Feature Recognition System Research Articles

Improvements to a Machine Learning Machining Feature Recognition System

Improvements to a Machine Learning Machining Feature Recognition System

Human Movement Recognition Based on 3D Point Cloud Spatiotemporal Information from Millimeter-Wave Radar

Human movement recognition is the use of perceptual technology to collect some of the limb or body movements presented. This practice involves the use of wireless signals, processing, and classification to identify some of the regular movements of the human body. It has a wide range of application prospects, including in intelligent pensions, remote health monitoring, and child supervision. Among the traditional human movement recognition methods, the widely used ones are video image-based recognition technology and Wi-Fi-based recognition technology. However, in some dim and imperfect weather environments, it is not easy to maintain a high performance and recognition rate for human movement recognition using video images. There is the problem of a low recognition degree for Wi-Fi recognition of human movement in the case of a complex environment. Most of the previous research on human movement recognition is based on LiDAR perception technology. LiDAR scanning using a three-dimensional static point cloud can only present the point cloud characteristics of static objects; it struggles to reflect all the characteristics of moving objects. In addition, due to its consideration of privacy and security issues, the dynamic millimeter-wave radar point cloud used in the previous study on the existing problems of human body movement recognition performance is better, with the recognition of human movement characteristics in non-line-of-sight situations as well as better protection of people’s privacy. In this paper, we propose a human motion feature recognition system (PNHM) based on spatiotemporal information of the 3D point cloud of millimeter-wave radar, design a neural network based on the network PointNet++ in order to effectively recognize human motion features, and study four human motions based on the threshold method. The data set of the four movements of the human body at two angles in two experimental environments was constructed. This paper compares four standard mainstream 3D point cloud human action recognition models for the system. The experimental results show that the recognition accuracy of the human body’s when walking upright can reach 94%, the recognition accuracy when moving from squatting to standing can reach 84%, that when moving from standing to sitting can reach 87%, and the recognition accuracy of falling can reach 93%.

Retracted: Optimization of Music Feature Recognition System for Internet of Things Environment Based on Dynamic Time Regularization Algorithm

Retracted: Optimization of Music Feature Recognition System for Internet of Things Environment Based on Dynamic Time Regularization Algorithm

Dual feature extraction system for ship-radiated noise and its application extension

The complexity of maritime noise and the time-variability of hydroacoustic channels make it more and more difficult to extract feature from ship-radiated noise effectively. To accurately recognize ship-radiated noise, a dual feature extraction and recognition system based on pre-processing-dual feature extraction-recognition is proposed. Pre-processing: The primary decomposition and secondary decomposition are performed for the first time using variational mode decomposition optimized by giant trevally optimizer, in which the threshold is adaptively determined. Dual feature extraction: Grey relational analysis is calculated for the intrinsic mode function component after primary decomposition and the high-intrinsic mode function component after secondary decomposition, and two eigenvectors are selected separately. Then entropy of the combination of weighted fluctuation-based dispersion entropy and Lempel-Ziv complexity is proposed to calculate the eigenvalues for classification. Recognition: The feature values are recognized using random forest recognizer optimized by tuna swarm optimization to obtain the final recognition rate. The experimental results show that the proposed entropy has an overwhelming advantage over the others, such as permutation entropy, and dispersion entropy. It is proved that the proposed system for ship-radiated noise has good classification performance and the recognition rate is up to 97.5%. In addition, it is successfully applied to simulated chaotic signals as well as measured aquatic life signals.

