Correct Recognition Rate Research Articles

Investigation of flexible graphene hybrid knitted sensor for joint motion recognition based on convolutional neural network fusion long short-term memory network

Wearable electronics have attracted have attracted widespread attentions for their promising applications in motion monitoring and human-computer interaction. This paper proposes a flexible wearable joint movement intelligent sensing and recognition system to achieve stable and reliable motion feature extraction and recognition. Flexible graphene hybrid knitted sensor were prepared by transferring graphenes (GNs) agent onto stretchable knitted products via a simple spray-drying approach. The small dynamic movement of human joints for the prepared GNs hybrid sensing gloves, elbow pads and knee pads were converted into electrical signals for sensitive detection. The convolutional neural network fusion long short-term memory (CNN-LSTM) network with self-attention mechanism (SAM) is established for feature training and intelligent dynamic recognition of the measured joint information. The interconnected conductive networks endowed knitted sensor with good flexibility and remarkable electrical conductivity of 37 S/m. The unique conductive networks in the fabric offered excellent linearity and repeatable resistance response variation for better detection of joint motion. The resistant signal was analyzed by feature extraction, data correlation capture and time sequence relationship modeling. Finally, the test results show that the proposed CNN-LSTM with SAM network achieves 97%, 96% and 100% correct recognition rates for gesture signals, elbow and wrist signals and knee signals respectively, which is obviously higher than other recognition algorithms. It has great application prospects in the fields of smart wear, medical detection, and smart elderly care.

Mining the Performance Characteristics of Yao Nationality Musical Instruments under Multivariate Statistical Analysis

Abstract In the context of the rapid development of modernization and urbanization, the inheritance and research of music and musical instruments by ethnic minorities are facing great challenges. It is, therefore, of enormous significance to uncover the performance characteristics of ethnic minority musical instruments. This paper focuses on four categories of Yao musical instruments, namely reed instruments, air-pipe instruments, wind instruments, and percussion instruments. We mine the performance features of Yao musical instruments using MFCC feature extraction, perceptual linear prediction parameter extraction, and other methods. The performance of this paper’s algorithm is explored by comparing the detection and recognition accuracy of its algorithm with SVM on four types of musical instruments. The performance characteristics of Yao musical instruments are explored by analyzing the timbre characteristics, auditory characteristics, and beat statistics of the four instruments. This paper’s algorithm significantly outperforms the SVM algorithm in the recognition correct rate of four different types of musical instruments, with differences of 2.1905%, 7.1574%, 5.3758%, and 3.6962%, respectively. The extracted performance features of Yao music instruments reveal that reed instruments and air-pipe instruments have a superior timbral effect than wind instruments and percussion instruments. The sound of pneumatic instruments and wind instruments is the best when it comes to audibility, and the beats of reed instruments and wind instruments are the best among the four instruments.

Identification of Baijiu based on the Raman spectroscopy and back-propagation neural network optimized using genetic algorithm

The traditional quality testing methods of Baijiu have several challenges including long duration of testing that increases the workload of the laboratory. In this experiment, Raman spectroscopy was used to efficiently control the quality of Baijiu. The spectroscopy were primarily preprocessed using the wavelet domain denoising technique and the principal component analysis (PCA). The rapid identification and classification of 496 samples of Baijiu with various alcohol concentrations (AC), flavor grade (FG), and production year (PY) were achieved using a neural network model constructed based on Raman spectroscopy and genetic algorithm-back propagation (GA-BP). The results indicated that in comparison to the BP neural network, the best GA-BP network model achieved 95.68%, 91.37%, and 93.96% correct recognition rates for the three indicators namely, AC, FG, and PY respectively. Additionally, the average correct recognition rate of the model was enhanced by 3.62%, 9.4%, and 8.45% for AC, FG, and PY respectively. In this study, for the first time, AC, FG, and PY of Baijiu were combined to construct a model that enabled the scientific identification of Baijiu samples in unique circumstances and it also provided a new reference method for the identification, detection, and classification of Baijiu.

