Real-time Identification Method Research Articles

Joint sound denoising with EEMD and improved wavelet threshold for real-time drilling lithology identification

The present study proposes a joint denoising method for real-time drilling lithology identification based on drilling sound signals. The proposed method employs ensemble empirical mode decomposition (EEMD) for preliminary sound signal denoising while retaining the effective intrinsic mode function (IMF) components, which then undergo secondary denoising using the improved wavelet threshold function. Finally, the sound signal is reconstructed using the denoised IMF components. The experimental results show that the proposed method achieves a higher signal-to-noise ratio and lower root mean square error, reaching 12.94 and 0.0442, respectively. Furthermore, the proposed method demonstrated good performance in denoising rock drilling sound signals and successfully addressed issues including the susceptibility of sound signals to external interference, nonadaptive threshold selection in wavelet threshold denoising, and high reconstruction errors during EEMD in drilling environments. Simulation results show that the proposed method significantly improves the accuracy of lithology recognition by 23%, with a resulting accuracy of 72.58%.

Revolutionizing crowd surveillance through voice-driven face recognition empowering rapid identification: towards development of sustainable smart cities

Recent global efforts to create sustainable smart cities have significantly transformed society and improved the lives of people. Nowadays, crowd surveillance (CS) has become essential in sustainable smart cities and society to protect public safety and security. In this regard, the face-based human detection system has received considerable attention because it is recognized as an emerging method in crowd surveillance applications. Thus, in this work, a new method for real-time identification of people for a crowd surveillance system (CSS) that uses facial and speech recognition technology has been introduced. In traditional CS systems, human operators are frequently used by crowd surveillance systems to watch and evaluate video feeds. Human error and operator weariness may result in lost opportunities or slow replies, which reduce the system’s efficacy. Certain procedures, including the initial identification and monitoring of people in video feeds, can be automated using a voice-activated system. To address the issues with the present CSS, a new framework Voice-Activated Face Recognition (VAFR) is proposed in this work. The proposed framework combines the speech and face recognition models for crowd surveillance. Experimental and simulation studies have been performed to analyze the performance of the proposed VAFR framework. The proposed framework uses the Viola-Jones algorithm for face identification and the Conformer architecture for speech analysis, reaching a noteworthy 99.8% accuracy rate in live video feeds. In addition, the ethical and safety aspect of the proposed VAFR system is presented.

An automatic Q-factor matching method for eliminating 77% of the ZRO of a MEMS vibratory gyroscope in rate mode

Mismatching quality factors (Q-factors) is one of the main factors causing zero-rate output (ZRO) in degenerate (DE) Micro-Electro-Mechanical Systems (MEMS) vibratory gyroscopes. To eliminate the ZRO of the DE MEMS gyroscope, this study introduces a method for real-time identification and automatic matching of Q-factors in rate mode. By leveraging the vibration characteristics of the DE MEMS vibratory gyroscope in rate mode, dedicated online test methods are designed to determine the Q-factors for both the drive and sense modes, enabling online identification of the Q-factor mismatching. Furthermore, an automatic Q-factor matching system is designed utilizing the mechanical-thermal dissipation mechanism of the resistive damper. The effectiveness of this proposed method is validated through simulations and experiments conducted on a MEMS disk resonator gyroscope (DRG). The results show a measurement error within 4% for Q-factor identification, and automatic Q-factor matching effectively reduces the ZRO by 77%. Employing this automatic Q-factor matching method successfully reduces the ZRO that is caused by the mismatching of Q-factors in the MEMS DRG from 0.11°/s to 0.025°/s and improves the bias instability (BI) from 0.40°/s to 0.19°/s.

TARGET RECOGINITION AND DETECTION USING DEEP LEARING TECHNIQUE

Object detection is a fundamental computer vision function for identifying and locating things in images or videos. Unlike image classification, object detection not only classifies the items in an image but also locates them inside the picture by constructing a bounding box around each one. Defence objects include a wide range of key assets needed for military and security activities. Tanks, armoured personnel carriers (APCs), military trucks, planes, helicopters, naval boats, and unmanned ground vehicles (UGVs) are among the types of vehicles included. Detection of these vehicles is critical for tracking troop movements, analysing battlefield dynamics, and maintaining operational preparedness. The modern defence scenario demands improved surveillance and security measures to successfully counter growing threats. This work suggests using the You Only Look Once (YOLO) method for real-time identification of numerous defence items. The project's goal is to improve security by properly detecting a variety of items, including cars, individuals, equipment, and structures, with a single deep learning model. Methodologically, a broad dataset containing photos and videos of defence items in various locations is collected and preprocessed. The YOLO algorithm is then trained on this dataset, with parameters optimised for high accuracy and recall rates. Performance evaluation include thorough testing on various datasets to determine accuracy, speed, and resilience. KEYWORDS: Target recognition, Detection, YOLO algorithm, Military applications, Flying objects, Vehicles, Sensor integration, Real-time processing, Threat detection, Situational awareness.

