Visible and near-infrared photodetectors are widely used in intelligent driving,health monitoring,and other fields.However,the application of photodetectors in the near-infrared region is significantly impacted by hig...Visible and near-infrared photodetectors are widely used in intelligent driving,health monitoring,and other fields.However,the application of photodetectors in the near-infrared region is significantly impacted by high dark current,which can greatly reduce their performance and sensitivity,thereby limiting their effectiveness in certain applications.In this work,the introduction of a C60 back interface layer successfully mitigated back interface reactions to decrease the thickness of the Mo(S,Se)_(2)layer,tailoring the back-contact barrier and preventing reverse charge injection,resulting in a kesterite photodetector with an ultralow dark current density of 5.2×10^(-9)mA/cm^(2)and ultra-weak-light detection at levels as low as 25 pW/cm^(2).Besides,under a self-powered operation,it demonstrates outstanding performance,achieving a peak responsivity of 0.68 A/W,a wide response range spanning from 300 to 1600 nm,and an impressive detectivity of 5.27×10^(14)Jones.In addition,it offers exceptionally rapid response times,with rise and decay times of 70 and 650 ns,respectively.This research offers important insights for developing high-performance self-powered near-infrared photodetectors that have high responsivity,rapid response times,and ultralow dark current.展开更多
Various novel conjugated polymers(CPs)have been developed for organic photodetectors(OPDs),but their application to practical image sensors such as X-ray,R/G/B,and fingerprint sensors is rare.In this article,we report...Various novel conjugated polymers(CPs)have been developed for organic photodetectors(OPDs),but their application to practical image sensors such as X-ray,R/G/B,and fingerprint sensors is rare.In this article,we report the entire process from the synthesis and molecular engineering of novel CPs to the development of OPDs and fingerprint image sensors.We synthesized six benzo[1,2-d:4,5-d’]bis(oxazole)(BBO)-based CPs by modifying the alkyl side chains of the CPs.Several relationships between the molecular structure and the OPD performance were revealed,and increasing the number of linear octyl side chains on the conjugated backbone was the best way to improve Jph and reduce Jd in the OPDs.The optimized CP demonstrated promising OPD performance with a responsivity(R)of 0.22 A/W,specific detectivity(D^(*))of 1.05×10^(13)Jones at a bias of-1 V,rising/falling response time of 2.9/6.9μs,and cut-off frequency(f_(-3dB))of 134 kHz under collimated 530 nm LED irradiation.Finally,a fingerprint image sensor was fabricated by stacking the POTB1-based OPD layer on the organic thin-film transistors(318 ppi).The image contrast caused by the valleys and ridges in the fingerprints was obtained as a digital signal.展开更多
Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are...Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.展开更多
Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Num...Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.展开更多
Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable...Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.展开更多
With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a c...With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a convenient and visual technique with low equipment requirements and high sensitivity for the field detection of GM plants is still lacking.On the basis of the existing recombinase polymerase amplification(RPA)technique,we developed a multiplex RPA(multi-RPA)method that can simultaneously detect three transgenic elements,including the cauliflower mosaic virus 35S gene(CaMV35S)promoter,neomycin phosphotransferaseⅡgene(NptⅡ)and hygromycin B phosphotransferase gene(Hyg),thus improving the detection rate.Moreover,we coupled this multi-RPA technique with the CRISPR/Cas12a reporter system,which enabled the detection results to be clearly observed by naked eyes under ultraviolet(UV)light(254 nm;which could be achieved by a portable UV flashlight),therefore establishing a multi-RPA visual detection technique.Compared with the traditional test strip detection method,this multi-RPA-CRISPR/Cas12a technique has the higher specificity,higher sensitivity,wider application range and lower cost.Compared with other polymerase chain reaction(PCR)techniques,it also has the advantages of low equipment requirements and visualization,making it a potentially feasible method for the field detection of GM plants.展开更多
Kesterite Cu_(2)ZnSn(S,Se)_(4)(CZTSSe)is a promising candidate for photodetector(PD)applications thanks to its excellent optoelectronic properties.In this work,a green solution-processed spin coating and selenization-...Kesterite Cu_(2)ZnSn(S,Se)_(4)(CZTSSe)is a promising candidate for photodetector(PD)applications thanks to its excellent optoelectronic properties.In this work,a green solution-processed spin coating and selenization-processed thermodynamic or kinetic growth of high-quality narrow bandgap kesterite CZTSSe thin film is developed.A self-powered CZTSSe/CdS thin-film PD is then successfully fabricated.Under optimization of light absorber and heterojunction interface,especially tailoring the defect and carrier kinetics,it can achieve broadband response from300 to 1300 nm,accompaniedwith a high responsivity of 1.37A/W,specific detectivity(D*)up to 4.0×10^(14)Jones under 5 nW/cm^(2),a linear dynamic range(LDR)of 126 dB,and a maximum Ilight/Idark ratio of 1.3×10^(8)within the LDR,and ultrafast response speed(rise/decay time of 16 ns/85 ns),representing the leading-level performance to date,which is superior to those of commercial andwell-researched photodiodes.Additionally,an imaging system with a 905nm laser is built for weak light response evaluation,and can respond to 718 pW weak light and infrared imaging at a wavelength as low as 5 nW/cm2.It has also been employed for photoplethysmography detection of pulsating signals at both the finger and wrist,presenting obvious arterial blood volume changes,demonstrating great application potential in broadband and weak light photodetection scenarios.展开更多
