Average Recognition Rate Research Articles

Virtual interaction and manipulation control of a hexacopter through hand gesture recognition from a data glove

AbstractThe purpose of this study is to realize virtual interaction and manipulation control of a hexacopter based on hand gesture recognition from a designed data glove, to provide an intuitive and visual real-time simulation system for flight control algorithm verification and external control equipment testing. First, the hand gesture recognition from a designed data glove is studied, which can recognize different actions, such as mobile ready, grab, loosen, landing, take-off, and hover. Then, the design of virtual simulation system for hexacopter capture is completed, with the model design of hexacopter and manipulator, and the simulation software design with $CoppeliaSim$ . Finally, virtual simulation experiment of hexacopter grasping and virtual flight control experiment based on data glove are tested, respectively, and quantitatively described. The overall recognition rate is 84.3%, indicating that the data glove produced has the ability to recognize gestures, but its recognition performance is not superior. In gesture recognition, the recognition rate of static gestures is relatively higher than that of dynamic gestures. Among the static gestures, the hover gesture has the highest recognition rate. The average correct rate of static gestures can reach 94%. The lowest recognition rate of dynamic gestures is upward movement, and the average recognition rate of dynamic gestures is 76.1%. The research can be used to remotely operate hexacopter using a data glove in the future and improve the control performance through virtual interaction and manipulation simulation before actual application.

Happy or sad? Recognizing emotions with wavelet coefficient energy mean of EEG signals.

Emotional intelligence plays a vital role in human-computer interaction, and EEG signals are an objective response to human emotions. We propose a method to extract the energy means of detail coefficients as feature values for emotion recognition helps to improve EEG signal-based emotion recognition accuracy. We used movie clips as the eliciting material to stimulate the real emotions of the subjects, preprocessed the collected EEG signals, extracted the feature values, and classified the emotions based on them using Support Vector Machine (SVM) and Stacked Auto-Encoder (SAE). The method was verified based on the SJTU emotion EEG database (SEED) and the self-acquisition experiment. The results show that the accuracy is better using SVM. The results based on the SEED database are 89.06% and 79.90% for positive-negative and positive-neutral-negative, respectively. The results based on the self-acquisition data are 98.05% and 89.83% for the same, with an average recognition rate of 86.57% for the four categories of fear, sad (negative), peace (neutral) and happy (positive). The results demonstrate the validity of the feature values and provide a theoretical basis for implementing human-computer interaction.

EEG-based emotion analysis using non-linear features and ensemble learning approaches

Recognition of emotions based on electroencephalography (EEG) has become one of the most emerging topics for healthcare, education system, knowledge sharing, gaming, and many other fields in the last few decades. This paper proposes three non-linear features (Higuchi fractal dimension, sample entropy, and permutation entropy) and eight ensemble learning approaches to predict six basic emotions (hope, interest, excitement, shame, fear, and sad). To increase the recognition rate of each classifier, we utilized a randomized grid search technique for tunning the hyperparameter of each algorithm. Moreover, the impact of electrodes on each emotion was observed using the recursive feature elimination (RFE) method. In addition, the synthetic minority oversampling technique (SMOTE) is used to handle the imbalanced sample distribution of each emotion. Then, we have conducted all the experiments on DEAP and AMIGOS datasets and calculated the computation time and accuracy to assess each algorithm's performance. Besides, we observe statistical significance to compare the algorithm's performance. From the experimental result, we achieved the highest average accuracy, 89.38% and 94.62%, using Higuchi fractal dimension on DEAP and AMIGOS datasets, respectively. We also observed that the Higuchi fractal dimension outperforms the sample entropy and permutation entropy in terms of accuracy. Our proposed method increased the average recognition rate by 8.22% and 1.77%, respectively, compared with existing approaches working on the same dataset. Along with accuracy, precision, recall, F-measure, and AUC-ROC have been evaluated using the same experimental setting on DEAP and AMIGOS datasets. The results of each classifier have been shown in the table and graph. Moreover, the proposed method outperforms existing approaches dealing with shallow machine learning techniques.

