Passive microseismic monitoring(PMM)serves as a fundamental technology for assessing hydraulic fracturing(HF)effectiveness,with a key focus on accurate and efficient phase detection/arrival picking and source location...Passive microseismic monitoring(PMM)serves as a fundamental technology for assessing hydraulic fracturing(HF)effectiveness,with a key focus on accurate and efficient phase detection/arrival picking and source location.In PMM data processing,the data-driven paradigm(deep learning based)outperforms the model-driven paradigm in characteristic extraction but lacks quality control and uncertainty quantification.Monte Carlo Dropout,a Bayesian uncertainty quantification technique,performs stochastic neuron deactivation through multiple forward propagation samplings.Therefore,this study proposes a deep learning neural network incorporating uncertainty quantification with manual quality control integration,establishing an optimized workflow spanning automated phase detection to robust source location.The methodology implementation comprises two principal components:(1)The MDNet employing Monte Carlo Dropout strategy enabling simultaneous phase detection/arrival picking and unce rtainty estimation;(2)an integrated hybrid-driven workflow with a traveltime-based inve rsion method for source location.Validation with field data demonstrates that MD-Net achieves superior performance under low signal-to-noise ratio conditions,maintaining detection accuracy exceeding 99%for both P-and S-waves.The phase arrival picking precision shows significant improvement,with a 40%reduction in standard deviation compared to the baseline model(P-S time difference decreasing from12.0 ms to 7.1 ms),while providing quantifiable uncertainty metrics for manual calibration.Source location results further reveal that our hybrid-driven workflow produces more physically plausible event distributions,with 100%of microseismic eve nts clustering along the primary fracture expanding direction.This performance surpasses traditional cross-correlation methods and single/multi-trace data-driven me thods in spatial rationality.This study establishes an inte rpretable,high-pre cision automated framework for HF-PMM applications,demonstrating potential for extension to diverse geological settings and monitoring configurations.展开更多
Buried pipelines are an essential component of the urban infrastructure of modern cities.Traditional buried pipes are mainly made of metal materials.With the development of material science and technology in recent ye...Buried pipelines are an essential component of the urban infrastructure of modern cities.Traditional buried pipes are mainly made of metal materials.With the development of material science and technology in recent years,non-metallic pipes,such as plastic pipes,ceramic pipes,and concrete pipes,are increasingly taking the place of pipes made from metal in various pipeline networks such as water supply,drainage,heat,industry,oil,and gas.The location technologies for the location of the buried metal pipeline have become mature,but detection and location technologies for the non-metallic pipelines are still developing.In this paper,current trends and future perspectives of detection and location of buried non-metallic pipelines are summarized.Initially,this paper reviews and analyzes electromagnetic induction technologies,electromagnetic wave technologies,and other physics-based technologies.It then focuses on acoustic detection and location technologies,and finally introduces emerging technologies.Then the technical characteristics of each detection and location method have been compared,with their strengths and weaknesses identified.The current trends and future perspectives of each buried non-metallic pipeline detection and location technology have also been defined.Finally,some suggestions for the future development of buried non-metallic pipeline detection and location technologies are provided.展开更多
Earthquake detection and location are essential in earthquake studies,which generally consists of two main classes:waveform-based and pick-based methods.To evaluate the ability of two different methods,a graphicsproce...Earthquake detection and location are essential in earthquake studies,which generally consists of two main classes:waveform-based and pick-based methods.To evaluate the ability of two different methods,a graphicsprocessing-unit-based Match&Locate(GPU-M&L)method and a rapid earthquake association and location(REAL)method are applied to continuous seismic data recorded by 24 digital seismic stations from Jiangsu Seismic Network during 2013 for comparison.GPU-M&L is one of waveform-based methods by waveform cross-correlations while REAL is one of pick-based method to associate arrivals of different seismic phases and locate events through counting the number of P and S picks and travel time residuals.Twenty-six templates are selected from the Jiangsu Seismic Network local catalog by using the GPU-M&L.The number of newly detected and located events is about 2.8 times more than those listed in the local catalog.We both utilize a deep-neural-network-based arrival-time picking method called PhaseNet and a shortterm/long-term average(STA/LTA)trigger algorithm for seismic phase detection and picking by applying the REAL.We then refine seismic locations using a least-squares location method(VELEST)and a high-precision relative location method(hypoDD).By applying STA/LTA and PhaseNet,1006 and 1893 events are associated and located,respectively.The newly detected events are mainly clustered and show steeply dipping fault planes.By analyzing the performance of these methods based on long-term continuous seismic data,the detected catalogs by the GPU-M&L and REAL show that the magnitudes of completeness are 1.4 and 0.8,respectively,which are smaller than 2.6 given by the local catalog.Although REAL provides improvement compared with GPU-M&L,REAL is highly dependent on phase detection and picking which is strongly affected by signal-noise ratio(SNR).Stations at southeast of the study region with low SNR may lead to few detections in the same area.展开更多
