Epilepsy is a common neurological disorder that occurs at all ages.Epilepsy not only brings physical pain to patients,but also brings a huge burden to the lives of patients and their families.At present,epilepsy detec...Epilepsy is a common neurological disorder that occurs at all ages.Epilepsy not only brings physical pain to patients,but also brings a huge burden to the lives of patients and their families.At present,epilepsy detection is still achieved through the observation of electroencephalography(EEG)by medical staff.However,this process takes a long time and consumes energy,which will create a huge workload to medical staff.Therefore,it is particularly important to realize the automatic detection of epilepsy.This paper introduces,in detail,the overall framework of EEG-based automatic epilepsy identification and the typical methods involved in each step.Aiming at the core modules,that is,signal acquisition analog front end(AFE),feature extraction and classifier selection,method summary and theoretical explanation are carried out.Finally,the future research directions in the field of automatic detection of epilepsy are prospected.展开更多
Since the atmospheric correction is a necessary preprocessing step of remote sensing image before detecting green tide, the introduced error directly affects the detection precision. Therefore, the detection method of...Since the atmospheric correction is a necessary preprocessing step of remote sensing image before detecting green tide, the introduced error directly affects the detection precision. Therefore, the detection method of green tide is presented from Landsat TM/ETM plus image which needs not the atmospheric correction. In order to achieve an automatic detection of green tide, a linear relationship（y =0.723 x＋0.504） between detection threshold y and subtraction x（x=λnir–λred） is found from the comparing Landsat TM/ETM plus image with the field surveys.Using this relationship, green tide patches can be detected automatically from Landsat TM/ETM plus image.Considering there is brightness difference between different regions in an image, the image will be divided into a plurality of windows（sub-images） with a same size firstly, and then each window will be detected using an adaptive detection threshold determined according to the discovered linear relationship. It is found that big errors will appear in some windows, such as those covered by clouds seriously. To solve this problem, the moving step k of windows is proposed to be less than the window width n. Using this mechanism, most pixels will be detected[n/k]×[n/k] times except the boundary pixels, then every pixel will be assigned the final class（green tide or sea water） according to majority rule voting strategy. It can be seen from the experiments, the proposed detection method using multi-windows and their adaptive thresholds can detect green tide from Landsat TM/ETM plus image automatically. Meanwhile, it avoids the reliance on the accurate atmospheric correction.展开更多
As a dispersive wave mode produced by lightning strokes, tweek atmospherics provide important hints of lower ionospheric(i.e., D-region) electron density. Based on data accumulation from the WHU ELF/VLF receiver syste...As a dispersive wave mode produced by lightning strokes, tweek atmospherics provide important hints of lower ionospheric(i.e., D-region) electron density. Based on data accumulation from the WHU ELF/VLF receiver system, we develop an automatic detection module in terms of the maximum-entropy-spectral-estimation(MESE) method to identify unambiguous instances of low latitude tweeks.We justify the feasibility of our procedure through a detailed analysis of the data observed at the Suizhou Station(31.57°N, 113.32°E) on17 February 2016. A total of 3961 tweeks were registered by visual inspection;the automatic detection method captured 4342 tweeks, of which 3361 were correct ones, producing a correctness percentage of 77.4%(= 3361/4342) and a false alarm rate of 22.6%(= 981/4342).A Short-Time Fourier Transformation(STFT) was also applied to trace the power spectral profiles of identified tweeks and to evaluate the tweek propagation distance. It is found that the fitting accuracy of the frequency–time curve and the relative difference of propagation distance between the two methods through the slope and through the intercept can be used to further improve the accuracy of automatic tweek identification. We suggest that our automatic tweek detection and analysis method therefore supplies a valuable means to investigate features of low latitude tweek atmospherics and associated ionospheric parameters comprehensively.展开更多
Co-seismic landslide detection is essential for post-disaster rescue and risk assessment after an earthquake event.However,a variety of ground objects,including roads and bare land,have spectral characteristics simila...Co-seismic landslide detection is essential for post-disaster rescue and risk assessment after an earthquake event.However,a variety of ground objects,including roads and bare land,have spectral characteristics similar to those of co-seismic landslides,making it difficult to gather information and assess their impact rapidly and accurately.Therefore,an automatic detection method based on a deep learning model,named ENVINet5,with multiple features(ENVINet5_MF)was proposed to solve this problem and improve the detection accuracy of co-seismic landslides.The ENVINet5_MF method is advantageous for co-seismic landslide detection because it features a landslide gain index(LGI)that effectively eliminates the spectral interference of bare land and roads.We conducted two experiments using multi-temporal PlanetScope images acquired in Hokkaido,Japan,and Mainling,China.The accuracy evaluation and rationality analysis show that ENVINet5_MF performed better than comparative methods and that the co-seismic landslide areas detected by ENVINet5_MF were the most consistent with ground reference data.The findings of this study suggest that ENVINet5_MF can provide an efficient and accurate method for coseismic landslide detection to ensure a rapid response to co-seismic landslide disasters.展开更多
This research focuses on the automatic detection and grading of microaneurysms in fundus images of diabetic retinopathy using artificial intelligence deep learning algorithms.By integrating multi-source fundus image d...This research focuses on the automatic detection and grading of microaneurysms in fundus images of diabetic retinopathy using artificial intelligence deep learning algorithms.By integrating multi-source fundus image data and undergoing a rigorous preprocessing workflow,a hybrid deep learning model architecture combining a modified U-Net and a residual neural network was adopted for the study.The experimental results show that the model achieved an accuracy of[X]%in microaneurysm detection,with a recall rate of[Y]%and a precision rate of[Z]%.In terms of grading diabetic retinopathy,the Cohen’s kappa coefficient for agreement with clinical grading was[K],and there were specific sensitivities and specificities for each grade.Compared with traditional methods,this model has significant advantages in processing speed and result consistency.However,it also has limitations such as insufficient data diversity,difficulties for the algorithm in detecting microaneurysms in severely hemorrhagic images,and high computational costs.The results of this research are of great significance for the early screening and clinical diagnosis decision support of diabetic retinopathy.In the future,it is necessary to further optimize the data and algorithms and promote clinical integration and telemedicine applications.展开更多