Gymnastics Action Recognition and Training Posture Analysis Based on Artificial Intelligence Sensor

In order to solve the problems that the gymnastics action recognition system cannot select gymnastics training items and difficulty modes, the calculation matching degree and its threshold angle are inaccurate, the effect and efficiency of action learning are low, and this research proposes a gymnastics action recognition and training pose analysis methods based on artificial intelligence sensors. This method completes the performance improvement of the traditional human action recognition algorithm and uses the skeletal features of the Kinect sensor to discriminate sports actions. Clustering based on static K -means algorithm increases the accuracy of pose selection, and each pose is recognized by human action using artificial neural network (ANN) and hidden Markov model (HMM). The obtained results are as follows: comparison of nonstatic and proposed static K -means algorithm on the training set and the overall accuracy of the proposed method is much better than the previous method. Among the four movements, the accuracy rate of “sitting” and “standing” movements is significantly higher, reaching 100%. In the gymnastics action recognition experiment, the average recognition rate of the system in this research is 93.6%, the false rejection rate is 5%, and the false acceptance rate is only 1.4%. It is proved that the system interface designed in this research can prompt the part that needs to be corrected, display the error on the output device, more efficiently assist the user to perform targeted training on the action to be learned, and improve the effect and efficiency of action learning.

A Novel Arabic Words Recognition System Using Hyperplane Classifier

Topic of exhaustive study for about past decades has been carried out in machine imitation of human reading. a small number of investigates have been accepted on the detection of cursive font writing like Arabic texts for its individual challenge and difficulty .In this work, a novel technique for automatic Arabic font recognition is proposed to demonstrate an suitable recognition rate for multi fonts styles and multi sizes of Arabic word images.
 The scheme can be classified into a number of steps. First, segmenting Arabic line into words depending on the vertical projection and dynamic threshold then we implicated each Arabic word as a class by ignoring segmenting the word into characters .Second ,normalizing step, the size of Arabic word images varies from each other .The system converts the images that contribution into a new size that is divisible by "N" without remainder, to decrease the difficulty of feature extraction and recognition of the system that may allow images from different resources, Third, feature extraction step which is based on apply the ratio of vertical sliding strips as a features. Finally, multi class support vector machine (one versus one technique)is used as a classifier .This method was estimated on off line printed fonts, five Arabic fonts, (Andalus, Arial, Simplified Arabic, Tahoma and Traditional Arabic) were used and the average recognition rate of all fonts was 95.744%.

An Improved Dictionary-Based Method for Gas Identification with Electronic Nose

The dictionary learning algorithm has been successfully applied to electronic noses because of its high recognition rate. However, most dictionary learning algorithms use l0-norm or l1-norm to regularize the sparse coefficients, which means that the electronic nose takes a long time to test samples and results in the inefficiency of the system. Aiming at accelerating the recognition speed of the electronic nose system, an efficient dictionary learning algorithm is proposed in this paper where the algorithm performs a multi-column atomic update. Meanwhile, to solve the problem that the singular value decomposition of the k-means (K-SVD) dictionary has little discriminative power, a novel classification model is proposed, a coefficient matrix is achieved by a linear projection to the training sample, and a constraint is imposed where the coefficients in the same category should keep a large coefficient and be closer to their class centers while coefficients in the different categories should keep sparsity. The algorithm was evaluated and analyzed based on the comparisons of several traditional classification algorithms. When the dimension of the sample was larger than 10, the average recognition rate of the algorithm was maintained above 92%, and the average training time was controlled within 4 s. The experimental results show that the improved algorithm is an effective method for the development of an electronic nose.