Exploring the Role of Chinese Language and Literature in the Transmission of Traditional Culture by Combining the Method of Internet Text Analysis

Abstract This paper constructs a Bayesian network text recognition model based on the Bayesian network and explores the role of Chinese language literature in the dissemination of traditional culture by analyzing the embodiment of traditional culture in Chinese language literature network texts. The collection process of Chinese language and literature data in network text is analyzed from the perspective of textual data interaction. The information of node variables in a Bayesian network is used to determine the mutual relationship between Chinese language literature and traditional culture. The degree of interdependence between Chinese literature and traditional culture can be measured by combining mutual information. The results show that the correct rate of text recognition of the Bayesian text recognition model decreases slightly when the training samples are (100-300), but the correct rate always stays around 0.85, thus reflecting the effectiveness of the network recognition model in this paper. Chinese language literature has a certain role in the dissemination of traditional culture, which proves that Chinese language literature, as a carrier of traditional culture, can improve the dissemination speed of traditional culture. This study focuses on the integration of Chinese literature and traditional communication to improve a new vision.

Evaluating the quality of green asparagus treated with cassava starch-based coating using laser scattering

AbstractThe presented study investigated the effects of edible coatings with concentration of 2%, 3% and 4% of starch (w/v) on the weight loss and firmness loss of green asparagus during 4 days of storage at room temperature (26 ± 2 °C, 65–70% RH). According to the results, the coated asparagus exhibited significantly slower deterioration rate than the uncoated control samples. This was indicated by the decrease in weight loss and increase in firmness (P < 0.05). After the storage period, the samples treated with 4% starch formula retained the highest quality. Furthermore, the assessment of asparagus quality throughout the storage period involved the use of the line laser scattering technique. Extracted parameters of laser scattering signal discriminated samples with linear discriminant analysis (LDA), in which the correct recognition rate of the treated groups was 75.26% and the storage time was 70.54%. This study showed the potential of laser scattering as a rapid, non-invasive, and practical optical method for assessing the quality of asparagus during storage.

Naive Bayesian classifier target recognition based on ridge features

Traditional time-frequency algorithms do not have a high feature recognition rate for targets in a low signal-to-noise background, and the number of operations for classification using plain Bayes is large and slow. To solve the above problems and achieve recognition of different ship echo signals in water, this paper proposes a target recognition method based on time-frequency ridge features by using collected audio data of different ship types, combined with Fourier Simultaneous Compression Transform (abbreviation: FSST), starting from time-frequency features, by extracting the FSST time-frequency ridge and finally using the Hopper Parsimonious Bayes classifier to obtain respectively. The recognition rate of ships under FSST and the recognition rate of ships based on ridge line features are obtained respectively. The final comparison shows that the correct recognition rate of the plain Bayesian classifier using the spine features of the time-frequency map is much higher than that of the conventional FSST time-frequency method, and it also outperforms the FSST in terms of computational power and speed.

Deep attention-based multi-task learning for underwater acoustic target recognition

Underwater acoustic target recognition is a key step in underwater acoustic signal processing, which is to judge the target attributes by extracting the features of the target radiated noise. However, the traditional feature extraction method is easily affected by the complex marine environment, resulting in a great decline in the correct recognition rate. In previous studies, the deep learning method was introduced to underwater acoustic target recognition and achieved good results. In this study, deep attention-based multi-task learning for underwater acoustic target recognition is proposed. This strategy trains encoder, decoder, and classifier in a multi-task framework to extract features that are more essential and suitable for classification. In addition, the attention mechanism is introduced in the process of feature extraction by the encoder, and the feature weight is given in the process of extraction, so as to improve the representation ability of the network. Experiments on measured underwater acoustic data are performed and the results show that this method can achieve a high accuracy rate in target recognition.