Traffic Sign Recognition using CNN (Convolutional Neural Network)

Effective recognition of traffic signs systems are essential for traffic management since road safety is a major problem. In this work, we introduce a unique method for computer vision and deep learning-based real-time traffic sign identification and categorization. Convolutional neural networks (CNNs), trained on an extensive collection of photographs of traffic signs, are the tool we use in our approach. We improve the quality of the input photos and extract relevant features by preprocessing them, which includes grayscale conversion and normalization. After that, the CNN model examines these characteristics to precisely determine the kind of traffic sign that is shown. To further improve classification accuracy, we also use bounding box localization to correctly outline and extract indicators. The effectiveness of our system is shown by experimental findings, which show that it can reliably detect traffic signs in a variety of environmental conditions. This study provides a reliable and effective method for identifying traffic signs, promoting road safety

Real-time identification of time-varying cable force for cable-stayed bridges based on vibration monitoring

Time-varying cable-force identification is fundamental for assessing the fatigue damage of stay cables. This study proposes a novel method for real-time time-varying cable force identification. This method updates the cable vibration signals through the sliding window and employs adaptive chirp mode decomposition (ACMD) to identify the instantaneous frequencies of the cables. Finally, the cable forces were calculated based on the taut string theory. The novelty of this study lies in the introduction of ACMD to enhance the accuracy of cable-force identification, while adaptively determining the key parameters of ACMD based on the scale space peak picking (SSPP) algorithm. The effectiveness of this method was been verified through numerical models, with the results showing that the maximum relative error in cable-force identification was 0.87%. Furthermore, this method was successfully applied to a sea-crossing cable-stayed bridge, demonstrating its ability to assess cable force accurately.

Real-time driver identification in IoV: A deep learning and cloud integration approach

The Internet of Vehicles (IoV) emerges as a pivotal extension of the Internet of Things (IoT), specifically geared towards transforming the automotive landscape. In this evolving ecosystem, the demand for a seamless end-to-end system becomes paramount for enhancing operational efficiency and safety. Hence, this study introduces an innovative method for real-time driver identification by integrating cloud computing with deep learning. Utilizing the integrated capabilities of Google Cloud, Thingsboard, and Apache Kafka, the developed solution tailored for IoV technology is adept at managing real-time data collection, processing, prediction, and visualization, with resilience against sensor data anomalies. Also, this research suggests an appropriate method for driver identification by utilizing a combination of Convolutional Neural Networks (CNN) and multi-head self-attention in the proposed approach. The proposed model is validated on two datasets: Security and collected. Moreover, the results show that the proposed model surpassed the previous works by achieving an accuracy and F1 score of 99.95%. Even when challenged with data anomalies, this model maintains a high accuracy of 96.2%. By achieving accurate driver identification results, the proposed end-to-end IoV system can aid in optimizing fleet management, vehicle security, personalized driving experiences, insurance, and risk assessment. This emphasizes its potential for road safety and managing transportation more effectively.

A method for real-time identification of muck flow state inside EPB shield chambers in fine-grained soils

This study aims to evaluate the effectiveness of the newly developed shield test device (VMS) by Tongji University in Shanghai for determining the muck state inside EPB shield chambers in fine-grained soils. Muck blockage inside shield chambers frequently results in significant stratum deformation and excessive damage to these machines. The engineering community requires real-time and effective monitoring technology to assess the muck flow state inside EPB shield chambers. Three motion modes of the muck composed of fine-grained soil near the partition have been identified: flow around the central axis, overall static and local adhesion. These modes correspond to the three characteristics of shear plate torque near the shield partition: the periodic fluctuation, the three-stage change of ‘rise-fall-stability’, and a noticeable amplitude difference at the top and lower sides of the chamber. Finally, a preliminarily validated wireless signalling-type shear plate device that can be assembled on the field shield and an actual measurement method was further developed to determine the muck state inside the shield chamber in real-time.