From Structural Optimization Results to Parametric CAD Modeling—Automated, Skeletonization-Based Truss Recognition

This paper presents an automated, skeletonization-based feature recognition system designed for use with biomimetic structural optimization results. It enables importing optimization results back to the CAD system as a set of parameterized geometries. The system decomposes the output of the structural optimization system into a set of simple CAD features, cylinders and spheres, enabling continuation of mechanical design workflow using native CAD representation. The system was designed to work in a fully automated mode accepting 3D objects as an input. The system uses mesh skeletonization to generate an initial solution which is refined using an evolutionary algorithm for the 3D geometry reconstruction. The system is designed as the last step of structural optimization. Applied for industrial use, it preserves unique features of this approach, such as excluding parts of the domain from optimization. The biomimetic topology optimization was used for structural optimization for all presented examples. The proposed algorithm is demonstrated using two cases: well-recognized cantilever beam optimization and industrial application of the structural optimization. For both cases, resultant geometry stress distribution is provided and analyzed.

Retracted: Design of the Vocal Music Feature Recognition System Based on the Internet of Things Technology

Retracted: Design of the Vocal Music Feature Recognition System Based on the Internet of Things Technology

Preprocessing and pattern recognition for Single-Lead cardiac dynamic model

ObjectiveSince time-domain/frequency-domain domain data can only provide limited cardiac features, this reduces the recognition accuracy of pathological variation signals. Therefore, this study designed a new feature recognition system to identify patients. MethodsThis research first designed a new single-lead cardiac dynamic model, which can be used to distinguish different types of diseases, and then used the new dynamic model to assist wavelet analysis. Subsequently, this research proposed a new discriminant for baseline drift noise, and employed a new dynamic model to assist in eliminating sampling noise and motion artifact noise. This research also designed a new dual-threshold algorithm. Afterwards, this research designed a new kurtosis-skewness clipper capable of clipping disease-damaged signals. Since the neural network needs to use samples of equal length, this research designed a new Signal stretch-feature Integrator, which can automatically select the best interpolation method. Finally, this research designed a new automatic machine learning model that can automatically build a neural network and complete the signal identification using only P wave/QRS wave/T wave. ResultsDynamic modeling enables intuitive comparison of signal processing effects. After preprocessing, the baseline drift of the signal was effectively removed and the noise was reduced. After clipping the signal damaged by the disease, the highest accuracy of individual discrimination reached 99.8951 % by using the automatic machine learning model. ConclusionAfter being analyzed and processed by the new dynamic model, the recognition accuracy can reach 99.4895% by using P wave / QRS wave / T wave alone.

Hybrid Japanese Language Teaching Aid System with Multi-Source Information Fusion Mapping

Learning Japanese can enhance competitiveness in a globalized economy, and we address the problems of poor open-source Japanese language teaching, cumbersome teaching tasks, and a single teaching model. We propose a hybrid Japanese teaching aid system with multiple information fusion mapping, which can effectively improve the efficiency of Japanese teaching and reduce the tedious human teaching procedures. The system is divided into two branches of Japanese language recognition, namely, the Japanese text recognition branch and the Japanese voice sequence recognition branch. In the Japanese text recognition branch, we integrate attention mechanisms and long short-term memory networks as the basic network for Japanese character text recognition. In addition, we set up separate text feature recognition systems for Japanese computer writing and handwriting to prevent feature overlap problems. For Japanese voice sequence recognition, we used a combination of memory gating unit and encoder, based on the network still extending the structure of the deep neural network and using the residual structure connection in the gating unit to avoid the gradient disappearance problem. At the end of the system, we use a softmax layer to connect the text recognition and voice recognition networks to form a Japanese language teaching aid system. To verify the efficiency of our system, we selected the Japanese text recognition public dataset and voice recognition public dataset for experimental validation. To match the practical application of the system, we created our dataset based on the dataset standard and conducted experimental validation. To compare other Japanese recognition methods, we selected the six most representative Japanese recognition algorithms for experimental comparison. To ensure the balance of the experiments, each algorithm is trained in a separate experimental environment for modeling and tuning parameters. Experimental performance and the experimental results show that our method significantly outperforms the other methods and has better system stability.