In the practice of healthcare,patient-reported outcomes(PROs)and PRO measures(PROMs)are used as an attempt to observe the changes in complex clinical situations.They guide us in making decisions based on the evidence ...In the practice of healthcare,patient-reported outcomes(PROs)and PRO measures(PROMs)are used as an attempt to observe the changes in complex clinical situations.They guide us in making decisions based on the evidence regarding patient care by recording the change in outcomes for a particular treatment to a given condition and finally to understand whether a patient will benefit from a particular treatment and to quantify the treatment effect.For any PROM to be usable in health care,we need it to be reliable,encapsulating the points of interest with the potential to detect any real change.Using structured outcome measures routinely in clinical practice helps the physician to understand the functional limitation of a patient that would otherwise not be clear in an office interview,and this allows the physician and patient to have a meaningful conver-sation as well as a customized plan for each patient.Having mentioned the rationale and the benefits of PROMs,understanding the quantification process is crucial before embarking on management decisions.A better interpretation of change needs to identify the treatment effect based on clinical relevance for a given condition.There are a multiple set of measurement indices to serve this effect and most of them are used interchangeably without clear demarcation on their differences.This article details the various quantification metrics used to evaluate the treatment effect using PROMs,their limitations and the scope of usage and implementation in clinical practice.展开更多
In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,par...In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.展开更多
The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approac...The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum(Al^(3+))and fluoride(F^(−))ions in aqueous solutions.The proposed method involves the synthesis of sulfur-functionalized carbon dots(C-dots)as fluorescence probes,with fluorescence enhancement upon interaction with Al^(3+)ions,achieving a detection limit of 4.2 nmol/L.Subsequently,in the presence of F^(−)ions,fluorescence is quenched,with a detection limit of 47.6 nmol/L.The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python,followed by data preprocessing.Subsequently,the fingerprint data is subjected to cluster analysis using the K-means model from machine learning,and the average Silhouette Coefficient indicates excellent model performance.Finally,a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions.The results demonstrate that the developed model excels in terms of accuracy and sensitivity.This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment,making it a valuable tool for safeguarding our ecosystems and public health.展开更多
OBJECTIVE:To propose an automatic acupuncture robot system for performing acupuncture operations.METHODS:The acupuncture robot system consists of three components:automatic acupoint localization,acupuncture manipulati...OBJECTIVE:To propose an automatic acupuncture robot system for performing acupuncture operations.METHODS:The acupuncture robot system consists of three components:automatic acupoint localization,acupuncture manipulations,and De Qi sensation detection.The OptiTrack motion capture system is used to locate acupoints,which are then translated into coordinates in the robot control system.A flexible collaborative robot with an intelligent gripper is then used to perform acupuncture manipulations with high precision.In addition,a De Qi sensation detection system is proposed to evaluate the effect of acupuncture.To verify the stability of the designed acupuncture robot,acupoints'coordinates localized by the acupuncture robot are compared with the Gold Standard labeled by a professional acupuncturist using significant level tests.RESULTS:Through repeated experiments for eight acupoints,the acupuncture robot achieved a positioning error within 3.3 mm,which is within the allowable range of needle extraction and acupoint insertion.During needle insertion,the robot arm followed the prescribed trajectory with a mean deviation distance of 0.02 mm and a deviation angle of less than 0.15°.The results of the lifting thrusting operation in the Xingzhen process show that the mean acupuncture depth error of the designed acupuncture robot is approximately 2 mm,which is within the recommended depth range for the Xingzhen operation.In addition,the average detection accuracy of the De Qi keywords is 94.52%,which meets the requirements of acupuncture effect testing for different dialects.CONCLUSION:The proposed acupuncture robot system streamlines the acupuncture process,increases efficiency,and reduces practitioner fatigue,while also allowing for the quantification of acupuncture manipulations and evaluation of therapeutic effects.The development of an acupuncture robot system has the potential to revolutionize low back pain treatment and improve patient outcomes.展开更多
