It is of great importance to obtain precise trace data,as traces are frequently the sole visible and measurable parameter in most outcrops.The manual recognition and detection of traces on high-resolution three-dimens...It is of great importance to obtain precise trace data,as traces are frequently the sole visible and measurable parameter in most outcrops.The manual recognition and detection of traces on high-resolution three-dimensional(3D)models are relatively straightforward but time-consuming.One potential solution to enhance this process is to use machine learning algorithms to detect the 3D traces.In this study,a unique pixel-wise texture mapper algorithm generates a dense point cloud representation of an outcrop with the precise resolution of the original textured 3D model.A virtual digital image rendering was then employed to capture virtual images of selected regions.This technique helps to overcome limitations caused by the surface morphology of the rock mass,such as restricted access,lighting conditions,and shading effects.After AI-powered trace detection on two-dimensional(2D)images,a 3D data structuring technique was applied to the selected trace pixels.In the 3D data structuring,the trace data were structured through 2D thinning,3D reprojection,clustering,segmentation,and segment linking.Finally,the linked segments were exported as 3D polylines,with each polyline in the output corresponding to a trace.The efficacy of the proposed method was assessed using a 3D model of a real-world case study,which was used to compare the results of artificial intelligence(AI)-aided and human intelligence trace detection.Rosette diagrams,which visualize the distribution of trace orientations,confirmed the high similarity between the automatically and manually generated trace maps.In conclusion,the proposed semi-automatic method was easy to use,fast,and accurate in detecting the dominant jointing system of the rock mass.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
Bladder urothelial carcinoma is the most common malignant tumor disease in urinary system,and its incidence rate ranks ninth in the world.In recent years,the continuous development of hyperspectral imaging technology ...Bladder urothelial carcinoma is the most common malignant tumor disease in urinary system,and its incidence rate ranks ninth in the world.In recent years,the continuous development of hyperspectral imaging technology has provided a new tool for the auxiliary diagnosis of bladder cancer.In this study,based on microscopic hyperspectral data,an automatic detection algorithm of bladder tumor cells combining color features and shape features is proposed.Support vector machine(SVM)is used to build classification models and compare the classification performance of spectral feature,spectral and shape fusion feature,and the fusion feature proposed in this paper on the same classifier.The results show that the sensitivity,specificity,and accuracy of our classification algorithm based on shape and color fusion features are 0.952,0.897,and 0.920,respectively,which are better than the classification algorithm only using spectral features.Therefore,this study can effectively extract the cell features of bladder urothelial carcinoma smear,thus achieving automatic,real-time,and noninvasive detection of bladder tumor cells,and then helping doctors improve the efficiency of pathological diagnosis of bladder urothelial cancer,and providing a reliable basis for doctors to choose treatment plans and judge the prognosis of the disease.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
Although quality assurance and quality control procedures are routinely applied in most air quality networks, outliers can still occur due to instrument malfunctions, the influence of harsh environments and the limita...Although quality assurance and quality control procedures are routinely applied in most air quality networks, outliers can still occur due to instrument malfunctions, the influence of harsh environments and the limitation of measuring methods. Such outliers pose challenges for data-powered applications such as data assimilation, statistical analysis of pollution characteristics and ensemble forecasting. Here, a fully automatic outlier detection method was developed based on the probability of residuals, which are the discrepancies between the observed and the estimated concentration values. The estimation can be conducted using filtering—or regressions when appropriate—to discriminate four types of outliers characterized by temporal and spatial inconsistency, instrument-induced low variances, periodic calibration exceptions, and less PM_(10) than PM_(2.5) in concentration observations, respectively. This probabilistic method was applied to detect all four types of outliers in hourly surface measurements of six pollutants(PM_(2.5), PM_(10),SO_2,NO_2,CO and O_3) from 1436 stations of the China National Environmental Monitoring Network during 2014-16. Among the measurements, 0.65%-5.68% are marked as outliers. with PM_(10) and CO more prone to outliers. Our method successfully identifies a trend of decreasing outliers from 2014 to 2016,which corresponds to known improvements in the quality assurance and quality control procedures of the China National Environmental Monitoring Network. The outliers can have a significant impact on the annual mean concentrations of PM_(2.5),with differences exceeding 10 μg m^(-3) at 66 sites.展开更多
