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A dual-RPA based lateral flow strip for sensitive,on-site detection of CP4-EPSPS and Cry1Ab/Ac genes in genetically modified crops 被引量:1
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作者 Jinbin Wang Yu Wang +7 位作者 Xiuwen Hu Yifan Chen Wei Jiang Xiaofeng Liu Juan Liu Lemei Zhu Haijuan Zeng Hua Liu 《Food Science and Human Wellness》 SCIE CSCD 2024年第1期183-190,共8页
Traditional transgenic detection methods require high test conditions and struggle to be both sensitive and efficient.In this study,a one-tube dual recombinase polymerase amplification(RPA)reaction system for CP4-EPSP... Traditional transgenic detection methods require high test conditions and struggle to be both sensitive and efficient.In this study,a one-tube dual recombinase polymerase amplification(RPA)reaction system for CP4-EPSPS and Cry1Ab/Ac was proposed and combined with a lateral flow immunochromatographic assay,named“Dual-RPA-LFD”,to visualize the dual detection of genetically modified(GM)crops.In which,the herbicide tolerance gene CP4-EPSPS and the insect resistance gene Cry1Ab/Ac were selected as targets taking into account the current status of the most widespread application of insect resistance and herbicide tolerance traits and their stacked traits.Gradient diluted plasmids,transgenic standards,and actual samples were used as templates to conduct sensitivity,specificity,and practicality assays,respectively.The constructed method achieved the visual detection of plasmid at levels as low as 100 copies,demonstrating its high sensitivity.In addition,good applicability to transgenic samples was observed,with no cross-interference between two test lines and no influence from other genes.In conclusion,this strategy achieved the expected purpose of simultaneous detection of the two popular targets in GM crops within 20 min at 37°C in a rapid,equipmentfree field manner,providing a new alternative for rapid screening for transgenic assays in the field. 展开更多
关键词 Genetically modifi ed crops On-site detection Lateral fl ow test strips Dual recombinase polymerase amplification (RPA)
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YOLO-MFD:Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head
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作者 Zhongyuan Zhang Wenqiu Zhu 《Computers, Materials & Continua》 SCIE EI 2024年第5期2547-2563,共17页
Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false... Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery.Additionally,these complexities contribute to inaccuracies in target localization and hinder precise target categorization.This paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withMulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery analysis.Specifically,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex backgrounds.To resolve these issues,we introduce a novel approach.First,we propose the implementation of a lightweight multi-scale module called CEF.This module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature information.It effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing imagery.Second,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone section.This allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed images.Finally,a dynamic head attentionmechanism is introduced.This allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different sizes.Consequently,the precision of object detection is significantly improved.The trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 and map@0.5:0.95,separately.These results illustrate the clear advantages of the method. 展开更多
关键词 Object detection YOLOv8 MULTI-SCALE attention mechanism dynamic detection head
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Target Detection Algorithm in Foggy Scenes Based on Dual Subnets
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作者 Yuecheng Yu Liming Cai +3 位作者 Anqi Ning Jinlong Shi Xudong Chen Shixin Huang 《Computers, Materials & Continua》 SCIE EI 2024年第2期1915-1931,共17页
Under the influence of air humidity,dust,aerosols,etc.,in real scenes,haze presents an uneven state.In this way,the image quality and contrast will decrease.In this case,It is difficult to detect the target in the ima... Under the influence of air humidity,dust,aerosols,etc.,in real scenes,haze presents an uneven state.In this way,the image quality and contrast will decrease.In this case,It is difficult to detect the target in the image by the universal detection network.Thus,a dual subnet based on multi-task collaborative training(DSMCT)is proposed in this paper.Firstly,in the training phase,the Gated Context Aggregation Network(GCANet)is used as the supervisory network of YOLOX to promote the extraction of clean information in foggy scenes.In the test phase,only the YOLOX branch needs to be activated to ensure the detection speed of the model.Secondly,the deformable convolution module is used to improve GCANet to enhance the model’s ability to capture details of non-homogeneous fog.Finally,the Coordinate Attention mechanism is introduced into the Vision Transformer and the backbone network of YOLOX is redesigned.In this way,the feature extraction ability of the network for deep-level information can be enhanced.The experimental results on artificial fog data set FOG_VOC and real fog data set RTTS show that the map value of DSMCT reached 86.56%and 62.39%,respectively,which was 2.27%and 4.41%higher than the current most advanced detection model.The DSMCT network has high practicality and effectiveness for target detection in real foggy scenes. 展开更多
