Recognition Success Rate Research Articles

Automatic tiling method for mosaic ceramic art images based on subpixel edge fitting localization and collaborative operation of multiple manipulators

Mosaic ceramics are not limited to use solely as building materials, they also possess artistic value. Artists can create images by arranging and combining mosaic ceramics, resulting in a perfect fusion of large-scale public art for external walls and ceramic materials. However, the current approach for artists to create mosaic ceramic exterior wall art images involves manual laying and assembling of individual mosaic ceramics. This manual process suffers from issues such as low efficiency, eye fatigue, selection errors, and the risk of high-altitude operations. These challenges significantly impact the quality and efficiency of creating mosaic ceramic exterior wall art image images. To address these problems, this paper proposes an automatic mosaic ceramic art image stitching method based on subpixel edge fitting positioning and collaborative operation of multiple robotic arms. Additionally, a new U + I type conveying method is designed for efficient and space-saving transportation of mosaic ceramics. Experimental results demonstrate a high success rate of recognition, absorption, and placement of multi-color mosaic ceramics using this method reaching 95.45%, with a positioning error within 0.5 mm. The method can also adapt to varying levels of light intensity or noise interference. This approach effectively enhances the quality and efficiency of creating mosaic ceramic exterior wall art images and promotes the development of mosaic ceramic exterior wall public art creation.

Separated spacecraft attitude algorithm research based on star sensor multi-information fusion

Faced with the problem of poor anti-interference ability of star map recognition technology, in order to improve its anti-interference ability and achieve higher attitude measurement accuracy, this article proposes a star map recognition method based on binary search trees. Star sensors and gyroscopes are combined to solve the problem of the poor real-time performance of star sensors, optimizing the Extended Kalman Filtering (EKF) algorithm. This improves attitude output accuracy and formulates corresponding attitude measurement schemes considering the situation where the star sensor is not visible. The results showed that the improved star pattern recognition algorithm had better adaptability and robustness than the rasterization algorithm, with a high recognition success rate of 95.6% when the star sensor field of view was 3°*3°, while the rasterization algorithm cannot recognize small fields of view. The recognition success rate of the algorithm was 98.6% when the standard deviation of magnitude noise was 2.0 pixels, which was 15.4% higher than the rasterization algorithm. Unlike the EKF algorithm, the optimized EKF algorithm had a higher attitude output accuracy, with an average error of about 0.000 211°; compared to before optimization, it has increased by 93.43%. In the attitude measurement scheme, the relative attitude angle measurement error was small, and the maximum attitude measurement error in the rolling angle direction was 1.8 × 10−3°. This paper adopts a method to achieve high-precision satellite attitude measurement with good adaptability, which can be applied in complex space missions.

Microstructured CNTs/Cellulose Aerogel for a Highly Sensitive Pressure Sensor.

Flexible sensors have been applied in human health monitoring and biomedical research, but producing high-performance piezoresistive sensors at low cost is still challenging. To address these shortcomings, we proposed a microstructured carbon nanotube (CNT)/cellulose aerogel-based pressure sensor. The sensor consists of three parts, i.e., cellulose/poly(vinyl alcohol)/CNT aerogel-based sensing layer and top and bottom thermoplastic polyurethane elastomer (TPU)/silver nanowire (Ag NW) nanofiber electrode. The aerogel is fabricated using a simple freeze-drying method and an easy electrospinning method to obtain the nanofiber-based electrode. Two TPU/Ag NW nanofiber electrodes sandwiched the aerogel with a microstructure in the middle. Benefiting from the microcone and micropore structures on the nanofiber electrode, the assembled sensors show a high sensitivity of 66.4 kPa-1, a significant detection boundary of 50 kPa, and an excellent response speed of 10 ms. The high sensing performance enables the sensor to monitor physiological signals, Morse code interactions, and gesture recognition. With the help of machine learning, the success rate of gesture recognition is as high as 98.8%. The preparation of this pressure sensor based on an aerogel shows excellent health and environmental monitoring potential as an artificial skin.

Contact State Recognition for Dual Peg-in-Hole Assembly of Tightly Coupled Dual Manipulator

Contact state recognition is a critical technology for enhancing the robustness of robotic assembly tasks. There have been many studies on contact state recognition for single-manipulator, single peg-in-hole assembly tasks. However, as the number of pegs and holes increases, the contact state becomes significantly more complex. Additionally, when a tightly coupled multi-manipulator is required, the estimation errors in the contact forces between pegs and holes make contact state recognition challenging. The current state recognition methods have not been tested in such tasks. This paper tested Support Vector Machine (SVM) and several neural network models on these tasks and analyzed the recognition accuracy, precision, recall, and F1 score. An ablation experiment was carried out to test the contributions of force, image, and position to the recognition performance. The experimental results show that SVM has better performance than the neural network models. However, when the size of the dataset is limited, SVM still faces generalization issues. By applying heuristic action, this paper proposes a two-stage recognition strategy that can improve the recognition success rate of the SVM.