Faced with the evolving attacks in recommender systems, many detection features have been proposed by human engineering and used in supervised or unsupervised detection methods. However, the detection features extract...Faced with the evolving attacks in recommender systems, many detection features have been proposed by human engineering and used in supervised or unsupervised detection methods. However, the detection features extracted by human engineering are usually aimed at some specific types of attacks. To further detect other new types of attacks, the traditional methods have to re-extract detection features with high knowledge cost. To address these limitations, the method for automatic extraction of robust features is proposed and then an Adaboost-based detection method is presented. Firstly, to obtain robust representation with prior knowledge, unlike uniform corruption rate in traditional mLDA(marginalized Linear Denoising Autoencoder), different corruption rates for items are calculated according to the ratings’ distribution. Secondly, the ratings sparsity is used to weight the mapping matrix to extract low-dimensional representation. Moreover, the uniform corruption rate is also set to the next layer in mSLDA(marginalized Stacked Linear Denoising Autoencoder) to extract the stable and robust user features. Finally, under the robust feature space, an Adaboost-based detection method is proposed to alleviate the imbalanced classification problem. Experimental results on the Netflix and Amazon review datasets indicate that the proposed method can effectively detect various attacks.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
Accurate detection and picking of the P-phase onset time in noisy microseismic data from underground mines remains a big challenge. Reliable P-phase onset time picking is necessary for accurate source location needed ...Accurate detection and picking of the P-phase onset time in noisy microseismic data from underground mines remains a big challenge. Reliable P-phase onset time picking is necessary for accurate source location needed for planning and rescue operations in the event of failures. In this paper, a new technique based on the discrete stationary wavelet transform （DSWT）and higher order statist!cs, is proposed for processing noisy data from underground mines. The objectives of this method are to （1） Improve manual detection and tPicking of P-phase onset; and （ii） provide an automatic means of detecting and picking P-phase onset me accurately. The DSWT is first used to filter the signal over several scales. The manual P-phase onset detection and picking are then obtained by computing the signal energy across selected scales with frequency bands that capture the signal of interest. The automatic P-phase onset, on the other hand, is achieved by using skewness- and kurtosis-based criterion applied to selected scales in a time-frequency domain. The method was tested using synthetic and field data from an underground limestone mine. Results were compared with results obtained by using the short-term to long-term average （STA/LTA） ratio and that by Reference Ge et al. （2009）. The results show that the me!hod provides a more reliable estimate of the P-phase onset arrival than the STA]LTA method when the signal to noise ratio is very low. Also, the results obtained from the field data matched accurately with the results from Reference Ge et al. （2009）.展开更多
The satellite-based automatic identification system (AIS) receiver has to encounter the frequency offset caused by the Doppler effect and the oscillator instability. This paper proposes a non-coherent sequence detecti...The satellite-based automatic identification system (AIS) receiver has to encounter the frequency offset caused by the Doppler effect and the oscillator instability. This paper proposes a non-coherent sequence detection scheme for the satellite-based AIS signal transmitted over the white Gaussian noise channel. Based on the maximum likelihood estimation and a Viterbi decoder, the proposed scheme is capable of tolerating a frequency offset up to 5% of the symbol rate. The complexity of the proposed scheme is reduced by the state-complexity reduction, which is based on per-survivor processing. Simulation results prove that the proposed non-coherent sequence detection scheme has high robustness to frequency offset compared to the relative scheme when messages collision exists.展开更多
In the process of bromine production,because of lag adjustment methods,there are problems of adjusting delay,raw material wastage and low growth rate.By considering the nature of bromine production,with the help of fu...In the process of bromine production,because of lag adjustment methods,there are problems of adjusting delay,raw material wastage and low growth rate.By considering the nature of bromine production,with the help of fuzzy data processing method,computer detection and display technique,we designed an automatic detection instrument for the ratio of chlorine to bromine in oxidized liquid of bromine production.This instrument can be used to detect the different parameters of raw materials adjustment and control in real time,and afford assurance that raw materials will be adjusted in time.This paper briefly introduces the working mechanism,hardware and software design of the instrument.展开更多
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