AIM:To develop an automated model for subfoveal choroidal thickness(SFCT)detection in optical coherence tomography(OCT)images,addressing manual fovea location and choroidal contour challenges.METHODS:Two procedures we...AIM:To develop an automated model for subfoveal choroidal thickness(SFCT)detection in optical coherence tomography(OCT)images,addressing manual fovea location and choroidal contour challenges.METHODS:Two procedures were proposed:defining the fovea and segmenting the choroid.Fovea localization from B-scan OCT image sequence with three-dimensional reconstruction(LocBscan-3D)predicted fovea location using central foveal depression features,and fovea localization from two-dimensional en-face OCT(LocEN-2D)used a mask region-based convolutional neural network(Mask R-CNN)model for optic disc detection,and determined the fovea location based on optic disc relative position.Choroid segmentation also employed Mask R-CNN.RESULTS:For 53 eyes in 28 healthy subjects,LocBscan-3D’s mean difference between manual and predicted fovea locations was 170.0μm,LocEN-2D yielded 675.9μm.LocEN-2D performed better in non-high myopia group(P=0.02).SFCT measurements from Mask R-CNN aligned with manual values.CONCLUSION:Our models accurately predict SFCT in OCT images.LocBscan-3D excels in precise fovea localization even with high myopia.LocEN-2D shows high detection rates but lower accuracy especially in the high myopia group.Combining both models offers a robust SFCT assessment approach,promising efficiency and accuracy for large-scale studies and clinical use.展开更多
Determination of ovulation time is one of the most important tasks in sow reproduction management.Temperature variation in the vulva of the sows can be used as a predictor of ovulation time.However,the skin temperatur...Determination of ovulation time is one of the most important tasks in sow reproduction management.Temperature variation in the vulva of the sows can be used as a predictor of ovulation time.However,the skin temperatures of sows in existing studies are obtained manually from infrared thermal images,posing an obstacle to the automatic prediction of ovulation time.In this study,an improved YOLO-V5s detector based on feature fusion and dilated convolution(FDYOLOV5s)was proposed for the automatic extraction of the vulva temperature of sows based on infrared thermal images.For the purpose of reducing the model complexity,the depthwise separable convolution and the modified lightweight ShuffleNet-V2 module were introduced in the backbone.Meanwhile,the feature fusion network structure of the model was simplified for efficiency,and a mixed dilated convolutional module was designed to obtain global features.The experimental results show that FD-YOLOV5s outperformed the other nine methods,with a mean average precision(mAP)of 99.1%,an average frame rate of 156.25 fps,and a model size of only 3.86 MB,indicating that the method effectively simplifies the model while ensuring detection accuracy.Using a linear regression between manual extraction and the results extracted using this method in randomly selected thermal images,the coefficients of determination for maximum and average vulvar temperatures reached 99.5%and 99.3%,respectively.The continuous vulva temperature of sows was obtained by the target detection algorithm,and the sow estrus detection was performed by the temperature trend and compared with the manually detected estrus results.The results showed that the sensitivity,specificity,and error rate of the estrus detection algorithm were 89.3%,94.5%,and 5.8%,respectively.The method achieves real-time and accurate extraction of sow vulva temperature and can be used for the automatic detection of sow estrus,which could be helpful for the automatic prediction of ovulation time.展开更多
Real-time, automatic, and accurate determination of seismic signals is critical for rapid earthquake reporting and early warning. In this study, we present a correction trigger function（CTF） for automatically detect...Real-time, automatic, and accurate determination of seismic signals is critical for rapid earthquake reporting and early warning. In this study, we present a correction trigger function（CTF） for automatically detecting regional seismic events and a fourth-order statistics algorithm with the Akaike information criterion（AIC） for determining the direct wave phase, based on the differences, or changes, in energy, frequency, and amplitude of the direct P- or S-waves signal and noise. Simulations suggest for that the proposed fourth-order statistics result in high resolution even for weak signal and noise variations at different amplitude, frequency, and polarization characteristics. To improve the precision of establishing the S-waves onset, first a specific segment of P-wave seismograms is selected and the polarization characteristics of the data are obtained. Second, the S-wave seismograms that contained the specific segment of P-wave seismograms are analyzed by S-wave polarization filtering. Finally, the S-wave phase onset times are estimated. The proposed algorithm was used to analyze regional earthquake data from the Shandong Seismic Network. The results suggest that compared with conventional methods, the proposed algorithm greatly decreased false and missed earthquake triggers, and improved the detection precision of direct P- and S-wave phases.展开更多
In order to realize the automatic monitoring of ruminant activities of cows,an automatic detection method for the mouth area of ruminant cows based on machine vision technology was studied.Optical flow was used to cal...In order to realize the automatic monitoring of ruminant activities of cows,an automatic detection method for the mouth area of ruminant cows based on machine vision technology was studied.Optical flow was used to calculate the relative motion speed of each pixel in the video frame images.The candidate mouth region with large motion ranges was extracted,and a series of processing methods,such as grayscale processing,threshold segmentation,pixel point expansion and adjacent region merging,were carried out to extract the real area of cows’mouth.To verify the accuracy of the proposed method,six videos with a total length of 96 min were selected for this research.The results showed that the highest accuracy was 87.80%,the average accuracy was 76.46%and the average running time of the algorithm was 6.39 s.All the results showed that this method can be used to detect the mouth area automatically,which lays the foundation for automatic monitoring of cows’ruminant behavior.展开更多