Design of the Vocal Music Feature Recognition System Based on the Internet of Things Technology

In order to meet the needs of vocal feature recognition, the author proposes a system design based on the Internet of things technology. The principle of the method is that the body sensory layer of the body puts the sound sensors in different positions, records the primary sound signal, and monitors and makes the sound signal using the TMS320VC5402 light red processor. The network transport layer audio signal processes and sends the voice signal database in the process layer. The sound characteristic analysis module in the application layer uses a real-time dynamic change algorithm to obtain the best similarity between the experimental model and the design, identify the characteristics of the sound set red, and identify the content of the voice feature, the music, and the emotional results of the song. Experiments have shown that the system is capable of recognizing speech in a noisy environment, with an accuracy of approximately 95%, resulting in better sound control and the ability to switch switches and remote control. The system, developed with the Internet of things technology, has been proven to improve voice recognition.

Facial Expression Recognition System Using Haar Cascades Classifier

Abstract: Facial expression conveys non-verbal cues, which play a crucial role in social relations. Facial Expression Recognition is a significant yet challenging task, as we can use it to identify the emotions and the mental state of an individual. In this system, using image processing and machine learning, we compare the captured image with the trained dataset and then display the emotional state of the image. To design a robust facial feature recognition system, Local Binary Pattern(LBP) is used. We then assess the performance of the suggested system by using a database that is trained, with the help of Neural Networks. The results show the competitive classification accuracy of various emotions.

English Speech Feature Recognition-Based Fuzzy Algorithm and Artificial Intelligent

It is necessary to study the application of digital technology in English speech feature recognition. This paper combines the actual needs of English speech feature recognition to improve the digital algorithm. Moreover, this paper combines fuzzy algorithm to analyze English speech features, analyzes the shortcomings of traditional algorithms, proposes the fuzzy digitized English speech recognition algorithm, and builds an English speech feature recognition model on this basis. In addition, this paper conducts time-frequency analysis on chaotic signals and speech signals, eliminates noise in English speech features, improves the recognition effect of English speech features, and builds an English speech feature recognition system based on digital means. Finally, this paper conducts grouping experiments by inputting students’ English pronunciation forms and counts the results of the experiments to test the performance of the system. The research results show that the method proposed in this paper has a certain effect.

Toward Privacy Protection of Sensed Biometric Features with Extended Visual Cryptography

The popularity of more powerful and smarter digital devices has improved the quality of life and poses new challenges to the privacy protection of personal information. Although traditional encryption algorithms are effective in most application scenarios, they may not be suitable in integrated sensing and communication environments due to their low processing capabilities. In this paper, we develop a novel extended visual cryptography scheme to store private information in separate databases, which eliminates the complex operations in privacy-preserving schemes based on cryptography and watermarking. Since shares do not reveal any feature about private sensed information, we can transmit sensitive information among sensors and smart home servers in plain. To abate the influence of noise due to the use of visual cryptography, we leverage the generalization ability of transfer learning to train a visual cryptography-based recognition network. Experimental results show that our proposed method keeps the high accuracy of the feature recognition system when providing security.

Analysis Model of Spoken English Evaluation Algorithm Based on Intelligent Algorithm of Internet of Things.

With the in-depth promotion of the national strategy for the integration of artificial intelligence technology and entity development, speech recognition processing technology, as an important medium of human-computer interaction, has received extensive attention and motivated research in industry and academia. However, the existing accurate speech recognition products are based on massive data platform, which has the problems of slow response and security risk, which makes it difficult for the existing speech recognition products to meet the application requirements for timely translation of speech with high response time and network security requirements under the condition of network instability and insecurity. Based on this, this paper studies the analysis model of oral English evaluation algorithm based on Internet of things intelligent algorithm in speech recognition technology. Firstly, based on the automatic machine learning and lightweight learning strategy, a lightweight technology of automatic speech recognition depth neural network adapted to the edge computing power is proposed. Secondly, the quantitative evaluation of Internet of things intelligent classification algorithm and big data analysis in this system is described. In the evaluation, the evaluation method of oral English characteristics is adopted. At the same time, the Internet of things intelligent classification algorithm and big data analysis strategy are used to evaluate the accuracy of oral English. Finally, the experimental results show that the oral English feature recognition system based on Internet of things intelligent classification algorithm and big data analysis has the advantages of good reliability, high intelligence, and strong ability to resist subjective factors, which proves the advantages of Internet of things intelligent classification algorithm and big data analysis in English feature recognition.

