Recognition Method Research Articles

Operating modal parameter identification of railway vehicle bogie based on k-value optimization variational mode decomposition and second-order blind identification method

Due to the variable operating conditions and strong background noise, the accuracy of modal parameter identification of the railway vehicle bogie is low. This paper proposes a method for identifying operational modal parameters by combining the K-value optimization variational mode decomposition (KVMD) and second-order blind identification (SOBI) method (KVMD–SOBI method). Firstly, the variational mode decomposition (VMD) algorithm is used to adaptively decompose a single-channel vibration signal into intrinsic mode function (IMF) components with different scale characteristics. The feature IMF components are selected through the K-value optimization method based on energy ratio and stability graph. Then, the SOBI algorithm is used to separate the matrix composed of the feature IMF components. And the mode shape and single-mode signal are obtained. Finally, the natural frequency and damping ratio of each order mode are extracted by the single-mode parameter recognition method. Through simulation and experimental verification, it is shown that the proposed method has good noise robustness and can effectively identify the first three modal parameters of railway vehicle bogies in variable-speed operation. At the same time, the feature IMF components extracted by KVMD algorithm eliminate the sensitivity of sensor installation positions and operating conditions to detection results. Therefore, this method is an excellent operational mode identification method based on a single-channel signal with good noise and working condition robustness.

Research on brain functional network property analysis and recognition methods targeting brain fatigue

At present, researches on brain fatigue recognition are still in the stage of single task and simple brain region network features, while researches on high-order brain functional network features and brain region state mechanisms during fatigue in multi-task scenarios are still insufficient, making it difficult to meet the needs of fatigue recognition under complex conditions. Therefore, this study utilized functional near-infrared spectroscopy (fNIRS) technology to explore the correlation and differences in the low-order and high-order brain functional network attributes of three task induced mental fatigue, and to explore the brain regions that have a major impact on mental fatigue. Self-training algorithms were used to identify the three levels of brain fatigue. The results showed that during the fatigue development, the overall connection strength of the endothelial cell metabolic activity and neural activity frequency bands of the low-order brain functional network first decreased and then increased, while the myogenic activity and heart rate activity frequency bands showed the opposite pattern. Network topology analysis indicated that from no fatigue to mild fatigue, the clustering coefficient of endothelial cell metabolic activity and myogenic activity frequency bands significantly decreased, while the characteristic path length of myogenic activity significantly increased; when experiencing severe fatigue, the small-world attribute of the neural frequency band significantly weakened. However, each frequency band maintained its small-world attribute, reflecting the self-optimization and adaptability of the network during the fatigue process. During mild fatigue, neuronal activity bands’ node degree, cluster coefficient, and efficiency rose in high-order brain networks, while low-order networks showed no significant changes. As fatigue progressed, the myogenic activity bands of high-order network properties dominated, but neural bands gained prominence in mild fatigue, approaching the level of myogenic bands in severe fatigue, indicating that brain fatigue orchestrated a shift from myogenic to neural dominance in frequency bands. In addition, during the process of fatigue, the four network attributes of the high-order network cluster composed of low-order nodes related to the prefrontal cortex region, left anterior motor region, motor assist region, and left frontal lobe eye movement region significantly increased, indicating that these brain regions had a significant impact on brain fatigue status. The accuracy of using both high-order and low-order features to identify fatigue levels reached 88.095%, indicating that the combined network features of both high-order and low-order fNIRS signals could effectively detect multi-level mental fatigue, providing innovative ideas for fatigue warning.