Modeling and Simulation of English Speech Optimization Teaching Recognition Based on Intelligent Edge Detection Algorithm

English speech modeling is one of the key problems in the field of speech recognition. Its accuracy directly affects the performance of the English speech recognition system, and how to establish a more accurate acoustic model has always been the focus of researchers. This paper is based on the intelligent edge detection algorithm of the English speech optimization teaching recognition modeling simulation analysis to improve the accuracy of acoustic models such as parameters and the performance of continuous speech recognition system as the main purpose. In this paper, the accuracy of the neural network is improved on the premise of improving the training speed and decoding speed of the model. It proposes a new model and how to use the intelligent edge detection algorithm to build a complete English speech optimization teaching recognition system. The whole system includes the mobile terminal and the server, which realizes the most basic business logic of the speech recognition system. The experimental results of this paper show that from the point of view of the average recognition rate, the recognition effect of the optimized feature set has been further improved compared with the fusion feature. From the point of view of the recognition rate under different SNR environments, the recognition rate of the optimized feature set PCA-Features2 decreased by 0.47% compared with the FFPRLS_D + TEOCC feature set under 10dB10 words. Compared with the FFPLMS_D + TEOCC feature set, the recognition rate under 5dB10 words also drops by 0.47%.

Space Modeling Design Art of Suspense Movies Based on Machine Vision

After suspense movie space has a substantial role, it plays an irreplaceable role in movie narration. Different spatial forms can give us different feelings and different spatial feelings and can cooperate with different stories to create a different atmosphere. In this paper, a new 3D human behavior recognition algorithm in MV (machine vision) perception is proposed. The new algorithm is achieved by collecting the human behavior images and detecting the contour areas of the images. By reducing the projection area of the spatial positioning error area of unknown nodes on the plane, the positioning accuracy of unknown nodes in each axis direction is improved. The necessary brightness correction and color saturation compensation are carried out in color space. The results show that the average recognition rate of human behavior in MV perception is 95%, which can effectively improve the recognition accuracy. The enhanced color image is not only clearer but also more vivid and bright, which achieves the expected purpose.

Scene Construction and Application of Panoramic Virtual Simulation in Interactive Dance Teaching Based on Artificial Intelligence Technology

In recent years, with the rapid development of human motion simulation technology and virtual simulation technology, natural human-computer interaction has become the main form of research in the computer industry. Due to the research capabilities and practical value of mobile video technology, it is widely used in advanced fields, such as video animation production, rehabilitation medicine, sports training, and game software, effectively realizing the connection between the three-dimensional world and the simulation world. However, the application of motion imaging technology in teaching activities is not perfect. This article is based on dance teaching, adopts a dance movement analysis method based on vector matching, and uses artificial intelligence technology to create a virtual panoramic scene. Experimental data show that the average recognition rate of gestures is 89.8%, which can meet the gesture switching interaction of working conditions in virtual simulation of equipment operation.

Research on Field Soybean Weed Identification Based on an Improved UNet Model Combined With a Channel Attention Mechanism.

Aiming at the problem that it is difficult to identify two types of weeds, grass weeds and broadleaf weeds, in complex field environments, this paper proposes a semantic segmentation method with an improved UNet structure and an embedded channel attention mechanism SE module. First, to eliminate the semantic gap between low-dimensional semantic features and high-dimensional semantic features, the UNet model structure is modified according to the characteristics of different types of weeds, and the feature maps after the first five down sampling tasks are restored to the same original image through the deconvolution layer. Hence, the final feature map used for prediction is obtained by the fusion of the upsampling feature map and the feature maps containing more low-dimensional semantic information in the first five layers. In addition, ResNet34 is used as the backbone network, and the channel attention mechanism SE module is embedded to improve useful features. The channel weight is determined, noise is suppressed, soybean and grass weeds are identified, and broadleaf weeds are extracted through digital image morphological processing, and segmented images of soybean plants, grass weeds and broadleaf weeds are generated. Moreover, compared with the standard semantic segmentation models, FCN, UNet, and SegNet, the experimental results show that the overall performance of the model in this paper is the best. The average intersection ratio and average pixel recognition rate in a complex field environment are 0.9282 and 96.11%, respectively. On the basis of weed classification, the identified weeds are further refined into two types of weeds to provide technical support for intelligent precision variable weed spraying.

