Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensi...Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensive applications in law enforcement and the commercial domain,and the rapid advancement of practical technologies.Despite the significant advancements,modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions,occlusion,and diverse facial postures.In such scenarios,human perception is still well above the capabilities of present technology.Using the systematic mapping study,this paper presents an in-depth review of face detection algorithms and face recognition algorithms,presenting a detailed survey of advancements made between 2015 and 2024.We analyze key methodologies,highlighting their strengths and restrictions in the application context.Additionally,we examine various datasets used for face detection/recognition datasets focusing on the task-specific applications,size,diversity,and complexity.By analyzing these algorithms and datasets,this survey works as a valuable resource for researchers,identifying the research gap in the field of face detection and recognition and outlining potential directions for future research.展开更多
A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can ...A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can bottom spray code number recognition.In the coding number detection stage,Differentiable Binarization Network is used as the backbone network,combined with the Attention and Dilation Convolutions Path Aggregation Network feature fusion structure to enhance the model detection effect.In terms of text recognition,using the Scene Visual Text Recognition coding number recognition network for end-to-end training can alleviate the problem of coding recognition errors caused by image color distortion due to variations in lighting and background noise.In addition,model pruning and quantization are used to reduce the number ofmodel parameters to meet deployment requirements in resource-constrained environments.A comparative experiment was conducted using the dataset of tank bottom spray code numbers collected on-site,and a transfer experiment was conducted using the dataset of packaging box production date.The experimental results show that the algorithm proposed in this study can effectively locate the coding of cans at different positions on the roller conveyor,and can accurately identify the coding numbers at high production line speeds.The Hmean value of the coding number detection is 97.32%,and the accuracy of the coding number recognition is 98.21%.This verifies that the algorithm proposed in this paper has high accuracy in coding number detection and recognition.展开更多
Load time series analysis is critical for resource management and optimization decisions,especially automated analysis techniques.Existing research has insufficiently interpreted the overall characteristics of samples...Load time series analysis is critical for resource management and optimization decisions,especially automated analysis techniques.Existing research has insufficiently interpreted the overall characteristics of samples,leading to significant differences in load level detection conclusions for samples with different characteristics(trend,seasonality,cyclicality).Achieving automated,feature-adaptive,and quantifiable analysis methods remains a challenge.This paper proposes a Threshold Recognition-based Load Level Detection Algorithm(TRLLD),which effectively identifies different load level regions in samples of arbitrary size and distribution type based on sample characteristics.By utilizing distribution density uniformity,the algorithm classifies data points and ultimately obtains normalized load values.In the feature recognition step,the algorithm employs the Density Uniformity Index Based on Differences(DUID),High Load Level Concentration(HLLC),and Low Load Level Concentration(LLLC)to assess sample characteristics,which are independent of specific load values,providing a standardized perspective on features,ensuring high efficiency and strong interpretability.Compared to traditional methods,the proposed approach demonstrates better adaptive and real-time analysis capabilities.Experimental results indicate that it can effectively identify high load and low load regions in 16 groups of time series samples with different load characteristics,yielding highly interpretable results.The correlation between the DUID and sample density distribution uniformity reaches 98.08%.When introducing 10% MAD intensity noise,the maximum relative error is 4.72%,showcasing high robustness.Notably,it exhibits significant advantages in general and low sample scenarios.展开更多
In this work,a novel electrochemical sensor based on covalent organic framework@carbon black@molecularly imprinted polydopamine(COF@CB@MPDA)was developed for selective recognition and determination of ciprofloxacin(CF...In this work,a novel electrochemical sensor based on covalent organic framework@carbon black@molecularly imprinted polydopamine(COF@CB@MPDA)was developed for selective recognition and determination of ciprofloxacin(CF).COF@CB@MPDA possessed good water dispersibility and was synthesized by the selfpolymerization of dopamine under alkaline conditions in the presence of the COF,CB and CF.The high surface area COF enhanced the adsorption of CF,whilst CB gave the composites high electrical conductivity to improve the sensitivity of the proposed COF@CB@MPDA/glassy carbon electrode(GCE)sensor.The specific recognition of CF by COF@CB@MPDA involved hydrogen bonding and van der Waals interactions.Under optimized conditions,the sensor showed a good linear relationship with CF concentration over the range of 5.0×10^(–7)and 1.0×10^(–4)mol/L,with a limit of detection(LOD)of 9.53×10^(–8)mol/L.Further,the developed sensor exhibited high selectivity,repeatability and stability for CF detection in milk and milk powders.The method used to fabricate the COF@CB@MPDA/GCE sensor could be easily adapted for the selective recognition and detection of other antibacterial agents and organic pollutants in the environment.展开更多
Enantiomer identification is of paramount industrial value and physiological significance.Construction of sensitive chiral sensors with high enantiomeric discrimination ability is highly desirable.In this work,a chira...Enantiomer identification is of paramount industrial value and physiological significance.Construction of sensitive chiral sensors with high enantiomeric discrimination ability is highly desirable.In this work,a chiral covalent organic framework/anodic aluminum oxide(c-COF/AAO)membrane was prepared for electrochemical enantioselective recognition and sensing.Benefiting from the remarkable asymmetry,the asprepared nanofluidic c-COF/AAO presents a distinct ion current rectification(ICR)characteristic,enabling sensitive bioanalysis.In addition,owing to the large surface area,high chemical stability and perfect ion selectivity of chiral COF,the prepared c-COF/AAO membrane presents exceptionally selective mass transport and thereby enables excellent chiral discrimination for S-/R-Naproxen(S-/R-Npx)enantiomers.It is especially noteworthy that the detection limit is achieved as low as 3.88 pmol/L.These results raise the possibility for a facile,stable and low-cost method to carry out sensitive enantioselective recognition and detection.展开更多