RT-CBCH: Real-Time VPN Traffic Service Identification Based on Sampled Data in High-Speed Networks

Virtual Private Network (VPN) technology can bypass censorship and access geographically locked services. Some harmful information may be hidden in VPN traffic and circumvent the surveillance systems, bringing a significant challenge to network security. Considering the increasing richness of service types in VPN traffic, identifying traffic service facilitates further targeting harmful VPN traffic. Therefore, VPN traffic service identification is critical in network management. The existing identification methods use complete traffic for analysis. However, massive data analysis in high-speed networks consumes enormous resources, limiting the real-time processing of traffic identification. This paper proposes a real-time VPN traffic service identification method named RT-CBCH. We construct features that are still available after sampling and design a fast traffic processing structure based on Counting Bloom Filter and Chained Hash Table (CBCH). Experimental results validate the real-time capability, stability and accuracy of our method. At the sampling ratio of 1/256, it takes only 23.63 seconds to process the mixed traffic of 900-second traffic generated on a 10 Gbps link and our collected V2Ray traffic, which is increasingly common in VPN traffic. Under different sampling ratios, the identification results remain respectable, with an overall accuracy of about 90% for application service and over 99% for V2Ray proxy service. Furthermore, comparisons with similar work illustrate the high accuracy and low resource consumption of RT-CBCH. Experimental results show that our method can stably implement real-time VPN traffic service identification from sampled data in high-speed networks.

The Real-Time Dynamic Prediction of Optimal Taxi Cruising Area Based on Deep Learning

A real-time, effective, and dynamic taxi cruising recommendation strategy is essential to solving the problem of taxi cruising passenger difficulty and urban road traffic congestion. This study focuses on two aspects of the real-time accessible range and pick-up ratio (PR) and proposes a real-time dynamic identification method for taxi optimal cruise-seeking area. Firstly, based on the cumulative opportunity method, a univariate temporal convolutional network (UTCN) accessible range dynamic prediction model is proposed to predict the real-time accessible range of taxis. Secondly, based on the gradient boosting decision tree (GBDT) model, the influencing factors with a high correlation with the PR are selected from the four dimensions of traffic characteristics, environmental meteorology, and time and space variables. Then, a multivariate univariate temporal convolutional network (MTCN) global grid PR prediction model is constructed, and the optimal taxi cruising area is identified based on the maximum PR. The results show that the taxi accessible range and PR of the same grid in different periods change with time, and based on the model comparison, the accessible range and PR prediction results of UTCN and MTCN algorithms in different periods are the best to identify the optimal cruising area of taxis in different periods. The main contribution of this study is that the proposed optimal cruising area prediction model has timeliness, accessibility, and dynamics. It can not only improve the probability of taxis receiving passengers and avoid taxis cruising aimlessly, but also solve the shortage of taxis in hotspots, thus shortening the waiting time of passengers. This provides a scientific basis for improving taxi cruising efficiency and the government’s formulation of taxi operation management policies, which can effectively promote the sustainable development of urban traffic.

MobileViT model-based real-time fiber identification method for cashmere and wool

The physical and morphological characteristics of wool and cashmere fibers exhibit notable similarities, making distinguishing them challenging. In this study, we propose a method based on a lightweight hybrid model called MobileViT, which combines a vision transformer and convolutional neural network, for the real-time identification of fiber categories. After training on a large sample dataset, the model was validated on a test set of 61,095 fiber images belonging to six categories; it took 26.2 s to achieve a recognition accuracy of 97.19%. This paper presents the first attempt to use a hybrid model of Transformer and Convolutional Neural Network (CNN) for the recognition of fiber images. Experimental results demonstrate that the model is capable of effectively extracting features from fibers, and it outperforms pure CNN models in terms of both speed and accuracy.

Real-time damage identification of hinge joints in multi-girder bridges using recursive least squares solution of the characteristic equation

Hinge joint damage is commonly observed in assembled multi-girder bridges, which may jeopardize their serviceability, safety, and sustainability. Timely detection of such damage is crucial for bridge management. Existing damage identification methods regarding hinge joints are either inconvenient in calculation or less sensitive to damage, and struggle with real-time monitoring. Therefore, to overcome these defects regarding assembled multi-girder bridges, this study proposes a real-time damage identification method that can directly estimate the stiffness of hinge joints without any complex finite element model. The proposed method is based on the recursive least squares solution of the characteristic equation of multiple beam systems, which is easy to implement and allows real-time evaluation. The effect of hinge joint damage on the dynamic characteristics of the bridge is quantified through numerical simulation, and the accuracy of the proposed method is verified. Finally, the proposed method is validated on an on-site measured bridge. The bridge consists of 16 girders with a length of 20 m. The results demonstrate that the proposed method practically achieves accurate identification of damage in the real bridge, indicating that the proposed method can be applied to the real-time monitoring of actual bridges to locate and assess hinge joint damage.