FM‐YOLOv8:Lightweight gesture recognition algorithm

AbstractIn practical production applications, the efficiency and success rate of gesture recognition directly affect the user experience and work efficiency. However, the existing gesture recognition models have the problem of a large number of model parameters and high computational complexity, which makes them unable to meet the needs of end‐to‐end industrial deployment. To solve these problems, this article proposes a gesture recognition model based on YOLOv8. First, FasterNet is adopted as the backbone of YOLOv8, which significantlydecrease the number of parameters and made the model more lightweight. By reducing the number of parameters, the computational complexity of the model can be reduced while maintaining the performance of the model, and the operation efficiency of the model can be improved. Second, recombination convolution ScConv is introduced to replace common convolution operations to further improve the model's efficiency. Recombination convolution can reduce the computation and make up for the loss of precision to some extent. Finally, the MDPIoU loss function is used to optimize target location and prediction, to improve the accuracy of the model. The MDPIoU loss function can better deal with the problem of target boundary frame positioning and prediction so that the model can locate and predict gestures more accurately in gesture recognition tasks. Experiments on a data set containing 10 types of gestures show that the number of parameters and floating point calculations of the improved network model are reduced by 45% and 42.7%, respectively, while the accuracy is unchanged. The improved model can be deployed on edge terminals, providing efficient and accurate gesture recognition.

Monocular Pose Estimation Method for Automatic Citrus Harvesting Using Semantic Segmentation and Rotating Target Detection.

The lack of spatial pose information and the low positioning accuracy of the picking target are the key factors affecting the picking function of citrus-picking robots. In this paper, a new method for automatic citrus fruit harvest is proposed, which uses semantic segmentation and rotating target detection to estimate the pose of a single culture. First, Faster R-CNN is used for grab detection to identify candidate grab frames. At the same time, the semantic segmentation network extracts the contour information of the citrus fruit to be harvested. Then, the capture frame with the highest confidence is selected for each target fruit using the semantic segmentation results, and the rough angle is estimated. The network uses image-processing technology and a camera-imaging model to further segment the mask image of the fruit and its epiphyllous branches and realize the fitting of contour, fruit centroid, and fruit minimum outer rectangular frame and three-dimensional boundary frame. The positional relationship of the citrus fruit to its epiphytic branches was used to estimate the three-dimensional pose of the citrus fruit. The effectiveness of the method was verified through citrus-planting experiments, and then field picking experiments were carried out in the natural environment of orchards. The results showed that the success rate of citrus fruit recognition and positioning was 93.6%, the average attitude estimation angle error was 7.9°, and the success rate of picking was 85.1%. The average picking time is 5.6 s, indicating that the robot can effectively perform intelligent picking operations.

X-ray pneumonia detection using angular and radial local binary patterns fusion

Detecting pneumonia via X-ray images is a crucial phase in determining any probable bacterial or viral disease. The automation of this stage is critical since it speeds up the diagnosing process. LBPs have excelled in investigating local conditions within a neighborhood, where they aggregate and analyze local statistical results. In this paper, we present a novel family of LBP descriptors based on local accumulative pixel disparities. We construct the new descriptors using a circular neighborhood acquired from the initial square filter. These descriptors consider angular and radial transitions that occur in both the microstructures and macrostructures of image textures. Eventually, a more accurate and comprehensive visual representation is obtained. Furthermore, we offer a data fusion step based on a voting mechanism to integrate the retrieved data efficiently. We show many types of analyses that demonstrate LBP's and the suggested extensions' capability to extract discriminant features from X-ray images. The suggested method is tested on two different datasets with large diversities that include images from various demographics and regions. Several measures, including accuracy rate, precision, sensitivity, F-measure, and specificity, are used to evaluate the system's efficacy. According to the testing results, the proposed system provides a best successful recognition rate of 82.7 %.