The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children a...The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children and adolescents who are increasingly exposed to online grooming crimes.Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims.However,research on grooming detection in South Korea remains limited,as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations,leading to inaccurate classifications.To address these issues,this study proposes a novel framework that integrates optical character recognition(OCR)technology with KcELECTRA,a deep learning-based natural language processing(NLP)model that shows excellent performance in processing the colloquial Korean language.In the proposed framework,the KcELECTRA model is fine-tuned by an extensive dataset,including Korean social media conversations,Korean ethical verification data from AI-Hub,and Korean hate speech data from Hug-gingFace,to enable more accurate classification of text extracted from social media conversation images.Experimental results show that the proposed framework achieves an accuracy of 0.953,outperforming existing transformer-based models.Furthermore,OCR technology shows high accuracy in extracting text from images,demonstrating that the proposed framework is effective for online grooming detection.The proposed framework is expected to contribute to the more accurate detection of grooming text and the prevention of grooming-related crimes.展开更多
In order to meet the requirements of accurate identification of surface defects on copper strip in industrial production,a detection model of surface defects based on machine vision,CSC-YOLO,is proposed.The model uses...In order to meet the requirements of accurate identification of surface defects on copper strip in industrial production,a detection model of surface defects based on machine vision,CSC-YOLO,is proposed.The model uses YOLOv4-tiny as the benchmark network.First,K-means clustering is introduced into the benchmark network to obtain anchor frames that match the self-built dataset.Second,a cross-region fusion module is introduced in the backbone network to solve the difficult target recognition problem by fusing contextual semantic information.Third,the spatial pyramid pooling-efficient channel attention network(SPP-E)module is introduced in the path aggregation network(PANet)to enhance the extraction of features.Fourth,to prevent the loss of channel information,a lightweight attention mechanism is introduced to improve the performance of the network.Finally,the performance of the model is improved by adding adjustment factors to correct the loss function for the dimensional characteristics of the surface defects.CSC-YOLO was tested on the self-built dataset of surface defects in copper strip,and the experimental results showed that the mAP of the model can reach 93.58%,which is a 3.37% improvement compared with the benchmark network,and FPS,although decreasing compared with the benchmark network,reached 104.CSC-YOLO takes into account the real-time requirements of copper strip production.The comparison experiments with Faster RCNN,SSD300,YOLOv3,YOLOv4,Resnet50-YOLOv4,YOLOv5s,YOLOv7,and other algorithms show that the algorithm obtains a faster computation speed while maintaining a higher detection accuracy.展开更多
During the test on transient pressure signal in explosion field,false trigger caused by field interference can lead to test failure.To improve the stability of test system,a signal detection and recognition technology...During the test on transient pressure signal in explosion field,false trigger caused by field interference can lead to test failure.To improve the stability of test system,a signal detection and recognition technology is proposed for transient pressure test system.In the process of signal acquisition,firstly,electrical levels are monitored in real time to find effective abrupt changes and mark them;then the effective data segments are detecdted totected;thus the effective signals can be acquired in turn finally.The experimental results show that the shock wave signal can be collected effectively and the reliability of the test system can be improved after removal of interferences.展开更多
To ensure revulsive driving of intelligent vehicles at intersections, a method is presented to detect and recognize the traffic lights. First, the stabling siding at intersections is detected by applying Hough transfo...To ensure revulsive driving of intelligent vehicles at intersections, a method is presented to detect and recognize the traffic lights. First, the stabling siding at intersections is detected by applying Hough transformation. Then, the colors of traffic lights are detected with color space transformation. Finally, self-associative memory is used to recognize the countdown characters of the traffic lights. Test results at 20 real intersections show that the ratio of correct stabling siding recognition reaches up to 90%;and the ratios of recognition of traffic lights and divided characters are 85% and 97%, respectively. The research proves that the method is efficient for the detection of stabling siding and is robust enough to recognize the characters from images with noise and broken edges.展开更多
An object learning and recognition system is implemented for humanoid robots to discover and memorize objects only by simple interactions with non-expert users. When the object is presented, the system makes use of th...An object learning and recognition system is implemented for humanoid robots to discover and memorize objects only by simple interactions with non-expert users. When the object is presented, the system makes use of the motion information over consecutive frames to extract object features and implements machine learning based on the bag of visual words approach. Instead of using a local feature descriptor only, the proposed system uses the co-occurring local features in order to increase feature discriminative power for both object model learning and inference stages. For different objects with different textures, a hybrid sampling strategy is considered. This hybrid approach minimizes the consumption of computation resources and helps achieving good performances demonstrated on a set of a dozen different daily objects.展开更多
An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction...An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction,was conducted to extract useful feature information and recognize and classify rock images using Tensor Flow-based convolutional neural network(CNN)and Py Qt5.A rock image dataset was established and separated into workouts,confirmation sets,and test sets.The framework was subsequently compiled and trained.The categorization approach was evaluated using image data from the validation and test datasets,and key metrics,such as accuracy,precision,and recall,were analyzed.Finally,the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image.The experimental results indicated that the method combining deep learning,Tensor Flow-based CNN,and Py Qt5 to recognize and classify rock images has an accuracy rate of up to 98.8%,and can be successfully utilized for rock image recognition.The system can be extended to geological exploration,mine engineering,and other rock and mineral resource development to more efficiently and accurately recognize rock samples.Moreover,it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme.The system can serve as a reference for supporting the design of other mining and underground space projects.展开更多
