Fast Detection Speed Research Articles

LG-YOLOv8: A Lightweight Safety Helmet Detection Algorithm Combined with Feature Enhancement

In the realm of construction site monitoring, ensuring the proper use of safety helmets is crucial. Addressing the issues of high parameter values and sluggish detection speed in current safety helmet detection algorithms, a feature-enhanced lightweight algorithm, LG-YOLOv8, was introduced. Firstly, we introduce C2f-GhostDynamicConv as a powerful tool. This module enhances feature extraction to represent safety helmet wearing features, aiming to improve the efficiency of computing resource utilization. Secondly, the Bi-directional Feature Pyramid (BiFPN) was employed to further enrich the feature information, integrating feature maps from various levels to achieve more comprehensive semantic information. Finally, to enhance the training speed of the model and achieve a more lightweight outcome, we introduce a novel lightweight asymmetric detection head (LADH-Head) to optimize the original YOLOv8-n’s detection head. Evaluations on the SWHD dataset confirm the effectiveness of the LG-YOLOv8 algorithm. Compared to the original YOLOv8-n algorithm, our approach achieves a mean Average Precision (mAP) of 94.1%, a 59.8% reduction in parameters, a 54.3% decrease in FLOPs, a 44.2% increase in FPS, and a 2.7 MB compression of the model size. Therefore, LG-YOLOv8 has high accuracy and fast detection speed for safety helmet detection, which realizes real-time accurate detection of safety helmets and an ideal lightweight effect.

Real-Time Detection and Localization of Weeds in Dictamnus dasycarpus Fields for Laser-Based Weeding Control

Traditional Chinese medicinal herbs have strict environmental requirements and are highly susceptible to weed damage, while conventional herbicides can adversely affect their quality. Laser weeding has emerged as an effective method for managing weeds in precious medicinal herbs. This technique allows for precise weed removal without chemical residue and protects the surrounding ecosystem. To maximize the effectiveness of this technology, accurate detection and localization of weeds in the medicinal herb fields are crucial. This paper studied seven species of weeds in the field of Dictamnus dasycarpus, a traditional Chinese medicinal herb. We propose a lightweight YOLO-Riny weed-detection algorithm and develop a YOLO-Riny-ByteTrack Multiple Object Tracking method by combining it with the ByteTrack algorithm. This approach enables accurate detection and localization of weeds in medicinal fields. The YOLO-Riny weed-detection algorithm is based on the YOLOv7-tiny network, which utilizes the FasterNet lightweight structure as the backbone, incorporates a lightweight upsampling operator, and adds structure reparameterization to the detection network for precise and rapid weed detection. The YOLO-Riny-ByteTrack Multiple Object Tracking method provides quick and accurate feedback on weed identification and location, reducing redundant weeding and saving on laser weeding costs. The experimental results indicate that (1) YOLO-Riny improves detection accuracy for Digitaria sanguinalis and Acalypha australis, ultimately amounting to 5.4% and 10%, respectively, compared to the original network. It also diminishes the model size by 2 MB and inference time by 10 ms, making it more suitable for resource-constrained edge devices. (2) YOLO-Riny-ByteTrack enhances Multiple Object Tracking accuracy by 3%, reduces ID switching by 14 times, and improves overall tracking accuracy by 3.4%. The proposed weed-detection and localization method for Dictamnus dasycarpus offers fast detection speed, high localization accuracy, and stable tracking, supporting the implementation of laser weeding during the seedling stage of Dictamnus dasycarpus.

