification of Citrus aurantium diseases and pests based on a deep convolutional neural network (DCNN). The initial images of Citrus aurantium leaves are collected by hardware equipment and then preprocessed using the techniques of cropping, enhancement, and morphological transformation. By using the neural network to divide the disease spots of Citrus aurantium images, accurate recognition results are obtained through feature matching. The comparative experimental results show that, compared with the traditional recognition method, the recognition rate of the proposed method has increased by about 11.9%, indicating its better performance. The proposed method can overcome the interference of the external environment to a certain extent and can provide reference data for the prevention and control of Citrus aurantium diseases and pests.

Traditional Ceramic Sculpture Feature Recognition Based on the Machine Learning Algorithm

Aiming at the problems of low recognition accuracy, high line noise, and high time cost of feature recognition in traditional ceramic sculpture modeling feature recognition methods, this paper designed a traditional ceramic sculpture modeling feature recognition method based on the machine learning algorithm. By constructing the sparse representation model of a traditional ceramic sculpture image, the posterior probability of a traditional ceramic sculpture image was determined and Gaussian distribution of pixels in the image was carried out to determine the distribution law of pixels in the image and the super-resolution reconstruction of the traditional ceramic sculpture image was realized. The feature of the line state of a traditional ceramic sculpture image was determined and classified by the kernel classification method. Finally, the machine learning algorithm red BP neural network is introduced to construct the traditional ceramic sculpture modeling feature recognition algorithm and the error threshold is constantly iterated to realize the traditional ceramic sculpture modeling feature recognition. The experimental results show that the recognition algorithm designed in this paper has high accuracy in identifying the traditional ceramic sculpture features, can effectively suppress the line noise, and has a short recognition time overhead, which has a certain feasibility.

Intelligent Robot English Speech Recognition Method Based on Online Database

In order to solve the problem of low accuracy of traditional English speech recognition, an intelligent robot English speech recognition method based on online database is proposed. A speech recognition device is installed on the intelligent robot as the hardware support for running the speech recognition method. The online English speech standard database is constructed to provide reference data for speech recognition. The real-time speech information is collected, and the speech signal is preprocessed by pre-emphasis, framing, windowing and other steps. According to the principle of speech signal generation, the features of speech signal are extracted, and the results of English speech recognition are obtained by similarity calculation and matching. Compared with the traditional recognition method, the experimental results show that the recognition rate of the optimised speech recognition method is improved by 1.3%, i.e. the recognition accuracy is improved.

DMS-SK/BLSTM-CTC Hybrid Network for Gesture/Speech Fusion and Its Application in Lunar Robot–Astronauts Interaction

In the future manned lunar exploration mission, astronauts would work with the lunar robots, which has a high requirement for human–robot interaction (HRI). As the accuracy of gesture recognition interaction does not fulfill the requirement for human–robot joint exploration missions, we propose the DMS-SK/BLSTM-CTC hybrid network to improve the performance of HRI. For gesture recognition, considering VGG-SK has low accuracy and complex architecture, we delete the fourth convolution module, optimize the last global pooling layer, introduce dilated convolution block and multiscale convolution block in VGG-SK, and get the DMS-SK-based gesture recognition sub-network. Compared with the traditional recognition methods, the accuracy and performance of DMS-SK improve. For speech recognition, considering that Bidirectional long–short-term memory unit (BLSTM) has the advantages of processing temporal information, and the Connectionist Temporal Classification (CTC) algorithm can simplify speech data preprocessing, we use BLSTM based on CTC as the speech recognition sub-network. Finally, we combine DMS-SK with BLSTM-CTC, and propose the DMS-SK/BLSTM-CTC hybrid network as the gesture/speech hybrid network. In addition, we use 10 gestures in the American Sign Language (ASL) dataset and 10 speech commands to construct the gesture/speech hybrid dataset. Experimental results show that compared with the pure gesture or pure speech networks, the recognition accuracy of the gesture-speech hybrid network improves by 2% and 12%, respectively, its accuracy reaches 97.38%, which fulfills the requirement of astronauts for HRI.

Application of Multimodal Neuroimaging in the Treatment of Neurological Patients.