As an important index of soil crushing performance of rotary tiller,the soil fragmentation rate is still limited to manual measurement.In this study,an automatic detection platform for soil fragmentation rate was desi...As an important index of soil crushing performance of rotary tiller,the soil fragmentation rate is still limited to manual measurement.In this study,an automatic detection platform for soil fragmentation rate was designed,which integrated soil intake,screening,weighing and calculation of soil fragmentation rate.This platform can solve the problem that the index of the soil fragmentation rate cannot be detected quickly and effectively after rotary tillage,which leads to difficulty in field quality evaluation.The platform was mainly composed of a shovel soil module,conveying module,screening module,weighing module and automatic control system,which could realize single-line and multi-point automatic soil fragmentation rate detection.Based on the homogeneous dry slope model,the tilting angles of soil intake and soil feeding after rotary tillage on the platform were determined to be 30.10°and 26.67°,respectively.According to the principle of flow conservation,a rotary circulation screening module was designed to obtain soil particle size grading.A method based on the principle of multi-line and multi-point measurement was developed to detect soil fragmentation rate.The influence of screening speed on screening effect was analyzed,and the reasonable value of screening speed was determined to be 0.5 m/s.A field performance test was carried out in October 2019 to verify the detection performance of the platform.The results showed that,compared with the manual test method,the maximum test error was no more than 11%,the minimum test error was less than 4%,the maximum single test time was no more than 2 min,and the total test time of each test area was no more than 30 min.The efficiency of single-point detection was significantly better than the manual detection,which indicated that the design in this study met the requirements of rapid detection of soil fragmentation rate,and provided a new idea for the automatic detection of quality of rotary tillage.展开更多
One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrom...One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrome can help in the process of recovery.Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition.This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies.Additionally,feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of classifiers.In this research,the tri-stage wrapper method is used because it reduces the computation time.The proposed study for the Automatic diagnosis of PCOS contains preprocessing,data normalization,feature selection,and classification.A dataset with 39 characteristics,including metabolism,neuroimaging,hormones,and biochemical information for 541 subjects,was employed in this scenario.To start,this research pre-processed the information.Next for feature selection,a tri-stage wrapper method such as Mutual Information,ReliefF,Chi-Square,and Xvariance is used.Then,various classification methods are tested and trained.Deep learning techniques including convolutional neural network(CNN),multi-layer perceptron(MLP),Recurrent neural network(RNN),and Bi long short-term memory(Bi-LSTM)are utilized for categorization.The experimental finding demonstrates that with effective feature extraction process using tri stage wrapper method+CNN delivers the highest precision(97%),high accuracy(98.67%),and recall(89%)when compared with other machine learning algorithms.展开更多
Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety.The task is challenging because the shadows on the pavement may have similar intensity with the crack,whi...Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety.The task is challenging because the shadows on the pavement may have similar intensity with the crack,which interfere with the crack detection performance.Till to the present,there still lacks efficient algorithm models and training datasets to deal with the interference brought by the shadows.To fill in the gap,we made several contributions as follows.First,we proposed a new pavement shadow and crack dataset,which contains a variety of shadow and pavement pixel size combinations.It also covers all common cracks(linear cracks and network cracks),placing higher demands on crack detection methods.Second,we designed a two-step shadow-removal-oriented crack detection approach:SROCD,which improves the performance of the algorithm by first removing the shadow and then detecting it.In addition to shadows,the method can cope with other noise disturbances.Third,we explored the mechanism of how shadows affect crack detection.Based on this mechanism,we propose a data augmentation method based on the difference in brightness values,which can adapt to brightness changes caused by seasonal and weather changes.Finally,we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters,and the algorithm improves the performance of the model overall.We compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset,and the experimental results demonstrate the superiority of our method.展开更多
Cows mounting behavior is a significant manifestation of estrus in cows.The timely detection of cows mounting behavior can make cows conceive in time,thereby improving milk production of cows and economic benefits of ...Cows mounting behavior is a significant manifestation of estrus in cows.The timely detection of cows mounting behavior can make cows conceive in time,thereby improving milk production of cows and economic benefits of the pasture.Existing methods of mounting behavior detection are difficult to achieve precise detection under occlusion and severe scale change environments and meet real-time requirements.Therefore,this study proposed a Cow-YOLO model to detect cows mounting behavior.To meet the needs of real-time performance,YOLOv5s model is used as the baseline model.In order to solve the problem of difficult detection of cows mounting behavior in an occluded environment,the CSPDarknet53 of YOLOv5s is replaced with Non-local CSPDarknet53,which enables the network to obtain global information and improves the model’s ability to detect the mounting cows.Next,the neck of YOLOv5s is redesigned to Multiscale Neck,reinforcing the multi-scale feature fusion capability of model to solve difficulty detection under dramatic scale changes.Then,to further increase the detection accuracy,the Coordinate Attention Head is integrated into YOLOv5s.Finally,these improvements form a novel cow mounting detection model called Cow-YOLO and make Cow-YOLO more suitable for cows mounting behavior detection in occluded and drastic scale changes environments.Cow-YOLO achieved a precision of 99.7%,a recall of 99.5%,a mean average precision of 99.5%,and a detection speed of 156.3 f/s on the test set.Compared with existing detection methods of cows mounting behavior,Cow-YOLO achieved higher detection accuracy and faster detection speed in an occluded and drastic scale-change environment.Cow-YOLO can assist ranch breeders in achieving real-time monitoring of cows estrus,enhancing ranch economic efficiency.展开更多
The analysis and exploration of auroral dynamics are very significant for studying auroral mechanisms. This paper proposes a method based on auroral dynamic processes for detecting auroral events automatically. We fir...The analysis and exploration of auroral dynamics are very significant for studying auroral mechanisms. This paper proposes a method based on auroral dynamic processes for detecting auroral events automatically. We first obtained the motion fields using the multiscale fluid flow estimator. Then, the auroral video frame sequence was represented by the spatiotemporal statistics of local motion vectors. Finally, automatic auroral event detection was achieved. The experimental results show that our methods could detect the required auroral events effectively and accurately, and that the detections were independent on any specific auroral event. The proposed method makes it feasible to statistically analyze a large number of continuous observations based on the auroral dynamic process.展开更多