关键词 Target detection fog target detection YOLOX twin network multi-task learning
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Artificial Immune Detection for Network Intrusion Data Based on Quantitative Matching Method
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作者 CaiMing Liu Yan Zhang +1 位作者 Zhihui Hu Chunming Xie 《Computers, Materials & Continua》 SCIE EI 2024年第2期2361-2389,共29页
Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune de... Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method.The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements.Then,to improve the accuracy of similarity calculation,a quantitative matching method is proposed.The model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions.The proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional methods.The experiment results show that the proposed model can detect intrusions effectively.It has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance. 展开更多
关键词 Immune detection network intrusion network data signature detection quantitative matching method
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SDH-FCOS:An Efficient Neural Network for Defect Detection in Urban Underground Pipelines
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作者 Bin Zhou Bo Li +2 位作者 Wenfei Lan Congwen Tian Wei Yao 《Computers, Materials & Continua》 SCIE EI 2024年第1期633-652,共20页
Urban underground pipelines are an important infrastructure in cities,and timely investigation of problems in underground pipelines can help ensure the normal operation of cities.Owing to the growing demand for defect... Urban underground pipelines are an important infrastructure in cities,and timely investigation of problems in underground pipelines can help ensure the normal operation of cities.Owing to the growing demand for defect detection in urban underground pipelines,this study developed an improved defect detection method for urban underground pipelines based on fully convolutional one-stage object detector(FCOS),called spatial pyramid pooling-fast(SPPF)feature fusion and dual detection heads based on FCOS(SDH-FCOS)model.This study improved the feature fusion component of the model network based on FCOS,introduced an SPPF network structure behind the last output feature layer of the backbone network,fused the local and global features,added a top-down path to accelerate the circulation of shallowinformation,and enriched the semantic information acquired by shallow features.The ability of the model to detect objects with multiple morphologies was strengthened by introducing dual detection heads.The experimental results using an open dataset of underground pipes show that the proposed SDH-FCOS model can recognize underground pipe defects more accurately;the average accuracy was improved by 2.7% compared with the original FCOS model,reducing the leakage rate to a large extent and achieving real-time detection.Also,our model achieved a good trade-off between accuracy and speed compared with other mainstream methods.This proved the effectiveness of the proposed model. 展开更多
关键词 Urban underground pipelines defect detection SDH-FCOS feature fusion SPPF dual detection heads
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A self-organization formation configuration based assignment probability and collision detection
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作者 SONG Wei WANG Tong +1 位作者 YANG Guangxin ZHANG Peng 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2024年第1期222-232,共11页
The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment pro... The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment probability is proposed to achieve the shortest overall formation path of multi-UAVs with low complexity and reduce the energy consumption.In order to avoid the collision between UAVs in the formation process,the concept of safety ball is introduced,and the collision detection based on continuous motion of two time slots and the lane occupation detection after motion is proposed to avoid collision between UAVs.Based on the idea of game theory,a method of UAV motion form setting based on the maximization of interests is proposed,including the maximization of self-interest and the maximization of formation interest is proposed,so that multi-UAVs can complete the formation task quickly and reasonably with the linear trajectory assigned in advance.Finally,through simulation verification,the multi-UAVs target assignment algorithm based on the assignment probability proposed in this paper can effectively reduce the total path length,and the UAV motion selection method based on the maximization interests can effectively complete the task formation. 展开更多
关键词 straight line trajectory assignment probability collision detection lane occupation detection maximization of interests
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Phenotypic Detection of Enterobacterales Strains Susceptible of Producing OXA-48 Carbapenemase
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作者 Abdoulaye Seck Abdou Diop +5 位作者 Babacar Ndiaye Assane Dieng Awa Ba Amadou Diop Chantal Mahou Douala-Djemba Thierno Abdoulaye Diallo 《Advances in Microbiology》 CAS 2024年第2期115-121,共7页