The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests suc...The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests such as the Mini-Mental State Examination and the Montreal Cognitive Assessment,exhibit inherent limitations with respect to accessibility,administration burden,and sensitivity to subtle cognitive decline,particularly among diverse populations.This commentary critically examines a recent study that champions a novel approach:The integration of gait and handwriting kinematic parameters analyzed via machine learning for MCI screening.The present study positions itself within the broader landscape of MCI detection,with a view to comparing its advantages against established neuropsychological batteries,advanced neuroimaging(e.g.,positron emission tomography,magnetic resonance imaging),and emerging fluid biomarkers(e.g.,cerebrospinal fluid,blood-based assays).While the study demonstrates promising accuracy(74.44%area under the curve 0.74 with gait and graphic handwriting)and addresses key unmet needs in accessibility and objectivity,we highlight its cross-sectional nature,limited sample diversity,and lack of dual-task assessment as areas for future refinement.This commentary posits that kinematic biomarkers offer a distinctive,scalable,and ecologically valid approach to widespread MCI screening,thereby complementing existing methods by providing real-world functional insights.Future research should prioritize longitudinal validation,expansion to diverse cohorts,integration with multimodal data including dual-tasking,and the development of highly portable,artificial intelligence-driven solutions to achieve the democratization of early MCI detection and enable timely interventions.展开更多
Since the coal mine in-pit personnel positioning system neither can effectively achieve the function to detect the uniqueness of in-pit coal-mine personnel nor can identify and eliminate violations in attendance manag...Since the coal mine in-pit personnel positioning system neither can effectively achieve the function to detect the uniqueness of in-pit coal-mine personnel nor can identify and eliminate violations in attendance management such as multiple cards for one person, and swiping one's cards by others in China at present. Therefore, the research introduces a uniqueness detection system and method for in-pit coal-mine personnel integrated into the in-pit coal mine personnel positioning system, establishing a system mode based on face recognition + recognition of personnel positioning card + release by automatic detection. Aiming at the facts that the in-pit personnel are wearing helmets and faces are prone to be stained during the face recognition, the study proposes the ideas that pre-process face images using the 2D-wavelet-transformation-based Mallat algorithm and extracts three face features: miner light, eyes and mouths, using the generalized symmetry transformation-based algorithm. This research carried out test with 40 clean face images with no helmets and 40 lightly-stained face images, and then compared with results with the one using the face feature extraction method based on grey-scale transformation and edge detection. The results show that the method described in the paper can detect accurately face features in the above-mentioned two cases, and the accuracy to detect face features is 97.5% in the case of wearing helmets and lightly-stained faces.展开更多
Words are the most indispensable information in human life.It is very important to analyze and understand the meaning of words.Compared with the general visual elements,the text conveys rich and high-level moral infor...Words are the most indispensable information in human life.It is very important to analyze and understand the meaning of words.Compared with the general visual elements,the text conveys rich and high-level moral information,which enables the computer to better understand the semantic content of the text.With the rapid development of computer technology,great achievements have been made in text information detection and recognition.However,when dealing with text characters in natural scene images,there are still some limitations in the detection and recognition of natural scene images.Because natural scene image has more interference and complexity than text,these factors make the detection and recognition of natural scene image text face many challenges.To solve this problem,a new text detection and recognition method based on depth convolution neural network is proposed for natural scene image in this paper.In text detection,this method obtains high-level visual features from the bottom pixels by ResNet network,and extracts the context features from character sequences by BLSTM layer,then introduce to the idea of faster R-CNN vertical anchor point to find the bounding box of the detected text,which effectively improves the effect of text object detection.In addition,in text recognition task,DenseNet model is used to construct character recognition based on Kares.Finally,the output of Softmax is used to classify each character.Our method can replace the artificially defined features with automatic learning and context-based features.It improves the efficiency and accuracy of recognition,and realizes text detection and recognition of natural scene images.And on the PAC2018 competition platform,the experimental results have achieved good results.展开更多
Anomaly detection and recognition are of prime importance in process industries.Faults are usually rare,and, therefore,predicting them is difficult.In this paper,a new greedy initialization method for the K-means algo...Anomaly detection and recognition are of prime importance in process industries.Faults are usually rare,and, therefore,predicting them is difficult.In this paper,a new greedy initialization method for the K-means algorithm is proposed to improve traditional K-means clustering techniques.The new initialization method tries to choose suitable initial points,which are well separated and have the potential to form high-quality clusters.Based on the clustering result of historical disqualification product data in manufacturing process which generated by the Improved-K-means algorithm,a prediction model which is used to detect and recognize the abnormal trend of the quality problems is constructed.This simple and robust alarm-system architecture for predicting incoming faults realizes the transition of quality problems from diagnosis afterward to prevention beforehand indeed.In the end,the alarm model was applied for prediction and avoidance of gear-wheel assembly faults at a gear-plant.展开更多