This paper presents an automatic system for failure detection in hydro-power generators. The main idea of this system is to detect failure using current and voltage signals acquired without any type of internal interf...This paper presents an automatic system for failure detection in hydro-power generators. The main idea of this system is to detect failure using current and voltage signals acquired without any type of internal interference in the generator operation. The detected failures could be mechanical or electrical origins, such as: problems in bearings, unwanted vibrations, partial discharges, misalignment, unbalancing, among others. It is possible because the generator acts as a transducer for mechanical problems, and they appear in current and voltage signals. This automatic system based on electric signature analysis has been installed in Itapebi Power Plant generators since 2012. Some results are presented in this paper.展开更多
In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set f...In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.展开更多
Multi-way principal component analysis（MPCA）has received considerable attention and been widely used in process monitoring.A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensio...Multi-way principal component analysis（MPCA）has received considerable attention and been widely used in process monitoring.A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces.However,low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model.This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information.The MPCA model and the knowledge base are built based on the new subspace.Then,fault detection and isolation with the squared prediction error（SPE）statistic and the Hotelling（T2）statistic are also realized in process monitoring.When a fault occurs,fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables.For fault isolation of subspace based on the T2 statistic,the relationship between the statistic indicator and state variables is constructed,and the constraint conditions are presented to check the validity of fault isolation.Then,to improve the robustness of fault isolation to unexpected disturbances,the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation.Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system（ASCS）to prove the correctness and effectiveness of the algorithm.The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model,and sets the relationship between the state variables and fault detection indicators for fault isolation.展开更多
Craters are salient terrain features on planetary surfaces, and provide useful information about the relative dating of geological unit of planets. In addition, they are ideal landmarks for spacecraft navigation. Due ...Craters are salient terrain features on planetary surfaces, and provide useful information about the relative dating of geological unit of planets. In addition, they are ideal landmarks for spacecraft navigation. Due to low contrast and uneven illumination, automatic extraction of craters remains a challenging task. This paper presents a saliency detection method for crater edges and a feature matching algorithm based on edges informa- tion. The craters are extracted through saliency edges detection, edge extraction and selection, feature matching of the same crater edges and robust ellipse fitting. In the edges matching algorithm, a crater feature model is proposed by analyzing the relationship between highlight region edges and shadow region ones. Then, crater edges are paired through the effective matching algorithm. Experiments of real planetary images show that the proposed approach is robust to different lights and topographies, and the detection rate is larger than 90%.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
Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for position...Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for positioning of general aviation aircraft.Aim to acquire the accurate position information of aircraft and detect anomaly data,the ADS-B anomaly data detection model based on deep learning and difference of Gaussian(DoG)approach is proposed.First,according to the characteristic of ADS-B data,the ADS-B position data are transformed into the coordinate system.And the origin of the coordinate system is set up as the take-off point.Then,based on the kinematic principle,the ADS-B anomaly data can be removed.Moreover,the details of the ADS-B position data can be got by the DoG approach.Finally,the long short-term memory(LSTM)neural network is used to optimize the recurrent neural network(RNN)with severe gradient reduction for processing ADS-B data.The position data of ADS-B are reconstructed by the sequence to sequence(seq2seq)model which is composed of LSTM neural network,and the reconstruction error is used to detect the anomalous data.Based on the real flight data of general aviation aircraft,the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model,and its running time is reduced.Compared with the RNN,the accuracy of anomaly detection is increased by 2.7%.The performance of the proposed model is better than that of the traditional anomaly detection models.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
基金supported by grants from the Human Resources Development program (Grant No.20204010600250)the Training Program of CCUS for the Green Growth (Grant No.20214000000500)by the Korea Institute of Energy Technology Evaluation and Planning (KETEP)funded by the Ministry of Trade,Industry,and Energy of the Korean Government (MOTIE).
