Traditional Detection Methods Research Articles

A Tacholess Order Tracking Method Based on the STFTSC Algorithm for Rotor Unbalance Fault Diagnosis Under Variable-Speed Conditions

Abstract Due to the fact that rotors usually operate in a non-stationary mode with changing speeds, the conventional rotor unbalance detection method based on the stationary signal will produce a major “spectrum ambiguity issue” and affect the accuracy of rotor unbalance detection. To this end, a tacholess order tracking method based on the STFTSC algorithm is suggested in this study, where the STFTSC algorithm is developed by combining the short-time Fourier transform and the seam carving algorithm. First, the STFTSC algorithm is utilized to accurately extract the instantaneous frequency (IF) of the rotor and calculate the instantaneous phase under variable-speed conditions. Subsequently, the original signal is resampled in the angular domain to transform the non-stationary time domain signal into a stable angle domain signal, eliminating the effect of the speed variations. Finally, the angular domain signal is transformed into the order domain signal, which uses the discrete Fourier transform and the discrete spectrum correction method to identify the amplitude and phase corresponding to the fundamental frequency component of the signal. The simulation results show that the IF extracted by the STFTSC algorithm has higher extraction accuracy compared with the traditional STFT spectral peak detection method and effectively eliminates the effect of speed fluctuations. A rotor dynamic-balancing experiment shows that the unbalance correction effect based on the STFTSC algorithm is remarkable, with the average unbalance amount decrease rate on the left and right sides being 90.02% and 92.56%, respectively, after a single correction.

Development of Multifunctional Detection Robot for Roller Coaster Track.

Recent advances in roller coasters accelerate the creation of complex tracks to provide stimulation and excitement for humans. As the main load-bearing component, tracks are prone to damage such as loose connecting bolts, paint peeling, corroded sleeper welds, corroded butt welds, reduced track wall thickness and surface cracks under complex environments and long-term alternating loads. However, inspection of the roller coaster tracks, especially the high-altitude rolling tracks, is a crucial problem that traditional manual detection methods have difficulty solving. In addition, traditional inspection is labor-intensive, time-consuming, and provides only discrete information. Here, a concept of the multifunctional detection robot with a mechanical structure, electrical control system, camera, electromagnetic ultrasonic probes and an array of eddy current probes for detecting large roller coaster tracks is reported. By optimizing the design layout, integrating multiple systems and completing machine testing, the multifunctional roller coaster track detection robot exhibits outstanding performance in track appearance, thickness and crack detection. This study provides great potential for intelligent detection in amusement equipment, railcar, train and so on.

BL-YOLOv8: An Improved Road Defect Detection Model Based on YOLOv8.

Road defect detection is a crucial task for promptly repairing road damage and ensuring road safety. Traditional manual detection methods are inefficient and costly. To overcome this issue, we propose an enhanced road defect detection algorithm called BL-YOLOv8, which is based on YOLOv8s. In this study, we optimized the YOLOv8s model by reconstructing its neck structure through the integration of the BiFPN concept. This optimization reduces the model's parameters, computational load, and overall size. Furthermore, to enhance the model's operation, we optimized the feature pyramid layer by introducing the SimSPPF module, which improves its speed. Moreover, we introduced LSK-attention, a dynamic large convolutional kernel attention mechanism, to expand the model's receptive field and enhance the accuracy of object detection. Finally, we compared the enhanced YOLOv8 model with other existing models to validate the effectiveness of our proposed improvements. The experimental results confirmed the effective recognition of road defects by the improved YOLOv8 algorithm. In comparison to the original model, an improvement of 3.3% in average precision mAP@0.5 was observed. Moreover, a reduction of 29.92% in parameter volume and a decrease of 11.45% in computational load were achieved. This proposed approach can serve as a valuable reference for the development of automatic road defect detection methods.

Establishment of two assays based on reverse transcription recombinase-aided amplification technology for rapid detection of H5 subtype avian influenza virus.