Holistic and in situ calibration method for a spectral-temporally modulated Mueller spectropolarimeter

The spectral-temporally modulated Mueller spectropolarimeter (STMSP) offers advantages of broader band limitation, better resolution, and faster detection speed. However, the current STMSP calibration method separates the polarization state generator and analyzer, necessitating subsequent recombination, which is inefficient and unstable. In this paper, a holistic and in situ calibration method for STMSP is proposed. It only requires insertion of a polarizer as a reference sample, eliminating the need for separate calibration and recombination. The STMSP is calibrated in situ as a whole, addressing the misalignment error of the spectral modulation module, the total polarimetric errors of the temporal modulation module, and the spectral modulation transfer function of the spectrometer. Experimental results demonstrate high accuracy, with a root mean square error (RMSE) of 0.0004, which is an order of magnitude lower than that of the dual-rotating retarder spectropolarimeter (DRRSP) after eigenvalue calibration. This demonstrates its potential for enabling faster and more accurate acquisition of the Mueller spectra.

Application of Microwave Imaging Combined with Electromagnetic & Ray Localization in Pipeline Deformation Detection

Abstract This article aims to explore a new type of non excavation pipeline detection method, which combines microwave imaging with electromagnetic and ray positioning methods, to improve the accuracy and efficiency of pipeline detection and accurately locate the deformation of pipelines. [Method]Firstly, the microwave imaging method is used to detect the pipeline, and the deformation of the pipeline is determined by the reflection of the microwave. At the same time, electromagnetic and X-ray positioning technologies are used to accurately locate the pipeline and its deformation situation, in order to more accurately understand the position and deformation of the pipeline. [Result] Through the inspection of actual pipelines, we found that microwave imaging combined with electromagnetic and ray positioning technology has high detection accuracy and reliability, and can effectively detect the deformation or blockage position information of pipelines. Meanwhile, this method also has the advantages of simple operation and fast detection speed.[Innovation]Microwave imaging method combines electromagnetic and ray positioning technology organically, and is a non-contact detection method. It solves the problems of self deformation caused by impact in existing diameter plate methods, damage to pipe walls, and imprecise positioning of pipe deformation positions. This method is expected to be applied to the detection and maintenance of various pipelines, improving the efficiency and accuracy of pipeline maintenance.

Fiber Bragg Grating Pulse and Systolic Blood Pressure Measurement System Based on Mach-Zehnder Interferometer.

A fiber Bragg grating (FBG) pulse and systolic blood pressure (SBP) measurement system based on the edge-filtering method is proposed. The edge filter is the Mach-Zehnder interferometer (MZI) fabricated by two fiber couplers with a linear slope of 52.45 dBm/nm. The developed system consists of a broadband light source, an edge filter, fiber Bragg gratings (FBGs), a coarse wavelength-division multiplexer (CWDM), and signal-processing circuits based on a field-programmable gate array (FPGA). It can simultaneously measure pulse pulsations of the radial artery in the wrist at three positions: Cun, Guan and Chi. The SBP can be calculated based on the pulse transit time (PTT) principle. The measurement results compared to a standard blood pressure monitor showed the mean absolute error (MAE) and standard deviation (STD) of the SBP were 0.93 ± 3.13 mmHg. The system meets the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) equipment standards. The proposed system can achieve continuous real-time measurement of pulse and SBP and has the advantages of fast detection speed, stable performance, and no compression sensation for subjects. The system has important application value in the fields of human health monitoring and medical device development.

Synchronous End-to-End Vehicle Pedestrian Detection Algorithm Based on Improved YOLOv8 in Complex Scenarios.