A distributed optical fiber disturbance detection system consisted of a Sagnac interferometer and a Mach-Zehnder interferometer is demonstrated. Two interferometers outputs are connected to an electric band-pass filte...A distributed optical fiber disturbance detection system consisted of a Sagnac interferometer and a Mach-Zehnder interferometer is demonstrated. Two interferometers outputs are connected to an electric band-pass filter via a detector respectively. The central frequencies of the two filters are selected adaptively according to the disturbance frequency. The disturbance frequency is obtained by either frequency spectrum of the two interferometers outputs. An alarm is given out only when the Sagnac interferometer output is changed. A disturbance position is determined by calculating a time difference with a cross-correlation method between the filter output connected to the Sagnac interferometer and derivative of the filter output connected to the Mach-Zehnder interferometer. The frequency spectrum, derivative and cross-correlation are obtained by a signal processing system. Theory analysis and simulation results are presented. They show that the system structure and location method are effective, accurate, and immune to environmental variations.展开更多
The valuation relation of potential difference with discharging time in Electrical Discharge Machining (EDM) is analyzed theoretically and tested and verified by experiments designed in this paper and the relation bet...The valuation relation of potential difference with discharging time in Electrical Discharge Machining (EDM) is analyzed theoretically and tested and verified by experiments designed in this paper and the relation between potential difference and spark location is induced and analyzed, and proceed by experiments under the condition of onedimension.展开更多
Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications i...Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.展开更多
Position-spoofing-based attacks seriously threaten the security of Vehicular Ad Hoc Network(VANET).An effective solution to detect position spoofing is location verification.However,since vehicles move fast and the to...Position-spoofing-based attacks seriously threaten the security of Vehicular Ad Hoc Network(VANET).An effective solution to detect position spoofing is location verification.However,since vehicles move fast and the topology changes quickly in VANET,the static location verification method in Wireless Sensor Network(WSN) is not suitable for VANET.Taking into account the dynamic changing topology of VANET and collusion,we propose a Time-Slice-based Location Verification scheme,named TSLV,to resist position spoofing in VANET.Specifically,TSLV transforms the dynamic topology into static topology by time slice and each time slice corresponds to a verification process.The verifier can implement location verification for the corresponding prover.During the verification process,the verifier first filters out vehicles which provide unreasonably claimed locations,and then uses the Mean Square Error(MSE)-based cluster approach to separate the consistent vehicles by time slice,and uses the consistent set for its verification.In addition,security analysis and simulation show that TSLV can defend against the collusion attack effectively.展开更多
A new vision-based long-distance lane perception and front vehicle location method was developed for decision making of full autonomous vehicles on highway roads,Firstly,a real-time long-distance lane detection approa...A new vision-based long-distance lane perception and front vehicle location method was developed for decision making of full autonomous vehicles on highway roads,Firstly,a real-time long-distance lane detection approach was presented based on a linear-cubic road model for two-lane highways.By using a novel robust lane marking feature which combines the constraints of intensity,edge and width,the lane markings in far regions were extracted accurately and efficiently.Next,the detected lane lines were selected and tracked by estimating the lateral offset and heading angle of ego vehicle with a Kalman filter,Finally,front vehicles were located on correct lanes using the tracked lane lines,Experiment results show that the proposed lane perception approach can achieve an average correct detection rate of 94.37% with an average false positive detection rate of 0.35%,The proposed approaches for long-distance lane perception and front vehicle location were validated in a 286 km full autonomous drive experiment under real traffic conditions.This successful experiment shows that the approaches are effective and robust enough for full autonomous vehicles on highway roads.展开更多