The avian influenza virus (AIV), a member of the type A influenza virus group, is harbored by avian hosts. The highly pathogenic H5 subtype of AIV has inflicted significant financial damage on the poultry industry and also presents a possible pandemic hazard to the worldwide human population. There is an urgent need for a detection method that is both rapid and accurate in identifying H5-AIV. In this study, two assays based on reverse transcription recombinase-aided amplification technology (RT-RAA) have been developed to detect H5-AIV, namely, real-time fluorescence and reverse transcription recombinase-aided amplification (RF-RT-RAA) and reverse transcription recombinase-aided amplification combined lateral flow dipstick (RT-RAA-LFD). The completion of the two methods can be achieved in 30 min, and there was no cross-reaction with the nucleic acid of other avian pathogens. The results indicated that the lowest detectable limit of RF-RT-RAA and RT-RAA-LFD for H5-AIV detection was 1 copy/μL, which was 100 times higher than that of RT-PCR (102 copies/μL). A total of 376 samples, consisting of 350 clinical and 26 experimental samples, underwent testing utilizing virus isolation, RF-RT-RAA and RT-RAA-LFD methods, respectively. The results demonstrated a high degree of consistency between those assays. In summary, both the RF-RT-RAA and RT-RAA-LFD methods demonstrated excellent specificity and sensitivity, making them ideal for use in both laboratory and field settings.IMPORTANCEAvian influenza virus (AIV) subtype H5 is a highly contagious zoonotic disease and a serious threat to the farming industry and public health. Traditional detection methods, including virus isolation and real-time PCR, require tertiary biological laboratories and are time-consuming and complex to perform, making it difficult to rapidly diagnose H5 subtype avian influenza viruses. In this study, we successfully developed two methods, namely, RF-RT-RAA and RT-RAA-LFD, for rapid detection of H5-AIV. The assays are characterized by their high specificity, sensitivity, and user-friendliness. Moreover, the results of the reaction can be visually assessed, which are suitable for both laboratory testing and grassroots farm screening for H5-AIV.

RepRCNN: A Structural Reparameterisation Convolutional Neural Network Object Detection Algorithm Based on Branch Matching

A CNN object detection method based on the structural reparameterisation technique using branch matching is proposed to address the problem of balancing accuracy and speed in object detection techniques. By the structural reparameterisation of the convolutional layer in the object detection network, the amount of computation and the number of parameters in the network inference are reduced, the memory overhead is lowered, and the use of the branch-matching method to improve the structural reparameterisation model improves the computational efficiency and speed of the network while maintaining the detection accuracy. Optimisation is also carried out in terms of target screening and loss function, and a new CPC NMS screening strategy was introduced to further improve the performance of the model. The experimental results show that the proposed method achieves competitive results on the PASCAL VOC2012 and MS COCO2017 datasets compared to the traditional object detection methods and the current mainstream models, achieving a better balance between the detection accuracy and detection speed.

HS-YOLO: Small Object Detection for Power Operation Scenarios

Object detection methods are commonly employed in power safety monitoring systems to detect violations in surveillance scenes. However, traditional object detection methods are ineffective for small objects that are similar to the background information in the power monitoring scene, which consequently affects the performance of violation behavior detection. This paper proposed a small object detection algorithm named HS-YOLO, based on High-Resolution Network (HRNet) and sub-pixel convolution. First, to fully extract the microfeature information of the object, a small object feature extraction backbone network is proposed based on the HRNet structure. The feature maps of different scales are processed by multiple parallel branches and fused with each other in the network. Then, to fully retain the effective features of small objects, the sub-pixel convolution module is incorporated as the upsampling operator in the feature fusion network. The low-resolution feature map is upsampled to a higher resolution by reorganizing pixel values and performing padding operations in this module. On our self-constructed power operation dataset, the HS-YOLO algorithm achieved a mAP of 87.2%, which is a 3.5% improvement compared to YOLOv5. Particularly, the dataset’s AP for detecting small objects such as cuffs, necklines, and safety belts is improved by 10.7%, 5.8%, and 4.4%, respectively. These results demonstrate the effectiveness of our proposed method in detecting small objects in power operation scenarios.

Early Season Monitoring of Tarnished Plant Bug, Lygus lineolaris, in Wild Hosts Using Pheromone Traps.

The tarnished plant bug, Lygus lineolaris (Hemiptera: Miridae), has a wide host range of over 700 plant species, including 130 crops of economic importance. During early spring, managing the field edges with weeds and other wild hosts is important in preventing early-season infestations of L. lineolaris in cotton to prevent damage to the squares and other fruiting structures. Scouting fields for L. lineolaris is time- and labor-intensive, and end-user variability associated with field sampling can lead to inaccuracies. Insect traps that combine visual cues and pheromones are more accurate, sustainable, and economically feasible in contrast to traditional insect detection methods. In this study, we investigated the application of red or white sticky cards baited with the female-produced sex pheromone to monitor overwintering L. lineolaris populations in early spring. Field experiments demonstrated that the red sticky cards baited with a pheromone blend containing hexyl butyrate, (E)-2-hexenyl butyrate, and (E)-4-oxo-2-hexenal in 4:10:7 ratio are highly effective in trapping L. lineolaris adults in early spring before the row crops are planted, and in monitoring their movement into a cotton crop. The monitoring of L. lineolaris should help growers to make judicious decisions on insecticide applications to control early pest infestations, thereby reducing economic damage to cotton.