Abstract LB543: tmHRD: Accurately estimating the homologous recombination deficiency status from clinical panel sequencing data

Abstract Estimating the status of homologous recombination deficiency (HRD) is of clinical interest because the homologous recombination-deficient cells are reported sensitive to poly-ADP ribose polymerase (PARP) inhibitors. As HRD status becomes a critical indicator, many approaches have been proposed to measure the HRD status, among which estimating HRD status from cancer sequencing data is the popular strategy, where the representative methods include HRDetect, SigMA, FoundationFocusCDx, MyChoiceHRD, etc. Although sequencing data is the same input of these bioinformatics tools, algorithms prefer different aspects. Some methods focus on germline variants in BRCA1/2 genes and somatic mutational signatures, while some others capture the genomic scars, including loss of heterozygosity (LOH), telomere allele imbalance (TAI) and large-scale state transition (LST). However, there are two major controversial points when applying these tools on clinical sequencing data. First, panel sequencing is the most popular sequencing plan in clinic, but gene panels usually carry only hundreds to a thousand genes. Thus, the panel sequencing can neither capture a large number of mutations to calculate mutational signatures, nor obtain large genomic scars, such as TAI and LST. Second, the associations between the mutations and HRD status is still unclear. Thus, scoring mutations and genomic scars is not reasonable. To solving these issues, we here proposed a machine learning-based method, called tmHRD, which estimates HRD status according to limited mutations and LOHs captured by clinical panel sequencing data. It consists of a semi-supervised learning (TRI) model and a multi-instance learning (MIL) model. The TRI model filters the false positives from mutation calls. Then, the filtered mutation calls from one patient is defined as a package, where each mutation call is an instance in the package. When training the MIL model, in each iteration, a package with known HRD status (label) is given to MIL model. MIL model calculates the correlations among instances and assigns an adjusted weight for each. Thus, the HRD status is only contributed by those mutations associated with. We tested tmHRD on several datasets obtained by the clinical trials we participated. Compared to the existing methods, tmHRD achieved better performance. The average recognition rate of tmHRD was approaching 90%, while the comparison methods show around 80%. The software package, tmHRD, is freely available at https://github.com/Sherwin-xjtu/TriMILhrd/ for academic usages only. Citation Format: Xuwen Wang, Ying Xu, Yanfang Guan, Xin Yi, Jiayin Wang. tmHRD: Accurately estimating the homologous recombination deficiency status from clinical panel sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB543.

Footprint Extraction and Sports Dance Action Recognition Method Based on Artificial Intelligence Distributed Edge Computing

Distributed edge computing technology for artificial intelligence refers to an emerging technology that integrates network core processing functions, computing functions, storage functions, etc. into one end source closer to objects or data on the basis of an open platform to optimize service quality. In this paper, distributed edge computing technology is applied to footprint extraction and sports dance action recognition, aimed at improving the recognition efficiency and recognition quality. Firstly, the overview of edge computing theory is introduced; these include edge computing concepts, edge computing characteristics, and edge computing platforms; and then, the classification of action recognition technology is described. Finally, the edge computing recognition technology and traditional recognition technology are compared and tested. The experimental results show that the average accuracy of edge computing technology for footprint extraction can reach 98.98, and the average recognition rate of sports dance movements can reach 80.21%, which verifies its practicability.

Motor Imagery EEG Classification Based on Transfer Learning and Multi-Scale Convolution Network.

For the successful application of brain-computer interface (BCI) systems, accurate recognition of electroencephalography (EEG) signals is one of the core issues. To solve the differences in individual EEG signals and the problem of less EEG data in classification and recognition, an attention mechanism-based multi-scale convolution network was designed; the transfer learning data alignment algorithm was then introduced to explore the application of transfer learning for analyzing motor imagery EEG signals. The data set 2a of BCI Competition IV was used to verify the designed dual channel attention module migration alignment with convolution neural network (MS-AFM). Experimental results showed that the classification recognition rate improved with the addition of the alignment algorithm and adaptive adjustment in transfer learning; the average classification recognition rate of nine subjects was 86.03%.