Using the spatial coordinates of detection stations and the time of arrival of lightning wave,the observation equations can be expressed.For the large lightning detection network,the least square method is used to pro...Using the spatial coordinates of detection stations and the time of arrival of lightning wave,the observation equations can be expressed.For the large lightning detection network,the least square method is used to process the adjustment of observation data to find the most probable value of lightning position,and the result is assessed by the mean error and dilution of precision.Lightning location precision is affected by figure factor.The conclusion can be used in the design of location network,data processing,and data analysis.展开更多
Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore waters.Current deep learning researches on optical image-based ship detection mainly focus on i...Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore waters.Current deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of detection.To solve this problem,we present a hybrid ship detection framework which is named EfficientShip in this paper.The core parts of the EfficientShip are DLA-backboned object location(DBOL)and CascadeRCNN-guided object classification(CROC).The DBOL is responsible for finding potential ship objects,and the CROC is used to categorize the potential ship objects.We also design a pixel-spatial-level data augmentation(PSDA)to reduce the risk of detection model overfitting.We compare the proposed EfficientShip with state-of-the-art(SOTA)literature on a ship detection dataset called Seaships.Experiments show our ship detection framework achieves a result of 99.63%(mAP)at 45 fps,which is much better than 8 SOTA approaches on detection accuracy and can also meet the requirements of real-time application scenarios.展开更多
Vehicle recognition system (VRS) plays a very important role in the field of intelligent transportation systems.A novel and intuitive method is proposed for vehicle location.The method we provide for vehicle location ...Vehicle recognition system (VRS) plays a very important role in the field of intelligent transportation systems.A novel and intuitive method is proposed for vehicle location.The method we provide for vehicle location is based on human visual perception model technique. The perception color space HSI in this algorithm is adopted.Three color components of a color image and more potential edge patterns are integrated for solving the feature extraction problem.A fast and automatic threshold technique based on human visual perception model is also developed.The vertical edge projection and horizontal edge projection are adopted for locating left-right boundary of vehicle and top-bottom boundary of vehicle, respectively. Very promising experimental results are obtained using real-time vehicle image sequences, which have confirmed that this proposed location vehicle method is efficient and reliable, and its calculation speed meets the needs of the VRS.展开更多
Visualizing lightning location data is necessary in analyzing and researching lightning activity patterns.This article uses C#and the cross-platform.NET framework to develop a lightning location data analysis class li...Visualizing lightning location data is necessary in analyzing and researching lightning activity patterns.This article uses C#and the cross-platform.NET framework to develop a lightning location data analysis class library and the data-driven client to help lightning researchers improve work efficiency by avoiding repeated wheel invention.Lightning Location System Data Analyzer(LLSDA)is a suite of software tools that includes a.NET class library for software developers and a desktop application for end users.It supports a wide range of lightning location data formats,such as the University of Washington Global Lightning Location System(WWLLN)and Beijing Huayun Dongfang ADTD Lightning Location System data format,and maintains scalability.The class library can easily read,parse,and analyze lightning location data,and combined with third-party frameworks can realize grid analysis.The desktop application can be combined with MeteoInfo(a GIS open-source project)for secondary development.展开更多
Using the data of the Lightning Location System( LLS) over Hubei Province,through the analysis of the distribution characteristics of CG( Cloud-to-Ground) flash density in 2015,it was found that the layout of the dete...Using the data of the Lightning Location System( LLS) over Hubei Province,through the analysis of the distribution characteristics of CG( Cloud-to-Ground) flash density in 2015,it was found that the layout of the detection station had influence on the spatial distribution of lightning.Grid CG flash density data were used to characterize the spatial distribution of the CG flash,and station distance factor was used to characterize the detection station layout. The result showed that there existed negative correlation between density and factor,significant correlation between the density component and the factor for the lightning current amplitude of 5 to 30 kA,and insignificant correlation between >30 kA of density component and factor. So it is necessary to revise the density to eliminate the influence of the station layout. On the basis of the linear regression method and its residual theory,the revision model of the grid CG flash density and the statistical model of relative detection efficiency were established. The result consistency of segment and non-segmented revision of the density was verified. Through the contrastive analysis of theoretical detection efficiency and relative detection efficiency,the feasibility for revision method of CG flash density and the statistical method of relative detection efficiency was also verified.展开更多