P-TIMA: a framework of T witter threat intelligence mining and analysis based on a prompt-learning NER model

Abstract Open-source information platforms such as Twitter continuously provide the latest threat intelligence, including new vulnerabilities and in-the-wild exploitations of advanced persistent threat (APT) groups. Automated extraction of threat intelligence from Twitter has become crucial for defenders to access up-to-date threat knowledge. However, existing studies mainly rely on supervised learning methods to extract threat intelligence knowledge, such as entities, which require a large amount of annotated data. This paper presents Threat Intelligence Mining and Analysis based on Prompt Learning (P-TIMA), a framework specifically crafted for extracting and analyzing threat intelligence from Twitter. P-TIMA employs our innovative few-shot entity recognition method, SecEntPrompt (SEP), built on prompt learning, to extract vulnerability intelligence from Twitter. Additionally, P-TIMA analyzes and profiles the overarching vulnerability intelligence obtained from Twitter, along with in-the-wild exploitation intelligence of APT groups. The SEP improves the average entity recognition F1 score by 3.62-4.40 compared with the best-performing comparison model and outperforms the method based on the large language model on recognition performance and inference time. To validate our framework, we apply P-TIMA to extract vulnerability-related threat intelligence from real Twitter data. Through case studies, we then analyze trends in vulnerability threats and the exploitation capabilities of APT groups. In conclusion, our framework provides a more efficient and accurate method for extracting threat intelligence from Twitter, enabling defenders to stay up-to-date with the latest threat trends and helping them improve their defense strategies against cyber attacks.

Improving lithofacies prediction in lacustrine shale by combining deep learning and well log curve morphology in Sanzhao Sag, Songliao Basin, China

The accurate identification of shale lithofacies is crucial for characterizing the hydrocarbon potential of lacustrine shale oil reservoirs. Petrophysical logging, serving as an effective tool for acquiring subsurface lithofacies information, provides a convenient and reliable lithofacies identification solution. Deep learning technology, capable of adapting to the nonlinearity and non-stationarity inherent in geological statistics, exhibits unique advantages in conventional reservoir lithofacies prediction. However, the lithofacies of lacustrine shale formations undergo rapid spatial and temporal changes, rendering lithofacies prediction more complex compared to conventional reservoirs. In this study, the lower Qingshankou member in the Sanzhao Sag was selected as the research target, and the Deep Residual Shrinkage Network (DRSN), known for its ability to handle complex nonlinear relationships and mitigate the effects of noisy data through residual connections and shrinkage mechanisms, was employed as a deep learning framework for predicting lithofacies in lacustrine shale formations for the first time. Well logging data, including natural gamma ray (GR), acoustic (AC), deep investigate double lateral resistivity log (RD), shallow investigate double lateral resistivity log (RS), and corrected compensated neutron log (CNC), were used as input features for the model. The results indicate that the DRSN model achieves an accuracy of 76.3% in predicting lithofacies in lacustrine shale formations. However, the DRSN model still exhibits shortcomings in capturing lithofacies change information. To enhance the model's ability to identify lithofacies change interfaces, this study further explicitly introduces Well Logging Curve Morphological Features (WLCM) as additional features and establishes a recognition method combining DRSN with WLCM. The combined DRSN-WLCM model was validated using a separate test dataset, demonstrating an improved accuracy of 85.5%, using the five well logging attributes and the derivative of the AC as inputs. Furthermore, the study reveals the lithofacies spatial distribution characteristics of the lower Qingshankou member in the Sanzhao Sag. This method can be widely applied to lithofacies delineation in lacustrine shale formations and similar stratigraphic units.

Comparative Analysis of Using Different Text Features, Models, and Methods in Text Author Recognition

Comparative Analysis of Using Different Text Features, Models, and Methods in Text Author Recognition

A Real-Time Global Re-Localization Framework for a 3D LiDAR-Based Navigation System