文摘It is of great importance to obtain precise trace data,as traces are frequently the sole visible and measurable parameter in most outcrops.The manual recognition and detection of traces on high-resolution three-dimensional(3D)models are relatively straightforward but time-consuming.One potential solution to enhance this process is to use machine learning algorithms to detect the 3D traces.In this study,a unique pixel-wise texture mapper algorithm generates a dense point cloud representation of an outcrop with the precise resolution of the original textured 3D model.A virtual digital image rendering was then employed to capture virtual images of selected regions.This technique helps to overcome limitations caused by the surface morphology of the rock mass,such as restricted access,lighting conditions,and shading effects.After AI-powered trace detection on two-dimensional(2D)images,a 3D data structuring technique was applied to the selected trace pixels.In the 3D data structuring,the trace data were structured through 2D thinning,3D reprojection,clustering,segmentation,and segment linking.Finally,the linked segments were exported as 3D polylines,with each polyline in the output corresponding to a trace.The efficacy of the proposed method was assessed using a 3D model of a real-world case study,which was used to compare the results of artificial intelligence(AI)-aided and human intelligence trace detection.Rosette diagrams,which visualize the distribution of trace orientations,confirmed the high similarity between the automatically and manually generated trace maps.In conclusion,the proposed semi-automatic method was easy to use,fast,and accurate in detecting the dominant jointing system of the rock mass.
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Bethune Medical Engineering and Instrument Center Fund(E10133Y8H0)Jilin province science and technology development plan project(20210204216YY,20210204146YY).
文摘Bladder urothelial carcinoma is the most common malignant tumor disease in urinary system,and its incidence rate ranks ninth in the world.In recent years,the continuous development of hyperspectral imaging technology has provided a new tool for the auxiliary diagnosis of bladder cancer.In this study,based on microscopic hyperspectral data,an automatic detection algorithm of bladder tumor cells combining color features and shape features is proposed.Support vector machine(SVM)is used to build classification models and compare the classification performance of spectral feature,spectral and shape fusion feature,and the fusion feature proposed in this paper on the same classifier.The results show that the sensitivity,specificity,and accuracy of our classification algorithm based on shape and color fusion features are 0.952,0.897,and 0.920,respectively,which are better than the classification algorithm only using spectral features.Therefore,this study can effectively extract the cell features of bladder urothelial carcinoma smear,thus achieving automatic,real-time,and noninvasive detection of bladder tumor cells,and then helping doctors improve the efficiency of pathological diagnosis of bladder urothelial cancer,and providing a reliable basis for doctors to choose treatment plans and judge the prognosis of the disease.
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supported by the National Natural Science Foundation (Grant Nos.91644216 and 41575128)the CAS Information Technology Program (Grant No.XXH13506-302)Guangdong Provincial Science and Technology Development Special Fund (No.2017B020216007)
文摘Although quality assurance and quality control procedures are routinely applied in most air quality networks, outliers can still occur due to instrument malfunctions, the influence of harsh environments and the limitation of measuring methods. Such outliers pose challenges for data-powered applications such as data assimilation, statistical analysis of pollution characteristics and ensemble forecasting. Here, a fully automatic outlier detection method was developed based on the probability of residuals, which are the discrepancies between the observed and the estimated concentration values. The estimation can be conducted using filtering—or regressions when appropriate—to discriminate four types of outliers characterized by temporal and spatial inconsistency, instrument-induced low variances, periodic calibration exceptions, and less PM_(10) than PM_(2.5) in concentration observations, respectively. This probabilistic method was applied to detect all four types of outliers in hourly surface measurements of six pollutants(PM_(2.5), PM_(10),SO_2,NO_2,CO and O_3) from 1436 stations of the China National Environmental Monitoring Network during 2014-16. Among the measurements, 0.65%-5.68% are marked as outliers. with PM_(10) and CO more prone to outliers. Our method successfully identifies a trend of decreasing outliers from 2014 to 2016,which corresponds to known improvements in the quality assurance and quality control procedures of the China National Environmental Monitoring Network. The outliers can have a significant impact on the annual mean concentrations of PM_(2.5),with differences exceeding 10 μg m^(-3) at 66 sites.