Objective In order to study the application of multimodal neuroimaging in the treatment of neurological patients, the brain of patients was scanned and identified through multimodal neuroimaging, so as to provide basis for doctors to judge their diseases and give treatment plans. Method Understand the principle of multimodal neural scene currently used through literature analysis, analyze the current level of neurological diseases in the medical field and the application of multimodal neural image in diseases, recalculate the imaging examination data through neurofuzzy algorithm, and obtain a more optimized identification method than the previous visual judgment method. Result The image inspection data is dimensioned through computer spatial convolution. At this time, the output two-dimensional array is not accurate enough. It needs to be processed again through fuzzy spatial convolution to obtain relatively accurate data output, generate a small two-dimensional array, and use the convolution core for the spatial convolution process of two-dimensional array. The algorithm uses neural network machine learning algorithm to identify and judge the inspection data. Conclusion Through this algorithm to identify the examination data, the early diagnosis sensitivity of intracranial space occupying lesions and intracranial hematogenous lesions are more than 20% higher than the previous traditional recognition methods, which provides a medical imaging basis for the early diagnosis and treatment of acoustic diseases, and improves the treatment probability and prognosis quality of life of neurological patients.

SONAR IMAGE RECOGNITION BASED ON MACHINE LEARNING FRAMEWORK

Abstract. In order to improve the robustness and generalization ability of model recognition, sonar images are enhanced by preprocessing such as conversion coordinates, interpolation, denoising and enhancement, and the transfer learning method under the Caffe framework of MATLAB as an interface is used respectively (mainly composed of 8 layers of network structure, including 5 convolutional layers and 3 full chain layers) And the transfer learning method under the Python deep learning framework Inception-Resnet-v2 model for sonar image training and recognition. First of all, part of the sonar image dataset (derived from the 2021 National Robot Underwater Competition online competition data), using MATLAB as the interface Caffe framework, the sonar image is trained to obtain a training model, and then through parameter adjustment, the convolutional neural network model of sonar image automatic recognition is obtained, and the transfer learning method can use less sonar image data to solve the problem of insufficient sonar image data, and then make the training achieve a higher recognition rate in a shorter time. When the training data is randomly sampled for testing, the sonar data recognition model based on the Caffe framework is quickly and fully recognized, and the recognition rate can reach 92% when the test sample does not participate in the training of sonar image data; The transfer learning method under the Inception-Resnet-v2 model of python deep learning framework is used to train recognition on sonar images, and the recognition rate reaches about 97%. Using the two models in this paper, it is feasible to identify sonar images with high recognition rate, which is much higher than traditional recognition methods such as SVM classifiers, and the two sonar image data recognition models based on deep learning have better recognition ability and generalization ability.

Deblurring Method of Face Recognition AI Technology Based on Deep Learning

As a common method of deep learning, a convolutional neural network (CNN) shows excellent performance in face recognition. The features extracted by traditional face recognition methods are greatly influenced by subjective factors and are time-consuming and laborious. In addition, these images are susceptible to illumination, expression, occlusion, posture, and other factors, which bring a lot of interference to the computer face recognition and increase recognition difficulty. Deep learning is the most important technical means in the field of computer vision. The participation of this technology reduces manual participation and can identify the identity of visitors from multiple aspects. This study, based on the introduction at all levels and on the fundamental principle of the colloidal neural network, combines the basic model and the common exploitation methods of aspects to make a model of a combination of multiple aspects. Then, an improved CNN-based multifeature fusion face recognition model is proposed, and the effectiveness of the model in face feature extraction is verified by experiments. With the experimental results, the identification rate for the ORL and Yale data sets is 98.2% and 98.8%, respectively. Accordingly, an online face detection system and recognition system based on the combination of element models are designed. The system can obtain dynamic facial recognition and meet the recognition rate of the design requirements. The system is training four detection models and online recognition, and the test results show that the noise component model has the highest recognition rate, and the recognition rate has improved by 13% compared with the baseline capacity, further verifying that a model of a combination of features can achieve more effectively.

Road Adhesion Coefficient Identification Method Based on Vehicle Dynamics Model and Multi-Algorithm Fusion