In this paper, we propose an analogy based immune recognition method that focuses on the implement of the clone selection process and the negative selection process by means of analogy similarity. This method is appli...In this paper, we propose an analogy based immune recognition method that focuses on the implement of the clone selection process and the negative selection process by means of analogy similarity. This method is applied in an IDS （Intrusion Detection System） following several steps. Firstly, the initial abnormal behaviours sample set is optimized through the combining of the AIS （Artificial Immune System） and the genetic algorithm. Then, the abnormity probability algorithm is raised considering the two sides of abnormality and normality. Finally, an intrusion detection system model is established based on the above algorithms and models.展开更多
The detection and recognition of radar signals play a critical role in the maintenance of future electronic warfare(EW).So far,however,there are still problems with signal detection and recognition,especially in the l...The detection and recognition of radar signals play a critical role in the maintenance of future electronic warfare(EW).So far,however,there are still problems with signal detection and recognition,especially in the low probability of intercept(LPI)radar.This paper explores the usefulness of such an algorithm in the scenario of LPI radar signal detection and recognition based on visibility graphs(VG).More network and feature information can be extracted in the VG two-dimensional space,this algorithm can solve the problem of signal recognition using the autocorrelation function.Wavelet denoising processing is introduced into the signal to be tested,and the denoised signal is converted to the VG domain.Then,the signal detection is performed by using the constant false alarm of the VG average degree.Next,weight the converted graph.Finally,perform feature extraction on the weighted image,and use the feature to complete the recognition.It is testified that the proposed algorithm offers significant improvements,such as robustness to noise,and the detection and recognition accuracy,over the recent researches.展开更多
The hypersonic target detection and recognition system is studied,on the basis of overall planning and design,a multi-agent system(MAS)structure and intelligent+information processing mechanism based on target detecti...The hypersonic target detection and recognition system is studied,on the basis of overall planning and design,a multi-agent system(MAS)structure and intelligent+information processing mechanism based on target detection and recognition are proposed,and the multi-agent operation process is analyzed and designed in detail.In the specific agents construction,the information fusion technology is introduced to defining the embedded agents and their interrelations in the system structure,and the intelligent processing ability of complex and uncertain problems is emphatically analyzed from the aspects of autonomy and collaboration.The aim is to optimize the information processing strategy of the hypersonic target detection and recognition system and improve the robustness and rapidity of the system.展开更多
Face recognition based on few training samples is a challenging task. In daily applications, sufficient training samples may not be obtained and most of the gained training samples are in various illuminations and pos...Face recognition based on few training samples is a challenging task. In daily applications, sufficient training samples may not be obtained and most of the gained training samples are in various illuminations and poses. Non-sufficient training samples could not effectively express various facial conditions, so the improvement of the face recognition rate under the non-sufficient training samples condition becomes a laborious mission. In our work, the facial pose pre-recognition(FPPR) model and the dualdictionary sparse representation classification(DD-SRC) are proposed for face recognition. The FPPR model is based on the facial geometric characteristic and machine learning, dividing a testing sample into full-face and profile. Different poses in a single dictionary are influenced by each other, which leads to a low face recognition rate. The DD-SRC contains two dictionaries, full-face dictionary and profile dictionary, and is able to reduce the interference. After FPPR, the sample is processed by the DD-SRC to find the most similar one in training samples. The experimental results show the performance of the proposed algorithm on olivetti research laboratory(ORL) and face recognition technology(FERET) databases, and also reflect comparisons with SRC, linear regression classification(LRC), and two-phase test sample sparse representation(TPTSSR).展开更多
Aptamers are specific nucleic acid sequences that can bind to a wide range of nucleic acid and non-nucleic acid targets with high affinity and specificity. Nucleic acid aptamers are selected in vitro from single stran...Aptamers are specific nucleic acid sequences that can bind to a wide range of nucleic acid and non-nucleic acid targets with high affinity and specificity. Nucleic acid aptamers are selected in vitro from single stranded DNA or RNA ligands containing random sequences of up to a few hundred nucleotides. Systematic evolution of ligands by exponential enrichment （SELEX） was used to select and PCR amplify DNA sequences （aptamers） capable of binding to and detecting Listeria monocytogenes, one of the major food-borne pathogens. A simplified affinity separation approach was employed, in which L. monocytogenes in exponential （log） phase of growth was used as the separation target. A fluorescently-labeled aptamer assay scheme was devised for detecting L. monoeytogenes. This report described a novel approach to the detection of L. monocytogenes using DNA aptamers. Aptamers were developed by nine rounds of SELEX. A high affinity aptamer was successfully selected from the initial random DNA pool, and its secondary structure was also investigated. One of aptamers named e01 with the highest affinity was further tested in aptamer-peroxidase and aptamer-fluorescence staining protocols. This study has proved the principle that the whole-cell SELEX could be a promising technique to design aptamer-based molecular probes for dectection of pathogenic microorganisms without tedious isolation and purification of complex markers or targets.展开更多
文摘Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensive applications in law enforcement and the commercial domain,and the rapid advancement of practical technologies.Despite the significant advancements,modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions,occlusion,and diverse facial postures.In such scenarios,human perception is still well above the capabilities of present technology.Using the systematic mapping study,this paper presents an in-depth review of face detection algorithms and face recognition algorithms,presenting a detailed survey of advancements made between 2015 and 2024.We analyze key methodologies,highlighting their strengths and restrictions in the application context.Additionally,we examine various datasets used for face detection/recognition datasets focusing on the task-specific applications,size,diversity,and complexity.By analyzing these algorithms and datasets,this survey works as a valuable resource for researchers,identifying the research gap in the field of face detection and recognition and outlining potential directions for future research.