In modern urban traffic, vehicles and pedestrians are fundamental elements in the study of traffic dynamics. Vehicle and pedestrian detection have significant practical value in fields like autonomous driving, traffic management, and public security. However, traditional detection methods struggle in complex environments due to challenges such as varying scales, target occlusion, and high computational costs, leading to lower detection accuracy and slower performance. To address these challenges, this paper proposes an improved vehicle and pedestrian detection algorithm based on YOLOv8, with the aim of enhancing detection in complex traffic scenes. The motivation behind our design is twofold: first, to address the limitations of traditional methods in handling targets of different scales and severe occlusions, and second, to improve the efficiency and accuracy of real-time detection. The new generation of dense pedestrian detection technology requires higher accuracy, less computing overhead, faster detection speed, and more convenient deployment. Based on the above background, this paper proposes a synchronous end-to-end vehicle pedestrian detection algorithm based on improved YOLOv8, aiming to solve the detection problem in complex scenes. First of all, we have improved YOLOv8 by designing a deformable convolutional improved backbone network and attention mechanism, optimized the network structure, and improved the detection accuracy and speed. Secondly, we introduced an end-to-end target search algorithm to make the algorithm more stable and accurate in vehicle and pedestrian detection. The experimental results show that, using the algorithm designed in this paper, our model achieves an 11.76% increase in precision and a 6.27% boost in mAP. In addition, the model maintains a real-time detection speed of 41.46 FPS, ensuring robust performance even in complex scenarios. These optimizations significantly enhance both the efficiency and robustness of vehicle and pedestrian detection, particularly in crowded urban environments. We further apply our improved YOLOv8 model for real-time detection in intelligent transportation systems and achieve exceptional performance with a mAP of 95.23%, outperforming state-of-the-art models like YOLOv5, YOLOv7, and Faster R-CNN.

LD-YOLO: A Lightweight Dynamic Forest Fire and Smoke Detection Model with Dysample and Spatial Context Awareness Module

The threat of forest fires to human life and property causes significant damage to human society. Early signs, such as small fires and smoke, are often difficult to detect. As a consequence, early detection of smoke and fires is crucial. Traditional forest fire detection models have shortcomings, including low detection accuracy and efficiency. The YOLOv8 model exhibits robust capabilities in detecting forest fires and smoke. However, it struggles to balance accuracy, model complexity, and detection speed. This paper proposes LD-YOLO, a lightweight dynamic model based on the YOLOv8, to detect forest fires and smoke. Firstly, GhostConv is introduced to generate more smoke feature maps in forest fires through low-cost linear transformations, while maintaining high accuracy and reducing model parameters. Secondly, we propose C2f-Ghost-DynamicConv as an effective tool for increasing feature extraction and representing smoke from forest fires. This method aims to optimize the use of computing resources. Thirdly, we introduce DySample to address the loss of fine-grained detail in initial forest fire images. A point-based sampling method is utilized to enhance the resolution of small-target fire images without imposing an additional computational burden. Fourthly, the Spatial Context Awareness Module (SCAM) is introduced to address insufficient feature representation and background interference. Also, a lightweight self-attention detection head (SADH) is designed to capture global forest fire and smoke features. Lastly, Shape-IoU, which emphasizes the importance of boundaries’ shape and scale, is used to improve smoke detection in forest fires. The experimental results show that LD-YOLO realizes an mAP0.5 of 86.3% on a custom forest fire dataset, which is 4.2% better than the original model, with 36.79% fewer parameters, 48.24% lower FLOPs, and 15.99% higher FPS. Therefore, LD-YOLO indicates forest fires and smoke with high accuracy, fast detection speed, and a low model complexity. This is crucial to the timely detection of forest fires.

A novel highly selective Fe@UiO-67-BDA/GCE sensor for efficient detecting Hg2+

A three-dimensional UiO-67-BDA with various pore structures and abundant active binding sites is constructed, and Fe@UiO-67-BDA composites with electrochemical sensing and recognition performance were further prepared by successfully loading Fe3+ ions. The different modified electrodes (Fe@UiO-67-BDA/GCE, UiO-67-BDA/GCE, Fe/GCE) and the bare electrode (GCE) were also characterized in terms of basic structure and electrochemistry, and it was found that the basic structure and electrochemical performances of Fe@UiO-67-BDA/GCE were better than those of the remaining three electrodes, with good selectivity, reproducibility and stability. And the Fe@UiO-67-BDA/GCE sensor with good electrochemical performance was constructed under the optimal working potential (Epa = 0.59 V) and experimental conditions, which was utilized to rapidly and accurately detect heavy metal ions Hg2+ by the DPV method, with a detection limit of up to 1.642 × 10−9 M. In addition, the electrochemical behavior mechanism of the Fe@UiO-67- BDA/GCE sensor was investigated. Finally, the sensor was applied to detect cosmetic heavy metal ions, and the experimental results also showed high reliability and practicality. Therefore, MOFs composite material is applied to the field of electrochemistry, because of its simple operation technology, fast detection speed and high sensitivity, so that it has a greater application prospect.