Decision Fusion and Micro-Doppler Effects in Moving Sonar Target Recognition

This paper proposes a method for underwater target recognition based on micro-Doppler effects (called STR_MD) using a majority voting ensemble classifier weighted with particle swarm optimization (PSO) (called MV-PSO). The micro-Doppler effect refers to amplitude/phase modulation of the received signal by rotating parts of a target such as propellers. Since different targets’ geometric and physical properties differ, their micro-Doppler signature is different. This inconsistency can be considered an effective issue (especially in the frequency domain) for sonar target recognition. To demonstrate the effectiveness of the proposed method, both simulated and practical micro-Doppler data are produced and applied to the designed STR_MD. Also, MV-PSO with six well-known basic classifiers, k-nearest neighbors (k-NN), Naive Bayes (NB), decision tree (DT), MLP_NN, support vector machine (SVM), and random forest (RF), has been used to evaluate the performance of the proposed method. This ensemble classifier assigns an instance to a class that most base classifiers agree on. However, basic classifiers in a set seldom work just as well. Therefore, in this case, one strategy is to weigh each classification depending on its performance using PSO. The performance parameters measured are the recognition score, reliability, and processing time. The simulation results showed that the correct recognition rate, reliability, and processing time for the simulated data at SNR = 5 dB and 10° viewing angle were 98.50, 98.89, and 9.81 s, respectively, and for the practical dataset with RPM = 1200, 100, 100, and 4.43, respectively. Thus, MV-PSO has a more encouraging performance in STR_MD for simulated and practical micro-Doppler sonar datasets.

A Novel Intelligent Fault Diagnosis Method for Self-Priming Centrifugal Pumps.

The real-time diagnostic monitoring of self-priming centrifugal pumps is essential to ensure their safe operation. Nevertheless, owing to the intricate structure and complex operational conditions inherent in such pumps, existing fault diagnosis methods encounter challenges in effectively extracting crucial fault feature information and accurately identifying fault types. Consequently, this paper introduces an intelligent fault diagnosis method tailored for self-priming centrifugal pumps. The approach amalgamates refined time-shift multiscale fluctuation dispersion entropy, cosine pairwise-constrained supervised manifold mapping, and adaptive chaotic Aquila optimization support vector machine techniques. To begin with, refined time-shift multiscale fluctuation dispersion entropy is employed to extract fault-related features, adeptly mitigating concerns related to entropy domain deviations and instability. Subsequently, the application of cosine pairwise-constrained supervised manifold mapping serves to reduce the dimensionality of the extracted fault features, thereby bolstering the efficiency and precision of the ensuing identification process. Ultimately, the utilization of an adaptive chaotic Aquila optimization support vector machine facilitates intelligent fault classification, leading to enhanced accuracy in fault identification. The experimental findings unequivocally affirm the efficacy of the proposed method in accurately discerning among various fault types in self-priming centrifugal pumps, achieving an exceptional recognition rate of 100%. Moreover, it is noteworthy that the average correct recognition rate achieved by the proposed method surpasses that of five existing intelligent fault diagnosis techniques by a significant margin, registering a notable increase of 15.97%.

Research on Language Evolution and Language Diversity Based on Chinese Speech Pitch Deviation Features

Abstract This paper constructs a Chinese speech evaluation system based on the characteristics of the vocal mechanism of Chinese speech and uses the detection framework of statistical speech recognition to realize the automatic detection function of pronunciation bias. Assuming that the amplitude spectra of speech and noise obey the generalized Gamma distribution and Gaussian distribution, respectively, the MMSE estimator of the logarithmic spectrum of the speech signal is derived. The speech presence probability under the generalized Gamma speech model is derived as a correction to the MMSE estimation based on language evolution and diversity features. The correct recognition rate of the proposed algorithm reaches 64%, which indicates that the proposed algorithm can effectively suppress the interference of convolutional noise on the speech signal features and improve the subjective perceptual quality of diverse languages.

Indigenous Knowledge and Perception Toward Mental Illnesses Within Goma City in Democratic Republic of Congo

Background: Prevailing wars for more than two decades have been associated with the increased burden of mental illnesses (MIs) in North-Kivu province (NKP), in Democratic Republic of Congo (DRC). The community perception of this claim has, however, not been fully documented. Aims: To assess the knowledge and perception of Goma city residents on MI and their associated factors. Methods: Cross sectional study, was conducted among 610 selected community residents including key informants of Goma city from April to July 2021. Quantitative data from semi-structured interviews was analyzed using descriptive statistics and binary logistic regression, while data from Focus group discussions (FGDs) were analyzed using qualitative thematic analysis. Results: Among residents of Goma city, rates of correct recognition of MIs namely schizophrenia, anxiety, depression and neurodevelopment disorders varied: 96.6%, 29.7%, 39.5% and 14.3%, respectively. The major causes of MIs perceived by respondents were spiritual (67.5%) and substance abuse (56.7%). Qualitative data from FGD’s revealed three major themes for poor perception of MI among residents as indigenous names of various MIs, reason of preferring traditional medicine versus conventional medicine (CM) and challenges faced in management of MIs using CM. Furthermore, demographic factors such as education, religion, occupation and age were not statistically significant (p˃0.05) in predicting one’s odds of MI perception among Goma city residents. Conclusion: The findings revealed that residents of Goma city have poor knowledge and perception on MIs regardless of education, religion, occupation and age.