Aircraft disturbs the adjacent atmospheric environment in flight,forming spatial distribution features of atmospheric density that differ from the natural background,which may potentially be utilized as tracer charact...Aircraft disturbs the adjacent atmospheric environment in flight,forming spatial distribution features of atmospheric density that differ from the natural background,which may potentially be utilized as tracer characteristics to introduce new technologies for indirectly sensing the presence of aircraft.In this paper,the concept of a long-range aircraft detection based on the atmospheric disturbance density field is proposed,and the detection mode of tomographic imaging of the scattering light of an atmospheric disturbance flow field is designed.By modeling the spatial distribution of the disturbance density field,the scattered echo signal images of active light towards the disturbance field at long distance are simulated.On this basis,the characteristics of the disturbance optical signal at the optimal detection resolution are analyzed.The results show that the atmospheric disturbance flow field of the supersonic aircraft presents circular in the light-scattering echo images.The disturbance signal can be further highlighted by differential processing of the adjacent scattering images.As the distance behind the aircraft increases,the diffusion range of the disturbance signal increases,and the signal intensity and contrast with the background decrease.Under the ground-based observation conditions of the aircraft at a height of 10000 m,a Mach number of1.6,and a detection distance of 100 km,the contrast between the disturbance signal and the back-ground was 30 d B at a distance of one time from the rear of the fuselage,and the diffusion diameter of the disturbance signal was 50 m.At a distance eight times the length of the aircraft,the contrast decreased to 10 dB,and the diameter increased to 290 m.The contrast was reduced to 3 dB at a distance nine times the length of the aircraft,and the diameter was diffused to 310 m.These results indicate the possibility of long-range aircraft detection based on the characteristics of the atmospheric density field.展开更多
Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time perfor...Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.展开更多
An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyram...An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.展开更多
Liver cancer,particularly hepatocellular carcinoma(HCC),remains a significant global health challenge due to its high mortality rate and late-stage diagnosis.The discovery of reliable biomarkers is crucial for improvi...Liver cancer,particularly hepatocellular carcinoma(HCC),remains a significant global health challenge due to its high mortality rate and late-stage diagnosis.The discovery of reliable biomarkers is crucial for improving early detection and patient outcomes.This review provides a comprehensive overview of current and emerging biomarkers for HCC,including alpha-fetoprotein,des-gamma-carboxy prothrombin,glypican-3,Golgi protein 73,osteopontin,and microRNAs.Despite advancements,the diagnostic limitations of existing biomarkers underscore the urgent need for novel markers that can detect HCC in its early stages.The review emphasizes the importance of integrating multi-omics approaches,combining genomics,proteomics,and metabolomics,to develop more robust biomarker panels.Such integrative methods have the potential to capture the complex molecular landscape of HCC,offering insights into disease mechanisms and identifying targets for personalized therapies.The significance of large-scale validation studies,collaboration between research institutions and clinical settings,and consideration of regulatory pathways for clinical implementation is also discussed.In conclusion,while substantial progress has been made in biomarker discovery,continued research and innovation are essential to address the remaining challenges.The successful translation of these discoveries into clinical practice will require rigorous validation,standardization of protocols,and crossdisciplinary collaboration.By advancing the development and application of novel biomarkers,we can improve the early detection and management of HCC,ultimately enhancing patient survival and quality of life.展开更多
Plants play a crucial role in maintaining ecological balance and biodiversity.However,plant health is easily affected by environmental stresses.Hence,the rapid and precise monitoring of plant health is crucial for glo...Plants play a crucial role in maintaining ecological balance and biodiversity.However,plant health is easily affected by environmental stresses.Hence,the rapid and precise monitoring of plant health is crucial for global food security and ecological balance.Currently,traditional detection strategies for monitoring plant health mainly rely on expensive equipment and complex operational procedures,which limit their widespread application.Fortunately,near-infrared(NIR)fluorescence and surface-enhanced Raman scattering(SERS)techniques have been recently highlighted in plants.NIR fluorescence imaging holds the advantages of being non-invasive,high-resolution and real-time,which is suitable for rapid screening in large-scale scenarios.While SERS enables highly sensitive and specific detection of trace chemical substances within plant tissues.Therefore,the complementarity of NIR fluorescence and SERS modalities can provide more comprehensive and accurate information for plant disease diagnosis and growth status monitoring.This article summarizes these two modalities in plant applications,and discusses the advantages of multimodal NIR fluorescence/SERS for a better understanding of a plant’s response to stress,thereby improving the accuracy and sensitivity of detection.展开更多