Immersive e-learning application in intelligent teaching of English composition based on neural network algorithm

Technical solutions for evaluating traditional English teaching composition and other problems, such as excessive subjectivity, waste of time, slow feedback, short scoring time, heavy tasks and so on. This paper first analyzes the theoretical basis of neural network algorithm, and completes the preprocessing of the composition text submitted by students from three aspects: text segmentation, text representation and text fragment retrieval. Then, an intelligent evaluation system of English composition is established, and the scoring standard of English composition is determined. On this basis, the design of English scoring module is improved, and an anti cheating module for common plagiarism problems in English composition is added. Finally, the English composition intelligent evaluation system is tested. It can effectively complete the target task, evaluate and correct the composition, and has a higher recognition success rate and accuracy rate. By studying the algorithm and applying it to the construction of English composition intelligent evaluation system, this paper successfully designs a kind of English composition intelligent evaluation system, which promotes the development of teaching work.

Research on puncture classification method of seed implantation robot based on force/image model

This article focuses on the use of a puncture classification method for prostate cancer seed implantation robots. To address the limitations of seed implantation surgery, different tissues in the human body are punctured and classified to improve the precision and success rate of seed implantation. This paper discusses an information fusion sensing technique that can be used in seed implantation surgery, which senses the real-time state of the puncture needle by fusing force and image modes and dramatically improves puncture accuracy and surgical tolerance. In addition, considering the multiscale feature problem of the image recognition process, this paper improves the YOLOv8 model by adding a global attention mechanism (GAM_Attention) to the backbone of the model, which significantly improves the image recognition success rate. Then, datasets of the puncture force and real-time puncture images were produced through puncture experiments. Preliminary puncture experiments with different models were completed, and the experimental results showed that the classification method improved the classification accuracy. The accuracy of seed implantation is improved by this method, which may provide a new idea for deep learning in seed implantation modality perception.

Sound classification with time-frequency features in forest environment

The study of forest sound classification has drawn more attention recently due to its potential for illegal activities and natural disaster monitoring. Based on the forest sound classification dataset (FSC22), a dataset specific to possible sound existing in the forest, five classification methods are utilized to investigate the relationship between recognition accuracy and the number of sound acoustic features, as well as the number of target classes. The results confirmed that extreme random forest is the best method for forest sound classification, with an accuracy of around 70% when the target class number is above 20. Further, Mel-frequency cepstral coefficients are the critical feature for sound classification, while fuzzy labels in the dataset may reduce the success rate of recognition.

Deep neural network-based emotion recognition using facial landmark features and particle swarm optimization

Relating specifically to human–computer interaction (HCI), computer vision research has placed a substantial emphasis on intelligent emotion recognition in recent years. The primary emphasis lies in investigating speech aspects and bodily motions, while the knowledge of recognizing emotions from facial expressions remains relatively unexplored. Automated facial emotion detection allows a machine to assess and understand a person's emotional state, allowing the system to predict intent by analyzing facial expressions. Therefore, this research provides a novel parameter selection strategy using swarm intelligence and a fitness function for intelligent recognition of micro emotions. This paper presents a novel method based on geometric visual representation obtained from facial landmark points. We employ the Deep Neural Networks (DNN) model to analyze the input features from the normalized angle and distance values derived from these landmarks. The results of the experiments show that Particle Swarm Optimization (PSO) worked very well by using only a few carefully chosen features. The method achieved a recognition success rate of 98.76% on the MUG dataset and 97.79% on the GEMEP datasets.

Exploring the Impact of Mismatch Conditions, Noisy Backgrounds, and Speaker Health on Convolutional Autoencoder-Based Speaker Recognition System with Limited Dataset

This paper presents a novel approach to enhance the success rate and accuracy of speaker recognition and identification systems. The methodology involves employing data augmentation techniques to enrich a small dataset with audio recordings from five speakers, covering both male and female voices. Python programming language is utilized for data processing, and a convolutional autoencoder is chosen as the model. Spectrograms are used to convert speech signals into images, serving as input for training the autoencoder. The developed speaker recognition system is compared against traditional systems relying on the MFCC feature extraction technique. In addition to addressing the challenges of a small dataset, the paper explores the impact of a "mismatch condition" by using different time durations of the audio signal during both training and testing phases. Through experiments involving various activation and loss functions, the optimal pair for the small dataset is identified, resulting in a high success rate of 92.4% in matched conditions. Traditionally, Mel-Frequency Cepstral Coefficients (MFCC) have been widely used for this purpose. However, the COVID-19 pandemic has drawn attention to the virus's impact on the human body, particularly on areas relevant to speech, such as the chest, throat, vocal cords, and related regions. COVID-19 symptoms, such as coughing, breathing difficulties, and throat swelling, raise questions about the influence of the virus on MFCC, pitch, jitter, and shimmer features. Therefore, this research aims to investigate and understand the potential effects of COVID-19 on these crucial features, contributing valuable insights to the development of robust speaker recognition systems.