Wind turbine fault detection based on spatial-temporal feature and neighbor operation state

Wind turbines (WTs) are complex pieces of engineering. They are generally exposed to harsh environmental conditions. Thus, major changeable working conditions and multidimensional dynamic correlation arise, resulting in unsatisfactory fault detection accuracy. A WT fault detection method based on spatio-temporal features and neighbor operation state is proposed in this study to tackle the aforementioned challenge. A spatio-temporal feature extraction method based on accumulated mutual information is proposed to describe the dynamic correlation of multiple features during the offline phase. Then, the spatio-temporal features are applied to build the weighted k-nearest neighbor fault detection model. In the online phase, the weighted distance between the online sample and the neighbor operation state is adopted to identify the anomaly state. Moreover, a dynamic threshold based on the neighbor sample is designed to cope with the changeable operating conditions. The proposed methods are demonstrated using FAST data and supervisory control and data acquisition data, which consider different fault types. The experiment results show that compared with similar methods and traditional fault detection methods, the operational characteristics of WTs’ components can be better described by the proposed spatio-temporal feature extraction method, and the false alarm rates can be considerably reduced by the dynamic threshold.

An electronic nose for harmful gas early detection based on a hybrid deep learning method H-CRNN

Among harmful gases, carbon monoxide (CO) has posed a significant threat to human safety due to its concealment and harmfulness. Therefore, it’s imperative for the preservation of human safety to implement early detection for CO concentration. However, traditional methods for gas detection are either plagued by the limitations of insufficient accuracy like electronic nose (E-nose) equipped with relatively simple machine learning algorithms such as vanilla SVM or RNN, or limited by the incapacity to make early prediction such as sensory analysis, which requires a large amount of manpower resources and time. To address this issue, a novel technique, namely H-CRNN, is presented for E-nose to perform early prediction of low concentration CO, leveraging the strengths of convolutional and recurrent neural network to efficiently capture long-term dependencies within data. Also, shortcut connection and a novel gated attention mechanism is applied to enhance the capacity to capture crucial features. Furthermore, a linear process unit has been incorporated to handle the scale insensitivity issue. The experimental results show that the proposed H-CRNN outperforms RNN with a notable average reduction of 50.51% in the root relative squared error and 53.8% in the relative absolute error, and exhibits varying degrees of improvement when compared to state-of-the-art algorithms (TCN, TPA-LSTM, STCN, LSTNet, LSTM). Additionally, the H-CRNN achieved an average accuracy rate of 96.42%, surpassing all other algorithms and demonstrating the highest level of performance. Therefore, our work substantiates the proposed H-CRNN as an applicable method for early prediction of CO concentration.

An Artificial Intelligence-Based Method for Crack Detection in Engineering Facilities around Subways

While the construction and operation of subways have brought convenience to commuters, it has also caused ground subsidence and cracks of facilities around subways. The industry mainly adopts traditional manual detection methods to monitor these settlements and cracks. The current approaches have difficulties in achieving all-weather, all-region dynamic monitoring, increasing the traffic burden of the city during the monitoring work. The study aims to provide a large-scale settlement detection approach based on PS-InSAR for the monitoring of subway facilities. Meanwhile, this paper proposes a crack detection method that is based on UAVs and the VGG16 algorithm to quantify the length and width of cracks. The experimental data of Shenzhen University Section of Metro Line 9 are used to verify the proposed settlement model and to illustrate the monitoring process. The developed model is innovative in that it can monitor the settlement of large-scale facilities around the subway with high accuracy around the clock and automatically identify and quantify the cracks in the settled facilities around the subway.