At present,the network security situation is becoming more and more serious.Malicious network attacks such as computer viruses,Trojans and hacker attacks are becoming more and more rampant.National and group network a...At present,the network security situation is becoming more and more serious.Malicious network attacks such as computer viruses,Trojans and hacker attacks are becoming more and more rampant.National and group network attacks such as network information war and network terrorism have a serious damage to the production and life of the whole society.At the same time,with the rapid development of Internet of Things and the arrival of 5G era,IoT devices as an important part of industrial Internet system,have become an important target of infiltration attacks by hostile forces.This paper describes the challenges facing firmware vulnerability detection at this stage,and introduces four automatic detection and utilization technologies in detail:based on patch comparison,based on control flow,based on data flow and ROP attack against buffer vulnerabilities.On the basis of clarifying its core idea,main steps and experimental results,the limitations of its method are proposed.Finally,combined with four automatic detection methods,this paper summarizes the known vulnerability detection steps based on firmware analysis,and looks forward to the follow-up work.展开更多
A wireless sensor network (WSN) is spatially distributing independent sensors to monitor physical and environmental characteristics such as temperature, sound, pressure and also provides different applications such as...A wireless sensor network (WSN) is spatially distributing independent sensors to monitor physical and environmental characteristics such as temperature, sound, pressure and also provides different applications such as battlefield inspection and biological detection. The Constrained Motion and Sensor (CMS) Model represents the features and explain k-step reach ability testing to describe the states. The description and calculation based on CMS model does not solve the problem in mobile robots. The ADD framework based on monitoring radio measurements creates a threshold. But the methods are not effective in dynamic coverage of complex environment. In this paper, a Localized Coverage based on Shape and Area Detection (LCSAD) Framework is developed to increase the dynamic coverage using mobile robots. To facilitate the measurement in mobile robots, two algorithms are designed to identify the coverage area, (i.e.,) the area of a coverage hole or not. The two algorithms are Localized Geometric Voronoi Hexagon (LGVH) and Acquaintance Area Hexagon (AAH). LGVH senses all the shapes and it is simple to show all the boundary area nodes. AAH based algorithm simply takes directional information by locating the area of local and global convex points of coverage area. Both these algorithms are applied to WSN of random topologies. The simulation result shows that the proposed LCSAD framework attains minimal energy utilization, lesser waiting time, and also achieves higher scalability, throughput, delivery rate and 8% maximal coverage connectivity in sensor network compared to state-of-art works.展开更多
As an important promising biomarker,high frequency oscillations(HFOs)can be used to track epileptic activity and localize epileptogenic zones.However,visual marking of HFOs from a large amount of intracranial electroe...As an important promising biomarker,high frequency oscillations(HFOs)can be used to track epileptic activity and localize epileptogenic zones.However,visual marking of HFOs from a large amount of intracranial electroencephalogram(iEEG)data requires a great deal of time and effort from researchers,and is also very dependent on visual features and easily influenced by subjective factors.Therefore,we proposed an automatic epileptic HFO detection method based on visual features and non-intuitive multi-domain features.To eliminate the interference of continuous oscillatory activity in detected sporadic short HFO events,the iEEG signals adjacent to the detected events were set as the neighboring environmental range while the number of oscillations and the peak–valley differences were calculated as the environmental reference features.The proposed method was developed as a MatLab-based HFO detector to automatically detect HFOs in multi-channel,long-distance iEEG signals.The performance of our detector was evaluated on iEEG recordings from epileptic mice and patients with intractable epilepsy.More than 90%of the HFO events detected by this method were confirmed by experts,while the average missed-detection rate was<10%.Compared with recent related research,the proposed method achieved a synchronous improvement of sensitivity and specificity,and a balance between low false-alarm rate and high detection rate.Detection results demonstrated that the proposed method performs well in sensitivity,specificity,and precision.As an auxiliary tool,our detector can greatly improve the efficiency of clinical experts in inspecting HFO events during the diagnosis and treatment of epilepsy.展开更多
Based on the Mg^(2+)complexation with acid chrome blue K(ACBK)at pH 10.2,an automatic system was designed to determine total hardness of water.The system consists of a vector colorimeter,a multi-channel sampling pump ...Based on the Mg^(2+)complexation with acid chrome blue K(ACBK)at pH 10.2,an automatic system was designed to determine total hardness of water.The system consists of a vector colorimeter,a multi-channel sampling pump and both reagents A and B.Two kinds of reagent solutions were prepared and used in this system,i.e.,ammoniacal buffer and ACBK—disodium magnesium EDTA solutions.The experimental results of the standard solutions containing 2 and 3 mg/L of total hardness showed that the relative standard deviations(RSDs)were 1.9%and 2.2%,respectively,and the limit of detection(LOD)was only 0.035 mg/L.The detection of four natural water samples showed that the recoveries were between 85.0%and 108.6%,consistent with those obtained by ICP-AES method.展开更多
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences,Grant No.XDA0330000 and Grant No.XDB44000000。
文摘Epilepsy is a common neurological disorder that occurs at all ages.Epilepsy not only brings physical pain to patients,but also brings a huge burden to the lives of patients and their families.At present,epilepsy detection is still achieved through the observation of electroencephalography(EEG)by medical staff.However,this process takes a long time and consumes energy,which will create a huge workload to medical staff.Therefore,it is particularly important to realize the automatic detection of epilepsy.This paper introduces,in detail,the overall framework of EEG-based automatic epilepsy identification and the typical methods involved in each step.Aiming at the core modules,that is,signal acquisition analog front end(AFE),feature extraction and classifier selection,method summary and theoretical explanation are carried out.Finally,the future research directions in the field of automatic detection of epilepsy are prospected.