Diver-robot communication dataset for underwater hand gesture recognition

In this paper, we present a dataset of diving gesture images used for human–robot interaction underwater. By offering this open access dataset, the paper aims at investigating the potential of using visual detection of diving gestures from an autonomous underwater vehicle (AUV) as a form of communication with a human diver. In addition to the image recording, the same dataset was recorded using a smart gesture recognition glove. The glove uses dielectric elastomer sensors and on-board processing to determine the selected gesture and transmit the command associated with the gesture to the AUV via acoustics. Although this method can be used under different visibility conditions and even without line of sight, it introduces a communication delay required for the acoustic transmission of the gesture command. To compare efficiency, the glove was equipped with visual markers proposed in a gesture-based language called CADDIAN and recorded with an underwater camera in parallel to the glove’s onboard recognition process. The dataset contains over 30,000 underwater frames of nearly 900 individual gestures annotated in corresponding snippet folders. The dataset was recorded in a balanced ratio with five different divers in sea and five different divers in pool conditions, with gestures recorded at 1, 2 and 3 metres from the camera. The glove gesture recognition statistics are reported in terms of average diver reaction time, average time taken to perform a gesture, recognition success rate, transmission times and more. The dataset presented should provide a good baseline for comparing the performance of state of the art visual diving gesture recognition techniques under different visibility conditions.

Recognition of pulse-in-pulse modulation type of radar signal based on feature extraction

In order to solve the problem of identifying radar signal modulation types in a complex electromagnetic environment, this paper analyzes and evaluates some of the existing identification methods, such as CW, LFM, NLFM, BPSK, QPSK, FSK, FSK + PSK, and LFM + BPSK modulation types, and proposes a hierarchical decision recognition algorithm based on feature extraction. First, the signal is preprocessed by FFT transformation and STFT transformation. Second, a new set of radar signal recognition characteristic parameters is proposed to identify and classify different signals. Then, by analyzing the characteristics of different signals, the signals are identified and classified one by one. Finally, the usability of the proposed algorithm is obtained by comparing several commonly used recognition algorithms. The simulation results show that the recognition success rate of this algorithm can reach 95% when the SNR is better than −7 dB, which has certain industrial application value.

Hollow-microsphere-integrated optofluidic immunochip for myocardial infarction biomarker microanalysis

This work developed an optofluidic immunochip that uses whispering gallery mode with fiber laser enhancement, for the rapid detection of a key biomarker cardiac troponin I for acute myocardial infarction (AMI). The immunochip adopted an innovative design, using perforated hollow glass microspheres (HGMS) as carriers, with antibodies immobilized on the inner surface of the HGMS, thereby achieving ultra-low sample consumption. The performance of the immunochip was improved by fiber laser, including spectral width compression to 0.019 nm, optical signal-to-noise ratio amplification to 63.17 dB, and an enhancement in the limit of detection to 5 pg/mL. Moreover, this immunochip can provide results within 15 minutes, making it highly suitable for early AMI risk management. Compared to the standard electrochemiluminescence detection method, although some differences exist in the results of the immunochip due to the principle of detection and differences in antibody affinity, its positive reference value can be calculated as 0.0754 ng/mL, with a successful recognition rate of 88% for positive patients. The immunosensor is integrated on a polydimethylsiloxane substrate, with a compact size suitable for use in point-of-care devices and AMI self-screening, as well as rapid disease screening and microanalysis of various biomarkers, offering new possibilities for applications in these fields.

Research on the Recognition Method of Dial Scales for Arrester Pointer Instruments Based on Deep Learning

To address the recognition challenges faced by arrester pointer instruments’ dial scales in various scenarios, this paper introduces a deep learning-based recognition method for pointer instrument scales. An attention module is integrated into the YOLOv5 network architecture, enhancing the accuracy and robustness of the model. After correcting the dial, dial recognition is conducted with OpenCV to achieve precise identification of the instrument scales. The proposed method was tested using images of arrester pointer instruments against diverse backgrounds. The experimental results demonstrate that the method processes instrument data images in an average time of 0.662 s and achieves a successful recognition rate of 96% with an average error of 0.923%. This method provides a rapid and efficient approach for recognizing instrument scales and offers a novel solution for identifying similar types of instruments.