Multi-task learning for arousal and sleep stage detection using fully convolutional networks

Objective. Sleep is a critical physiological process that plays a vital role in maintaining physical and mental health. Accurate detection of arousals and sleep stages is essential for the diagnosis of sleep disorders, as frequent and excessive occurrences of arousals disrupt sleep stage patterns and lead to poor sleep quality, negatively impacting physical and mental health. Polysomnography is a traditional method for arousal and sleep stage detection that is time-consuming and prone to high variability among experts. Approach. In this paper, we propose a novel multi-task learning approach for arousal and sleep stage detection using fully convolutional neural networks. Our model, FullSleepNet, accepts a full-night single-channel EEG signal as input and produces segmentation masks for arousal and sleep stage labels. FullSleepNet comprises four modules: a convolutional module to extract local features, a recurrent module to capture long-range dependencies, an attention mechanism to focus on relevant parts of the input, and a segmentation module to output final predictions. Main results. By unifying the two interrelated tasks as segmentation problems and employing a multi-task learning approach, FullSleepNet achieves state-of-the-art performance for arousal detection with an area under the precision-recall curve of 0.70 on Sleep Heart Health Study and Multi-Ethnic Study of Atherosclerosis datasets. For sleep stage classification, FullSleepNet obtains comparable performance on both datasets, achieving an accuracy of 0.88 and an F1-score of 0.80 on the former and an accuracy of 0.83 and an F1-score of 0.76 on the latter. Significance. Our results demonstrate that FullSleepNet offers improved practicality, efficiency, and accuracy for the detection of arousal and classification of sleep stages using raw EEG signals as input.

Fast Attack Detection Method for Imbalanced Data in Industrial Cyber-Physical Systems

Abstract Integrating industrial cyber-physical systems (ICPSs) with modern information technologies (5G, artificial intelligence, and big data analytics) has led to the development of industrial intelligence. Still, it has increased the vulnerability of such systems regarding cybersecurity. Traditional network intrusion detection methods for ICPSs are limited in identifying minority attack categories and suffer from high time complexity. To address these issues, this paper proposes a network intrusion detection scheme, which includes an information-theoretic hybrid feature selection method to reduce data dimensionality and the ALLKNN-LightGBM intrusion detection framework. Experimental results on three industrial datasets demonstrate that the proposed method outperforms four mainstream machine learning methods and other advanced intrusion detection techniques regarding accuracy, F-score, and run time complexity.

ARMing the Edge: Designing Edge Computing–Capable Machine Learning Algorithms to Target ARM Doppler Lidar Processing

Abstract There is a need for long-term observations of cloud and precipitation fall speeds in validating and improving rainfall forecasts from climate models. To this end, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) site at Lamont, Oklahoma, hosts five ARM Doppler lidars that can measure cloud and aerosol properties. In particular, the ARM Doppler lidars record Doppler spectra that contain information about the fall speeds of cloud and precipitation particles. However, due to bandwidth and storage constraints, the Doppler spectra are not routinely stored. This calls for the automation of cloud and rain detection in ARM Doppler lidar data so that the spectral data in clouds can be selectively saved and further analyzed. During the ARMing the Edge field experiment, a Waggle node capable of performing machine learning applications in situ was deployed at the ARM SGP site for this purpose. In this paper, we develop and test four algorithms for the Waggle node to automatically classify ARM Doppler lidar data. We demonstrate that supervised learning using a ResNet50-based classifier will classify 97.6% of the clear-air images and 94.7% of cloudy images correctly, outperforming traditional peak detection methods. We also show that a convolutional autoencoder paired with k-means clustering identifies 10 clusters in the ARM Doppler lidar data. Three clusters correspond to mostly clear conditions with scattered high clouds, and seven others correspond to cloudy conditions with varying cloud-base heights.

Chemically modified electrochemical sensors for the detection of β-blockers: an overview of electrode modifiers

β-blockers are a significant class of medications generally used to treat cardiovascular related diseases, but toxicity from overdoses can lead to serious side effects. In addition to this, they may also be used as doping agents by athletes due to their sedative effects. Therefore, it is crucial for their detection. Traditional detection methods include chromatography, spectroscopy, etc., but these techniques are tedious and labor-intensive, and the instruments are cumbersome. Electrochemical methods have become an important alternative to traditional methods because of their high selectivity, high sensitivity, minimal expense, and portability. For the purpose of enhancing the performance of electrochemical methods, this article focuses on three types of electrode modification materials: molecularly imprinted polymers (MIPs), carbon nanomaterials, and metal nanoparticles. MIPs are an important class of selective adsorption materials, which can adsorb a certain type of β-blockers through hydrophobic interaction, π-π interaction, hydrogen bonding, etc., thereby improving the selectivity and sensitivity of electrochemical sensors. Carbon nanomaterials and metal nanoparticles have strong electroconductivity and enormous specific surface area, which can promote the electron transfer rate on the working electrode surface and improve the sensitivity of the sensor. In addition, they also have some unique properties respectively. Furthermore, the trend of electrochemical detection of β-blockers and the prospect of future development are suggested.