Tomato plant diseases often first manifest on the leaves,making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry.However,conventional deep learning models face challenges ...Tomato plant diseases often first manifest on the leaves,making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry.However,conventional deep learning models face challenges such as large model sizes and slow detection speeds when deployed on resource-constrained platforms and agricultural machinery.This paper proposes a lightweight model for detecting tomato leaf diseases,named LT-YOLO,based on the YOLOv8n architecture.First,we enhance the C2f module into a RepViT Block(RVB)with decoupled token and channel mixers to reduce the cost of feature extraction.Next,we incorporate a novel Efficient Multi-Scale Attention(EMA)mechanism in the deeper layers of the backbone to improve detection of critical disease features.Additionally,we design a lightweight detection head,LT-Detect,using Partial Convolution(PConv)to significantly reduce the classification and localization costs during detection.Finally,we introduce a Receptive Field Block(RFB)in the shallow layers of the backbone to expand the model’s receptive field,enabling effective detection of diseases at various scales.The improved model reduces the number of parameters by 43%and the computational load by 50%.Additionally,it achieves a mean Average Precision(mAP)of 90.9%on a publicly available dataset containing 3641 images of tomato leaf diseases,with only a 0.7%decrease compared to the baseline model.This demonstrates that the model maintains excellent accuracy while being lightweight,making it suitable for rapid detection of tomato leaf diseases.展开更多
The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more e...The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.展开更多
Highly sensitive gas sensors play an important role in applications,such as environmental monitoring,medical diagnostics and food testing.This paper reviews recent advances in metal-doped and noble metal-decorated che...Highly sensitive gas sensors play an important role in applications,such as environmental monitoring,medical diagnostics and food testing.This paper reviews recent advances in metal-doped and noble metal-decorated chemo-resistive gas sensors with different nanostructures(ZnO,SnO_(2),In_(2)O_(3)and Fe_(2)O_(3)).It mainly includes the doping of metals such as Al,Fe and Cu,and the modification of noble metals such as Pd,Pt and Au,and introduces the bimetallic-modified materials possessing greater advantages than single metals in enhancing gas-sensitive performance.The results and problems of room-temperature detection of perovskite and metal oxide composite structural materials are also discussed.In addition,the potential applications of micro-electro-mechanical system(MEMS)gas sensing arrays and electronic nose smart sensing devices in disease diagnosis and environmental monitoring are presented through their limitations and development trends in areas such as smart homes.Finally,the main challenges and future prospects of metal oxide gas sensors are presented.展开更多
Anomaly Detection (AD) has been extensively adopted in industrial settings to facilitate quality control of products. It is critical to industrial production, especially to areas such as aircraft manufacturing, which ...Anomaly Detection (AD) has been extensively adopted in industrial settings to facilitate quality control of products. It is critical to industrial production, especially to areas such as aircraft manufacturing, which require strict part qualification rates. Although being more efficient and practical, few-shot AD has not been well explored. The existing AD methods only extract features in a single frequency while defects exist in multiple frequency domains. Moreover, current methods have not fully leveraged the few-shot support samples to extract input-related normal patterns. To address these issues, we propose an industrial few-shot AD method, Feature Extender for Anomaly Detection (FEAD), which extracts normal patterns in multiple frequency domains from few-shot samples under the guidance of the input sample. Firstly, to achieve better coverage of normal patterns in the input sample, we introduce a Sample-Conditioned Transformation Module (SCTM), which transforms support features under the guidance of the input sample to obtain extra normal patterns. Secondly, to effectively distinguish and localize anomaly patterns in multiple frequency domains, we devise an Adaptive Descriptor Construction Module (ADCM) to build and select pattern descriptors in a series of frequencies adaptively. Finally, an auxiliary task for SCTM is designed to ensure the diversity of transformations and include more normal patterns into support features. Extensive experiments on two widely used industrial AD datasets (MVTec-AD and VisA) demonstrate the effectiveness of the proposed FEAD.展开更多
基金supported by the National Natural Science Foundation of China(No.52472225)the Science and Technology Plan Project of Shenzhen(No.20220808165025003),China。
文摘Visible and near-infrared photodetectors are widely used in intelligent driving,health monitoring,and other fields.However,the application of photodetectors in the near-infrared region is significantly impacted by high dark current,which can greatly reduce their performance and sensitivity,thereby limiting their effectiveness in certain applications.In this work,the introduction of a C60 back interface layer successfully mitigated back interface reactions to decrease the thickness of the Mo(S,Se)_(2)layer,tailoring the back-contact barrier and preventing reverse charge injection,resulting in a kesterite photodetector with an ultralow dark current density of 5.2×10^(-9)mA/cm^(2)and ultra-weak-light detection at levels as low as 25 pW/cm^(2).Besides,under a self-powered operation,it demonstrates outstanding performance,achieving a peak responsivity of 0.68 A/W,a wide response range spanning from 300 to 1600 nm,and an impressive detectivity of 5.27×10^(14)Jones.In addition,it offers exceptionally rapid response times,with rise and decay times of 70 and 650 ns,respectively.This research offers important insights for developing high-performance self-powered near-infrared photodetectors that have high responsivity,rapid response times,and ultralow dark current.