Deep Stacked Autoencoder-Based Automatic Emotion Recognition Using an Efficient Hybrid Local Texture Descriptor

Extracting an effective facial feature representation is the critical task for an automatic expression recognition system. Local Binary Pattern (LBP) is known to be a popular texture feature for facial expression recognition. However, only a few approaches utilize the relationship between local neighborhood pixels itself. This paper presents a Hybrid Local Texture Descriptor (HLTD) which is derived from the logical fusion of Local Neighborhood XNOR Patterns (LNXP) and LBP to investigate the potential of positional pixel relationship in automatic emotion recognition. The LNXP encodes texture information based on two nearest vertical and/or horizontal neighboring pixel of the current pixel whereas LBP encodes the center pixel relationship of the neighboring pixel. After logical feature fusion, the Deep Stacked Autoencoder (DSA) is established on the CK+, MMI and KDEF-dyn dataset and the results show that the proposed HLTD based approach outperforms many of the state of art methods with an average recognition rate of 97.5% for CK+, 94.1% for MMI and 88.5% for KDEF.

Dynamic Recognition and Analysis of Gait Contour of Dance Movements Based on Generative Adversarial Networks.

With the generation of images, videos, and other data, how to identify the gait of the action in the video has gradually become the focus of research. Aiming at the problems of complex and changeable movements, strong coherence, and serious occlusion in dance video images, this paper proposes a dynamic recognition model of gait contour of dance movements based on GAN (generative adversarial networks). GAN method is used to convert the gait diagrams in any state into a group of gait diagrams in normal state with multiple angles, which are arranged in turn. In order to retain as much original feature information as possible, multiple loss strategy is adopted to optimize the network, increase the distance between classes, and reduce the distance within classes. Experimental results show that the average recognition rates of this model at 50°, 90°, and 120°are 93.24, 98.24, and 97.93, respectively, which shows that the recognition accuracy of dance movement recognition method is high. And this method can effectively improve the dynamic recognition of gait contour of dance movements.

Modeling and analysis of fatigue detection with multi-channel data fusion

To address the problem of accurate assessment of training effects of flight simulators, an intelligent algorithm using neural networks and reinforcement learning is proposed in the paper. The multi-dimensional data of facial expression features and EEG and EM physiological signals are analyzed. A new evaluation model for assessing human spatial balance, attention distribution, neurological weakness, and other pilot training states is studied through facial expression experiments mainly and eye-movement and EEG experiments supplemented. The EEG acquisition and analysis during pilot training subjects (take-off and landing) are completed, and the emotional characteristics of pilots during training are identified. We completed data fusion of multi-dimensional channels, constructed mathematical models of pilot maneuver reaction time and attention allocation, monitored and evaluated flight training effects, and conducted controlled experiments. The experimental results show that the average recognition rates of 92.598% and 87.013% were achieved for expression and neurasthenia recognition, and the human ergonomic information of facial expression and EEG and EM were effectively fused.

Flow characterization of gas-liquid with different liquid properties in a Y-type microchannel using electrical resistance tomography and volume of fluid model

BackgroundFlow characteristics in microscale reactors are one of the key factors in fast reaction systems, such as fine chemicals, pharmaceuticals, and nanopowders. In this work, the effects of liquid properties on the bubble flow characteristics in vertical circular microchannels are studied. MethodsUsing experiments (electrical resistance tomography and high-speed camera) and CFD simulation (volume of fluid method - continuous surface force) the effects of liquid phase properties on flow pattern transitions, liquid films, were investigated. Significant findingsThe average recognition rates of ERT in air - glycerol (µ = 0.885 mPa•s–1.912 mPa•s) and air - ethanol (σ = 38.7 mN/m - 72.0 mN/m) aqueous solution are over 84%. It was found that the liquid surface tension and liquid viscosity can change the internal shear force and interfacial force in the microchannel. When Ca > 0.0093, the bubbles and the inner wall of the microchannel form a liquid film. In addition, based on the experimental data, a modified equation for the gas holdup in the microchannel is proposed, which provides a reliable reference for the application of Y-type microchannels.