Recognition and Error Correction of Piano Playing Features Based on Filtering Technology

The extraction of piano features needs to be based on mature technology, but there is no mature technology for feature recognition of music to ensure the correct rate of feature extraction. This article combines filter processing technology to carry out the research on piano playing feature recognition and builds an intelligent error correction system, and it realizes the feature recognition and error correction of piano playing with the support of filter technology. Moreover, this article conducts experimental research on the system constructed. Through experimental research, it can be seen that the piano performance feature recognition system proposed in this article can effectively recognize the piano practitioner’s playing effect and record it for error correction. This intelligent method can effectively improve the piano playing effect.

A Privacy-Preserving Biometric Recognition System with Visual Cryptography

The popularity of more powerful and smarter digital devices has improved the quality of life and poses new challenges to the privacy protection of personal information. In this paper, we propose a biometric recognition system with visual cryptography, which preserves the privacy of biometric features by storing biometric features in separate databases. Visual cryptography combines perfect ciphers and secret sharing in cryptography with images, thus eliminating the complex operations in existing privacy-preserving schemes based on cryptography or watermarking. Since shares do not reveal any feature about biometric information, we can efficiently transmit sensitive information among sensors and smart devices in plain. To abate the influence of noise in visual cryptography, we leverage the generalization ability of transfer learning to train a visual cryptography-based recognition network. Experimental results show that our proposed method keeps the high accuracy of the feature recognition system when providing security.

Using Image Feature Extraction to Identification of Ancient Ceramics Based on Partial Differential Equation

This paper presents an in-depth study and analysis of the image feature extraction technique for ancient ceramic identification using an algorithm of partial differential equations. Image features of ancient ceramics are closely related to specific raw material selection and process technology, and complete acquisition of image features of ancient ceramics is a prerequisite for achieving image feature identification of ancient ceramics, since the quality of extracted area-grown ancient ceramic image feature extraction method is closely related to the background pixels and does not have generalizability. In this paper, we propose a deep learning-based extraction method, using Eased as a deep learning support platform, to extract and validate 5834 images of 272 types of ancient ceramics from kilns, celadon, and Yue kilns after manual labelling and training learning, and the results show that the average complete extraction rate is higher than 99%. The implementation of the deep learning method is summarized and compared with the traditional region growth extraction method, and the results show that the method is robust with the increase of the learning amount and has generalizability, which is a new method to effectively achieve the complete image feature extraction of ancient ceramics. The main content of the finite difference method is to use the ratio of the difference between the function values of two adjacent points and the distance between the two points to approximate the partial derivative of the function with respect to the variable. This idea was used to turn the problem of division into a problem of difference. Recognition of ancient ceramic image features was realized based on the extraction of the overall image features of ancient ceramics, the extraction and recognition of vessel type features, the quantitative recognition of multidimensional feature fusion ornamentation image features, and the implementation of deep learning based on inscription model recognition image feature classification recognition method; three-layer B/S architecture web application system and cross-platform system language called as the architectural support; and database services, deep learning packaging, and digital image processing. The specific implementation method is based on database service, deep learning encapsulation, digital image processing, and third-party invocation, and the service layer fusion and relearning mechanism is proposed to achieve the preliminary intelligent recognition system of ancient ceramic vessel type and ornament image features. The results of the validation test meet the expectation and verify the effectiveness of the ancient ceramic vessel type and ornament image feature recognition system.