基金funded by the National Research Foundation(NRF)of Korea(2020M3H4A3081816,RS-2023-00304936,and RS-2024-00398065).
文摘Various novel conjugated polymers(CPs)have been developed for organic photodetectors(OPDs),but their application to practical image sensors such as X-ray,R/G/B,and fingerprint sensors is rare.In this article,we report the entire process from the synthesis and molecular engineering of novel CPs to the development of OPDs and fingerprint image sensors.We synthesized six benzo[1,2-d:4,5-d’]bis(oxazole)(BBO)-based CPs by modifying the alkyl side chains of the CPs.Several relationships between the molecular structure and the OPD performance were revealed,and increasing the number of linear octyl side chains on the conjugated backbone was the best way to improve Jph and reduce Jd in the OPDs.The optimized CP demonstrated promising OPD performance with a responsivity(R)of 0.22 A/W,specific detectivity(D^(*))of 1.05×10^(13)Jones at a bias of-1 V,rising/falling response time of 2.9/6.9μs,and cut-off frequency(f_(-3dB))of 134 kHz under collimated 530 nm LED irradiation.Finally,a fingerprint image sensor was fabricated by stacking the POTB1-based OPD layer on the organic thin-film transistors(318 ppi).The image contrast caused by the valleys and ridges in the fingerprints was obtained as a digital signal.
基金supported by the Ministry of Science and Technology of China,No.2020AAA0109605(to XL)Meizhou Major Scientific and Technological Innovation PlatformsProjects of Guangdong Provincial Science & Technology Plan Projects,No.2019A0102005(to HW).
文摘Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.
文摘Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.
文摘Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.
基金the Experimental Technology Research Project of Zhejiang University(SYB202138)National Natural Science Foundation of China(32000195)。
文摘With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a convenient and visual technique with low equipment requirements and high sensitivity for the field detection of GM plants is still lacking.On the basis of the existing recombinase polymerase amplification(RPA)technique,we developed a multiplex RPA(multi-RPA)method that can simultaneously detect three transgenic elements,including the cauliflower mosaic virus 35S gene(CaMV35S)promoter,neomycin phosphotransferaseⅡgene(NptⅡ)and hygromycin B phosphotransferase gene(Hyg),thus improving the detection rate.Moreover,we coupled this multi-RPA technique with the CRISPR/Cas12a reporter system,which enabled the detection results to be clearly observed by naked eyes under ultraviolet(UV)light(254 nm;which could be achieved by a portable UV flashlight),therefore establishing a multi-RPA visual detection technique.Compared with the traditional test strip detection method,this multi-RPA-CRISPR/Cas12a technique has the higher specificity,higher sensitivity,wider application range and lower cost.Compared with other polymerase chain reaction(PCR)techniques,it also has the advantages of low equipment requirements and visualization,making it a potentially feasible method for the field detection of GM plants.
基金National Natural Science Foundation of China,Grant/Award Numbers:62074102,62104156,21961160720Open Research Fund of Songshan Lake Materials Laboratory,Grant/Award Number:2021SLABFK02+1 种基金Guangdong Basic and Applied Basic Research Foundation,Grant/Award Numbers:2022A1515010979,2023A1515011256Science and Technology Plan Project of Shenzhen,Grant/Award。
文摘Kesterite Cu_(2)ZnSn(S,Se)_(4)(CZTSSe)is a promising candidate for photodetector(PD)applications thanks to its excellent optoelectronic properties.In this work,a green solution-processed spin coating and selenization-processed thermodynamic or kinetic growth of high-quality narrow bandgap kesterite CZTSSe thin film is developed.A self-powered CZTSSe/CdS thin-film PD is then successfully fabricated.Under optimization of light absorber and heterojunction interface,especially tailoring the defect and carrier kinetics,it can achieve broadband response from300 to 1300 nm,accompaniedwith a high responsivity of 1.37A/W,specific detectivity(D*)up to 4.0×10^(14)Jones under 5 nW/cm^(2),a linear dynamic range(LDR)of 126 dB,and a maximum Ilight/Idark ratio of 1.3×10^(8)within the LDR,and ultrafast response speed(rise/decay time of 16 ns/85 ns),representing the leading-level performance to date,which is superior to those of commercial andwell-researched photodiodes.Additionally,an imaging system with a 905nm laser is built for weak light response evaluation,and can respond to 718 pW weak light and infrared imaging at a wavelength as low as 5 nW/cm2.It has also been employed for photoplethysmography detection of pulsating signals at both the finger and wrist,presenting obvious arterial blood volume changes,demonstrating great application potential in broadband and weak light photodetection scenarios.