文摘A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can bottom spray code number recognition.In the coding number detection stage,Differentiable Binarization Network is used as the backbone network,combined with the Attention and Dilation Convolutions Path Aggregation Network feature fusion structure to enhance the model detection effect.In terms of text recognition,using the Scene Visual Text Recognition coding number recognition network for end-to-end training can alleviate the problem of coding recognition errors caused by image color distortion due to variations in lighting and background noise.In addition,model pruning and quantization are used to reduce the number ofmodel parameters to meet deployment requirements in resource-constrained environments.A comparative experiment was conducted using the dataset of tank bottom spray code numbers collected on-site,and a transfer experiment was conducted using the dataset of packaging box production date.The experimental results show that the algorithm proposed in this study can effectively locate the coding of cans at different positions on the roller conveyor,and can accurately identify the coding numbers at high production line speeds.The Hmean value of the coding number detection is 97.32%,and the accuracy of the coding number recognition is 98.21%.This verifies that the algorithm proposed in this paper has high accuracy in coding number detection and recognition.
文摘Load time series analysis is critical for resource management and optimization decisions,especially automated analysis techniques.Existing research has insufficiently interpreted the overall characteristics of samples,leading to significant differences in load level detection conclusions for samples with different characteristics(trend,seasonality,cyclicality).Achieving automated,feature-adaptive,and quantifiable analysis methods remains a challenge.This paper proposes a Threshold Recognition-based Load Level Detection Algorithm(TRLLD),which effectively identifies different load level regions in samples of arbitrary size and distribution type based on sample characteristics.By utilizing distribution density uniformity,the algorithm classifies data points and ultimately obtains normalized load values.In the feature recognition step,the algorithm employs the Density Uniformity Index Based on Differences(DUID),High Load Level Concentration(HLLC),and Low Load Level Concentration(LLLC)to assess sample characteristics,which are independent of specific load values,providing a standardized perspective on features,ensuring high efficiency and strong interpretability.Compared to traditional methods,the proposed approach demonstrates better adaptive and real-time analysis capabilities.Experimental results indicate that it can effectively identify high load and low load regions in 16 groups of time series samples with different load characteristics,yielding highly interpretable results.The correlation between the DUID and sample density distribution uniformity reaches 98.08%.When introducing 10% MAD intensity noise,the maximum relative error is 4.72%,showcasing high robustness.Notably,it exhibits significant advantages in general and low sample scenarios.
基金supported by the Project of Key R&D Program of Shandong Province(2023CXGC010712).Geoffrey I.N.
文摘In this work,a novel electrochemical sensor based on covalent organic framework@carbon black@molecularly imprinted polydopamine(COF@CB@MPDA)was developed for selective recognition and determination of ciprofloxacin(CF).COF@CB@MPDA possessed good water dispersibility and was synthesized by the selfpolymerization of dopamine under alkaline conditions in the presence of the COF,CB and CF.The high surface area COF enhanced the adsorption of CF,whilst CB gave the composites high electrical conductivity to improve the sensitivity of the proposed COF@CB@MPDA/glassy carbon electrode(GCE)sensor.The specific recognition of CF by COF@CB@MPDA involved hydrogen bonding and van der Waals interactions.Under optimized conditions,the sensor showed a good linear relationship with CF concentration over the range of 5.0×10^(–7)and 1.0×10^(–4)mol/L,with a limit of detection(LOD)of 9.53×10^(–8)mol/L.Further,the developed sensor exhibited high selectivity,repeatability and stability for CF detection in milk and milk powders.The method used to fabricate the COF@CB@MPDA/GCE sensor could be easily adapted for the selective recognition and detection of other antibacterial agents and organic pollutants in the environment.