基金The National Natural Science Foundation of China under contract Nos 41506198 and 41476101the Natural Science Foundation Projects of Shandong Province of China under contract No.ZR2012FZ003the Science and Technology Development Plan of Qingdao City of China under contract No.13-1-4-121-jch
文摘Since the atmospheric correction is a necessary preprocessing step of remote sensing image before detecting green tide, the introduced error directly affects the detection precision. Therefore, the detection method of green tide is presented from Landsat TM/ETM plus image which needs not the atmospheric correction. In order to achieve an automatic detection of green tide, a linear relationship（y =0.723 x＋0.504） between detection threshold y and subtraction x（x=λnir–λred） is found from the comparing Landsat TM/ETM plus image with the field surveys.Using this relationship, green tide patches can be detected automatically from Landsat TM/ETM plus image.Considering there is brightness difference between different regions in an image, the image will be divided into a plurality of windows（sub-images） with a same size firstly, and then each window will be detected using an adaptive detection threshold determined according to the discovered linear relationship. It is found that big errors will appear in some windows, such as those covered by clouds seriously. To solve this problem, the moving step k of windows is proposed to be less than the window width n. Using this mechanism, most pixels will be detected[n/k]×[n/k] times except the boundary pixels, then every pixel will be assigned the final class（green tide or sea water） according to majority rule voting strategy. It can be seen from the experiments, the proposed detection method using multi-windows and their adaptive thresholds can detect green tide from Landsat TM/ETM plus image automatically. Meanwhile, it avoids the reliance on the accurate atmospheric correction.
基金supported by the National Natural Science Foundation of China (Grants Nos. 41674163, 41474141, 41204120, 41304127, 41304130, and 41574160)the Projects funded by China Postdoctoral Science Foundation (Grants Nos. 2013M542051, 2014T70732)+2 种基金the Hubei Province Natural Science Excellent Youth Foundation (2016CFA044)the Project Supported by the Specialized Research Fund for State Key Laboratoriesthe 985 funded project of School of Electronic information, Wuhan University
文摘As a dispersive wave mode produced by lightning strokes, tweek atmospherics provide important hints of lower ionospheric(i.e., D-region) electron density. Based on data accumulation from the WHU ELF/VLF receiver system, we develop an automatic detection module in terms of the maximum-entropy-spectral-estimation(MESE) method to identify unambiguous instances of low latitude tweeks.We justify the feasibility of our procedure through a detailed analysis of the data observed at the Suizhou Station(31.57°N, 113.32°E) on17 February 2016. A total of 3961 tweeks were registered by visual inspection;the automatic detection method captured 4342 tweeks, of which 3361 were correct ones, producing a correctness percentage of 77.4%(= 3361/4342) and a false alarm rate of 22.6%(= 981/4342).A Short-Time Fourier Transformation(STFT) was also applied to trace the power spectral profiles of identified tweeks and to evaluate the tweek propagation distance. It is found that the fitting accuracy of the frequency–time curve and the relative difference of propagation distance between the two methods through the slope and through the intercept can be used to further improve the accuracy of automatic tweek identification. We suggest that our automatic tweek detection and analysis method therefore supplies a valuable means to investigate features of low latitude tweek atmospherics and associated ionospheric parameters comprehensively.
基金supported by the National Natural Science Foundation of China(Grant No.42271078)the Key Research and Development Program of Shaanxi(Grant No.2024SF-YBXM669)the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(Grant No.2019QZKK0902)。
文摘Co-seismic landslide detection is essential for post-disaster rescue and risk assessment after an earthquake event.However,a variety of ground objects,including roads and bare land,have spectral characteristics similar to those of co-seismic landslides,making it difficult to gather information and assess their impact rapidly and accurately.Therefore,an automatic detection method based on a deep learning model,named ENVINet5,with multiple features(ENVINet5_MF)was proposed to solve this problem and improve the detection accuracy of co-seismic landslides.The ENVINet5_MF method is advantageous for co-seismic landslide detection because it features a landslide gain index(LGI)that effectively eliminates the spectral interference of bare land and roads.We conducted two experiments using multi-temporal PlanetScope images acquired in Hokkaido,Japan,and Mainling,China.The accuracy evaluation and rationality analysis show that ENVINet5_MF performed better than comparative methods and that the co-seismic landslide areas detected by ENVINet5_MF were the most consistent with ground reference data.The findings of this study suggest that ENVINet5_MF can provide an efficient and accurate method for coseismic landslide detection to ensure a rapid response to co-seismic landslide disasters.