文摘In the practice of healthcare,patient-reported outcomes(PROs)and PRO measures(PROMs)are used as an attempt to observe the changes in complex clinical situations.They guide us in making decisions based on the evidence regarding patient care by recording the change in outcomes for a particular treatment to a given condition and finally to understand whether a patient will benefit from a particular treatment and to quantify the treatment effect.For any PROM to be usable in health care,we need it to be reliable,encapsulating the points of interest with the potential to detect any real change.Using structured outcome measures routinely in clinical practice helps the physician to understand the functional limitation of a patient that would otherwise not be clear in an office interview,and this allows the physician and patient to have a meaningful conver-sation as well as a customized plan for each patient.Having mentioned the rationale and the benefits of PROMs,understanding the quantification process is crucial before embarking on management decisions.A better interpretation of change needs to identify the treatment effect based on clinical relevance for a given condition.There are a multiple set of measurement indices to serve this effect and most of them are used interchangeably without clear demarcation on their differences.This article details the various quantification metrics used to evaluate the treatment effect using PROMs,their limitations and the scope of usage and implementation in clinical practice.
文摘In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.
基金supported by the National Natural Science Foundation of China(No.U21A20290)Guangdong Basic and Applied Basic Research Foundation(No.2022A1515011656)+2 种基金the Projects of Talents Recruitment of GDUPT(No.2023rcyj1003)the 2022“Sail Plan”Project of Maoming Green Chemical Industry Research Institute(No.MMGCIRI2022YFJH-Y-024)Maoming Science and Technology Project(No.2023382).
文摘The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum(Al^(3+))and fluoride(F^(−))ions in aqueous solutions.The proposed method involves the synthesis of sulfur-functionalized carbon dots(C-dots)as fluorescence probes,with fluorescence enhancement upon interaction with Al^(3+)ions,achieving a detection limit of 4.2 nmol/L.Subsequently,in the presence of F^(−)ions,fluorescence is quenched,with a detection limit of 47.6 nmol/L.The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python,followed by data preprocessing.Subsequently,the fingerprint data is subjected to cluster analysis using the K-means model from machine learning,and the average Silhouette Coefficient indicates excellent model performance.Finally,a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions.The results demonstrate that the developed model excels in terms of accuracy and sensitivity.This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment,making it a valuable tool for safeguarding our ecosystems and public health.
基金Modernization of Traditional Chinese Medicine Project of National Key R&D Program of China:The construction of the theoretical system of Traditional Chinese Medicine nonpharmacological therapy based on body surface stimulation(2023YFC3502704)Sichuan Provincial Science and Technology Program Project:Research and Development of Chinese Medicine Intelligent Tongue Diagnosis Equipment for Digestive System Chinese Medicine Advantageous Diseases(2023YFS0327)+2 种基金Research and Development of Chinese Medicine Intelligent Detection System for Intestinal Functions(2024YFFK0044)Research and Application of Chinese Medicine Diagnosis and Treatment Program for Herpes Zoster Treated by Shu Pai Fire Acupuncture(2024YFFK0089)Major Research and Development Project of The China Academy of Chinese Medical Sciences Innovation:Construction and application of the theoretical research mode of Traditional Chinese Medicine diagnosis and treatment of modern diseases(CI2021A00104)。
文摘OBJECTIVE:To propose an automatic acupuncture robot system for performing acupuncture operations.METHODS:The acupuncture robot system consists of three components:automatic acupoint localization,acupuncture manipulations,and De Qi sensation detection.The OptiTrack motion capture system is used to locate acupoints,which are then translated into coordinates in the robot control system.A flexible collaborative robot with an intelligent gripper is then used to perform acupuncture manipulations with high precision.In addition,a De Qi sensation detection system is proposed to evaluate the effect of acupuncture.To verify the stability of the designed acupuncture robot,acupoints'coordinates localized by the acupuncture robot are compared with the Gold Standard labeled by a professional acupuncturist using significant level tests.RESULTS:Through repeated experiments for eight acupoints,the acupuncture robot achieved a positioning error within 3.3 mm,which is within the allowable range of needle extraction and acupoint insertion.During needle insertion,the robot arm followed the prescribed trajectory with a mean deviation distance of 0.02 mm and a deviation angle of less than 0.15°.The results of the lifting thrusting operation in the Xingzhen process show that the mean acupuncture depth error of the designed acupuncture robot is approximately 2 mm,which is within the recommended depth range for the Xingzhen operation.In addition,the average detection accuracy of the De Qi keywords is 94.52%,which meets the requirements of acupuncture effect testing for different dialects.CONCLUSION:The proposed acupuncture robot system streamlines the acupuncture process,increases efficiency,and reduces practitioner fatigue,while also allowing for the quantification of acupuncture manipulations and evaluation of therapeutic effects.The development of an acupuncture robot system has the potential to revolutionize low back pain treatment and improve patient outcomes.