基金supported by grants from the National Natural Science Foundation of China(Nos.22274076,22304084)the Primary Research&Development Plan of Jiangsu Province(No.BE2022793)+1 种基金the Natural Science Foundation of Jiangsu Province of China(No.BK20230377)Jiangsu Provincial Department of Education(No.211090B52303)。
文摘Enantiomer identification is of paramount industrial value and physiological significance.Construction of sensitive chiral sensors with high enantiomeric discrimination ability is highly desirable.In this work,a chiral covalent organic framework/anodic aluminum oxide(c-COF/AAO)membrane was prepared for electrochemical enantioselective recognition and sensing.Benefiting from the remarkable asymmetry,the asprepared nanofluidic c-COF/AAO presents a distinct ion current rectification(ICR)characteristic,enabling sensitive bioanalysis.In addition,owing to the large surface area,high chemical stability and perfect ion selectivity of chiral COF,the prepared c-COF/AAO membrane presents exceptionally selective mass transport and thereby enables excellent chiral discrimination for S-/R-Naproxen(S-/R-Npx)enantiomers.It is especially noteworthy that the detection limit is achieved as low as 3.88 pmol/L.These results raise the possibility for a facile,stable and low-cost method to carry out sensitive enantioselective recognition and detection.
基金supported by the IITP(Institute of Information&Communications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korean government(Ministry of Science and ICT)(IITP-2025-RS-2024-00438056).
文摘The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children and adolescents who are increasingly exposed to online grooming crimes.Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims.However,research on grooming detection in South Korea remains limited,as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations,leading to inaccurate classifications.To address these issues,this study proposes a novel framework that integrates optical character recognition(OCR)technology with KcELECTRA,a deep learning-based natural language processing(NLP)model that shows excellent performance in processing the colloquial Korean language.In the proposed framework,the KcELECTRA model is fine-tuned by an extensive dataset,including Korean social media conversations,Korean ethical verification data from AI-Hub,and Korean hate speech data from Hug-gingFace,to enable more accurate classification of text extracted from social media conversation images.Experimental results show that the proposed framework achieves an accuracy of 0.953,outperforming existing transformer-based models.Furthermore,OCR technology shows high accuracy in extracting text from images,demonstrating that the proposed framework is effective for online grooming detection.The proposed framework is expected to contribute to the more accurate detection of grooming text and the prevention of grooming-related crimes.
基金the Key Project of Basic Research of Yunnan Province(No.202101AS070016)。
文摘In order to meet the requirements of accurate identification of surface defects on copper strip in industrial production,a detection model of surface defects based on machine vision,CSC-YOLO,is proposed.The model uses YOLOv4-tiny as the benchmark network.First,K-means clustering is introduced into the benchmark network to obtain anchor frames that match the self-built dataset.Second,a cross-region fusion module is introduced in the backbone network to solve the difficult target recognition problem by fusing contextual semantic information.Third,the spatial pyramid pooling-efficient channel attention network(SPP-E)module is introduced in the path aggregation network(PANet)to enhance the extraction of features.Fourth,to prevent the loss of channel information,a lightweight attention mechanism is introduced to improve the performance of the network.Finally,the performance of the model is improved by adding adjustment factors to correct the loss function for the dimensional characteristics of the surface defects.CSC-YOLO was tested on the self-built dataset of surface defects in copper strip,and the experimental results showed that the mAP of the model can reach 93.58%,which is a 3.37% improvement compared with the benchmark network,and FPS,although decreasing compared with the benchmark network,reached 104.CSC-YOLO takes into account the real-time requirements of copper strip production.The comparison experiments with Faster RCNN,SSD300,YOLOv3,YOLOv4,Resnet50-YOLOv4,YOLOv5s,YOLOv7,and other algorithms show that the algorithm obtains a faster computation speed while maintaining a higher detection accuracy.
基金The 11th Postgraduate Technology Innovation Project of North University of China(No.20141142)
文摘During the test on transient pressure signal in explosion field,false trigger caused by field interference can lead to test failure.To improve the stability of test system,a signal detection and recognition technology is proposed for transient pressure test system.In the process of signal acquisition,firstly,electrical levels are monitored in real time to find effective abrupt changes and mark them;then the effective data segments are detecdted totected;thus the effective signals can be acquired in turn finally.The experimental results show that the shock wave signal can be collected effectively and the reliability of the test system can be improved after removal of interferences.
基金The Cultivation Fund of the Key Scientific and Technical Innovation Project of Higher Education of Ministry of Education (No.705020)
文摘To ensure revulsive driving of intelligent vehicles at intersections, a method is presented to detect and recognize the traffic lights. First, the stabling siding at intersections is detected by applying Hough transformation. Then, the colors of traffic lights are detected with color space transformation. Finally, self-associative memory is used to recognize the countdown characters of the traffic lights. Test results at 20 real intersections show that the ratio of correct stabling siding recognition reaches up to 90%;and the ratios of recognition of traffic lights and divided characters are 85% and 97%, respectively. The research proves that the method is efficient for the detection of stabling siding and is robust enough to recognize the characters from images with noise and broken edges.
基金The National Natural Science Foundation of China(No.60672094,60971098)
文摘An object learning and recognition system is implemented for humanoid robots to discover and memorize objects only by simple interactions with non-expert users. When the object is presented, the system makes use of the motion information over consecutive frames to extract object features and implements machine learning based on the bag of visual words approach. Instead of using a local feature descriptor only, the proposed system uses the co-occurring local features in order to increase feature discriminative power for both object model learning and inference stages. For different objects with different textures, a hybrid sampling strategy is considered. This hybrid approach minimizes the consumption of computation resources and helps achieving good performances demonstrated on a set of a dozen different daily objects.