Detection of 2-dimention spark locations by electromagnetic detection method in electrical discharge machining (EDM) is studied. The method, which is applied and investigated, is based on the fact that the release of ...Detection of 2-dimention spark locations by electromagnetic detection method in electrical discharge machining (EDM) is studied. The method, which is applied and investigated, is based on the fact that the release of energy from a spark is transformed into electromagnetic wave around the workpiece. A new sensor system composed of high precision linear Hall components and cubic ferrite is used to detect the intensity of magnetic field. Relation equation between the output of the sensor system and 2-dimention spark locations experiment under a spiculate electrode is introduced, and its diagram of curve is drawn. As a result, the information that can be achieved by detecting spark’s location gives new possibilities for an extended analysis of the EDM-process.展开更多
A method is proposed for detecting damage to shear structures by using Support Vector Machine (SVM) and only the first three natural frequencies of the translational modes. This method is able to determine the damage ...A method is proposed for detecting damage to shear structures by using Support Vector Machine (SVM) and only the first three natural frequencies of the translational modes. This method is able to determine the damage location in any story of a shear building with only two vibration sensors;to obtain modal frequencies, one sensor on the ground detects an input and the other on the roof detects the output. Based on the shifts in the first three natural frequencies, damage location indicators are proposed, and used as new feature vectors for SVM. Simulations of five-story, nine-story and twenty-one-story shear structures and experiments on a five-story steel model were used to test the performance of the proposed method.展开更多
The detection of hypersonic targets usually confronts range migration(RM)issue before coherent integration(CI).The traditional methods aiming at correcting RM to obtain CI mainly considers the narrow-band radar condit...The detection of hypersonic targets usually confronts range migration(RM)issue before coherent integration(CI).The traditional methods aiming at correcting RM to obtain CI mainly considers the narrow-band radar condition.However,with the increasing requirement of far-range detection,the time bandwidth product,which is corresponding to radar’s mean power,should be promoted in actual application.Thus,the echo signal generates the scale effect(SE)at large time bandwidth product situation,influencing the intra and inter pulse integration performance.To eliminate SE and correct RM,this paper proposes an effective algorithm,i.e.,scaled location rotation transform(ScLRT).The ScLRT can remove SE to obtain the matching pulse compression(PC)as well as correct RM to complete CI via the location rotation transform,being implemented by seeking the actual rotation angle.Compared to the traditional coherent detection algorithms,Sc LRT can address the SE problem to achieve better detection/estimation capabilities.At last,this paper gives several simulations to assess the viability of ScLRT.展开更多
The increase in the number of sensitive loads in power systems has made power quality,particularly voltage sag,a prominent problem due to its effects on consumers from both the utility and customer perspectives.Thus,t...The increase in the number of sensitive loads in power systems has made power quality,particularly voltage sag,a prominent problem due to its effects on consumers from both the utility and customer perspectives.Thus,to evaluate the effects of voltage sag caused by short circuits,it is necessary to determine the areas of vulnerability(AOVs).In this paper,a new method is proposed for the AOV determination that is applicable to large-scale networks.The false position method(FPM)is proposed for the precise calculation of the critical points of the system lines.Furthermore,a new method is proposed for the voltage sag monitor(VSM)placement to detect the fault locations.A systematic placement scheme is used to provide the highest fault location detection(FLD)index at buses and lines for various short-circuit fault types.To assess the efficiency of the proposed methods for AOV determination and VSM placement,simulations are conducted in IEEE standard systems.The results demonstrate the accuracy of the proposed method for AOV determination.In addition,through VSM placement,the fault locations at buses and lines are detected.展开更多