文摘Aircraft disturbs the adjacent atmospheric environment in flight,forming spatial distribution features of atmospheric density that differ from the natural background,which may potentially be utilized as tracer characteristics to introduce new technologies for indirectly sensing the presence of aircraft.In this paper,the concept of a long-range aircraft detection based on the atmospheric disturbance density field is proposed,and the detection mode of tomographic imaging of the scattering light of an atmospheric disturbance flow field is designed.By modeling the spatial distribution of the disturbance density field,the scattered echo signal images of active light towards the disturbance field at long distance are simulated.On this basis,the characteristics of the disturbance optical signal at the optimal detection resolution are analyzed.The results show that the atmospheric disturbance flow field of the supersonic aircraft presents circular in the light-scattering echo images.The disturbance signal can be further highlighted by differential processing of the adjacent scattering images.As the distance behind the aircraft increases,the diffusion range of the disturbance signal increases,and the signal intensity and contrast with the background decrease.Under the ground-based observation conditions of the aircraft at a height of 10000 m,a Mach number of1.6,and a detection distance of 100 km,the contrast between the disturbance signal and the back-ground was 30 d B at a distance of one time from the rear of the fuselage,and the diffusion diameter of the disturbance signal was 50 m.At a distance eight times the length of the aircraft,the contrast decreased to 10 dB,and the diameter increased to 290 m.The contrast was reduced to 3 dB at a distance nine times the length of the aircraft,and the diameter was diffused to 310 m.These results indicate the possibility of long-range aircraft detection based on the characteristics of the atmospheric density field.
基金supported by the National Key Research and Development Project of China(No.2023YFB3709605)the National Natural Science Foundation of China(No.62073193)the National College Student Innovation Training Program(No.202310422122)。
文摘Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.
基金supported by the National Natural Science Foundation of China(No.62241109)the Tianjin Science and Technology Commissioner Project(No.20YDTPJC01110)。
文摘An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.
文摘Liver cancer,particularly hepatocellular carcinoma(HCC),remains a significant global health challenge due to its high mortality rate and late-stage diagnosis.The discovery of reliable biomarkers is crucial for improving early detection and patient outcomes.This review provides a comprehensive overview of current and emerging biomarkers for HCC,including alpha-fetoprotein,des-gamma-carboxy prothrombin,glypican-3,Golgi protein 73,osteopontin,and microRNAs.Despite advancements,the diagnostic limitations of existing biomarkers underscore the urgent need for novel markers that can detect HCC in its early stages.The review emphasizes the importance of integrating multi-omics approaches,combining genomics,proteomics,and metabolomics,to develop more robust biomarker panels.Such integrative methods have the potential to capture the complex molecular landscape of HCC,offering insights into disease mechanisms and identifying targets for personalized therapies.The significance of large-scale validation studies,collaboration between research institutions and clinical settings,and consideration of regulatory pathways for clinical implementation is also discussed.In conclusion,while substantial progress has been made in biomarker discovery,continued research and innovation are essential to address the remaining challenges.The successful translation of these discoveries into clinical practice will require rigorous validation,standardization of protocols,and crossdisciplinary collaboration.By advancing the development and application of novel biomarkers,we can improve the early detection and management of HCC,ultimately enhancing patient survival and quality of life.