Background: Nowadays, emergence of Carbapenemase-Producing Enterobacterales (CPE) throughout the world has become a public health problem, especially in countries with limited resources. In recent years, CPE of type O... Background: Nowadays, emergence of Carbapenemase-Producing Enterobacterales (CPE) throughout the world has become a public health problem, especially in countries with limited resources. In recent years, CPE of type OXA-48 (Ambler class D) have been identified in Dakar. The aim of this study was to evaluate the phenotypic detection of OXA-48 CPE using a temocillin disc (30 μg). Methodology: A retrospective study was carried out at Medical Biology Laboratory of Pasteur Institute in Dakar on Ertapenem-Resistant Enterobacterales (ERE) strains isolated from 2015 to 2017. These strains were then tested with a 30 μg temocillin disc. Any strain resistant to temocillin (inhibition diameter Results: Forty-one ERE isolated during the study period were tested, of which 34 (82.9%) were OXA-48 based on phenotypic detection using temocillin disc and confirmed by PCR (100%). OXA-48 CPE strains detected were composed of Klebsiella pneumoniae (n = 14;41.2%), Enterobacter cloacae (n = 8;23.5%), Escherichia coli (n = 7, 20.5%), Citrobacter freundii (n = 3;8.8%), Cronobacter sakazakii (n = 1;3%) and Morganella morganii (n = 1;3%). Conclusion: Temocillin resistance has a good positive predictive value for detecting OXA-48 CPE by phenotypic method, confirmed by PCR. Temocillin is therefore a good marker for detection of OXA-48 CPE except Hafnia alvei. 展开更多
关键词 ERTAPENEM Temocillin Phenotypic detection Carbapenemase-Producing Enterobacterales OXA-48
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Strengthening Network Security: Deep Learning Models for Intrusion Detectionwith Optimized Feature Subset and Effective Imbalance Handling
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作者 Bayi Xu Lei Sun +2 位作者 Xiuqing Mao Chengwei Liu Zhiyi Ding 《Computers, Materials & Continua》 SCIE EI 2024年第2期1995-2022,共28页
In recent years,frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security.This paper presents a novel intrusion detection system consisting of a data prep... In recent years,frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security.This paper presents a novel intrusion detection system consisting of a data prepro-cessing stage and a deep learning model for accurately identifying network attacks.We have proposed four deep neural network models,which are constructed using architectures such as Convolutional Neural Networks(CNN),Bi-directional Long Short-Term Memory(BiLSTM),Bidirectional Gate Recurrent Unit(BiGRU),and Attention mechanism.These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the models,we apply various preprocessing techniques and employ the particle swarm optimization algorithm to perform feature selection on the NSL-KDD dataset,resulting in an optimized feature subset.Moreover,we address class imbalance in the dataset using focal loss.Finally,we employ the BO-TPE algorithm to optimize the hyperparameters of the four models,maximizing their detection performance.The test results demonstrate that the proposed model is capable of extracting the spatiotemporal features of network traffic data effectively.In binary and multiclass experiments,it achieved accuracy rates of 0.999158 and 0.999091,respectively,surpassing other state-of-the-art methods. 展开更多
关键词 Intrusion detection CNN BiLSTM BiGRU ATTENTION
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Anomaly Detection Algorithm of Power System Based on Graph Structure and Anomaly Attention
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作者 Yifan Gao Jieming Zhang +1 位作者 Zhanchen Chen Xianchao Chen 《Computers, Materials & Continua》 SCIE EI 2024年第4期493-507,共15页
In this paper, we propose a novel anomaly detection method for data centers based on a combination of graphstructure and abnormal attention mechanism. The method leverages the sensor monitoring data from targetpower s... In this paper, we propose a novel anomaly detection method for data centers based on a combination of graphstructure and abnormal attention mechanism. The method leverages the sensor monitoring data from targetpower substations to construct multidimensional time series. These time series are subsequently transformed intograph structures, and corresponding adjacency matrices are obtained. By incorporating the adjacency matricesand additional weights associated with the graph structure, an aggregation matrix is derived. The aggregationmatrix is then fed into a pre-trained graph convolutional neural network (GCN) to extract graph structure features.Moreover, both themultidimensional time series segments and the graph structure features are inputted into a pretrainedanomaly detectionmodel, resulting in corresponding anomaly detection results that help identify abnormaldata. The anomaly detection model consists of a multi-level encoder-decoder module, wherein each level includesa transformer encoder and decoder based on correlation differences. The attention module in the encoding layeradopts an abnormal attention module with a dual-branch structure. Experimental results demonstrate that ourproposed method significantly improves the accuracy and stability of anomaly detection. 展开更多
关键词 Anomaly detection TRANSFORMER graph structure