基金supported by the National Natural Science Foundation of China [Nos. 61772452, 61379116]the Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi [No.2019L0847]the Natural Science Foundation of Hebei Province, China [No. F2015203046]
文摘Faced with the evolving attacks in recommender systems, many detection features have been proposed by human engineering and used in supervised or unsupervised detection methods. However, the detection features extracted by human engineering are usually aimed at some specific types of attacks. To further detect other new types of attacks, the traditional methods have to re-extract detection features with high knowledge cost. To address these limitations, the method for automatic extraction of robust features is proposed and then an Adaboost-based detection method is presented. Firstly, to obtain robust representation with prior knowledge, unlike uniform corruption rate in traditional mLDA(marginalized Linear Denoising Autoencoder), different corruption rates for items are calculated according to the ratings’ distribution. Secondly, the ratings sparsity is used to weight the mapping matrix to extract low-dimensional representation. Moreover, the uniform corruption rate is also set to the next layer in mSLDA(marginalized Stacked Linear Denoising Autoencoder) to extract the stable and robust user features. Finally, under the robust feature space, an Adaboost-based detection method is proposed to alleviate the imbalanced classification problem. Experimental results on the Netflix and Amazon review datasets indicate that the proposed method can effectively detect various attacks.
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.
文摘Accurate detection and picking of the P-phase onset time in noisy microseismic data from underground mines remains a big challenge. Reliable P-phase onset time picking is necessary for accurate source location needed for planning and rescue operations in the event of failures. In this paper, a new technique based on the discrete stationary wavelet transform （DSWT）and higher order statist!cs, is proposed for processing noisy data from underground mines. The objectives of this method are to （1） Improve manual detection and tPicking of P-phase onset; and （ii） provide an automatic means of detecting and picking P-phase onset me accurately. The DSWT is first used to filter the signal over several scales. The manual P-phase onset detection and picking are then obtained by computing the signal energy across selected scales with frequency bands that capture the signal of interest. The automatic P-phase onset, on the other hand, is achieved by using skewness- and kurtosis-based criterion applied to selected scales in a time-frequency domain. The method was tested using synthetic and field data from an underground limestone mine. Results were compared with results obtained by using the short-term to long-term average （STA/LTA） ratio and that by Reference Ge et al. （2009）. The results show that the me!hod provides a more reliable estimate of the P-phase onset arrival than the STA]LTA method when the signal to noise ratio is very low. Also, the results obtained from the field data matched accurately with the results from Reference Ge et al. （2009）.
文摘The satellite-based automatic identification system (AIS) receiver has to encounter the frequency offset caused by the Doppler effect and the oscillator instability. This paper proposes a non-coherent sequence detection scheme for the satellite-based AIS signal transmitted over the white Gaussian noise channel. Based on the maximum likelihood estimation and a Viterbi decoder, the proposed scheme is capable of tolerating a frequency offset up to 5% of the symbol rate. The complexity of the proposed scheme is reduced by the state-complexity reduction, which is based on per-survivor processing. Simulation results prove that the proposed non-coherent sequence detection scheme has high robustness to frequency offset compared to the relative scheme when messages collision exists.
文摘In the process of bromine production,because of lag adjustment methods,there are problems of adjusting delay,raw material wastage and low growth rate.By considering the nature of bromine production,with the help of fuzzy data processing method,computer detection and display technique,we designed an automatic detection instrument for the ratio of chlorine to bromine in oxidized liquid of bromine production.This instrument can be used to detect the different parameters of raw materials adjustment and control in real time,and afford assurance that raw materials will be adjusted in time.This paper briefly introduces the working mechanism,hardware and software design of the instrument.
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.