Vote-Based Feature Selection Method for Stratigraphic Recognition in Tunnelling Process of Shield Machine

Shield machines are currently the main tool for underground tunnel construction. Due to the complexity and variability of the underground construction environment, it is necessary to accurately identify the ground in real-time during the tunnel construction process to match and adjust the tunnel parameters according to the geological conditions to ensure construction safety. Compared with the traditional method of stratum identification based on staged drilling sampling, the real-time stratum identification method based on construction data has the advantages of low cost and high precision. Due to the huge amount of sensor data of the ultra-large diameter mud-water balance shield machine, in order to balance the identification time and recognition accuracy of the formation, it is necessary to screen the multivariate data features collected by hundreds of sensors. In response to this problem, this paper proposes a voting-based feature extraction method (VFS), which integrates multiple feature extraction algorithms FSM, and the frequency of each feature in all feature extraction algorithms is the basis for voting. At the same time, in order to verify the wide applicability of the method, several commonly used classification models are used to train and test the obtained effective feature data, and the model accuracy and recognition time are used as evaluation indicators, and the classification with the best combination with VFS is obtained. The experimental results of shield machine data of 6 different geological structures show that the average accuracy of 13 features obtained by VFS combined with different classification algorithms is 91%; among them, the random forest model takes less time and has the highest recognition accuracy, reaching 93%, showing best compatibility with VFS. Therefore, the VFS algorithm proposed in this paper has high reliability and wide applicability for stratum identification in the process of tunnel construction, and can be matched with a variety of classifier algorithms. By combining 13 features selected from shield machine data features with random forest, the identification of the construction stratum environment of shield tunnels can be well realized, and further theoretical guidance for underground engineering construction can be provided.

Real-Time Recognition Method for Key Signals of Rock Fracture Acoustic Emissions Based on Deep Learning.

The characteristics of acoustic emission signals generated in the process of rock deformation and fission contain rich information on internal rock damage. The use of acoustic emissions monitoring technology can analyze and identify the precursor information of rock failure. At present, in the field of acoustic emissions monitoring and the early warning of rock fracture disasters, there is no real-time identification method for a disaster precursor characteristic signal. It is easy to lose information by analyzing the characteristic parameters of traditional acoustic emissions to find signals that serve as precursors to disasters, and analysis has mostly been based on post-analysis, which leads to poor real-time recognition of disaster precursor characteristics and low application levels in the engineering field. Based on this, this paper regards the acoustic emissions signal of rock fracture as a kind of speech signal generated by rock fracture uses this idea of speech recognition for reference alongside spectral analysis (STFT) and Mel frequency analysis to realize the feature extraction of acoustic emissions from rock fracture. In deep learning, based on the VGG16 convolutional neural network and AlexNet convolutional neural network, six intelligent real-time recognition models of rock fracture and key acoustic emission signals were constructed, and the network structure and loss function of traditional VGG16 were optimized. The experimental results show that these six deep-learning models can achieve the real-time intelligent recognition of key signals, and Mel, combined with the improved VGG16, achieved the best performance with 87.68% accuracy and 81.05% recall. Then, by comparing multiple groups of signal recognition models, Mel+VGG-FL proposed in this paper was verified as having a high recognition accuracy and certain recognition efficiency, performing the intelligent real-time recognition of key acoustic emission signals in the process of rock fracture more accurately, which can provide new ideas and methods for related research and the real-time intelligent recognition of rock fracture precursor characteristics.

A weigh-in-motion system with automatic data reliability estimation

Weigh-in-motion stations are used on roads for automatic recording of data on vehicles axle loads. In order to achieve direct legal enforcement, WIM stations should feature an internal real-time method for identification of unreliable measurements. In this paper, two groups of factors were used to assess the degree of measurement reliability. The first group included factors relating to ambient conditions; the second group included factors describing the trajectory and the dynamics of the crossing event (passage). The influence of the factors on the accuracy of the measurements was determined on the basis of an experimental study. The method is based on an appropriately-equipped WIM station and uses an algorithm with fuzzy logic. The results of the study indicate that almost 80% of the measurements are to be considered reliable, of which 40% of the measurements recorded at the WIM station were characterised by very high reliability.

Advances in optical molecular imaging for neural visualization.