基金financially supported by the National Science and Technology Major Project——Deep Earth Probe and Mineral Resources Exploration(No.2024ZD1003701)the National Key R&D Program of China(No.2022YFC2905004)。
文摘An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction,was conducted to extract useful feature information and recognize and classify rock images using Tensor Flow-based convolutional neural network(CNN)and Py Qt5.A rock image dataset was established and separated into workouts,confirmation sets,and test sets.The framework was subsequently compiled and trained.The categorization approach was evaluated using image data from the validation and test datasets,and key metrics,such as accuracy,precision,and recall,were analyzed.Finally,the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image.The experimental results indicated that the method combining deep learning,Tensor Flow-based CNN,and Py Qt5 to recognize and classify rock images has an accuracy rate of up to 98.8%,and can be successfully utilized for rock image recognition.The system can be extended to geological exploration,mine engineering,and other rock and mineral resource development to more efficiently and accurately recognize rock samples.Moreover,it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme.The system can serve as a reference for supporting the design of other mining and underground space projects.
文摘The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests such as the Mini-Mental State Examination and the Montreal Cognitive Assessment,exhibit inherent limitations with respect to accessibility,administration burden,and sensitivity to subtle cognitive decline,particularly among diverse populations.This commentary critically examines a recent study that champions a novel approach:The integration of gait and handwriting kinematic parameters analyzed via machine learning for MCI screening.The present study positions itself within the broader landscape of MCI detection,with a view to comparing its advantages against established neuropsychological batteries,advanced neuroimaging(e.g.,positron emission tomography,magnetic resonance imaging),and emerging fluid biomarkers(e.g.,cerebrospinal fluid,blood-based assays).While the study demonstrates promising accuracy(74.44%area under the curve 0.74 with gait and graphic handwriting)and addresses key unmet needs in accessibility and objectivity,we highlight its cross-sectional nature,limited sample diversity,and lack of dual-task assessment as areas for future refinement.This commentary posits that kinematic biomarkers offer a distinctive,scalable,and ecologically valid approach to widespread MCI screening,thereby complementing existing methods by providing real-world functional insights.Future research should prioritize longitudinal validation,expansion to diverse cohorts,integration with multimodal data including dual-tasking,and the development of highly portable,artificial intelligence-driven solutions to achieve the democratization of early MCI detection and enable timely interventions.
基金financial supports from the National Natural Science Foundation of China (No. 51134024)the National High Technology Research and Development Program of China (No. 2012AA062203)are gratefully acknowledged
文摘Since the coal mine in-pit personnel positioning system neither can effectively achieve the function to detect the uniqueness of in-pit coal-mine personnel nor can identify and eliminate violations in attendance management such as multiple cards for one person, and swiping one's cards by others in China at present. Therefore, the research introduces a uniqueness detection system and method for in-pit coal-mine personnel integrated into the in-pit coal mine personnel positioning system, establishing a system mode based on face recognition + recognition of personnel positioning card + release by automatic detection. Aiming at the facts that the in-pit personnel are wearing helmets and faces are prone to be stained during the face recognition, the study proposes the ideas that pre-process face images using the 2D-wavelet-transformation-based Mallat algorithm and extracts three face features: miner light, eyes and mouths, using the generalized symmetry transformation-based algorithm. This research carried out test with 40 clean face images with no helmets and 40 lightly-stained face images, and then compared with results with the one using the face feature extraction method based on grey-scale transformation and edge detection. The results show that the method described in the paper can detect accurately face features in the above-mentioned two cases, and the accuracy to detect face features is 97.5% in the case of wearing helmets and lightly-stained faces.
文摘Words are the most indispensable information in human life.It is very important to analyze and understand the meaning of words.Compared with the general visual elements,the text conveys rich and high-level moral information,which enables the computer to better understand the semantic content of the text.With the rapid development of computer technology,great achievements have been made in text information detection and recognition.However,when dealing with text characters in natural scene images,there are still some limitations in the detection and recognition of natural scene images.Because natural scene image has more interference and complexity than text,these factors make the detection and recognition of natural scene image text face many challenges.To solve this problem,a new text detection and recognition method based on depth convolution neural network is proposed for natural scene image in this paper.In text detection,this method obtains high-level visual features from the bottom pixels by ResNet network,and extracts the context features from character sequences by BLSTM layer,then introduce to the idea of faster R-CNN vertical anchor point to find the bounding box of the detected text,which effectively improves the effect of text object detection.In addition,in text recognition task,DenseNet model is used to construct character recognition based on Kares.Finally,the output of Softmax is used to classify each character.Our method can replace the artificially defined features with automatic learning and context-based features.It improves the efficiency and accuracy of recognition,and realizes text detection and recognition of natural scene images.And on the PAC2018 competition platform,the experimental results have achieved good results.