Contiguity of Unmanned Aerial Vehicles for Agricultural Approaches

Abstract: Conventional techniques applied for the agricultural field work is being mechanized but is still not human friendly. Most of the traditional methods of sowing, surveillance monitoring, disease detection and post-harvest management involves a lot of drudgery and is time consuming and sometimes is also hazardous in nature when application of chemicals like fertilizers, weedicides, pesticides etc. is applied on the crop canopy. Utilization of drone techniques is newest invention that is being employed in doing agricultural activities as it leads to efficient performance and is time intensive for better agri-practices. The present study was an attempt to conceptualize the various agricultural techniques that can be performed through unmanned aerial vehicles. The features of quadcopter and hexacopter were also studied for flight uptake and payloads. The hexacopter was found better employed in agricultural activities like surveillance, monitoring, seed sowing, chemical application, disease detection and also in post harvest management of agricultural produce including storage, package and delivery of the products. Overall it was seen that utilization of drone technologies in agricultural work could be very productive and the gross income of the farmers and growers will also increase after implementation of this technology.

Breast Cancer Segmentation from Ultrasound Images Using ResNext-based U-Net Model

Breast cancer is a type of cancer caused by the uncontrolled growth and proliferation of cells in the breast tissue. Differentiating between benign and malignant tumors is critical in the detection and treatment of breast cancer. Traditional methods of cancer detection by manual analysis of radiological images are time-consuming and error-prone due to human factors. Modern approaches based on image classifier deep learning models provide significant results in disease detection, but are not suitable for clinical use due to their black-box structure. This paper presents a semantic segmentation method for breast cancer detection from ultrasound images. First, an ultrasound image of any resolution is divided into 256×256 pixel patches by passing it through an image cropping function. These patches are sequentially numbered and given as input to the model. Features are extracted from the 256×256 pixel patches with pre-trained ResNext models placed in the encoder network of the U-Net model. These features are processed in the default decoder network of the U-Net model and estimated at the output with three different pixel values: benign tumor areas (1), malignant tumor areas (2) and background areas (0). The prediction masks obtained at the output of the decoder network are combined sequentially to obtain the final prediction mask. The proposed method is validated on a publicly available dataset of 780 ultrasound images of female patients. The ResNext-based U-Net model achieved 73.17% intersection over union (IoU) and 83.42% dice coefficient (DC) on the test images. ResNext-based U-Net models perform better than the default U-Net model. Experts could use the proposed pixel-based segmentation method for breast cancer diagnosis and monitoring.

A sensitive mass spectrometry-based method to identify common respiratory pathogens in children.

Public health threats posed by emerging respiratory infections are a significant concern, particularly in children and infants. Traditional culture-based detection methods are time-consuming and typically require 1-3 days. Herein, we developed and evaluated a 23-plex common respiratory pathogen mass spectrometry assay that enables the simultaneous detection of 18 common respiratory pathogens in children. This assay combines matrix-assisted laser desorption/ionization time of flight mass spectrometry with multiplex reverse transcription-PCR and targets 11 bacterial and 7 viral pathogens (including 10 subtypes), and two internal controls. The detection limit of the common respiratory pathogen mass spectrometry assay was as low as 1 copy/µL, with no cross-reactivity with other organisms. We assessed the clinical performance of the common respiratory pathogen mass spectrometry assay using respiratory samples from 450 children. The total 450 clinical specimens underwent analysis via matrix-assisted laser desorption/ionization time of flight mass spectrometry, and the outcomes were juxtaposed with those derived from real-time reverse-transcriptase PCR conducted concurrently. The concordance between these methods was 96.0%, and the multiple infection identification rate was 7.1%. This innovative approach enables the simultaneous analysis of numerous outcomes from a solitary examination across 192 specimens within a timeframe of approximately 7 hours, with a dramatically reduced sample use and cost. In summary, the common respiratory pathogen mass spectrometry assay is a sensitive, accurate, and cost-effective method for detecting common respiratory pathogens in children and has the potential to revolutionize the diagnosis of respiratory tract infections. IMPORTANCE This study aimed to present and evaluate a novel co-detection method that enables the simultaneous identification of 11 bacterial and 7 viral pathogens in about 7 hours using matrix-assisted laser desorption/ionization time of flight mass spectrometry. Our approach utilizes a combination of multiplex reverse transcription-PCR and matrix-assisted laser desorption/ionization time of flight mass spectrometry, which overcomes the limitations of conventional assays, which include a long assessment time, technical difficulty, and high costs. As a screening method for common respiratory pathogens in children, common respiratory pathogen mass spectrometry assay has the potential to revolutionize the diagnosis of respiratory tract infections by providing an accurate etiological diagnosis. The common respiratory pathogen mass spectrometry assay is expected to be a critical tool for the diagnosis of respiratory infections in children, offering a more efficient, cost-effective, and accurate approach for the detection of common respiratory pathogens.