Place recognition is widely used to re-localize robots in pre-built point cloud maps for navigation. However, current place recognition methods can only be used to recognize previously visited places. Moreover, these methods are limited by the requirement of using the same types of sensors in the re-localization process and the process is time consuming. In this paper, a template-matching-based global re-localization framework is proposed to address these challenges. The proposed framework includes an offline building stage and an online matching stage. In the offline stage, virtual LiDAR scans are densely resampled in the map and rotation-invariant descriptors can be extracted as templates. These templates are hierarchically clustered to build a template library. The map used to collect virtual LiDAR scans can be built either by the robot itself previously, or by other heterogeneous sensors. So, an important feature of the proposed framework is that it can be used in environments that have never been visited by the robot before. In the online stage, a cascade coarse-to-fine template matching method is proposed for efficient matching, considering both computational efficiency and accuracy. In the simulation with 100 K templates, the proposed framework achieves a 99% success rate and around 11 Hz matching speed when the re-localization error threshold is 1.0 m. In the validation on The Newer College Dataset with 40 K templates, it achieves a 94.67% success rate and around 7 Hz matching speed when the re-localization error threshold is 1.0 m. All the results show that the proposed framework has high accuracy, excellent efficiency, and the capability to achieve global re-localization in heterogeneous maps.

A novel rehabilitation action recognition approach using AF-LiteFormer from millimeter wave radar

Abstract Rehabilitation action recognition is a hot research topic in the medical field, which serves as the foundation for achieving remote healthcare, unsupervised exercise, intelligent home healthcare, and possesses extensive application value. Currently, vision-based action recognition methods are susceptible to limitations imposed by factors such as range of motion and environmental lighting during human motion capture. Due to its ability to effectively protect patient privacy and its immunity to lighting conditions, this paper proposed a millimeter-wave radar-based rehabilitation action recognition system, AF-LiteFormer. Firstly, EfficientFormerV2 is employed as the baseline, then a Lite-MSLA-FFN block-and-layer is designed to replace MHSA in EfficientFormerV2, which improves the diversity of attention and reduces computational complexity. Meanwhile, a Lite-Subsample block-and-layer is designed to replace the dual-path downsampling part that composed of attention downsampling and stride attention to realize global modeling and multi-scale learning.
Secondly, the iterative attention feature fusion (iAFF) mechanism is introduced to improve the recognition accuracy of rehabilitation actions. Finally, the effectiveness of the AF-LiteFormer model is validated on a self-collected rehabilitation action dataset and a publicly available micro-Doppler dataset. Experimental results show that the overall performance of the AF-LiteFormer model is better than the State-of-the-Art model (SOTA), the recognition accuracy of rehabilitation actions is as high as 99.7%, and it has strong generalization ability.

Radio frequency fingerprint recognition method based on prior information

The open wireless communication environment is vulnerable to various malicious attacks. Wireless communication hardware devices have unique physical layer characteristics. As an inherent unique feature of wireless signals, radio frequency fingerprints provide a guarantee for the identification and verification of wireless signals. Most of the existing radio frequency fingerprint identification methods only extract fingerprints from one of the steady-state signals or transient signals. Neglecting the connection between the two wireless communication signals results in low identification accuracy of the radio frequency fingerprint identification method under the condition of a low signal-to-noise ratio. Aiming at the respective characteristics of these two signals, a radio frequency fingerprinting method combining transient and steady-state signals based on prior information of wireless signals is proposed. This method combines the characteristic stability of steady-state signals and the integrity characteristics of transient signals, which can effectively identify and classify wireless signals and achieve excellent recognition under low signal-to-noise ratio conditions. The effectiveness of the proposed method is verified by experimental comparison with the traditional radio frequency fingerprinting method on the LFM signal dataset.

Face recognition method based on fusion of improved MobileFaceNet and adaptive Gamma algorithm

