A recently developed method, on the bases of “multifractal spectrum” filters for mineral exploration, is introduced in this paper. The “multifractal spectrum” filters, a group of irregularly shaped filters that a...A recently developed method, on the bases of “multifractal spectrum” filters for mineral exploration, is introduced in this paper. The “multifractal spectrum” filters, a group of irregularly shaped filters that are constructed on each processed datum, can be used to separate various types of geochemical and geophysical anomalies. The basic model, with an emphasis on the GIS based implementation and the application to the geochemical and geophysical data processing for mineral exploration in southern Nova Scotia, Canada, indicates its advantage in the separation of multiple anomalies from the background.展开更多
Ensemble learning for anomaly detection of data structured into a complex network has been barely studied due to the inconsistent performance of complex network characteristics and the lack of inherent objective funct...Ensemble learning for anomaly detection of data structured into a complex network has been barely studied due to the inconsistent performance of complex network characteristics and the lack of inherent objective function. We propose the intuitionistic fuzzy set（IFS）-based anomaly detection, a new two-phase ensemble method for anomaly detection based on IFS, and apply it to the abnormal behavior detection problem in temporal complex networks.Firstly, it constructs the IFS of a single network characteristic, which quantifies the degree of membership,non-membership and hesitation of each network characteristic to the defined linguistic variables so that makes the unuseful or noise characteristics become part of the detection. To build an objective intuitionistic fuzzy relationship, we propose a Gaussian distribution-based membership function which gives a variable hesitation degree. Then, for the fuzzification of multiple network characteristics, the intuitionistic fuzzy weighted geometric operator is adopted to fuse multiple IFSs and to avoid the inconsistence of multiple characteristics. Finally, the score function and precision function are used to sort the fused IFS. Finally, we carry out extensive experiments on several complex network datasets for anomaly detection, and the results demonstrate the superiority of our method to state-of-the-art approaches, validating the effectiveness of our method.展开更多
This paper reports statistical results of Seismo-Ionospheric Anomalies(SIAs) of the Total Electron Content(TEC) in the Global Ionosphere Map(GIM) associated with 56 M≥6.0 earthquakes in China during 1998—2012.To det...This paper reports statistical results of Seismo-Ionospheric Anomalies(SIAs) of the Total Electron Content(TEC) in the Global Ionosphere Map(GIM) associated with 56 M≥6.0 earthquakes in China during 1998—2012.To detect SIA,a quartile-based(i.e.median-based) process is performed.TEC anomalies for the period of earthquakes without being led by magnetic storms about 10 days are further isolated and examined to confirm the SIP existence.Results show that SIA is the TEC significantly decrease in the afternoon period 2—9 days before the earthquakes in China,which is in a good agreement with the SIA appearing before the 12 May 2008 M 8.0 Wenchuan earthquake.展开更多
Low visibility episodes (visibility < 1000 m) were studied by applying the anomaly-based weather analysis method. A regional episode of low visibility associated with a coastal fog that occurred from 27 to 28 Janua...Low visibility episodes (visibility < 1000 m) were studied by applying the anomaly-based weather analysis method. A regional episode of low visibility associated with a coastal fog that occurred from 27 to 28 January 2016 over Ningbo- Zhoushan Port, Zhejiang Province, East China, was first examined. Some basic features from the anomalous weather analysis for this case were identified:(1) the process of low visibility mainly caused by coastal fog was a direct response to anomalous temperature inversion in the lower troposphere, with a warm center around the 925 hPa level, which was formed by a positive geopotential height (GPH) anomaly in the upper troposphere and a negative GPH anomaly near the surface;(2) the positive humidity anomaly was conducive to the formation of coastal fog and rain;(3) regional coastal fog formed at the moment when the southwesterly wind anomalies transferred to northeasterly wind anomalies. Other cases confirmed that the low visibility associated with coastal fog depends upon low-level inversion, a positive humidity anomaly, and a change of wind anomalies from southwesterly to northeasterly, rain and stratus cloud amount. The correlation coefficients of six-hourly inversion, 850?925-hPa-averaged temperature, GPH and humidity anomalies against visibility are ?0.31, 0.40 and ?0.48, respectively, reaching the 99% confidence level in the first half-years of 2015 and 2016. By applying the anomaly-based weather analysis method to medium-range model output products, such as ensemble prediction systems, the anomalous temperature?pressure pattern and humidity?wind pattern can be used to predict the process of low visibility associated with coastal fog at several days in advance.展开更多