<div class="section abstract"><div class="htmlview paragraph">As an important input parameter of intelligent vehicle active safety technology, road adhesion coefficient is of great significance in autonomous collision avoidance, emergency braking and collision avoidance, and variable adhesion road motion control. Traditional recognition methods based on vehicle dynamics require large data volume and low solution accuracy. This paper proposes an adhesion coefficient recognition method based on Elman neural network and Kalman filter. By establishing a seven-degree-of-freedom vehicle dynamics model, dynamic parameters such as yaw angular velocity, longitudinal velocity, lateral velocity, and angular velocity of each wheel, which are easy to measure and strongly related to the road adhesion coefficient, are analyzed as the input of the neural network model. The square root cubature Kalman filter algorithm is used to remove the noise of the input of the neural network model, and Q-learning is used to strengthen learning, and the weight coefficient and bias coefficient of the model are continuously rewarded and punished, so that the predicted value does not exceed the normal range of values. The algorithm was pre-trained through CarSim/Simulink co-simulation, 9 sets of simulation conditions were established, and 4 sets of verification schemes were designed for identification and inspection. The average error of the simulation process is 4.93%, and the accuracy is 91.23%. Compared with the traditional Elman neural network, the average recognition error of this method is reduced by 2.23%, and the accuracy rate is increased by 9.83%. Real vehicle experiments were carried out on wet asphalt pavement and dry asphalt pavement, verifying the feasibility of the method. This paper proposes a road adhesion coefficient recognition method, which can improve the applicability of intelligent vehicle active safety systems to complex scenarios.</div></div>

Digital Music Feature Recognition Based on Wireless Sensing Technology

With the rapid development of information technology, digital music is subsequently increasing in large quantities, and how a good integration of vocal input and recognition technology can be transformed into digital music can greatly improve the efficiency of music production while ensuring the quality and effect of music. This paper focuses on the implementation and application of human voice input and recognition technology in digital music creation, enabling users to generate digital music forms by simply humming a melodic fragment of a piece of music into a microphone. The paper begins with an introduction to digital music and speech recognition technology and goes on to describe the respective characteristics of various audio formats, which are selected as data sources for digital music creation based on the advantages of the files in terms of retrieval. Following that, the method of extracting musical information from music is described, and the main melody is successfully extracted from the multitrack file to extract the corresponding musical performance information. The feature extraction of humming input melody is further described in detail. The traditional speech recognition method of using short-time energy and short-time overzero rate features for speech endpoint detection is analyzed. Combining the characteristics of humming music, the method of cutting notes by two-stage cutting mode, i.e., combining energy saliency index, overzero rate, and pitch change, is adopted to cut notes, which leads to a substantial improvement in performance. The algorithm uses the melody extraction algorithm to obtain the melody line, merges the short-time segments of the melody line to reduce the error rate of emotion recognition, uses the melody line to segment the music signal to generate segmented segments, then abstracts the features of the segmented segments through a CNN-based structural model, and inputs the output of the model to the regressor in cascade with the melody contour features of the corresponding segmented segments to finally obtain the emotion V / A value of the segmented segments.

Cutting-state identification of machine tools based on improved Dempster-Shafer evidence theory

The reliability of machine tools is highly influenced by the cutting state. The traditional recognition method of cutting state is emphasized on a single classifier, which has the weakness of low identification accuracy and strong randomness. This paper proposes a cutting-state identification method based on improved Dempster-Shafer (DS) evidence theory. This method is divided into multi-classifier preliminary-diagnosis layer and improved DS information-fusion layer. The wavelet packet analysis method is extracted as the input of multi-classifier (Back Propagation (BP) neural network, genetic algorithm (GA) optimized BP neural network and thinking evolution (mind evolutionary algorithms) MEA optimized BP neural network). After the preliminary judgment, the improved DS information-fusion method is integrated as the final judgment, and finally, the effectiveness and feasibility of the method are verified.

Gesture Tracking and Recognition Algorithm for Dynamic Human Motion Using Multimodal Deep Learning

To address the problems of the traditional human motion gesture tracking and recognition methods, such as poor tracking effect, low recognition accuracy, high frame loss rate, and long-time cost, a dynamic human motion gesture tracking and recognition algorithm using multimode deep learning was proposed. Firstly, the collected human motion images are repaired in the three-dimensional (3D) environment, and the multimodal 3D human motion model is reconstructed using the processed images. Secondly, according to the results of model reconstruction, the camera gesture and other parameters of the keyframe are used to construct the target tracking optimization function so as to achieve the purpose of accurate tracking of human motion. Finally, for multimodal human motion gesture learning, a convolutional neural network (CNN) is developed. The trained CNN is utilized to complete dynamic human motion recognition after convolutional and pooling calculations. The results demonstrate that the proposed algorithm is effective in tracking human motion gestures. The average recognition accuracy is 96%, the average frame loss rate is 8.8%, the time cost is low, and the proposed algorithm has a high F-measure and much lower power consumption than other algorithms, indicating that the proposed algorithm is effective.