Iatrogenic nerve injury is a significant complication in surgery, which can negatively impact patients' quality of life. Currently, the main clinical neuroimaging methods, such as computed tomography, magnetic resonance imaging, and high-resolution ultrasonography, do not offer precise real-time positioning images for doctors during surgery. The clinical application of optical molecular imaging technology has led to the emergence of new concepts such as optical molecular imaging surgery, targeted surgery, and molecular-guided surgery. These advancements have made it possible to directly visualize surgical target areas, thereby providing a novel method for real-time identification of nerves during surgery planning. Unlike traditional white light imaging, optical molecular imaging technology enables precise positioning and identifies the cation of intraoperative nerves through the presentation of color images. Although a large number of experiments and data support its development, there are few reports on its actual clinical application. This paper summarizes the research results of optical molecular imaging technology and its ability to realize neural visualization. Additionally, it discusses the challenges neural visualization recognition faces and future development opportunities.

Biological characters identification for hard clam larva based on the improved YOLOX-s

This study presents a real-time identification method for improving the accuracy and efficiency of counting hard clam larvae, overcoming the limitations of traditional manual methods. The proposed approach enhances the YOLOX-S network by incorporating attention mechanism modules into feature layers and up-sampling processes. The proposed model is subjected to comparative evaluations with YOLOv3, YOLOv4, YOLOv5, and Faster R-CNN networks, demonstrating its superior performance. After 150 epochs, the model achieves a training loss of approximately 1.42 and a mean average precision of 96.50%, outperforming models without attention mechanisms. It accurately identifies the quantity, survival rate, length, and width of hard clam larvae, providing valuable insights for quality prediction in the aquaculture industry. These findings contribute to the advancement of shellfish farming practices, offering a precise and efficient tool for larval assessment and promoting sustainable aquaculture development.

Joint Real-Time Identification for Mutual Inductance and Load Charging Parameters of IPT System

Accurate and fast identification for mutual inductance and load charging parameters of inductive wireless power transfer (IPT) system is essential for impedance matching, and the output control of communication-free IPT system. A joint real-time identification method for mutual inductance and load charging parameters of IPT system is proposed in this paper. Only via measuring the RMS value of fundamental voltage and current on primary side, the double equivalent impedance modulus equations about mutual inductance and equivalent load resistance are established. To obtain real-time identification results of mutual inductance and equivalent load resistance from the high-order implicit function equations, an improved adaptive filtering algorithm based on Least Mean Squares (LMS) is proposed. Then, the identification values of load charging parameters are obtained according to the identified mutual inductance and equivalent load resistance. Finally, a 3.6kW platform is built to verify the validity of the proposed method. And the experiment results show that, compared with other existing achievements, the proposed method can identify <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">U_Bat</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I_Bat</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R_L</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> simultaneously in real-time without changing the system operation, and it has faster rate and higher accuracy of identification.

Genetic population structure of Listeria monocytogenes strains isolated from salmon and trout sectors in France

Smoked salmon and smoked trout are ready-to-eat and potentially contaminated with the pathogenic bacterium Listeria monocytogenes making them high risk for the consumer. This raises questions about the presence of hypervirulent or persistent strains in the salmon and trout industries. Knowledge of the genetic diversity of circulating strains in these sectors is essential to evaluate the risk associated with this pathogen and improve food safety. We analyzed the genetic structure of 698 strains of L. monocytogenes isolated from 2006 to 2017 in France, based on their serogroup, lineage and clonal complexes (CCs) determined by Multilocus sequence typing (MLST). Most of the CCs were identified by mapping the strains PFGE profiles and a novel high-throughput real-time PCR method for CC identification. We identified thirteen CCs and one sequence type (ST) with variable distribution in salmon and trout samples (food, environment). The three most prevalent CCs were CC121, CC26 and CC204. Strains from ST191 and CC54 were detected for the first time in these sectors, while less than 0.6% of the isolates belonged to the hyper-virulent CC1, CC6 and CC20. No CC was exclusively associated with the salmon sector. This project allowed us to assess the population diversity of CCs of L. monocytogenes in the salmon and trout industries.

Differential Settlement of Track Foundations Identification Based on GRU Neural Network

The timely identification of differential settlement of track foundations is of great significance for the safety of train operation and the maintenance of track structures. However, traditional monitoring techniques cannot meet the requirements of efficient, real-time, and automatic monitoring of track foundation settlement. In order to solve these problems, a real-time identification method based on a gated recurrent unit (GRU) neural network is proposed for the differential settlement of track foundations monitoring. According to parameter sensitivity analysis, the vertical acceleration of the vehicle is selected as the known data fed into the GRU network for differential settlement identification. Then the GRU network is employed to establish the nonlinear relationship between the vertical acceleration of the vehicle and the differential settlement of the track foundation. The results indicate that the longitudinal continuous differential settlement distribution curve of track foundations could be accurately identified with GRU neural network through the real-time vibration response of the vehicle–track. The current method may provide a new means for the real-time and efficient identification of the differential settlement of track foundations.