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A Secure and Cost-Effective Training Framework Atop Serverless Computing for Object Detection in Blasting
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作者 Tianming Zhang Zebin Chen +4 位作者 Haonan Guo Bojun Ren Quanmin Xie Mengke Tian Yong Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第5期2139-2154,共16页
The data analysis of blasting sites has always been the research goal of relevant researchers.The rise of mobile blasting robots has aroused many researchers’interest in machine learning methods for target detection ... The data analysis of blasting sites has always been the research goal of relevant researchers.The rise of mobile blasting robots has aroused many researchers’interest in machine learning methods for target detection in the field of blasting.Serverless Computing can provide a variety of computing services for people without hardware foundations and rich software development experience,which has aroused people’s interest in how to use it in the field ofmachine learning.In this paper,we design a distributedmachine learning training application based on the AWS Lambda platform.Based on data parallelism,the data aggregation and training synchronization in Function as a Service(FaaS)are effectively realized.It also encrypts the data set,effectively reducing the risk of data leakage.We rent a cloud server and a Lambda,and then we conduct experiments to evaluate our applications.Our results indicate the effectiveness,rapidity,and economy of distributed training on FaaS. 展开更多
关键词 Serverless computing object detection BLASTING
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Analysis of the joint detection capability of the SMILE satellite and EISCAT-3D radar 被引量:1
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作者 JiaoJiao Zhang TianRan Sun +7 位作者 XiZheng Yu DaLin Li Hang Li JiaQi Guo ZongHua Ding Tao Chen Jian Wu Chi Wang 《Earth and Planetary Physics》 EI CSCD 2024年第1期299-306,共8页
The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite is a small magnetosphere–ionosphere link explorer developed cooperatively between China and Europe.It pioneers the use of X-ray imaging technology... The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite is a small magnetosphere–ionosphere link explorer developed cooperatively between China and Europe.It pioneers the use of X-ray imaging technology to perform large-scale imaging of the Earth’s magnetosheath and polar cusp regions.It uses a high-precision ultraviolet imager to image the overall configuration of the aurora and monitor changes in the source of solar wind in real time,using in situ detection instruments to improve human understanding of the relationship between solar activity and changes in the Earth’s magnetic field.The SMILE satellite is scheduled to launch in 2025.The European Incoherent Scatter Sciences Association(EISCAT)-3D radar is a new generation of European incoherent scatter radar constructed by EISCAT and is the most advanced ground-based ionospheric experimental device in the high-latitude polar region.It has multibeam and multidirectional quasi-real-time three-dimensional(3D)imaging capabilities,continuous monitoring and operation capabilities,and multiple-baseline interferometry capabilities.Joint detection by the SMILE satellite and the EISCAT-3D radar is of great significance for revealing the coupling process of the solar wind–magnetosphere–ionosphere.Therefore,we performed an analysis of the joint detection capability of the SMILE satellite and EISCAT-3D,analyzed the period during which the two can perform joint detection,and defined the key scientific problems that can be solved by joint detection.In addition,we developed Web-based software to search for and visualize the joint detection period of the SMILE satellite and EISCAT-3D radar,which lays the foundation for subsequent joint detection experiments and scientific research. 展开更多
关键词 Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite European Incoherent Scatter Sciences Association(EISCAT)-3D radar joint detection
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Finite element model updating for structural damage detection using transmissibility data
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作者 Ahmad Izadi Akbar Esfandiari 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2024年第1期87-101,共15页
This paper presents a new finite element model updating method for estimating structural parameters and detecting structural damage location and severity based on the structural responses(output-only data).The method ... This paper presents a new finite element model updating method for estimating structural parameters and detecting structural damage location and severity based on the structural responses(output-only data).The method uses the sensitivity relation of transmissibility data through a least-squares algorithm and appropriate normalization of the extracted equations.The proposed transmissibility-based sensitivity equation produces a more significant number of equations than the sensitivity equations based on the frequency response function(FRF),which can estimate the structural parameters with higher accuracy.The abilities of the proposed method are assessed by using numerical data of a two-story two-bay frame model and a plate structure model.In evaluating different damage cases,the number,location,and stiffness reduction of the damaged elements and the severity of the simulated damage have been accurately identified.The reliability and stability of the presented method against measurement and modeling errors are examined using error-contaminated data.The parameter estimation results prove the method’s capabilities as an accurate model updating algorithm. 展开更多