Traditional protection methods are not suitable for hybrid(cable and overhead)transmission lines in voltage source converter based high-voltage direct current(VSC-HVDC)systems.Accordingly,this paper presents the robus...Traditional protection methods are not suitable for hybrid(cable and overhead)transmission lines in voltage source converter based high-voltage direct current(VSC-HVDC)systems.Accordingly,this paper presents the robust fault detection,classification,and location based on the empirical wavelet transform-Teager energy operator(EWT-TEO)and artificial neural network(ANN)for hybrid transmission lines in VSC-HVDC systems.The operational scheme of the proposed protection method consists of two loops①an EWT-TEO based feature extraction loop,②and an ANN-based fault detection,classification,and location loop.Under the proposed protection method,the voltage and current signals are decomposed into several sub-passbands with low and high frequencies using the empirical wavelet transform(EWT)method.The energy content extracted by the EWT is fed into the ANN for fault detection,classification,and location.Various fault cases,including the high-impedance fault(HIF)as well as noises,are performed to train the ANN with two hidden layers.The test system and signal decomposition are conducted by PSCAD/EMTDC and MATLAB,respectively.The performance of the proposed protection method is compared with that of the traditional non-pilot traveling wave(TW)based protection method.The results confirm the high accuracy of the proposed protection method for hybrid transmission lines in VSC-HVDC systems,where a mean percentage error of approximately 0.1%is achieved.展开更多
基金funded by the Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project(Grant No.2024ZD1002503)。
文摘Passive microseismic monitoring(PMM)serves as a fundamental technology for assessing hydraulic fracturing(HF)effectiveness,with a key focus on accurate and efficient phase detection/arrival picking and source location.In PMM data processing,the data-driven paradigm(deep learning based)outperforms the model-driven paradigm in characteristic extraction but lacks quality control and uncertainty quantification.Monte Carlo Dropout,a Bayesian uncertainty quantification technique,performs stochastic neuron deactivation through multiple forward propagation samplings.Therefore,this study proposes a deep learning neural network incorporating uncertainty quantification with manual quality control integration,establishing an optimized workflow spanning automated phase detection to robust source location.The methodology implementation comprises two principal components:(1)The MDNet employing Monte Carlo Dropout strategy enabling simultaneous phase detection/arrival picking and unce rtainty estimation;(2)an integrated hybrid-driven workflow with a traveltime-based inve rsion method for source location.Validation with field data demonstrates that MD-Net achieves superior performance under low signal-to-noise ratio conditions,maintaining detection accuracy exceeding 99%for both P-and S-waves.The phase arrival picking precision shows significant improvement,with a 40%reduction in standard deviation compared to the baseline model(P-S time difference decreasing from12.0 ms to 7.1 ms),while providing quantifiable uncertainty metrics for manual calibration.Source location results further reveal that our hybrid-driven workflow produces more physically plausible event distributions,with 100%of microseismic eve nts clustering along the primary fracture expanding direction.This performance surpasses traditional cross-correlation methods and single/multi-trace data-driven me thods in spatial rationality.This study establishes an inte rpretable,high-pre cision automated framework for HF-PMM applications,demonstrating potential for extension to diverse geological settings and monitoring configurations.
基金Supported by Downhole Intelligent Measurement and Control Science and Technology Innovation Team of Southwest Petroleum University(Grant No.2018CXTD04)National Natural Science Foundation of China(Grant Nos.61701085,51974273)+1 种基金Chengdu Municipal international science and technology cooperation project of China(Grant Nos.2020-GH02-00016-HZ)2020 National Mountain Highway Engineering Technology Research Center Open Fund Project(Grant No.GSGZJ-2020-01).
文摘Buried pipelines are an essential component of the urban infrastructure of modern cities.Traditional buried pipes are mainly made of metal materials.With the development of material science and technology in recent years,non-metallic pipes,such as plastic pipes,ceramic pipes,and concrete pipes,are increasingly taking the place of pipes made from metal in various pipeline networks such as water supply,drainage,heat,industry,oil,and gas.The location technologies for the location of the buried metal pipeline have become mature,but detection and location technologies for the non-metallic pipelines are still developing.In this paper,current trends and future perspectives of detection and location of buried non-metallic pipelines are summarized.Initially,this paper reviews and analyzes electromagnetic induction technologies,electromagnetic wave technologies,and other physics-based technologies.It then focuses on acoustic detection and location technologies,and finally introduces emerging technologies.Then the technical characteristics of each detection and location method have been compared,with their strengths and weaknesses identified.The current trends and future perspectives of each buried non-metallic pipeline detection and location technology have also been defined.Finally,some suggestions for the future development of buried non-metallic pipeline detection and location technologies are provided.