Research on face emotion recognition algorithm based on deep learning neural network

Abstract This paper explores face emotion recognition based on deep learning neural networks in order to make the computer recognize facial emotion more accurately. Firstly, we introduce the convolutional neural network, which consists of a convolutional layer, pooling layer and fully connected layer in the deep learning neural network, and propose the variational self-encoder face emotion recognition algorithm based on the convolutional neural network in combination with the facial emotion recognition process. The correct recognition rates achieved by EVA using 32×32 pixels, LBP features and 50-dimensional Gabor wavelet features are 95.13%, 96.74% and 98.84%, respectively. In terms of emotion recognition ability, the recognition accuracy percentages were around 70% for large emotion fluctuations and around 30% for small emotion fluctuations. Since the neutral facial emotion is not well distinguished, the recognition degree of neutral facial emotion is only 80%, and the recognition degree using the self-encoder-based face emotion recognition algorithm in several facial features extraction is above 80%, and the recognition degree with neutral emotion removed is up to about 90%. Therefore, the algorithm in this paper has good classification performance and can recognize and analyze facial emotion features accurately.

A tool developed for Chinese olfactory screening: culturally adapted version of “Quick olfactory Sniffin’ Sticks Test (Q-Sticks)”

Background The odor identification test is culture-dependent. Objectives This study aimed to develop a culturally adapted version of the 5-item Quick olfactory Sniffin’ Sticks Test (Q-Stick) to adapt to the clinical environment in China. Methods The study included 3 phases: (1) develop a culturally adapted version of Q-Stick by replacing unfamiliar odors in the original Q-Stick test (N = 344); (2) validate the culturally adapted version of Q-Stick against two widely used olfactory tests: the Sniffin’ Sticks olfactory test and the 12-item Cross-Cultural Smell Identification Test (CC-SIT) (N = 286); 3) evaluate the test-retest reliability of the culturally adapted version of Q-Stick (N = 60). Results After modification of the interference items, the correct recognition rate of leather was changed from 54.65% to 90.00%. The adapted version of the Q-Stick score significantly correlated with both the Sniffin’ Sticks score (r = 0.7642, p < .0001) and CC-SIT score (r = 0.7403, p < .0001). Normal olfaction is indicated if the culturally adapted version of the Q-Stick score > 4 (specificity 70.00%, sensitivity 83.33%, Youden index 0.533) and Q-Stick score ≤ 4 indicates olfactory dysfunction. The 3-month test-retest reliability coefficient was as high as 0.96. Conclusions and significance The culturally adapted version of Q-Stick is an effective and stable screening tool to identify patients with olfactory dysfunction in China.

A Secure and Efficient Biometric Template Protection Scheme for Palmprint Recognition System