文摘This research focuses on the automatic detection and grading of microaneurysms in fundus images of diabetic retinopathy using artificial intelligence deep learning algorithms.By integrating multi-source fundus image data and undergoing a rigorous preprocessing workflow,a hybrid deep learning model architecture combining a modified U-Net and a residual neural network was adopted for the study.The experimental results show that the model achieved an accuracy of[X]%in microaneurysm detection,with a recall rate of[Y]%and a precision rate of[Z]%.In terms of grading diabetic retinopathy,the Cohen’s kappa coefficient for agreement with clinical grading was[K],and there were specific sensitivities and specificities for each grade.Compared with traditional methods,this model has significant advantages in processing speed and result consistency.However,it also has limitations such as insufficient data diversity,difficulties for the algorithm in detecting microaneurysms in severely hemorrhagic images,and high computational costs.The results of this research are of great significance for the early screening and clinical diagnosis decision support of diabetic retinopathy.In the future,it is necessary to further optimize the data and algorithms and promote clinical integration and telemedicine applications.
文摘AIM:To develop an automated model for subfoveal choroidal thickness(SFCT)detection in optical coherence tomography(OCT)images,addressing manual fovea location and choroidal contour challenges.METHODS:Two procedures were proposed:defining the fovea and segmenting the choroid.Fovea localization from B-scan OCT image sequence with three-dimensional reconstruction(LocBscan-3D)predicted fovea location using central foveal depression features,and fovea localization from two-dimensional en-face OCT(LocEN-2D)used a mask region-based convolutional neural network(Mask R-CNN)model for optic disc detection,and determined the fovea location based on optic disc relative position.Choroid segmentation also employed Mask R-CNN.RESULTS:For 53 eyes in 28 healthy subjects,LocBscan-3D’s mean difference between manual and predicted fovea locations was 170.0μm,LocEN-2D yielded 675.9μm.LocEN-2D performed better in non-high myopia group(P=0.02).SFCT measurements from Mask R-CNN aligned with manual values.CONCLUSION:Our models accurately predict SFCT in OCT images.LocBscan-3D excels in precise fovea localization even with high myopia.LocEN-2D shows high detection rates but lower accuracy especially in the high myopia group.Combining both models offers a robust SFCT assessment approach,promising efficiency and accuracy for large-scale studies and clinical use.
基金This work was financially supported by the Tianjin Key Research and Development Program Science and Technology Support Key Project(Grant No.20YFZCSN00220)the Central Government Leading Local Science and Technology Development Special Project(Grant No.21ZYCGSN00590)the Inner Mongolia Autonomous Region Science and Technology Department Project(Grant No.2020GG0068).
文摘Determination of ovulation time is one of the most important tasks in sow reproduction management.Temperature variation in the vulva of the sows can be used as a predictor of ovulation time.However,the skin temperatures of sows in existing studies are obtained manually from infrared thermal images,posing an obstacle to the automatic prediction of ovulation time.In this study,an improved YOLO-V5s detector based on feature fusion and dilated convolution(FDYOLOV5s)was proposed for the automatic extraction of the vulva temperature of sows based on infrared thermal images.For the purpose of reducing the model complexity,the depthwise separable convolution and the modified lightweight ShuffleNet-V2 module were introduced in the backbone.Meanwhile,the feature fusion network structure of the model was simplified for efficiency,and a mixed dilated convolutional module was designed to obtain global features.The experimental results show that FD-YOLOV5s outperformed the other nine methods,with a mean average precision(mAP)of 99.1%,an average frame rate of 156.25 fps,and a model size of only 3.86 MB,indicating that the method effectively simplifies the model while ensuring detection accuracy.Using a linear regression between manual extraction and the results extracted using this method in randomly selected thermal images,the coefficients of determination for maximum and average vulvar temperatures reached 99.5%and 99.3%,respectively.The continuous vulva temperature of sows was obtained by the target detection algorithm,and the sow estrus detection was performed by the temperature trend and compared with the manually detected estrus results.The results showed that the sensitivity,specificity,and error rate of the estrus detection algorithm were 89.3%,94.5%,and 5.8%,respectively.The method achieves real-time and accurate extraction of sow vulva temperature and can be used for the automatic detection of sow estrus,which could be helpful for the automatic prediction of ovulation time.
基金supported by the National Science and Technology Project(Grant No.2012BAK19B04)the Spark Program of Earthquake Sciences,China Earthquake Administration(Grant No.XH12029)
文摘Real-time, automatic, and accurate determination of seismic signals is critical for rapid earthquake reporting and early warning. In this study, we present a correction trigger function（CTF） for automatically detecting regional seismic events and a fourth-order statistics algorithm with the Akaike information criterion（AIC） for determining the direct wave phase, based on the differences, or changes, in energy, frequency, and amplitude of the direct P- or S-waves signal and noise. Simulations suggest for that the proposed fourth-order statistics result in high resolution even for weak signal and noise variations at different amplitude, frequency, and polarization characteristics. To improve the precision of establishing the S-waves onset, first a specific segment of P-wave seismograms is selected and the polarization characteristics of the data are obtained. Second, the S-wave seismograms that contained the specific segment of P-wave seismograms are analyzed by S-wave polarization filtering. Finally, the S-wave phase onset times are estimated. The proposed algorithm was used to analyze regional earthquake data from the Shandong Seismic Network. The results suggest that compared with conventional methods, the proposed algorithm greatly decreased false and missed earthquake triggers, and improved the detection precision of direct P- and S-wave phases.
基金This work was supported by the National Key Research and Development Program of China(2017YFD0701603)Natural Science Foundation of China(61473235).
文摘In order to realize the automatic monitoring of ruminant activities of cows,an automatic detection method for the mouth area of ruminant cows based on machine vision technology was studied.Optical flow was used to calculate the relative motion speed of each pixel in the video frame images.The candidate mouth region with large motion ranges was extracted,and a series of processing methods,such as grayscale processing,threshold segmentation,pixel point expansion and adjacent region merging,were carried out to extract the real area of cows’mouth.To verify the accuracy of the proposed method,six videos with a total length of 96 min were selected for this research.The results showed that the highest accuracy was 87.80%,the average accuracy was 76.46%and the average running time of the algorithm was 6.39 s.All the results showed that this method can be used to detect the mouth area automatically,which lays the foundation for automatic monitoring of cows’ruminant behavior.