Carbon dots-based dual-mode sensor for highly selective detection of nitrite in food substrates through diazo coupling reaction

As an antioxidant and preservative agent, nitrite (NO2−) plays an essential role in the food industry to maintain freshness or inhibit microbial growth. However, excessive addition of NO2− is detrimental to health, so accurate and portable detection of NO2− is critical for food quality control. Notably, the selectivity of most carbon dots (CDs)-based fluorescence sensors was not enough due to the nonspecific interaction mechanism of hydrogen bond, electrostatic interaction and inner filter effect etc. Herein, a novel fluorescence/UV–vis absorption (FL/UV–vis) dual-mode sensor was developed on basis of mC-CDs, which were prepared by simple solvothermal treatment of m-Phenylenediamine (m-PDA) and cyanidin cation (CC). The fluorescence of these mC-CDs could be selectively responded by NO2− through the specific diazo coupling reaction between NO2− and amino groups on the surface of mC-CDs, thus effectively improving the selectivity of NO2− detection. The CDs-based fluorescence sensor possessed a low detection limit of 0.091 μM and 0.143 μM for FL and UV–vis methods and the excellent linear range of 0.0–60.0 μM. Furthermore, the mC-CDs sensor was employed to detect NO2− in real samples with a recovery rate of 97.11 %–104.15 % for quantitative addition. Moreover, the smartphone-assisted fluorescence sensing platform developed could identify the subtle color changes that could not be distinguished by the naked eye, and had the advantages of fast detection speed and intelligence. More importantly, the portable solid phase sensor based on mC-CDs had been successfully applied to the specific fluorescence identification and concentration monitoring of NO2−. Accordingly, the designed sensor provided a new strategy for the highly selective and convenient sensing of NO2− in food substrates, and paved the way for the wide application of CDs-based nanomaterials in the detection of food safety.

Low detection limit of uric acid based on Prussian blue-enhanced photothermal effect with microfiber knot resonator

The human serum uric acid (UA) level is a crucial indicator for diagnosing dementia in middle-aged and elderly individuals, with decreased levels being expressed in patients. Therefore, developing a high-performance sensor for online uric acid detection is of significant research interest. Herein, a microfiber knot resonator (MKR) sensor for quantitative detection of UA levels is reported. Combining polydimethylsiloxane (PDMS) with an adiabatic MKR, the specificity of UA detection in serum is improved using the photothermal effect of Prussian blue-enhanced uricase reaction. During the sensor operation, UA generates the photothermal effect under 650 nm laser irradiation, causing the PDMS film to expand with heat absorption, thereby shifting the resonance spectrum of the PDMS-MKR sensor. Experimental results demonstrate a sensitivity of 0.0559 nm/(μmol/L) within a UA concentration range of 40–120 μmol/L and a detection limit as low as 0.3578 μmol/L. The proposed sensor shows potential applications in clinical point-of-care early diagnosis and prognosis of dementia due to its specificity, fast detection speed, robustness, and high integration.

Detection methods for antibiotics in wastewater: a review.

Antibiotics are widely used as fungicides because of their antibacterial and bactericidal effects. However, it is necessary to control their dosage. If the amount of antbiotics is too much, it cannot be completely metabolized and absorbed, will pollute the environment, and have a great impact on human health. Many antibiotics usually left in factory or aquaculture wastewater pollute the environment, so it is vital to detect the content of antibiotics in wastewater. This article summarizes several common methods of antibiotic detection and pretreatment steps. The detection methods of antibiotics in wastewater mainly include immunoassay, instrumental analysis method, and sensor. Studies have shown that immunoassay can detect deficient concentrations of antibiotics, but it is affected by external factors leading to errors. The detection speed of the instrumental analysis method is fast, but the repeatability is poor, the price is high, and the operation is complicated. The sensor is a method that is currently increasingly studied, including electrochemical sensors, optical sensors, biosensors, photoelectrochemical sensors, and surface plasmon resonance sensors. It has the advantages of fast detection speed, high accuracy, and strong sensitivity. However, the reproducibility and stability of the sensor are poor. At present, there is no method that can comprehensively integrate the advantages. This paper aims to review the enrichment and detection methods of antibiotics in wastewater from 2020 to the present. It also aims to provide some ideas for future research directions in this field.