Small- and medium-sized rice fields identification in hilly areas using all available sentinel-1/2 images

BackgroundMastering the spatial distribution and planting area of paddy can provide a scientific basis for monitoring rice production, and planning grain production layout. Previous remote sensing studies on paddy concentrated in the plain areas with large-sized fields, ignored the fact that rice is also widely planted in vast hilly regions. In addition, the land cover types here are diverse, rice fields are characterized by a scattered and fragmented distribution with small- or medium-sized, which pose difficulties for high-precision rice recognition.MethodsIn the paper, we proposed a solution based on Sentinel-1 SAR, Sentinel-2 MSI, DEM, and rice calendar data to focus on the rice fields identification in hilly areas. This solution mainly included the construction of rice feature dataset at four crucial phenological periods, the generation of rice standard spectral curve, and the proposal of spectral similarity algorithm for rice identification.ResultsThe solution, integrating topographical and rice phenological characteristics, manifested its effectiveness with overall accuracy exceeding 0.85. Comparing the results with UAV, it presented that rice fields with an area exceeding 400 m2 (equivalent to 4 pixels) exhibited a recognition success rate of over 79%, which reached to 89% for fields exceeding 800 m2. ConclusionsThe study illustrated that the proposed solution, integrating topographical and rice phenological characteristics, has the capability for charting various rice field sizes with fragmented and dispersed distribution. It also revealed that the synergy of Sentinel-1 SAR and Sentinel-2 MSI data significantly enhanced the recognition ability of rice paddy fields ranging from 400 m2 to 2000 m2.

Exploring the Diverse Practices of Artificial Intelligence in Blended Music Teaching in Colleges and University

Abstract With the development of the information age and the change in media technology, the education industry has also ushered in a new change, and this paper focuses on the application of artificial intelligence in blended music teaching in colleges and universities. The first part of the article utilizes artificial intelligence to appreciate music emotions, utilizing Thayer’s two-dimensional emotion model and emotion vectors to express emotions in music. Then, the short- and long-time feature extraction of music emotion is carried out separately and normalized. Then, a music emotion recognition model based on the CNN-SVM model is constructed by combining a deep learning network and a space vector machine model. The constructed music emotion recognition model and SPOC hybrid teaching are utilized to establish a blended music diversity curriculum in colleges and universities and practical exploration is conducted. The results show that the time range of the change in music emotion perception is about 2.5~3.2s, and the recognition success rate is more than 4/5 for all kinds of music emotion segments. The correlation between the effect of music teaching and music diversification practice and students’ satisfaction is less than 0.01. The diversification of music teaching in this study can effectively stimulate students’ learning interest and improve learning efficiency in the music classroom.

Exploring the Digital Innovation Development Mode of Chinese Sports Intangible Cultural Heritage

Abstract This paper explores the innovative development of digitization of Chinese sports intangible cultural heritage, focusing on applying advanced technologies such as three-dimensional reconstruction and movement recognition to protect and pass on this unique cultural heritage. The article digitally captures and analyzes traditional sports movements through 3D reconstruction and movement recognition techniques. Subsequently, a feature extraction method is applied to capture the critical features of the movements and Bessel curves are utilized to enhance the realism of the virtual character model. The experimental results show that this method can effectively recognize and reproduce traditional sports movements, with a recognition success rate as high as 97.05%, and accurately determine the standard degree of the movements. In addition, this technique helps to reduce training costs and improve training efficiency. Through these techniques, not only can the intangible cultural heritage of sports be preserved and inherited, but also promote its integration with modern science and technology and open up new ways for its development.

Artificial Intelligence-Assisted Training and Skill Enhancement Strategies for Athletes

Abstract The continuous progress of science and technology promotes the continuous development of artificial intelligence, and digital intelligence empowerment has gradually become a focus topic of widespread social concern. Considering that traditional sports training is difficult to meet the higher requirements put forward by athletes on training methods, training load, and training equipment, this study combines artificial intelligence technology to explore the path of skill enhancement. In this paper, based on the skeletal key points for athlete training action recognition, the improved DHRNet network is optimized for the detection of skeletal key points and then for the complexity of the athlete’s training state, the occlusion of the skeletal points and the repair of motion information are carried out. On this basis, we construct an artificial intelligence-assisted training system composed of a video image input function system, action recognition function system, and behavior analysis function system. After completing the design, the core module of the system, the motion recognition system, was tested. The average recognition success rates of basic and sport movements were between 90.8~95.6% and 84.5~96.5%, respectively. The overall recognition correctness rate remained above 90% in the actual application test, which can meet the training needs of athletes. The average score for all abilities of the athletes using the AI-supported system is 85.242, which is 6.21 higher than that of the traditional training method. The AI-assisted system is of great help to athletes’ training, and it also enables the early realization of intelligent training.