基金This work was supported by the National Key R&D Program of China(Grant No.2017YFD0700300)Key Research and Development Program of Yunnan Province(Grant No.2018ZC001-3)Intelligent Manufacturing&standardization of the Ministry of Industry and Information Technology of the People’s Republic China(No.2018GXZ1101011).
文摘As an important index of soil crushing performance of rotary tiller,the soil fragmentation rate is still limited to manual measurement.In this study,an automatic detection platform for soil fragmentation rate was designed,which integrated soil intake,screening,weighing and calculation of soil fragmentation rate.This platform can solve the problem that the index of the soil fragmentation rate cannot be detected quickly and effectively after rotary tillage,which leads to difficulty in field quality evaluation.The platform was mainly composed of a shovel soil module,conveying module,screening module,weighing module and automatic control system,which could realize single-line and multi-point automatic soil fragmentation rate detection.Based on the homogeneous dry slope model,the tilting angles of soil intake and soil feeding after rotary tillage on the platform were determined to be 30.10°and 26.67°,respectively.According to the principle of flow conservation,a rotary circulation screening module was designed to obtain soil particle size grading.A method based on the principle of multi-line and multi-point measurement was developed to detect soil fragmentation rate.The influence of screening speed on screening effect was analyzed,and the reasonable value of screening speed was determined to be 0.5 m/s.A field performance test was carried out in October 2019 to verify the detection performance of the platform.The results showed that,compared with the manual test method,the maximum test error was no more than 11%,the minimum test error was less than 4%,the maximum single test time was no more than 2 min,and the total test time of each test area was no more than 30 min.The efficiency of single-point detection was significantly better than the manual detection,which indicated that the design in this study met the requirements of rapid detection of soil fragmentation rate,and provided a new idea for the automatic detection of quality of rotary tillage.
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through Project Number WE-44-0033.
文摘One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrome can help in the process of recovery.Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition.This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies.Additionally,feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of classifiers.In this research,the tri-stage wrapper method is used because it reduces the computation time.The proposed study for the Automatic diagnosis of PCOS contains preprocessing,data normalization,feature selection,and classification.A dataset with 39 characteristics,including metabolism,neuroimaging,hormones,and biochemical information for 541 subjects,was employed in this scenario.To start,this research pre-processed the information.Next for feature selection,a tri-stage wrapper method such as Mutual Information,ReliefF,Chi-Square,and Xvariance is used.Then,various classification methods are tested and trained.Deep learning techniques including convolutional neural network(CNN),multi-layer perceptron(MLP),Recurrent neural network(RNN),and Bi long short-term memory(Bi-LSTM)are utilized for categorization.The experimental finding demonstrates that with effective feature extraction process using tri stage wrapper method+CNN delivers the highest precision(97%),high accuracy(98.67%),and recall(89%)when compared with other machine learning algorithms.
基金supported in part by the 14th Five-Year Project of Ministry of Science and Technology of China(2021YFD2000304)Fundamental Research Funds for the Central Universities(531118010509)Natural Science Foundation of Hunan Province,China(2021JJ40114)。
文摘Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety.The task is challenging because the shadows on the pavement may have similar intensity with the crack,which interfere with the crack detection performance.Till to the present,there still lacks efficient algorithm models and training datasets to deal with the interference brought by the shadows.To fill in the gap,we made several contributions as follows.First,we proposed a new pavement shadow and crack dataset,which contains a variety of shadow and pavement pixel size combinations.It also covers all common cracks(linear cracks and network cracks),placing higher demands on crack detection methods.Second,we designed a two-step shadow-removal-oriented crack detection approach:SROCD,which improves the performance of the algorithm by first removing the shadow and then detecting it.In addition to shadows,the method can cope with other noise disturbances.Third,we explored the mechanism of how shadows affect crack detection.Based on this mechanism,we propose a data augmentation method based on the difference in brightness values,which can adapt to brightness changes caused by seasonal and weather changes.Finally,we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters,and the algorithm improves the performance of the model overall.We compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset,and the experimental results demonstrate the superiority of our method.
基金supported by the National Natural Science Foundation of China(Grant No.32072788,31902210,32002227,32172784)the National Key Research and Development Program of China(Grant No.2019YFE0125600)the earmarked fund(Grant No.CARS36).
文摘Cows mounting behavior is a significant manifestation of estrus in cows.The timely detection of cows mounting behavior can make cows conceive in time,thereby improving milk production of cows and economic benefits of the pasture.Existing methods of mounting behavior detection are difficult to achieve precise detection under occlusion and severe scale change environments and meet real-time requirements.Therefore,this study proposed a Cow-YOLO model to detect cows mounting behavior.To meet the needs of real-time performance,YOLOv5s model is used as the baseline model.In order to solve the problem of difficult detection of cows mounting behavior in an occluded environment,the CSPDarknet53 of YOLOv5s is replaced with Non-local CSPDarknet53,which enables the network to obtain global information and improves the model’s ability to detect the mounting cows.Next,the neck of YOLOv5s is redesigned to Multiscale Neck,reinforcing the multi-scale feature fusion capability of model to solve difficulty detection under dramatic scale changes.Then,to further increase the detection accuracy,the Coordinate Attention Head is integrated into YOLOv5s.Finally,these improvements form a novel cow mounting detection model called Cow-YOLO and make Cow-YOLO more suitable for cows mounting behavior detection in occluded and drastic scale changes environments.Cow-YOLO achieved a precision of 99.7%,a recall of 99.5%,a mean average precision of 99.5%,and a detection speed of 156.3 f/s on the test set.Compared with existing detection methods of cows mounting behavior,Cow-YOLO achieved higher detection accuracy and faster detection speed in an occluded and drastic scale-change environment.Cow-YOLO can assist ranch breeders in achieving real-time monitoring of cows estrus,enhancing ranch economic efficiency.