MobileFaceNet face recognition algorithm is a relatively mainstream face recognition algorithm at present. Its advantages of small memory and fast running speed make it widely used in embedded devices. Due to the limited face image acquisition capability of embedded devices, the accuracy of face recognition is often reduced due to uneven illumination and poor exposure quality. In order to solve this problem, a face recognition algorithm based on the fusion of MobileFaceNet and adaptive Gamma algorithm is proposed. The application of the algorithm proposed in this paper in image preprocessing is as follows. Firstly, adaptive Gamma correction is used to improve the brightness of the face image. Then, the edge of the face image is enhanced by the Laplace operator. Finally, a linear weighted fusion was performed between the Gamma corrected image and the enhanced edge image to obtain the pre-processed face image. At the same time, we have improved the traditional MobileFaceNet network. The feature extraction network MobileFaceNet has been improved by adding a Stylebased Recall Module (SRM) attention mechanism to its bottom neck layer, utilizing the mean and standard deviation of input features to improve the ability to capture global information and enhance more important feature information. Finally, the proposed method was verified on the LFW and Agedb face test set. The experimental results show that the adaptive Gamma algorithm proposed in this paper and the improvement of MobileFaceNet can achieve a face recognition accuracy of 99.27 % on LFW dataset and 90.18 % on Agedb dataset while only increasing the model size by 0.4 M and the processing speed for each image is enhanced by 4 ms. which can effectively improve the accuracy of face recognition and better application prospects on embedded devices. The method presented in this article has certain practical significance.

Deep Learning Approach for Wildland Fire Recognition Using RGB and Thermal Infrared Aerial Image

Wildfires cause severe consequences, including property loss, threats to human life, damage to natural resources, biodiversity, and economic impacts. Consequently, numerous wildland fire detection systems were developed over the years to identify fires at an early stage and prevent their damage to both the environment and human lives. Recently, deep learning methods were employed for recognizing wildfires, showing interesting results. However, numerous challenges are still present, including background complexity and small wildfire and smoke areas. To address these challenging limitations, two deep learning models, namely CT-Fire and DC-Fire, were adopted to recognize wildfires using both visible and infrared aerial images. Infrared images detect temperature gradients, showing areas of high heat and indicating active flames. RGB images provide the visual context to identify smoke and forest fires. Using both visible and infrared images provides a diversified data for learning deep learning models. The diverse characteristics of wildfires and smoke enable these models to learn a complete visual representation of wildland fires and smoke scenarios. Testing results showed that CT-Fire and DC-Fire achieved higher performance compared to baseline wildfire recognition methods using a large dataset, which includes RGB and infrared aerial images. CT-Fire and DC-Fire also showed the reliability of deep learning models in identifying and recognizing patterns and features related to wildland smoke and fires and surpassing challenges, including background complexity, which can include vegetation, weather conditions, and diverse terrain, detecting small wildfire areas, and wildland fires and smoke variety in terms of size, intensity, and shape. CT-Fire and DC-Fire also reached faster processing speeds, enabling their use for early detection of smoke and forest fires in both night and day conditions.

MSS-JDA: Multi-source self-selected joint domain adaptation method based on cross-subject EEG emotion recognition

The electroencephalogram (EEG) is widely used to identify different emotional states. However, cross-subject emotion recognition based on EEG data is challenging due to physiological differences among participants. Many studies have proposed using multi-source domain adaptation methods to address cross-subject issues and have achieved promising results. multi-source domain adaptation involves constructing independent branches for EEG data from multiple subjects, using one-to-one domain adaptation, and extracting domain-specific features, followed by reasoning through multiple branches. However, a current practical obstacle is the increasing computational time cost as the number of source domains grows. Moreover, too many source domains may lead to domain shift issues. To address these challenges, we propose a novel emotion recognition method based on Multi-Source Domain Branch Self-Selected Joint Domain Adaptation (MSS-JDA). Initially, we construct a shared feature extractor for multiple source domain branches to extract common low-level features. Next, we use a one-to-one associated joint adaptation method to extract features specific to each domain. During training, our method of multi-source domain branch self-selection, guided by prior knowledge in the early training phase, prunes source domain branches that deviate significantly from the target domain, retaining only a certain number of source domain branches close to the target domain for continued training. Finally, domain-specific features are input into their respective classifiers to obtain N predictions, and their average is taken as the final result. Extensive experiments on the SEED and SEED-IV datasets demonstrate that the MSS-JDA model achieves accuracy and standard deviation scores of 93.78%, 3.39 and 78.93%, 7.39 respectively, surpassing most existing models. The experiments indicate that the proposed MSS-JDA method improves the accuracy of EEG emotion recognition classification, thus benefiting practical applications in EEG classification.