文摘Anomaly detection and recognition are of prime importance in process industries.Faults are usually rare,and, therefore,predicting them is difficult.In this paper,a new greedy initialization method for the K-means algorithm is proposed to improve traditional K-means clustering techniques.The new initialization method tries to choose suitable initial points,which are well separated and have the potential to form high-quality clusters.Based on the clustering result of historical disqualification product data in manufacturing process which generated by the Improved-K-means algorithm,a prediction model which is used to detect and recognize the abnormal trend of the quality problems is constructed.This simple and robust alarm-system architecture for predicting incoming faults realizes the transition of quality problems from diagnosis afterward to prevention beforehand indeed.In the end,the alarm model was applied for prediction and avoidance of gear-wheel assembly faults at a gear-plant.
基金Supported by the National Natural Science Foundation ofChina (60563002) Scientific Research Programof the Higher EducationInstitution of Xinjiang (XJEDU2004I03)
文摘In this paper, we propose an analogy based immune recognition method that focuses on the implement of the clone selection process and the negative selection process by means of analogy similarity. This method is applied in an IDS （Intrusion Detection System） following several steps. Firstly, the initial abnormal behaviours sample set is optimized through the combining of the AIS （Artificial Immune System） and the genetic algorithm. Then, the abnormity probability algorithm is raised considering the two sides of abnormality and normality. Finally, an intrusion detection system model is established based on the above algorithms and models.
基金This work was supported by the National Defence Pre-research Foundation of China(30502010103).
文摘The detection and recognition of radar signals play a critical role in the maintenance of future electronic warfare(EW).So far,however,there are still problems with signal detection and recognition,especially in the low probability of intercept(LPI)radar.This paper explores the usefulness of such an algorithm in the scenario of LPI radar signal detection and recognition based on visibility graphs(VG).More network and feature information can be extracted in the VG two-dimensional space,this algorithm can solve the problem of signal recognition using the autocorrelation function.Wavelet denoising processing is introduced into the signal to be tested,and the denoised signal is converted to the VG domain.Then,the signal detection is performed by using the constant false alarm of the VG average degree.Next,weight the converted graph.Finally,perform feature extraction on the weighted image,and use the feature to complete the recognition.It is testified that the proposed algorithm offers significant improvements,such as robustness to noise,and the detection and recognition accuracy,over the recent researches.
基金This work was supported by the National Natural Science Foundation of China(61471391).
文摘The hypersonic target detection and recognition system is studied,on the basis of overall planning and design,a multi-agent system(MAS)structure and intelligent+information processing mechanism based on target detection and recognition are proposed,and the multi-agent operation process is analyzed and designed in detail.In the specific agents construction,the information fusion technology is introduced to defining the embedded agents and their interrelations in the system structure,and the intelligent processing ability of complex and uncertain problems is emphatically analyzed from the aspects of autonomy and collaboration.The aim is to optimize the information processing strategy of the hypersonic target detection and recognition system and improve the robustness and rapidity of the system.
基金supported by the National Natural Science Foundation of China(6137901061772421)
文摘Face recognition based on few training samples is a challenging task. In daily applications, sufficient training samples may not be obtained and most of the gained training samples are in various illuminations and poses. Non-sufficient training samples could not effectively express various facial conditions, so the improvement of the face recognition rate under the non-sufficient training samples condition becomes a laborious mission. In our work, the facial pose pre-recognition(FPPR) model and the dualdictionary sparse representation classification(DD-SRC) are proposed for face recognition. The FPPR model is based on the facial geometric characteristic and machine learning, dividing a testing sample into full-face and profile. Different poses in a single dictionary are influenced by each other, which leads to a low face recognition rate. The DD-SRC contains two dictionaries, full-face dictionary and profile dictionary, and is able to reduce the interference. After FPPR, the sample is processed by the DD-SRC to find the most similar one in training samples. The experimental results show the performance of the proposed algorithm on olivetti research laboratory(ORL) and face recognition technology(FERET) databases, and also reflect comparisons with SRC, linear regression classification(LRC), and two-phase test sample sparse representation(TPTSSR).
文摘Aptamers are specific nucleic acid sequences that can bind to a wide range of nucleic acid and non-nucleic acid targets with high affinity and specificity. Nucleic acid aptamers are selected in vitro from single stranded DNA or RNA ligands containing random sequences of up to a few hundred nucleotides. Systematic evolution of ligands by exponential enrichment （SELEX） was used to select and PCR amplify DNA sequences （aptamers） capable of binding to and detecting Listeria monocytogenes, one of the major food-borne pathogens. A simplified affinity separation approach was employed, in which L. monocytogenes in exponential （log） phase of growth was used as the separation target. A fluorescently-labeled aptamer assay scheme was devised for detecting L. monoeytogenes. This report described a novel approach to the detection of L. monocytogenes using DNA aptamers. Aptamers were developed by nine rounds of SELEX. A high affinity aptamer was successfully selected from the initial random DNA pool, and its secondary structure was also investigated. One of aptamers named e01 with the highest affinity was further tested in aptamer-peroxidase and aptamer-fluorescence staining protocols. This study has proved the principle that the whole-cell SELEX could be a promising technique to design aptamer-based molecular probes for dectection of pathogenic microorganisms without tedious isolation and purification of complex markers or targets.