基金supported by the National Natural Science Foundation of China(Grant nos.41274164,41031064)the Ocean Public Welfare Scientific Research Project of China(Grant no.201005017)+1 种基金the Foundation of Shaanxi Educational Committee(Grant no.12JK0543)the Youth Research Project of the Xi'an University of Posts and Telecommunications(Grant no.ZL2012-01)
文摘The analysis and exploration of auroral dynamics are very significant for studying auroral mechanisms. This paper proposes a method based on auroral dynamic processes for detecting auroral events automatically. We first obtained the motion fields using the multiscale fluid flow estimator. Then, the auroral video frame sequence was represented by the spatiotemporal statistics of local motion vectors. Finally, automatic auroral event detection was achieved. The experimental results show that our methods could detect the required auroral events effectively and accurately, and that the detections were independent on any specific auroral event. The proposed method makes it feasible to statistically analyze a large number of continuous observations based on the auroral dynamic process.
文摘At present,the network security situation is becoming more and more serious.Malicious network attacks such as computer viruses,Trojans and hacker attacks are becoming more and more rampant.National and group network attacks such as network information war and network terrorism have a serious damage to the production and life of the whole society.At the same time,with the rapid development of Internet of Things and the arrival of 5G era,IoT devices as an important part of industrial Internet system,have become an important target of infiltration attacks by hostile forces.This paper describes the challenges facing firmware vulnerability detection at this stage,and introduces four automatic detection and utilization technologies in detail:based on patch comparison,based on control flow,based on data flow and ROP attack against buffer vulnerabilities.On the basis of clarifying its core idea,main steps and experimental results,the limitations of its method are proposed.Finally,combined with four automatic detection methods,this paper summarizes the known vulnerability detection steps based on firmware analysis,and looks forward to the follow-up work.
文摘A wireless sensor network (WSN) is spatially distributing independent sensors to monitor physical and environmental characteristics such as temperature, sound, pressure and also provides different applications such as battlefield inspection and biological detection. The Constrained Motion and Sensor (CMS) Model represents the features and explain k-step reach ability testing to describe the states. The description and calculation based on CMS model does not solve the problem in mobile robots. The ADD framework based on monitoring radio measurements creates a threshold. But the methods are not effective in dynamic coverage of complex environment. In this paper, a Localized Coverage based on Shape and Area Detection (LCSAD) Framework is developed to increase the dynamic coverage using mobile robots. To facilitate the measurement in mobile robots, two algorithms are designed to identify the coverage area, (i.e.,) the area of a coverage hole or not. The two algorithms are Localized Geometric Voronoi Hexagon (LGVH) and Acquaintance Area Hexagon (AAH). LGVH senses all the shapes and it is simple to show all the boundary area nodes. AAH based algorithm simply takes directional information by locating the area of local and global convex points of coverage area. Both these algorithms are applied to WSN of random topologies. The simulation result shows that the proposed LCSAD framework attains minimal energy utilization, lesser waiting time, and also achieves higher scalability, throughput, delivery rate and 8% maximal coverage connectivity in sensor network compared to state-of-art works.
基金This work was supported by the National Natural Science Foundation of China(81630098,81671282,and 61471314).
文摘As an important promising biomarker,high frequency oscillations(HFOs)can be used to track epileptic activity and localize epileptogenic zones.However,visual marking of HFOs from a large amount of intracranial electroencephalogram(iEEG)data requires a great deal of time and effort from researchers,and is also very dependent on visual features and easily influenced by subjective factors.Therefore,we proposed an automatic epileptic HFO detection method based on visual features and non-intuitive multi-domain features.To eliminate the interference of continuous oscillatory activity in detected sporadic short HFO events,the iEEG signals adjacent to the detected events were set as the neighboring environmental range while the number of oscillations and the peak–valley differences were calculated as the environmental reference features.The proposed method was developed as a MatLab-based HFO detector to automatically detect HFOs in multi-channel,long-distance iEEG signals.The performance of our detector was evaluated on iEEG recordings from epileptic mice and patients with intractable epilepsy.More than 90%of the HFO events detected by this method were confirmed by experts,while the average missed-detection rate was<10%.Compared with recent related research,the proposed method achieved a synchronous improvement of sensitivity and specificity,and a balance between low false-alarm rate and high detection rate.Detection results demonstrated that the proposed method performs well in sensitivity,specificity,and precision.As an auxiliary tool,our detector can greatly improve the efficiency of clinical experts in inspecting HFO events during the diagnosis and treatment of epilepsy.
基金supported by both the Foundation(PCRRK21005)of State Key Laboratory of Pollution Control and Resource Reuse(Tongji University)The National Key Research and Development Program of China(2019YFC1805300)
文摘Based on the Mg^(2+)complexation with acid chrome blue K(ACBK)at pH 10.2,an automatic system was designed to determine total hardness of water.The system consists of a vector colorimeter,a multi-channel sampling pump and both reagents A and B.Two kinds of reagent solutions were prepared and used in this system,i.e.,ammoniacal buffer and ACBK—disodium magnesium EDTA solutions.The experimental results of the standard solutions containing 2 and 3 mg/L of total hardness showed that the relative standard deviations(RSDs)were 1.9%and 2.2%,respectively,and the limit of detection(LOD)was only 0.035 mg/L.The detection of four natural water samples showed that the recoveries were between 85.0%and 108.6%,consistent with those obtained by ICP-AES method.