Multiplex reverse transcription recombinase polymerase amplification combined with lateral flow biosensor for simultaneous detection of three viral pathogens in cattle

Bovine viral diarrhea virus (BVDV), bovine epidemic fever virus (BEFV), and bovine respiratory syncytial virus (BRSV) cause respiratory symptoms in cattle. The absence of rapid, precise, and easily accessible diagnostic methods poses difficulties for herders and veterinary epidemiologists during outbreaks of major infectious animal diseases. Considering the mixed infection of viruses, a multiple-detection method, reverse transcription recombinase polymerase amplification (mRT-RPA) combined with a lateral flow biosensor (LFB), was established to simultaneously detect the three pathogens. This technique is based on the specific binding of three differently labeled RT-RPA products (DNA sequences) to antibodies on the three test lines of the LFB, achieving multiplex detection through the presence or absence of coloration on the LFB test lines. The fluorescence values of the LFB test lines are recorded by a test strip reader. The mRT-RPA-LFB assay completes detection at a constant temperature of 41 °C within 33 min. The limits of detection (LODs) for BVDV, BEFV and BRSV were 2.62 × 101, 2.42 × 101 and 2.56 × 101 copies/μL, respectively. No cross-reactivity was observed with the other six bovine viruses. The developed method showed satisfactory intra- and inter-assay precision, and the average coefficients of variation were ranged from 2.92 % to 3.99 %. The diagnostic sensitivity and specificity were 98.11 % and 100 %, respectively, which were highly consistent with the RT-qPCR assay, and the kappa value was 0.988 (95 % confidence interval, CI). In general, the mRT-RPA-LFB assay has the potential to become a powerful tool for rapid screening of cattle diseases because of its advantages such as fast detection speed, convenient operation, strong specificity, and high sensitivity.

Development of a Portable Residual Chlorine Detection Device with a Combination of Microfluidic Chips and LS-BP Algorithm to Achieve Accurate Detection of Residual Chlorine in Water.

Chlorine is widely used for sterilization and disinfection of water, but the presence of excess residual chlorine in water poses a substantial threat to human health. At present, there is no portable device which can achieve accurate, rapid, low-cost, and convenient detection of residual chlorine in water. Therefore, it is necessary to develop a device that can perform accurate, rapid, low-cost, and convenient detection of residual chlorine in water. In this study, a portable residual chlorine detection device was developed. A microfluidic chip was studied to achieve efficient mixing of two-phase flow. This microfluidic chip was used for rapid mixing of reagents in the portable residual chlorine detection device, reducing the consumption of reagents, detection time, and device volume. A deep learning algorithm was proposed for predicting residual chlorine concentration in water, achieving precise detection. Firstly, the microfluidic chip structure for detecting mixed reagents was optimized, and the microfluidic chip was fabricated by a 3D-printing method. Secondly, a deep learning (LS-BP) algorithm was constructed and proposed for predicting residual chlorine concentration in water, which can realize dual-channel signal reading. Thirdly, the corresponding portable residual chlorine detection device was developed, and the detection device was compared with residual chlorine detection devices and methods in other studies. The comparison results indicate that the portable residual chlorine detection device has high detection accuracy, fast detection speed, low cost, and good convenience. The excellent performance of the portable residual chlorine detection device makes it suitable for detecting residual chlorine in drinking water, swimming pool water, aquaculture and other fields.