The accuracy and security of a biometric system are the two sides of a coin. A biometric system must be simple, flexible, efficient, and secure enough from unauthorized access. Concerning these requirements, this paper presents an enhanced palmprint recognition system with superior performance and improved biometric template protection schemes. This method comprises five components: image preprocessing, feature extraction, cancelable biometrics, classification, and Bio-cryptosystem. The palm texture region is extracted from the input hand image during preprocessing. A feature representation technique is then used to extract discriminant features from the palmprint texture patterns. The extracted features are then analyzed using an information coding scheme to compute a user-specific token. Here, the user-specific tokens along with the original palmprint features are used to calculate the feature vectors for the cancelable biometric system. A novel Bio-Cryptosystem has been proposed to protect templates with high security where there is no need to remember the users&#x2019; tokens. Cancelable feature vectors are kept online in encrypted form using the proposed Bio-Cryptosystem, which is used to identify or verify the subjects after decryption. The original feature vectors are kept offline to prevent these from external attacks and misuse. Finally, the multi-class linear support vector machine has been used for user classification. For experimental purposes, two benchmark hand image databases: BOSPHORUS and CASIA-Palmprint, are employed, and the proposed system has achieved nearly 100&#x0025; correct recognition rate in cancelable domain for both the employed databases. Finally, the performance comparison in terms of both verification and identification for the palmprint and the cancelable palmprint recognition systems with the existing state-of-the-art methods shows the superiority of the proposed system. <i>Impact Statement</i> &#x2014;We have improved the biometric template protection scheme by modifying the cancelable biometric technique with two security levels capable of protecting the biometric information from the dictionary, brute-force, database, channel, replay, and spoofing attacks. To avoid the difficulty of remembering long user passwords/tokens, an automatic key generation technique of user passwords has been proposed. Two different keys are generated: the first one used for cancelable biometrics and the second for encryption-decryption processes. Both these keys are not needed to store or remember and are helpful for a multi-factor authentication system. A novel Bio-cryptographic algorithm instead of traditional Bio-Cryptographic algorithms have been introduced within the proposed system. Several attacks and challenging issues of biometric recognition systems have been considered with maintaining outstanding performance in terms of accuracy and security.

Innovative Reform Strategies of Artistic Practice of College Ethnic Music Education Based on Information Fusion Technology

Abstract To seek the path and method of innovation and reform of art practice of ethnic music education in colleges and universities. Based on information fusion technology, this paper introduces the Kalman filter algorithm for analysis and recognition of ethnic music education in colleges and universities and understands the application of information fusion technology in ethnic music education in colleges and universities through the correct recognition rate and recognition time of music signals. We also apply independent experiential and problem-based experiments to verify the feasibility of information fusion technology in innovative reform. The experimental results show that the Kalman filter algorithm based on information fusion technology has a 96.57% correct recognition rate for ethnic music signals, and the recognition time is faster than the support vector machine and BP neural network by 3.587s and 1.291s on average. 73.47% of the students, on average, think that the innovative reform of art practice based on information fusion technology in college ethnic music education is very effective, which is higher than the original The average percentage of students who thought the effect of the innovation reform of folk music education based on information integration technology was very good was 73.47%, which was 60.37 percentage points higher than the original research results. The average percentage of students who thought it was ineffective was only 9.19%, 52.31 percentage points lower than the original research results. The above results prove the feasibility of information fusion technology in reforming art practice innovation in university ethnomusicology education and provide a new direction for reforming art practice innovation in university ethnomusicology education.

Improving prediction model robustness with virtual sample construction for near-infrared spectra analysis

In a qualitative analysis of near-infrared spectroscopy (NIRS), when the samples to be analyzed are difficult to obtain or there are few counterexamples, the robustness of the models is poor, resulting in the decline of the generalization ability of the models. In this case, the effective method is to construct virtual samples to achieve the balance of categories. In this contribution, three virtual spectrum construction strategies including Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), and Deep Convolutional Generative Adversarial Network (DCGAN) were explored to deal with the problem of insufficient or imbalanced sample numbers in NIRS analysis. The strategies were tested with the melamine and Yali pears two spectral datasets. The PLS-DA and Correct Recognition Rate (CRR) were used for discriminant model construction and accuracy evaluation, respectively. The results show that SMOTE, ADASYN, and DCGAN processing strategies can all improve the global CRR (CRRglob). The SMOTE and ADASYN can improve the CRR for majority class sample (CRRmaj), but the CRR for minority class sample (CRRmin) has decreased. For the DCGAN method, the CRRglob, CRRmaj, and CRRmin were all improved. The standard deviation of the results of the multiple parallel calculations demonstrates the robustness of DCGAN generation method. Therefore, the DCGAN method has good reliability and practicability, and can increase the robustness and generalization ability of the NIRS model.