基金This research is co-supported by National Key R&D Program of China(No.2017YFC1500402)National Natural Science Foundation of China(Nos.41874063 and U1939203)Shanghai Sheshan National Geophysical Observatory(No.2020K02)。
文摘Earthquake detection and location are essential in earthquake studies,which generally consists of two main classes:waveform-based and pick-based methods.To evaluate the ability of two different methods,a graphicsprocessing-unit-based Match&Locate(GPU-M&L)method and a rapid earthquake association and location(REAL)method are applied to continuous seismic data recorded by 24 digital seismic stations from Jiangsu Seismic Network during 2013 for comparison.GPU-M&L is one of waveform-based methods by waveform cross-correlations while REAL is one of pick-based method to associate arrivals of different seismic phases and locate events through counting the number of P and S picks and travel time residuals.Twenty-six templates are selected from the Jiangsu Seismic Network local catalog by using the GPU-M&L.The number of newly detected and located events is about 2.8 times more than those listed in the local catalog.We both utilize a deep-neural-network-based arrival-time picking method called PhaseNet and a shortterm/long-term average(STA/LTA)trigger algorithm for seismic phase detection and picking by applying the REAL.We then refine seismic locations using a least-squares location method(VELEST)and a high-precision relative location method(hypoDD).By applying STA/LTA and PhaseNet,1006 and 1893 events are associated and located,respectively.The newly detected events are mainly clustered and show steeply dipping fault planes.By analyzing the performance of these methods based on long-term continuous seismic data,the detected catalogs by the GPU-M&L and REAL show that the magnitudes of completeness are 1.4 and 0.8,respectively,which are smaller than 2.6 given by the local catalog.Although REAL provides improvement compared with GPU-M&L,REAL is highly dependent on phase detection and picking which is strongly affected by signal-noise ratio(SNR).Stations at southeast of the study region with low SNR may lead to few detections in the same area.
基金Project supported by the Innovation Program of Education Commission of Shanghai Municipality (Grant No.10YZ19)the Shanghai Leading Academic Discipline Project (Grant No.S30108)the Shanghai Key Laboratory of Specialty Fiber Optics and Optical Access Networks (Grant No.SKLSFO200903)
文摘A distributed optical fiber disturbance detection system consisted of a Sagnac interferometer and a Mach-Zehnder interferometer is demonstrated. Two interferometers outputs are connected to an electric band-pass filter via a detector respectively. The central frequencies of the two filters are selected adaptively according to the disturbance frequency. The disturbance frequency is obtained by either frequency spectrum of the two interferometers outputs. An alarm is given out only when the Sagnac interferometer output is changed. A disturbance position is determined by calculating a time difference with a cross-correlation method between the filter output connected to the Sagnac interferometer and derivative of the filter output connected to the Mach-Zehnder interferometer. The frequency spectrum, derivative and cross-correlation are obtained by a signal processing system. Theory analysis and simulation results are presented. They show that the system structure and location method are effective, accurate, and immune to environmental variations.
文摘The valuation relation of potential difference with discharging time in Electrical Discharge Machining (EDM) is analyzed theoretically and tested and verified by experiments designed in this paper and the relation between potential difference and spark location is induced and analyzed, and proceed by experiments under the condition of onedimension.
基金This research was partly supported by the National Science and Technology Council,Taiwan with Grant Numbers 112-2221-E-992-045,112-2221-E-992-057-MY3 and 112-2622-8-992-009-TD1.
文摘Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.
基金supported by National Natural Science Foundation of China under Grant No.60972036
文摘Position-spoofing-based attacks seriously threaten the security of Vehicular Ad Hoc Network(VANET).An effective solution to detect position spoofing is location verification.However,since vehicles move fast and the topology changes quickly in VANET,the static location verification method in Wireless Sensor Network(WSN) is not suitable for VANET.Taking into account the dynamic changing topology of VANET and collusion,we propose a Time-Slice-based Location Verification scheme,named TSLV,to resist position spoofing in VANET.Specifically,TSLV transforms the dynamic topology into static topology by time slice and each time slice corresponds to a verification process.The verifier can implement location verification for the corresponding prover.During the verification process,the verifier first filters out vehicles which provide unreasonably claimed locations,and then uses the Mean Square Error(MSE)-based cluster approach to separate the consistent vehicles by time slice,and uses the consistent set for its verification.In addition,security analysis and simulation show that TSLV can defend against the collusion attack effectively.
基金Project(90820302) supported by the National Natural Science Foundation of China
文摘A new vision-based long-distance lane perception and front vehicle location method was developed for decision making of full autonomous vehicles on highway roads,Firstly,a real-time long-distance lane detection approach was presented based on a linear-cubic road model for two-lane highways.By using a novel robust lane marking feature which combines the constraints of intensity,edge and width,the lane markings in far regions were extracted accurately and efficiently.Next,the detected lane lines were selected and tracked by estimating the lateral offset and heading angle of ego vehicle with a Kalman filter,Finally,front vehicles were located on correct lanes using the tracked lane lines,Experiment results show that the proposed lane perception approach can achieve an average correct detection rate of 94.37% with an average false positive detection rate of 0.35%,The proposed approaches for long-distance lane perception and front vehicle location were validated in a 286 km full autonomous drive experiment under real traffic conditions.This successful experiment shows that the approaches are effective and robust enough for full autonomous vehicles on highway roads.