Safety Helmet Wearing Detection Model Based on Improved YOLO-M

Abstract: The existing helmet-wearing detection algorithm's weaknesses such as many parameters, substantial detection interferences, and poor detection accuracy. We proposes a new helmet wearing detection model called YOLO-M, which uses MobileNetv3 as the backbone set of connections for feature removal, which can reduce the quantity of model parameters and model size. A residual edge for feature fusion, and At last, by changing the connection between CAM and SAM, a new attention module BiCAM is designed. The experiments show that YOLO-M has a higher detection accuracy and faster detection speed than other models, while also reducing the quantity of model parameters

Honeysuckle flower stage identification based on improved YOLOv5s

AbstractThe medicinal constituents of Chinese herbal medicine honeysuckle (Lonicera japonica Thunb) vary at different flower stages. In order to ensure that the medicinal value is maximized, it is necessary to identify its flower stage before harvesting. However, at present, this study can only be accomplished by manual visual recognition, which is inefficient and costly. Therefore, there is an urgent need to develop an automatic detection technique with high maturity, fast detection speed, and strong model deployment capability. In order to adapt to the problems of different flower size and color texture similarity and complex background, this study chooses YOLOv5s algorithm for adaptive modification. First, a small detection layer is added to the network to enhance feature extraction and improve the accuracy of identifying small honeysuckle. Second, attention mechanism is incorporated into the backbone network to suppress background interference and improve identification accuracy. Finally, the original IoU‐NMS is replaced by the DIoU‐NMS algorithm, which improves the bounding box regression rate while reducing the leakage rate when overlapping or occluded. The test results showed that the P was increased from 80.0% to 92.7%, the R was increased from 78.6% to 80.2%, and the mean average precision was increased from 86.2% to 90.6%. Furthermore, the model was verified at both long range and short range, and the tests data indicate that the identification accuracy was no less than 90% in 3 m without serious occlusion. This study laid a solid foundation for accurate honeysuckle flower stage identification and provided technical support for real‐time machine picking honeysuckle.

Hyperspectral imaging coupled with deep learning model for visualization and detection of early bruises on apples

Early bruise on apples caused by external impacts during the transportation process is commonly difficult to be detected on the apple surface, limiting the application of traditional machine vision methods in determining fruit quality. In recent years, hyperspectral imaging (HSI) has emerged as a promising technology for identifying early bruise of fruits due to its efficient and nondestructive detection. In this study, HSI data in the shortwave infrared range were collected at 2-hour and 6-hour intervals after mechanical damage. The combination of the successive projections algorithm (SPA) and principal component analysis (PCA) was used to select three key feature bands, namely, 1074 nm, 1269 nm and 1441 nm. Pseudo color transformation and band ratio algorithm were then employed to improve the contrast between damaged and healthy apple tissues for image enhancement. The fast and precise YOLOv5 (FP-YOLOv5) model achieved effective identification of apple bruises, with a high recognition rate of 95 % and a fast detection speed at 130 fps. Overall, the proposed framework based on band selection and image enhancement exhibits better performance in the detection of early apple bruises, providing useful insights for HSI combined with a deep learning model in the grading evaluation of fruit quality.

EHSGNet: A novel edge and high-level semantic guided network for CFRP subsurface defects detection

Infrared thermography, due to its fast detection speed, low cost, and intuitive results, is widely used in carbon fiber reinforced plastics (CFRP) materials detection. However, CFRP infrared image noise and lower resolution lead to intrinsic similarly and edge disruption, making it impossible to obtain satisfactory results. Additionally, the current methods require a lot of preprocessing and post-processing operations. Since camouflaged object detection has achieved good results in solving intrinsic similarly and edge disruption. Therefore, this paper proposes an edge and high-level semantic guided network (EHSGNet) based on camouflaged object detection for infrared thermography CFRP subsurface defect detection, achieving end-to-end detection of CFRP. This paper also constructed a dataset containing CFRP samples with both natural defects and artificially simulated internal defects to validate our method. Since the temperature change is achieved in the temperature-controlled chamber by means of hot air heating, it can heat the sample uniformly and improve the quality of CFRP infrared images, so our experiments will be carried out in the temperature-controlled chamber. The experiments were conducted on CFRP specimens with different sizes, shapes and depths of artificial defects. The experimental results demonstrate that the proposed EHSGNet outperforms current approaches significantly on our dataset, with precision (PRC) and recall (RCL) reaching 94.3% and 93.1%, respectively.