Integrated Passive Sensing Chip for Highly Sensitive and Reusable Detection of Differential-Charged Nanoplastics Concentration.

In this work, a new type, highly sensitive, and reusable nanoplastics (NPs) microwave detection method is proposed, which can be used to rapidly analyze NPs with different surface charges and sizes. The effective dielectric constant of NPs varies according to the different concentrations, particle sizes, and surface charges of NPs in aqueous solution. The feasibility of the microwave method for differential-charged NPs detection is verified using a complementary split ring resonator sensor manufactured on a cost-effective printed circuit board, which shows a high sensitivity only for positively charged NPs (PS-NH2) detection. To achieve microwave detection of both positively and negatively charged NPs (PS-SO3H), a microscale spiral-coupled resonator sensing chip is manufactured through integrated passive technology, which demonstrates extremely low detection limits and high sensitivity for both PS-NH2 and PS-SO3H, with different concentrations, particle sizes, and charges. In addition, for NPs solution doped with methyl orange, the device can still perform stable measurements, overcoming the inability of traditional NPs molecular element determination and optical detection methods to detect NPs aqueous solution with organic matter doping and color presence. The proposed microwave detection method could also be extended to sensing detection for detecting other hazardous environmental substances.

Metagenomic next-generation sequencing of bronchoalveolar lavage fluid assists in the diagnosis of pathogens associated with lower respiratory tract infections in children.

Worldwide, lower respiratory tract infections (LRTI) are an important cause of hospitalization in children. Due to the relative limitations of traditional pathogen detection methods, new detection methods are needed. The purpose of this study was to evaluate the value of metagenomic next-generation sequencing (mNGS) of bronchoalveolar lavage fluid (BALF) samples for diagnosing children with LRTI based on the interpretation of sequencing results. A total of 211 children with LRTI admitted to the First Affiliated Hospital of Guangzhou Medical University from May 2019 to December 2020 were enrolled. The diagnostic performance of mNGS versus traditional methods for detecting pathogens was compared. The positive rate for the BALF mNGS analysis reached 95.48% (95% confidence interval [CI] 92.39% to 98.57%), which was superior to the culture method (44.07%, 95% CI 36.68% to 51.45%). For the detection of specific pathogens, mNGS showed similar diagnostic performance to PCR and antigen detection, except for Streptococcus pneumoniae, for which mNGS performed better than antigen detection. S. pneumoniae, cytomegalovirus and Candida albicans were the most common bacterial, viral and fungal pathogens. Common infections in children with LRTI were bacterial, viral and mixed bacterial-viral infections. Immunocompromised children with LRTI were highly susceptible to mixed and fungal infections. The initial diagnosis was modified based on mNGS in 29.6% (37/125) of patients. Receiver operating characteristic (ROC) curve analysis was performed to predict the relationship between inflammation indicators and the type of pathogen infection. BALF mNGS improves the sensitivity of pathogen detection and provides guidance in clinical practice for diagnosing LRTI in children.

Cybersecurity threat perception technology based on knowledge graph

<p class="Abstract">The issue of complex sources, difficult to understand and share security threat intelligence, this paper realizes deep learning of threat intelligence features based on Restricted Boltzmann Machine, which graphs the original threat intelligence features from high dimensional space to low dimensional space layer by layer, and constructs the cyberspace security threat knowledge graphs. The deep learning used to build a multi-level and structured knowledge graph of cyberspace security threats can reflect the structural characteristics of the knowledge graph, making the graph have a lower dimension and a higher level of abstraction. The experiment verifies the feasibility of constructing the cyberspace security threat knowledge graph, and verifies the security threat perception method based on the knowledge graph is more suitable for the perception of high-intensity security threats by comparing with traditional threat detection methods.<strong></strong></p>