基金funded by the National Natural Science Foundation of China(Nos.22374055,22022404,22074050,82172055)the National Natural Science Foundation of Hubei Province(No.22022CFA033)the Fundamental Research Funds for the Central Universities(Nos.CCNU24JCPT001,CCNU24JCPT020)。
文摘Plants play a crucial role in maintaining ecological balance and biodiversity.However,plant health is easily affected by environmental stresses.Hence,the rapid and precise monitoring of plant health is crucial for global food security and ecological balance.Currently,traditional detection strategies for monitoring plant health mainly rely on expensive equipment and complex operational procedures,which limit their widespread application.Fortunately,near-infrared(NIR)fluorescence and surface-enhanced Raman scattering(SERS)techniques have been recently highlighted in plants.NIR fluorescence imaging holds the advantages of being non-invasive,high-resolution and real-time,which is suitable for rapid screening in large-scale scenarios.While SERS enables highly sensitive and specific detection of trace chemical substances within plant tissues.Therefore,the complementarity of NIR fluorescence and SERS modalities can provide more comprehensive and accurate information for plant disease diagnosis and growth status monitoring.This article summarizes these two modalities in plant applications,and discusses the advantages of multimodal NIR fluorescence/SERS for a better understanding of a plant’s response to stress,thereby improving the accuracy and sensitivity of detection.
文摘Tomato plant diseases often first manifest on the leaves,making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry.However,conventional deep learning models face challenges such as large model sizes and slow detection speeds when deployed on resource-constrained platforms and agricultural machinery.This paper proposes a lightweight model for detecting tomato leaf diseases,named LT-YOLO,based on the YOLOv8n architecture.First,we enhance the C2f module into a RepViT Block(RVB)with decoupled token and channel mixers to reduce the cost of feature extraction.Next,we incorporate a novel Efficient Multi-Scale Attention(EMA)mechanism in the deeper layers of the backbone to improve detection of critical disease features.Additionally,we design a lightweight detection head,LT-Detect,using Partial Convolution(PConv)to significantly reduce the classification and localization costs during detection.Finally,we introduce a Receptive Field Block(RFB)in the shallow layers of the backbone to expand the model’s receptive field,enabling effective detection of diseases at various scales.The improved model reduces the number of parameters by 43%and the computational load by 50%.Additionally,it achieves a mean Average Precision(mAP)of 90.9%on a publicly available dataset containing 3641 images of tomato leaf diseases,with only a 0.7%decrease compared to the baseline model.This demonstrates that the model maintains excellent accuracy while being lightweight,making it suitable for rapid detection of tomato leaf diseases.
文摘The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.
基金supported by the National Natural Science Foundation of China(Nos.52174222,52303356 and 62204260)the Natural Science Fund project in Jiangsu Province(No.BK20210494)the Department of Science and Technology of Anhui Province(No.2022CSJGG0703).
文摘Highly sensitive gas sensors play an important role in applications,such as environmental monitoring,medical diagnostics and food testing.This paper reviews recent advances in metal-doped and noble metal-decorated chemo-resistive gas sensors with different nanostructures(ZnO,SnO_(2),In_(2)O_(3)and Fe_(2)O_(3)).It mainly includes the doping of metals such as Al,Fe and Cu,and the modification of noble metals such as Pd,Pt and Au,and introduces the bimetallic-modified materials possessing greater advantages than single metals in enhancing gas-sensitive performance.The results and problems of room-temperature detection of perovskite and metal oxide composite structural materials are also discussed.In addition,the potential applications of micro-electro-mechanical system(MEMS)gas sensing arrays and electronic nose smart sensing devices in disease diagnosis and environmental monitoring are presented through their limitations and development trends in areas such as smart homes.Finally,the main challenges and future prospects of metal oxide gas sensors are presented.
基金supported by the National Natural Science Foundation of China(No.52188102).
文摘Anomaly Detection (AD) has been extensively adopted in industrial settings to facilitate quality control of products. It is critical to industrial production, especially to areas such as aircraft manufacturing, which require strict part qualification rates. Although being more efficient and practical, few-shot AD has not been well explored. The existing AD methods only extract features in a single frequency while defects exist in multiple frequency domains. Moreover, current methods have not fully leveraged the few-shot support samples to extract input-related normal patterns. To address these issues, we propose an industrial few-shot AD method, Feature Extender for Anomaly Detection (FEAD), which extracts normal patterns in multiple frequency domains from few-shot samples under the guidance of the input sample. Firstly, to achieve better coverage of normal patterns in the input sample, we introduce a Sample-Conditioned Transformation Module (SCTM), which transforms support features under the guidance of the input sample to obtain extra normal patterns. Secondly, to effectively distinguish and localize anomaly patterns in multiple frequency domains, we devise an Adaptive Descriptor Construction Module (ADCM) to build and select pattern descriptors in a series of frequencies adaptively. Finally, an auxiliary task for SCTM is designed to ensure the diversity of transformations and include more normal patterns into support features. Extensive experiments on two widely used industrial AD datasets (MVTec-AD and VisA) demonstrate the effectiveness of the proposed FEAD.