Defect detection of photovoltaic modules based on improved VarifocalNet

Detecting and replacing defective photovoltaic modules is essential as they directly impact power generation efficiency. Many current deep learning-based methods for detecting defects in photovoltaic modules focus solely on either detection speed or accuracy, which limits their practical application. To address this issue, an improved VarifocalNet has been proposed to enhance both the detection speed and accuracy of defective photovoltaic modules. Firstly, a new bottleneck module is designed to replace the first bottleneck module of the last stage convolution group in the backbone. This new module includes both standard convolution and dilated convolution, enabling an increase in network depth and receptive field without reducing the output feature map size. This improvement can help to enhance the accuracy of defect detection for photovoltaic modules. Secondly, another bottleneck module is also designed and used to replace the original bottleneck module used in the fourth stage convolution group of the backbone. This new module has smaller parameters than the original bottleneck module, which is useful to improve the defect detection speed of the photovoltaic module. Thirdly, a feature interactor is designed in the detection head to enhance feature expression in the classification branch. This helps improve detection accuracy. Besides, an improved intersection over union is proposed and introduced into the loss function to measure the difference between the predicted and ground truth boxes. This is useful for improving defect detection accuracy. Compared to other methods, the proposed method has the highest detection accuracy. Additionally, it also has a faster detection speed than other methods except for the DDH-YOLOv5 method and the improved YOLOv7 method.

A novel multi-modal incremental tensor decomposition for anomaly detection in large-scale networks

Network traffic anomaly detection is a crucial task for today's network monitoring and maintenance. However, with the rapid growth of network data volume, the data structure has become more and more complex, showing multi-modal characteristics, which makes traffic anomaly detection face a great challenge. The earlier proposed anomaly detection methods have the following deficiencies, i) Most of them are static or dynamic detection methods that only grow along the temporal modality. ii) Lower detection rate or higher computational cost. To address these deficiencies, this article proposes a traffic anomaly detection framework based on multi-modal incremental tensor decomposition, which has the following three highlights, i) Constructing traffic data as a tensor model to fully mine the correlation between data, and the proposed framework is applicable to the situation where traffic data grows dynamically along multiple modes. ii) Using the multi-modal incremental tensor decomposition method to process dynamically growing data without decomposing all the data, greatly reducing computational cost and improving data quality. iii) Using the XGBoost classification algorithm for anomaly detection to improve detection accuracy. Finally, the results of experiments on two real network traffic datasets NSL-KDD and CICDDOS 2019 show that the proposed framework can achieve a high detection rate of 99.21%, and has the characteristics of good scalability and fast detection speed.

Harmonic Detection Methods and Optimization Strategies for Charger in Power Grid

The charger’s operation in the power grid will cause harmonic pollution, affecting the service life of the equipment and the quality of the power grid. The harmonic characteristics and detection during the charging process are studied. The simulation model of charger is established first using MATLAB, the results of the experiment show that odd harmonics are mainly generated during charging, and the distortion rate of the current harmonics remains between 0.231 and 0.443 during the charging process. A new wavelet neural network is proposed to detect the harmonic method. Experimental results indicate that, compared with previous detection methods, this network has better detection efficiency and accuracy for harmonics. But detection result of this network is unstable and does not even converge. A new adaptive bat algorithm with improved focusing distance is presented here to optimize the wavelet neural network, which effectively solves the defect of being sensitive to the node number of hidden layer, resulting in higher detection accuracy, faster detection speed, and stable convergence. The detection error range is reduced from −0.03 to 0.02, and the number of iterations is reduced to around 1000.