关键词 damage detection model updating output-only TRANSMISSIBILITY sensitivity equation
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Microstrip Patch Antenna with an Inverted T-Type Notch in the Partial Ground for Breast Cancer Detections
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作者 Nure Alam Chowdhury Lulu Wang +2 位作者 Md Shazzadul Islam Linxia Gu Mehmet Kaya 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第2期1301-1322,共22页
This study designs a microstrip patch antenna with an inverted T-type notch in the partial ground to detect tumorcells inside the human breast.The size of the current antenna is small enough(18mm×21mm×1.6mm)... This study designs a microstrip patch antenna with an inverted T-type notch in the partial ground to detect tumorcells inside the human breast.The size of the current antenna is small enough(18mm×21mm×1.6mm)todistribute around the breast phantom.The operating frequency has been observed from6–14GHzwith a minimumreturn loss of−61.18 dB and themaximumgain of current proposed antenna is 5.8 dBiwhich is flexiblewith respectto the size of antenna.After the distribution of eight antennas around the breast phantom,the return loss curveswere observed in the presence and absence of tumor cells inside the breast phantom,and these observations showa sharp difference between the presence and absence of tumor cells.The simulated results show that this proposedantenna is suitable for early detection of cancerous cells inside the breast. 展开更多
关键词 Antenna microwave wideband cancer breast phantom tumor detection
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Robust Malicious Executable Detection Using Host-Based Machine Learning Classifier
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作者 Khaled Soliman Mohamed Sobh Ayman MBahaa-Eldin 《Computers, Materials & Continua》 SCIE EI 2024年第4期1419-1439,共21页
The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are ins... The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are insufficientto prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious ExecutableDetection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE)files in hosts using Windows operating systems through collecting PE headers and applying machine learningmechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031benign files and 179,071 malware samples from diverse sources to ensure the efficiency of RMED approach.The most effective PE headers that can highly differentiate between benign and malware files were selected totrain the model on 15 PE features to speed up the classification process and achieve real-time detection formalicious executables. The evaluation results showed that RMED succeeded in shrinking the classification timeto 91 milliseconds for each file while reaching an accuracy of 98.42% with a false positive rate equal to 1.58. Inconclusion, this paper contributes to the field of cybersecurity by presenting a comprehensive framework thatleverages Artificial Intelligence (AI) methods to proactively detect and prevent cyber-attacks. 展开更多
关键词 Portable executable MALWARE intrusion detection CYBERSECURITY zero-day threats Host Intrusiondetection System(HIDS) machine learning Anomaly-based Intrusion detection System(AIDS) deep learning
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Development of a High-throughput Sequencing Platform for Detection of Viral Encephalitis Pathogens Based on Amplicon Sequencing
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作者 ZHANG Ya Li SU Wen Zhe +16 位作者 WANG Rui Chen LI Yan ZHANG Jun Feng LIU Sheng Hui HU Dan He XU Chong Xiao YIN Jia Yu YIN Qi Kai HE Ying LI Fan FU Shi Hong NIE Kai LIANG Guo Dong TAO Yong XU Song Tao MA Chao Feng WANG Huan Yu 《Biomedical and Environmental Sciences》 SCIE CAS CSCD 2024年第3期294-302,共9页
Objective Viral encephalitis is an infectious disease severely affecting human health.It is caused by a wide variety of viral pathogens,including herpes viruses,flaviviruses,enteroviruses,and other viruses.The laborat... Objective Viral encephalitis is an infectious disease severely affecting human health.It is caused by a wide variety of viral pathogens,including herpes viruses,flaviviruses,enteroviruses,and other viruses.The laboratory diagnosis of viral encephalitis is a worldwide challenge.Recently,high-throughput sequencing technology has provided new tools for diagnosing central nervous system infections.Thus,In this study,we established a multipathogen detection platform for viral encephalitis based on amplicon sequencing.Methods We designed nine pairs of specific polymerase chain reaction(PCR)primers for the 12 viruses by reviewing the relevant literature.The detection ability of the primers was verified by software simulation and the detection of known positive samples.Amplicon sequencing was used to validate the samples,and consistency was compared with Sanger sequencing.Results The results showed that the target sequences of various pathogens were obtained at a coverage depth level greater than 20×,and the sequence lengths were consistent with the sizes of the predicted amplicons.The sequences were verified using the National Center for Biotechnology Information BLAST,and all results were consistent with the results of Sanger sequencing.Conclusion Amplicon-based high-throughput sequencing technology is feasible as a supplementary method for the pathogenic detection of viral encephalitis.It is also a useful tool for the high-volume screening of clinical samples. 展开更多