Research on Iris Feature Extraction and Recognition Technology Based on Deep Learning

Abstract Certain biological information or behavioral information of a person can achieve the effect of characterizing an individual, and by combining the computer to extract the corresponding information, identity authentication is achieved. In a variety of biometrics, iris relative to fingerprints, handwriting and face, belongs to the structure within the human eye, if you want to steal is very difficult, in order to improve the safety factor, so the iris is used for authentication to achieve iris recognition. This study is based on deep learning iris recognition matching, in order to be able to effectively improve the accuracy of iris recognition, experiments are carried out. Evaluation metrics are performed through Hamming distance to calculate the correct recognition rate to ensure that the iris information can be accurately represented. This study mainly uses the improved PCHIP-LMD algorithm and CNN algorithm, the LMD algorithm is more context-aware, has better generalization ability, flexibility and scalability, while the CNN algorithm has the advantages of local awareness, parameter sharing and automatic parameter learning. In this study, we compare the correct recognition rate of iris recognition between improved PCHIP-LMD and CNN algorithms and get the conclusion that the correct recognition rate of the improved PCHIP-LMD algorithm is only 78%, which is much smaller than that of the CNN algorithm which is 92%, and we get the conclusion that LMD algorithm is suitable for iris recognition with few samples, and it is more suitable to use CNN algorithm when the sample images are too many. CNN algorithm. With the development of technology, the application of iris recognition will be more and more, I believe that soon will be widely popularized in daily life and work.

Research on Intelligent Recognition Method of Egg cracks Based on EfficientNet Network Model

Aiming at the egg crack detection task, a 6-position egg image acquisition method is proposed. EfficientNet was used to classify egg cracks. Using transfer learning technology, the model is pre-trained with CIFAR-10 data set to obtain initial weights. The efficiency of model training is improved. Compared with the current mainstream CNN models of Alexnet, VGG16 and ResNet50, in terms of egg crack recognition, the average correct recognition rate of EfficientNet model is as high as 98.6 %, which is significantly better than Alexnet, VGG16 and ResNet50 models. Among them, the correct recognition rate of EfficientNetB2 model is 99.5 %, the training time is only 29 min, and the comprehensive performance is the best. It is the most suitable method for egg crack detection. Finally, three learning rates are set for the EfficientNetB2 model. The experimental results show that the model has the best performance when the learning rate is 0.001. The improved model proposed in this paper improves the accuracy of egg crack detection and reduces the model training time.

Fiber optic detection method for intrinsic defects in carbon fiber composite core overhead conductors considering bending performance

Carbon fiber composite core wire in the carbon fiber core rod bending strength is low, easy to break, and thus affects the detection effect. In this regard, it is proposed to consider the bending performance of carbon fiber composite core overhead wire intrinsic defects fiber detection method. The overhead wire is X-ray scanned, and the image is bent compensated by the filtering algorithm. The image is reconstructed using an iterative reconstruction algorithm, and the Tamura texture feature operator is used to extract the roughness feature parameters in the texture feature enhancement map of each conductor to achieve the identification of defects. In the experiments, the proposed method is verified for detection accuracy. The analysis of the experimental results shows that the proposed method has a high correct defect recognition rate and has a more excellent detection accuracy when the proposed method is used to detect the inherent defects of overhead conductors.

Classification of female body shape based on two-dimensional image and computer vision technology

Traditional body classification methods are usually based on three-dimensional human body data. With the development of computer vision technology, two-dimensional (2D) anthropometry technology has garnered a great deal of research attention in the field of anthropometry. This paper presents a body shape classification and discrimination method using 2D images based on computer vision technology. The research included three main parts. (1) Index extraction of body shape classification based on computer vision. The orthogonal 2D human body image information of 362 young female samples was extracted. After normalizing the body height, three body shape classification indexes were separated: the body height pixel value ( H), the feature of the projected unit area ( ρ), and the feature of the projected area ratio of the front and side of the human body ( F). (2) Two-dimensional human body shape classification based on the two-step cluster model. The optimal classification number was determined, and the characteristics of each type of body shape were analyzed. (3) Automatic discrimination of the 2D human body shape based on the Bayesian algorithm. The correct rate of recognition was 94.8%. The results indicate that the body shape classification method based on computer vision technology and the selection of the proposed classification indexes are effective, and the accuracy of body shape recognition is high. In this paper, the classification of human body shape based on 2D digital images was realized, and this method can be applied to 2D anthropometry and other related fields.