Sequence-Information Recognition Method Based on Integrated mDTW

Sequence-Information Recognition Method Based on Integrated mDTW

Research on Ore Fragmentation Recognition Method Based on Deep Learning

Research on Ore Fragmentation Recognition Method Based on Deep Learning

A Multi-Fruit Recognition Method for a Fruit-Harvesting Robot Using MSA-Net and Hough Transform Elliptical Detection Compensation

A Multi-Fruit Recognition Method for a Fruit-Harvesting Robot Using MSA-Net and Hough Transform Elliptical Detection Compensation

Cutting-Edge Methods in Visage Expression Recognition and Ocular Tracking for Analyzing Remote Video Interviews

This paper focuses to augment an in the flesh image and video processor, capacitated with an artificial intelligence (AI) arbitrator that can speculate a job interviewee’s behavioral adeptness according to the facial dictions or expressions. We put forward a sentiment analysis model using machine learning and CNN for the reinforcement of decision-making in the job interview process. This is bought up by histogram of oriented gradients and support vector machine (HOG-SVM) with the addition of convolutional neural network (CNN) recognition in real-time video recorded interviews. The goal of applying this technology, is to develop a method that could automatically decode a candidate’s behavior by his or her facial language (or micro expressions) based on the behavioral ecology view of facial displays (BECV) which is different from the classical perspective of recognizing emotional states. This paper dispenses a trailblazing technique of determining the performance of a candidate in a video interview. To do this, a delineation of the analysis of sentiments and eye tracking technique study is intricated in which the results can be processed on a single screen to select the right person for the hire. Key Words: Artificial Intelligence(AI), Histogram of Oriented Gradients(HOG), Support Vector Machine(SVM), Convolutional Neural Network(CNN), Behavioral Ecology View of Facial Displays, Eye Tracking Technique, AVI(Automatic Visual Inspection).

On image transformation for partial discharge source identification in vehicle cable terminals of high‐speed trains

AbstractPartial discharge (PD) detection of cable terminals is crucial for the safe operation of the traction power system in trains. However, similar PD signals in complex train‐operating environments cause difficulty to recognise the insulation defects. Therefore, a PD signal image transformation recognition method is proposed for PD detection of cable terminal defects to identify defects in cable terminals with similar PD characteristics accurately. In the proposed method, the raw PD signals are firstly transformed to images via the Gramian angular field (GAF) representation. This can reveal the discriminative characteristics embedded in the original PD signals and subsequently facilitate differentiating the PD sources, which exhibit similar characteristic in the time domain. The obtained GAF representation of PD signals (named as PD GAF images) is extracted from local and global features to train an efficient MobileVIT model, which is then utilised to identify similar types of PD sources in cable terminals. The results show that the proposed method achieves 97.5% recognition accuracy in the field experiment, which is superior to other methods.

Research on the Cable-to-Terminal Connection Recognition Based on the YOLOv8-Pose Estimation Model