关键词 Viral encephalitis Amplicon sequencing High-throughput sequencing Multipathogen detection
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Overview of radar detection methods for low altitude targets in marine environments
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作者 YANG Yong YANG Boyu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2024年第1期1-13,共13页
In this paper,a comprehensive overview of radar detection methods for low-altitude targets in maritime environments is presented,focusing on the challenges posed by sea clutter and multipath scattering.The performance... In this paper,a comprehensive overview of radar detection methods for low-altitude targets in maritime environments is presented,focusing on the challenges posed by sea clutter and multipath scattering.The performance of the radar detection methods under sea clutter,multipath,and combined conditions is categorized and summarized,and future research directions are outlined to enhance radar detection performance for low-altitude targets in maritime environments. 展开更多
关键词 RADAR sea clutter multipath scattering detection low altitude target
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Real-Time Detection and Instance Segmentation of Strawberry in Unstructured Environment
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作者 Chengjun Wang Fan Ding +4 位作者 Yiwen Wang Renyuan Wu Xingyu Yao Chengjie Jiang Liuyi Ling 《Computers, Materials & Continua》 SCIE EI 2024年第1期1481-1501,共21页
The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-r... The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-ronment is a challenging task.Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy.To this end,the present study proposes an Efficient YOLACT(E-YOLACT)algorithm for strawberry detection and segmentation based on the YOLACT framework.The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism,pyramid squeeze shuffle attention(PSSA),for efficient feature extraction.Additionally,an attention-guided context-feature pyramid network(AC-FPN)is employed instead of FPN to optimize the architecture’s performance.Furthermore,a feature-enhanced model(FEM)is introduced to enhance the prediction head’s capabilities,while efficient fast non-maximum suppression(EF-NMS)is devised to improve non-maximum suppression.The experimental results demonstrate that the E-YOLACT achieves a Box-mAP and Mask-mAP of 77.9 and 76.6,respectively,on the custom dataset.Moreover,it exhibits an impressive category accuracy of 93.5%.Notably,the E-YOLACT also demonstrates a remarkable real-time detection capability with a speed of 34.8 FPS.The method proposed in this article presents an efficient approach for the vision system of a strawberry-picking robot. 展开更多
关键词 YOLACT real-time detection instance segmentation attention mechanism STRAWBERRY
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YOLO-DD:Improved YOLOv5 for Defect Detection
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作者 Jinhai Wang Wei Wang +4 位作者 Zongyin Zhang Xuemin Lin Jingxian Zhao Mingyou Chen Lufeng Luo 《Computers, Materials & Continua》 SCIE EI 2024年第1期759-780,共22页
As computer technology continues to advance,factories have increasingly higher demands for detecting defects.However,detecting defects in a plant environment remains a challenging task due to the presence of complex b... As computer technology continues to advance,factories have increasingly higher demands for detecting defects.However,detecting defects in a plant environment remains a challenging task due to the presence of complex backgrounds and defects of varying shapes and sizes.To address this issue,this paper proposes YOLO-DD,a defect detectionmodel based on YOLOv5 that is effective and robust.To improve the feature extraction process and better capture global information,the vanilla YOLOv5 is augmented with a new module called Relative-Distance-Aware Transformer(RDAT).Additionally,an Information Gap Filling Strategy(IGFS)is proposed to improve the fusion of features at different scales.The classic lightweight attention mechanism Squeeze-and-Excitation(SE)module is also incorporated into the neck section to enhance feature expression and improve the model’s performance.Experimental results on the NEU-DET dataset demonstrate that YOLO-DDachieves competitive results compared to state-of-the-art methods,with a 2.0% increase in accuracy compared to the original YOLOv5,achieving 82.41% accuracy and38.25FPS(framesper second).Themodel is also testedon a self-constructed fabric defect dataset,and the results show that YOLO-DD is more stable and has higher accuracy than the original YOLOv5,demonstrating its stability and generalization ability.The high efficiency of YOLO-DD enables it to meet the requirements of industrial high accuracy and real-time detection. 展开更多
关键词 YOLO-DD defect detection feature fusion attention mechanism
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Learning Discriminatory Information for Object Detection on Urine Sediment Image