Development and performance evaluation of a web-based feature extraction and recognition system for sheet metal bending process planning operations

ABSTRACT Sheet metal bending manufacturing companies require changeable and adaptable process planning systems to shorten the production cycle time and reduce operations costs. This is due to globalisation and the rapid change in market demands for sheet metal products. In light of this, this paper proposes a web-based feature extraction and recognition system. The system would ensure automated planning of various processes used by a bending machine to produce varieties of sheet metal products. The algorithms were implemented using C++ to produce the geometric and feature models used to extract, and recognise the bending features in various CAD files acquired from literature. Five (5) CAD files of various sheet metals were utilised to test the functionality of this system. The results from the feature recognition process have proven to be precisely what the user has designed and saved in the model file. Bend radius and bend angle. Finally, the developed system is able to transform a STEP file into a feature model and display the 3D CAD model within the least time. It is a cost-efficient standalone system that provides data storage, allows collaboration and data sharing. can be used anywhere where there is internet access.

Retracted] Optimization of Music Feature Recognition System for Internet of Things Environment Based on Dynamic Time Regularization Algorithm

Because of the difficulty of music feature recognition due to the complex and varied music theory knowledge influenced by music specialization, we designed a music feature recognition system based on Internet of Things (IoT) technology. The physical sensing layer of the system places sound sensors at different locations to collect the original music signals and uses a digital signal processor to carry out music signal analysis and processing. The network transmission layer transmits the completed music signals to the music signal database in the application layer of the system. The music feature analysis module of the application layer uses a dynamic time regularization algorithm to obtain the maximum similarity between the test template and the reference. The music feature analysis module of the application layer uses the dynamic time regularization algorithm to obtain the maximum similarity between the test template and the reference template to realize the feature recognition of the music signal and determine the music pattern and music emotion corresponding to the music feature content according to the recognition result. The experimental results show that the system operates stably, can capture high‐quality music signals, and can correctly identify music style features and emotion features. The results of this study can meet the needs of composers’ assisted creation and music researchers’ analysis of a large amount of music data, and the results can be further transferred to deep music learning research, human‐computer interaction music creation, application‐based music creation, and other fields for expansion.

Machine Learning Algorithm for Surface Quality Analysis of Friction Stir Welded Joint

Abstract The Friction Stir Welding process usually produces weld members of good quality compared to composite weld made with a standard welding process. However, there is a possibility of the formation of various defects if the input parameters are not properly selected. In the recent case study, an image-based feature recognition system using the Fourier conversion method which is a computer visual recognition tool is developed. Five types of filters like Ideal Filter, Butterworth Filter, Low Filter, Gaussian Filter, and High Pass Filter. The results showed that the high pass filter has more ability to detect surface defects compared to the other four filters. It has also been observed that the Ideal filter has a lot of distortions compared to the Gaussian Filter and the Butterworth Filter.

Use of an Automated System to Evaluate Feature Dissimilarities in Handwriting Under a Two-Stage Evaluative Process*, †.

The two-stage evaluative process is an established framework utilized by forensic document examiners (FDEs) for reaching a conclusion about the source(s) of handwritten evidence. In the second, or discrimination, stage, the examiner attempts to estimate the rarity of observations in a relevant background population. Unfortunately, control samples from a relevant background population are often unavailable, leaving the FDE to reach this determination based on subjective experience. Automated handwriting feature recognition systems are capable of performing both feature comparison and discrimination, yet these systems have not been subjected to empirical validation studies. In the present study, we repurposed a commercially available automated system to generate empirical distributions for ranking feature dissimilarity scores among pairs of handwritten phrases. The blinded results of this automated process were used to survey an international cohort of 36 FDEs regarding their strength of support for same- and different-writer propositions. The survey served to cross-validate FDE decision-making under the two-stage approach. Results from the survey demonstrated a clear pattern of response consistent with ground truth. Predictive regression analyses indicated that the automated feature dissimilarity scores and the log of their cumulative distribution functions accounted for 72% of the variability in FDE opinions. This study demonstrated that feature dissimilarity scores acquired using automated processes and their distributions are closely aligned with FDE decision-making processes supporting the heuristic value of the two-stage evaluative framework.