文摘This paper presents an automatic system for failure detection in hydro-power generators. The main idea of this system is to detect failure using current and voltage signals acquired without any type of internal interference in the generator operation. The detected failures could be mechanical or electrical origins, such as: problems in bearings, unwanted vibrations, partial discharges, misalignment, unbalancing, among others. It is possible because the generator acts as a transducer for mechanical problems, and they appear in current and voltage signals. This automatic system based on electric signature analysis has been installed in Itapebi Power Plant generators since 2012. Some results are presented in this paper.
基金National Natural Science Foundation of China(U2133208,U20A20161)National Natural Science Foundation of China(No.62273244)Sichuan Science and Technology Program(No.2022YFG0180).
文摘In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.
基金Supported by National Hi-tech Research and Development Program of China(863 Program,Grant No.2011AA11A223)
文摘Multi-way principal component analysis（MPCA）has received considerable attention and been widely used in process monitoring.A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces.However,low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model.This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information.The MPCA model and the knowledge base are built based on the new subspace.Then,fault detection and isolation with the squared prediction error（SPE）statistic and the Hotelling（T2）statistic are also realized in process monitoring.When a fault occurs,fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables.For fault isolation of subspace based on the T2 statistic,the relationship between the statistic indicator and state variables is constructed,and the constraint conditions are presented to check the validity of fault isolation.Then,to improve the robustness of fault isolation to unexpected disturbances,the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation.Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system（ASCS）to prove the correctness and effectiveness of the algorithm.The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model,and sets the relationship between the state variables and fault detection indicators for fault isolation.
基金supported by the National Natural Science Foundation of China(61210012)
文摘Craters are salient terrain features on planetary surfaces, and provide useful information about the relative dating of geological unit of planets. In addition, they are ideal landmarks for spacecraft navigation. Due to low contrast and uneven illumination, automatic extraction of craters remains a challenging task. This paper presents a saliency detection method for crater edges and a feature matching algorithm based on edges informa- tion. The craters are extracted through saliency edges detection, edge extraction and selection, feature matching of the same crater edges and robust ellipse fitting. In the edges matching algorithm, a crater feature model is proposed by analyzing the relationship between highlight region edges and shadow region ones. Then, crater edges are paired through the effective matching algorithm. Experiments of real planetary images show that the proposed approach is robust to different lights and topographies, and the detection rate is larger than 90%.
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 Key R&D Program of China(No.2018AAA0100804)the Talent Project of Revitalization Liaoning(No.XLYC1907022)+5 种基金the Key R&D Projects of Liaoning Province(No.2020JH2/10100045)the Capacity Building of Civil Aviation Safety(No.TMSA1614)the Natural Science Foundation of Liaoning Province(No.2019-MS-251)the Scientific Research Project of Liaoning Provincial Department of Education(Nos.L201705,L201716)the High-Level Innovation Talent Project of Shenyang(No.RC190030)the Second Young and Middle-Aged Talents Support Program of Shenyang Aerospace University.
文摘Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for positioning of general aviation aircraft.Aim to acquire the accurate position information of aircraft and detect anomaly data,the ADS-B anomaly data detection model based on deep learning and difference of Gaussian(DoG)approach is proposed.First,according to the characteristic of ADS-B data,the ADS-B position data are transformed into the coordinate system.And the origin of the coordinate system is set up as the take-off point.Then,based on the kinematic principle,the ADS-B anomaly data can be removed.Moreover,the details of the ADS-B position data can be got by the DoG approach.Finally,the long short-term memory(LSTM)neural network is used to optimize the recurrent neural network(RNN)with severe gradient reduction for processing ADS-B data.The position data of ADS-B are reconstructed by the sequence to sequence(seq2seq)model which is composed of LSTM neural network,and the reconstruction error is used to detect the anomalous data.Based on the real flight data of general aviation aircraft,the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model,and its running time is reduced.Compared with the RNN,the accuracy of anomaly detection is increased by 2.7%.The performance of the proposed model is better than that of the traditional anomaly detection models.
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.