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作者 Sixian Chan Binghui Wu +2 位作者 Guodao Zhang Yuan Yao Hongqiang Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第1期411-428,共18页
In clinical practice,the microscopic examination of urine sediment is considered an important in vitro examination with many broad applications.Measuring the amount of each type of urine sediment allows for screening,... In clinical practice,the microscopic examination of urine sediment is considered an important in vitro examination with many broad applications.Measuring the amount of each type of urine sediment allows for screening,diagnosis and evaluation of kidney and urinary tract disease,providing insight into the specific type and severity.However,manual urine sediment examination is labor-intensive,time-consuming,and subjective.Traditional machine learning based object detection methods require hand-crafted features for localization and classification,which have poor generalization capabilities and are difficult to quickly and accurately detect the number of urine sediments.Deep learning based object detection methods have the potential to address the challenges mentioned above,but these methods require access to large urine sediment image datasets.Unfortunately,only a limited number of publicly available urine sediment datasets are currently available.To alleviate the lack of urine sediment datasets in medical image analysis,we propose a new dataset named UriSed2K,which contains 2465 high-quality images annotated with expert guidance.Two main challenges are associated with our dataset:a large number of small objects and the occlusion between these small objects.Our manuscript focuses on applying deep learning object detection methods to the urine sediment dataset and addressing the challenges presented by this dataset.Specifically,our goal is to improve the accuracy and efficiency of the detection algorithm and,in doing so,provide medical professionals with an automatic detector that saves time and effort.We propose an improved lightweight one-stage object detection algorithm called Discriminatory-YOLO.The proposed algorithm comprises a local context attention module and a global background suppression module,which aid the detector in distinguishing urine sediment features in the image.The local context attention module captures context information beyond the object region,while the global background suppression module emphasizes objects in uninformative backgrounds.We comprehensively evaluate our method on the UriSed2K dataset,which includes seven categories of urine sediments,such as erythrocytes(red blood cells),leukocytes(white blood cells),epithelial cells,crystals,mycetes,broken erythrocytes,and broken leukocytes,achieving the best average precision(AP)of 95.3%while taking only 10 ms per image.The source code and dataset are available at https://github.com/binghuiwu98/discriminatoryyolov5. 展开更多
关键词 Object detection attention mechanism medical image urine sediment
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ResNeSt-biGRU: An Intrusion DetectionModel Based on Internet of Things
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作者 Yan Xiang Daofeng Li +2 位作者 XinyiMeng Chengfeng Dong Guanglin Qin 《Computers, Materials & Continua》 SCIE EI 2024年第4期1005-1023,共19页
The rapid expansion of Internet of Things (IoT) devices across various sectors is driven by steadily increasingdemands for interconnected and smart technologies. Nevertheless, the surge in the number of IoT device has... The rapid expansion of Internet of Things (IoT) devices across various sectors is driven by steadily increasingdemands for interconnected and smart technologies. Nevertheless, the surge in the number of IoT device hascaught the attention of cyber hackers, as it provides them with expanded avenues to access valuable data. Thishas resulted in a myriad of security challenges, including information leakage, malware propagation, and financialloss, among others. Consequently, developing an intrusion detection system to identify both active and potentialintrusion traffic in IoT networks is of paramount importance. In this paper, we propose ResNeSt-biGRU, a practicalintrusion detection model that combines the strengths of ResNeSt, a variant of Residual Neural Network, andbidirectionalGated RecurrentUnitNetwork (biGRU).Our ResNeSt-biGRUframework diverges fromconventionalintrusion detection systems (IDS) by employing this dual-layeredmechanism that exploits the temporal continuityand spatial feature within network data streams, a methodological innovation that enhances detection accuracy.In conjunction with this, we introduce the PreIoT dataset, a compilation of prevalent IoT network behaviors, totrain and evaluate IDSmodels with a focus on identifying potential intrusion traffics. The effectiveness of proposedscheme is demonstrated through testing, wherein it achieved an average accuracy of 99.90% on theN-BaIoT datasetas well as on the PreIoT dataset and 94.45% on UNSW-NB15 dataset. The outcomes of this research reveal thepotential of ResNeSt-biGRU to bolster security measures, diminish intrusion-related vulnerabilities, and preservethe overall security of IoT ecosystems. 展开更多
关键词 Internet of Things cyberattack intrusion detection internet security
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