An FCM clustering algorithm based on the identification of accounting statement whitewashing behavior in universities

Abstract The traditional recognition method of whitewash behavior of accounting statements needs to analyze a large number of special data samples. The learning rate of the algorithm is low, resulting in low recognition accuracy. To solve the aforementioned problems, this article proposes a method to identify the whitewash behavior of university accounting statements based on the FCM clustering algorithm. This article analyzes the motivation of university accounting statement whitewashing behavior, studies the common means of statement whitewashing, and establishes a fuzzy set for the identification of university accounting statement whitewashing behavior. By calculating the fuzzy partition coefficient, the membership matrix of whitewash behavior recognition is established, and the whitewash behavior is classified through the iteration of the FCM algorithm. The comparative experimental results show that the recognition method has good recognition performance, low recognition error rate, and recognition accuracy of 82%.

Research on Detection Algorithm of Tablet Surface Defect Based on Yolov3

Tablet surface defect detection is an important part of tablet quality inspection, and manual detection and traditional pattern recognition methods are difficult to achieve the expected results. In this regard, this paper proposes a method for detecting tablet defects based on YOLOV3, which firstly uses industrial cameras to complete the collection of tablet defect images and creates a data set; then uses Daeknet-53 as the backbone network to initially extract features; secondly, constructs FPN feature pyramid enhancement Extraction of features; finally use yolo-head to obtain prediction results; detection mAP reaches 92.97% on the test set of the self-built data set. The experimental results show that the method has certain applicability and feasibility.

Face Recognition Method Based on Residual Convolution Neural Network

With the advancement of information technology and societal growth, social security has become more important than ever. Face recognition, as compared to other traditional recognition methods like fingerprint recognition, palm recognition, etc, has the benefit that it is contact less, and now it is becoming one among the most prominent technologies in development. Although there are numerous recognition systems that use DNNs in the field of facial expression recognition, their accuracy and practicality are still insufficient for real-world applications. A facial recognition approach based on Resnet 152 v2 has been proposed in this work. In this paper, a residual learning approach is presented to make the training of networks that are far deeper than previously employed networks easier. The proposed method, employs the AT&T face dataset, and supposing that normalization and segmentation are complete, we concentrate on the subtask of person verification and recognition, demonstrating performance using a testing database comprising illumination, pose, expression and occlusion variations. SoftMax is the activation function that has been used, which adjusts the output sum up to one allowing it to be understood as probabilities. Then, the model would generate a judgment depending on which option has a strong likelihood. This system employs Adam as an optimizer to control the learning rate through training and categorical cross entropy as its loss function. The proposed approach has a 97 percent face recognition accuracy on AT&T dataset, showing its efficacy after a significant number of analyses and experimental verification.

Motion magnification multi-feature relation network for facial microexpression recognition

Microexpressions cannot be observed easily due to their short duration and small-expression range. These properties pose considerable challenges for the recognition of microexpressions. Thus, video motion magnification techniques help us to see small motions previously invisible to the naked eye. This study aimed to enhance the microexpression features with the help of motion amplification technology. Also, a motion magnification multi-feature relation network (MMFRN) combining video motion amplification and two feature relation modules was proposed. The spatial feature is enlarged while completing the spatial feature extraction, which is used for classification. In addition, we transferred Resnet50 network to extract the global features and improve feature comprehensiveness. The magnification of the features is controlled through hyperparameter amplification factor α. The effects of different magnification factors on the results are compared, and the best is selected. The experiments have verified that the enlarged network can resolve the misclassification problem caused by the one-to-one correspondence between microexpressions and facial action coding units. On CASME II datasets, MMFRN outperforms the traditional recognition methods and other neural networks.

Analysis of Image Evolution of Ancient Large Figurines Based on Deep Neural Network

Unlike traditional image recognition technology, DL can automatically extract features and improve recognition accuracy by combining feature extraction and classification. The challenges and shortcomings of traditional image recognition methods are discussed in this article, as well as the development process and research status of DL. Related theories in image recognition based on deep learning (DL) are proposed, DL’s basic models and methods are analyzed, and related image data sets are demonstrated experimentally. Furthermore, because DL is typically used for large sample sets, this paper proposes an improved algorithm based on small samples, as well as a DNN-based analysis model for the evolution of ancient large figurine images. This model, when compared to the traditional neural network model, can speed up the network’s convergence speed and reduce training time to a certain extent. This model improves the rate of image recognition while lowering the error rate.