Exploring the Impact of Parallel Architecture on Improving Adaptable Neuro-Fuzzy Inference Systems for Gas-Insulated Switch Defect Recognition

Gas-insulated switchgear malfunctions during power system operation may occur due to electrical, thermal, or human errors in the manufacturing process. The leading causes of insulation deterioration of gas-insulated switchgear are discharging along the surface caused by dirt on the insulating material, internal discharge caused by impurities and cavities in the insulating material, corona discharge caused by poor assembly or construction at the site, and electric tree channel discharge caused by the intense internal discharge. Since different defects produce different partial discharge characteristics, the operating power equipment can be analyzed using measurement instruments to detect partial discharge for preventive equipment fault diagnosis, avoiding unnecessary power outages and losses; therefore, evaluating the defects in gas-insulated switchgear is essential. In this study, three gas-insulated switchgears were prefabricated with different defects before encapsulation, and the partial discharge data of each defect were measured by applying different test voltages. The adaptive neuro-fuzzy inference system (ANFIS) input data were used to evaluate the recognition effect, showing that the average recognition rate of the core for all defects was over 90%. The proposed system architecture can continuously accumulate the defect measurement database of gas-insulated switchgear and be used as a reference for constructing electrical equipment defect recognition systems.

Character Recognition in Endangered Archives: Shui Manuscripts Dataset, Detection and Application Realization

Shui manuscripts provide a historical testimony of the national identity and spirit of the Shui people. In response to the lack of a high-quality Shui manuscripts dataset, we collected Shui manuscript images in the Shui area and used various methods to enhance them. Through our efforts, we created a well-labeled and sizable Shui manuscripts dataset, named Shuishu_T, which is the largest of its kind. Then, we applied target detection technology for Shui manuscript characters recognition. Specifically, we compared the advantages and disadvantages of Faster R-CNN, you only look once (YOLO), and single shot multibox detector (SSD), and subsequently chose Faster R-CNN to detect and recognize Shui manuscript characters. We trained and tested 111 classes of Shui manuscript characters with Faster R-CNN and achieved an average recognition rate of 87.8%. Finally, we designed a WeChat applet that can be used to quickly identify Shui manuscript characters in images obtained by scanning Shui manuscripts with a mobile phone. This work provides a basis for realizing the recognition of characters in Shui manuscripts on mobile terminals. Our research enables the intangible cultural heritage of the Shui people to be preserved, promoted, and shared, which is of great significance for the conservation and inheritance of Shui manuscripts.

Applying Machine Learning to Finger Movements Using Electromyography and Visualization in Opensim.

Electromyographic signals have been used with low-degree-of-freedom prostheses, and recently with multifunctional prostheses. Currently, they are also being used as inputs in the human–computer interface that controls interaction through hand gestures. Although there is a gap between academic publications on the control of an upper-limb prosthesis developed in laboratories and its service in the natural environment, there are attempts to achieve easier control using multiple muscle signals. This work contributes to this, using a database and biomechanical simulation software, both open access, to seek simplicity in the classifiers, anticipating their implementation in microcontrollers and their execution in real time. Fifteen predefined finger movements of the hand were identified using classic classifiers such as Bayes, linear and quadratic discriminant analysis. The idealized movements of the database were modeled with Opensim for visualization. Combinations of two preprocessing methods—the forward sequential selection method and the feature normalization method—were evaluated to increase the efficiency of these classifiers. The statistical methods of cross-validation, analysis of variance (ANOVA) and Duncan were used to validate the results. Furthermore, the classifier with the best recognition result was redesigned into a new feature space using the sparse matrix algorithm to improve it, and to determine which features can be eliminated without degrading the classification. The classifiers yielded promising results—the quadratic discriminant being the best, achieving an average recognition rate for each individual considered of 96.16%, and with 78.36% for the total sample group of the eight subjects, in an independent test dataset. The study ends with the visual analysis under Opensim of the classified movements, in which the usefulness of this simulation tool is appreciated by revealing the muscular participation, which can be useful during the design of a multifunctional prosthesis.