基金Supported by the National Key Technologies R&D Program of China(2008BAC36B00)
文摘Using the spatial coordinates of detection stations and the time of arrival of lightning wave,the observation equations can be expressed.For the large lightning detection network,the least square method is used to process the adjustment of observation data to find the most probable value of lightning position,and the result is assessed by the mean error and dilution of precision.Lightning location precision is affected by figure factor.The conclusion can be used in the design of location network,data processing,and data analysis.
基金This work was supported by the Outstanding Youth Science and Technology Innovation Team Project of Colleges and Universities in Hubei Province(Grant No.T201923)Key Science and Technology Project of Jingmen(Grant Nos.2021ZDYF024,2022ZDYF019)+2 种基金LIAS Pioneering Partnerships Award,UK(Grant No.P202ED10)Data Science Enhancement Fund,UK(Grant No.P202RE237)Cultivation Project of Jingchu University of Technology(Grant No.PY201904).
文摘Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore waters.Current deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of detection.To solve this problem,we present a hybrid ship detection framework which is named EfficientShip in this paper.The core parts of the EfficientShip are DLA-backboned object location(DBOL)and CascadeRCNN-guided object classification(CROC).The DBOL is responsible for finding potential ship objects,and the CROC is used to categorize the potential ship objects.We also design a pixel-spatial-level data augmentation(PSDA)to reduce the risk of detection model overfitting.We compare the proposed EfficientShip with state-of-the-art(SOTA)literature on a ship detection dataset called Seaships.Experiments show our ship detection framework achieves a result of 99.63%(mAP)at 45 fps,which is much better than 8 SOTA approaches on detection accuracy and can also meet the requirements of real-time application scenarios.
文摘Vehicle recognition system (VRS) plays a very important role in the field of intelligent transportation systems.A novel and intuitive method is proposed for vehicle location.The method we provide for vehicle location is based on human visual perception model technique. The perception color space HSI in this algorithm is adopted.Three color components of a color image and more potential edge patterns are integrated for solving the feature extraction problem.A fast and automatic threshold technique based on human visual perception model is also developed.The vertical edge projection and horizontal edge projection are adopted for locating left-right boundary of vehicle and top-bottom boundary of vehicle, respectively. Very promising experimental results are obtained using real-time vehicle image sequences, which have confirmed that this proposed location vehicle method is efficient and reliable, and its calculation speed meets the needs of the VRS.
文摘Visualizing lightning location data is necessary in analyzing and researching lightning activity patterns.This article uses C#and the cross-platform.NET framework to develop a lightning location data analysis class library and the data-driven client to help lightning researchers improve work efficiency by avoiding repeated wheel invention.Lightning Location System Data Analyzer(LLSDA)is a suite of software tools that includes a.NET class library for software developers and a desktop application for end users.It supports a wide range of lightning location data formats,such as the University of Washington Global Lightning Location System(WWLLN)and Beijing Huayun Dongfang ADTD Lightning Location System data format,and maintains scalability.The class library can easily read,parse,and analyze lightning location data,and combined with third-party frameworks can realize grid analysis.The desktop application can be combined with MeteoInfo(a GIS open-source project)for secondary development.
文摘Using the data of the Lightning Location System( LLS) over Hubei Province,through the analysis of the distribution characteristics of CG( Cloud-to-Ground) flash density in 2015,it was found that the layout of the detection station had influence on the spatial distribution of lightning.Grid CG flash density data were used to characterize the spatial distribution of the CG flash,and station distance factor was used to characterize the detection station layout. The result showed that there existed negative correlation between density and factor,significant correlation between the density component and the factor for the lightning current amplitude of 5 to 30 kA,and insignificant correlation between >30 kA of density component and factor. So it is necessary to revise the density to eliminate the influence of the station layout. On the basis of the linear regression method and its residual theory,the revision model of the grid CG flash density and the statistical model of relative detection efficiency were established. The result consistency of segment and non-segmented revision of the density was verified. Through the contrastive analysis of theoretical detection efficiency and relative detection efficiency,the feasibility for revision method of CG flash density and the statistical method of relative detection efficiency was also verified.