Substations, as critical nodes for power transmission and distribution, play a pivotal role in ensuring the stability and security of the entire power grid. With the ever-increasing demand for electricity and the growing complexity of grid structures, traditional manual inspection methods for substations can no longer meet the requirements for efficient and safe operation and maintenance. The advent of automated inspection systems has brought revolutionary changes to the power industry. These systems utilize advanced sensor technology, image processing techniques, and artificial intelligence algorithms to achieve real-time monitoring and fault diagnosis of substation equipment. Among these, the recognition of cable-to-terminal connection relationships is a key task for automated inspection systems, and its accuracy directly impacts the system’s diagnostic capabilities and fault prevention levels. However, traditional methods face numerous limitations when dealing with complex power environments, such as inadequate recognition performance under conditions of significant perspective angles and geometric distortions. This paper proposes a cable-to-terminal connection relationship recognition method based on the YOLOv8-pose model. The YOLOv8-pose model combines object detection and pose estimation techniques, significantly improving detection accuracy and real-time performance in environments with small targets and dense occlusions through optimized feature extraction algorithms and enhanced receptive fields. The model achieves an average inference time of 74 milliseconds on the test set, with an accuracy of 92.8%, a recall rate of 91.5%, and an average precision mean of 90.2%. Experimental results demonstrate that the YOLOv8-pose model performs excellently under different angles and complex backgrounds, accurately identifying the connection relationships between terminals and cables, providing reliable technical support for automated substation inspection systems. This research offers an innovative solution for automated substation inspection systems, with significant application prospects.

Tennis action recognition and evaluation with inertial measurement unit and SVM

Action recognition in tennis plays a crucial role for athletes and coaches, aiding in understanding and evaluating the players' skill levels to formulate more effective training plans and tactical strategies. To enhance the recognition and grading of tennis player actions, this study introduces the use of inertial measurement units and flexible resistive sensors for data collection. An improved Support Vector Machine is employed for data classification to achieve efficient action recognition. The results demonstrated that the proposed classification algorithm achieved an average accuracy of 95.35 % in recognizing actions of elite athletes, with the highest accuracy (96.38 %) observed in forehand strokes. In the case of sub-elite athletes, the algorithm achieved an impressive average accuracy of 97.67 %. For amateur enthusiasts, the algorithm exhibited an average accuracy of 94.08 %. Furthermore, elite athletes exhibited larger peak values in the three-axis acceleration waveform during ball striking. Specifically, the absolute peak value of acceleration in the Y-axis for elite athletes reached 78 m/s², representing an increase of 39 m/s² and 8 m/s² compared to the other two levels of athletes, respectively. Additionally, on the X and Z axes, elite athletes' acceleration peak values reached 59 m/s² and 78 m/s², significantly higher than those of sub-elite athletes and amateur enthusiasts. Moreover, the acceleration curves of elite athletes demonstrated a higher overall regularity. These findings indicate that the proposed action recognition method has a significant impact on recognition and evaluation, providing valuable insights for action recognition and assessment across various domains and advancing the application of artificial intelligence technology in the field of sports.

Correction to: Research on badminton take-off recognition method based on improved deep learning

Correction to: Research on badminton take-off recognition method based on improved deep learning

Condition assessment of concrete structures using automated crack detection method for different concrete surface types based on image processing

In the inspection and diagnosis of concrete construction, crack detection is highly recommended in the earliest phases to prevent any potential risks later. However, the flaws in concrete surfaces cannot be reliably and effectively identified using traditional crack detection techniques. The suggested algorithm is a supportive tool for agents or authorities to use in crack detection mechanisms to monitor and assess the current condition of buildings or bridges. The researchers aim to establish an intelligent model for automatic crack detection on different concrete surfaces based on image processing technology. Three different concrete surfaces—bridge decks, walls, and concrete cubes—are used to test the model. A subset of the public dataset of bridge decks and walls from SDNET (2018) and 150*150*150 mm of concrete cubes taken from the material laboratory of the faculty of engineering at Ain Shams University are applied to the model. The model F1-score measures are 98.87%, 97.43%, and 74.11% for detecting cracks in bridges, walls, and concrete cubes, respectively. The validation of the applicability of the suggested novel approach is based on a comparison with recent methods for crack recognition. The contribution of this study is that it could be applied to detect cracks efficiently on three different types of concrete surfaces, including uneven concrete surfaces, random noise, voids, dents, colour changes, and stain marks. The proposed method is transparent in its workflow and has a lower computational cost compared with deep learning frameworks. Thus, the outcomes of this model demonstrate its effectiveness in concrete defect field investigation.