文摘Detection of 2-dimention spark locations by electromagnetic detection method in electrical discharge machining (EDM) is studied. The method, which is applied and investigated, is based on the fact that the release of energy from a spark is transformed into electromagnetic wave around the workpiece. A new sensor system composed of high precision linear Hall components and cubic ferrite is used to detect the intensity of magnetic field. Relation equation between the output of the sensor system and 2-dimention spark locations experiment under a spiculate electrode is introduced, and its diagram of curve is drawn. As a result, the information that can be achieved by detecting spark’s location gives new possibilities for an extended analysis of the EDM-process.
文摘A method is proposed for detecting damage to shear structures by using Support Vector Machine (SVM) and only the first three natural frequencies of the translational modes. This method is able to determine the damage location in any story of a shear building with only two vibration sensors;to obtain modal frequencies, one sensor on the ground detects an input and the other on the roof detects the output. Based on the shifts in the first three natural frequencies, damage location indicators are proposed, and used as new feature vectors for SVM. Simulations of five-story, nine-story and twenty-one-story shear structures and experiments on a five-story steel model were used to test the performance of the proposed method.
基金supported by the National Natural Science Foundation of China(62101099)the Chinese Postdoctoral Science Foundation(2021M690558,2022T150100,2018M633352,2019T120825)+3 种基金the Young Elite Scientist Sponsorship Program(YESS20200082)the Aeronautical Science Foundation of China(2022Z017080001)the Open Foundation of Science and Technology on Electronic Information Control Laboratorythe Natural Science Foundation of Sichuan Province(2023NSFSC1386)。
文摘The detection of hypersonic targets usually confronts range migration(RM)issue before coherent integration(CI).The traditional methods aiming at correcting RM to obtain CI mainly considers the narrow-band radar condition.However,with the increasing requirement of far-range detection,the time bandwidth product,which is corresponding to radar’s mean power,should be promoted in actual application.Thus,the echo signal generates the scale effect(SE)at large time bandwidth product situation,influencing the intra and inter pulse integration performance.To eliminate SE and correct RM,this paper proposes an effective algorithm,i.e.,scaled location rotation transform(ScLRT).The ScLRT can remove SE to obtain the matching pulse compression(PC)as well as correct RM to complete CI via the location rotation transform,being implemented by seeking the actual rotation angle.Compared to the traditional coherent detection algorithms,Sc LRT can address the SE problem to achieve better detection/estimation capabilities.At last,this paper gives several simulations to assess the viability of ScLRT.
文摘The increase in the number of sensitive loads in power systems has made power quality,particularly voltage sag,a prominent problem due to its effects on consumers from both the utility and customer perspectives.Thus,to evaluate the effects of voltage sag caused by short circuits,it is necessary to determine the areas of vulnerability(AOVs).In this paper,a new method is proposed for the AOV determination that is applicable to large-scale networks.The false position method(FPM)is proposed for the precise calculation of the critical points of the system lines.Furthermore,a new method is proposed for the voltage sag monitor(VSM)placement to detect the fault locations.A systematic placement scheme is used to provide the highest fault location detection(FLD)index at buses and lines for various short-circuit fault types.To assess the efficiency of the proposed methods for AOV determination and VSM placement,simulations are conducted in IEEE standard systems.The results demonstrate the accuracy of the proposed method for AOV determination.In addition,through VSM placement,the fault locations at buses and lines are detected.
文摘Traditional protection methods are not suitable for hybrid(cable and overhead)transmission lines in voltage source converter based high-voltage direct current(VSC-HVDC)systems.Accordingly,this paper presents the robust fault detection,classification,and location based on the empirical wavelet transform-Teager energy operator(EWT-TEO)and artificial neural network(ANN)for hybrid transmission lines in VSC-HVDC systems.The operational scheme of the proposed protection method consists of two loops①an EWT-TEO based feature extraction loop,②and an ANN-based fault detection,classification,and location loop.Under the proposed protection method,the voltage and current signals are decomposed into several sub-passbands with low and high frequencies using the empirical wavelet transform(EWT)method.The energy content extracted by the EWT is fed into the ANN for fault detection,classification,and location.Various fault cases,including the high-impedance fault(HIF)as well as noises,are performed to train the ANN with two hidden layers.The test system and signal decomposition are conducted by PSCAD/EMTDC and MATLAB,respectively.The performance of the proposed protection method is compared with that of the traditional non-pilot traveling wave(TW)based protection method.The results confirm the high accuracy of the proposed protection method for hybrid transmission lines in VSC-HVDC systems,where a mean percentage error of approximately 0.1%is achieved.