The Global Geopotential Models (GGMs) of GOCE (Gravity Recovery and steady- state Ocean Circulation Explorer) differ globally as well as regionally in their accuracy and resolution based on the maximum degree and orde...The Global Geopotential Models (GGMs) of GOCE (Gravity Recovery and steady- state Ocean Circulation Explorer) differ globally as well as regionally in their accuracy and resolution based on the maximum degree and order (d/o) of the fully normalized spherical harmonic (SH) coefficients, which express each GGM. The main idea of this study is to compare the free-air gravity anomalies and quasi geoid heights determined from several recent GOCE-based GGMs with the corresponding ones from the Earth Gravitational Model 2008 (EGM2008) over Egypt on the one hand and with ground-based measurements on the other hand. The results regarding to the comparison of GOCE-based GGMs with terrestrial gravity and GPS/levelling data provide better improvement with respect to EGM2008. The 4th release GOCE-based GGM developed with the use of space-wise solution strategy (SPW_R4) approximates the gravity field well over the Egyptian region. The SPW_R4 model is accordingly suggested as a reference model for recovering the long wavelength (up to SH d/o 200) components of quasi geoid heights when modelling the gravimetric quasi-geoid over the Egypt. Finally, three types of transformation models: Four-, Five- and Seven-parameter transformations have been applied to reduce the data biases and to provide a better fitting of quasi geoid heights obtained from the studied GOCE-based GGMs to those from GPS/levelling data. These models reveal that the standard deviation of vertical datum over Egypt is at the level of about 32 cm.展开更多
The gravity field and steady-state ocean circulation explorer (GOCE) satellite mission has provided numerous Global Geopotential Models (GGMs) with different processing methodologies and model accuracies. In the curre...The gravity field and steady-state ocean circulation explorer (GOCE) satellite mission has provided numerous Global Geopotential Models (GGMs) with different processing methodologies and model accuracies. In the current contribution, the latest releases of GOCE-based GGMs are evaluated on the regional scale using the available terrestrial GPS/Levelling and gravity data collected over Egypt. To overcome the spectral inconsistency between the GOCE-based GGMs and the ground-based data, the spectral enhancement method (SEM) is applied. Five of GOCE-based GGMs have been used, namely GOSG01S, IGGT_R1, IfE_GOCE05s_ GO_CONS_GCF_2_SPW_R5 (SPW_R5 in the following) and NULP-02. The evaluation process of GOCE-based GGMs with the available ground data over Egypt considering the SEM method shows remarkable improvements obtained from the SPW_R5 model. The model provides lower differences of the standard deviations with respect to the EGM2008 and the other applied geopotential gravity models as well as the applied ground-based gravity and GPS/Levelling data. The findings regarding the ground-based data show obvious reductions of about 15.16% and 32.22% achieved by the GOCE-based model in term of standard deviations differences of gravity anomalies and geoid heights, respectively. Therefore, the SPW_R5 model is recommended to be applied as a reference model for compensating the long-to-short wavelength (up to spherical harmonics degree/order 280) components when modelling the gravimetric geoid over Egypt.展开更多
The positron annihilation lifetime and Doppler broadened line-shapeparameter have been measured between 77 and 300 K for Bi<sub>1.8</sub>Pb<sub>0.1</sub>Sb<sub>0.1</sub>Sr<sub>...The positron annihilation lifetime and Doppler broadened line-shapeparameter have been measured between 77 and 300 K for Bi<sub>1.8</sub>Pb<sub>0.1</sub>Sb<sub>0.1</sub>Sr<sub>2</sub>Ca<sub>2</sub>Cu<sub>3</sub>O<sub>x</sub>,Bi<sub>1.8</sub>Sb<sub>0.2</sub>Sr<sub>2</sub>Ca<sub>2</sub>Cu<sub>3</sub>O<sub>x</sub> and Bi<sub>1.7</sub>Pb<sub>0.2</sub>Sb<sub>0.1</sub>Sr<sub>2</sub>Ca<sub>2</sub>Cu<sub>2</sub>O<sub>x</sub>. The charge transfer from Cu-Olayers to Bi-O layers has been observed across T<sub>c</sub>s for all samples.Three samples allshow two normal state anomalies around 160 K and 240 K,respectively.The anomalyaround 160 K is attributed to the structural instability and that around 240 Kpresumably to the displacement phase transition.展开更多
Anomaly detection is crucial to the flight safety and maintenance of unmanned aerial vehicles(UAVs)and has attracted extensive attention from scholars.Knowledge-based approaches rely on prior knowledge,while model-bas...Anomaly detection is crucial to the flight safety and maintenance of unmanned aerial vehicles(UAVs)and has attracted extensive attention from scholars.Knowledge-based approaches rely on prior knowledge,while model-based approaches are challenging for constructing accurate and complex physical models of unmanned aerial systems(UASs).Although data-driven methods do not require extensive prior knowledge and accurate physical UAS models,they often lack parameter selection and are limited by the cost of labeling anomalous data.Furthermore,flight data with random noise pose a significant challenge for anomaly detection.This work proposes a spatiotemporal correlation based on long short-term memory and autoencoder(STCLSTM-AE)neural network data-driven method for unsupervised anomaly detection and recovery of UAV flight data.First,UAV flight data are preprocessed by combining the Savitzky-Golay filter data processing technique to mitigate the effect of noise in the original historical flight data on the model.Correlation-based feature subset selection is subsequently performed to reduce the reliance on expert knowledge.Then,the extracted features are used as the input of the designed LSTM-AE model to achieve the anomaly detection and recovery of UAV flight data in an unsupervised manner.Finally,the method's effectiveness is validated on real UAV flight data.展开更多
文摘A recently developed method, on the bases of “multifractal spectrum” filters for mineral exploration, is introduced in this paper. The “multifractal spectrum” filters, a group of irregularly shaped filters that are constructed on each processed datum, can be used to separate various types of geochemical and geophysical anomalies. The basic model, with an emphasis on the GIS based implementation and the application to the geochemical and geophysical data processing for mineral exploration in southern Nova Scotia, Canada, indicates its advantage in the separation of multiple anomalies from the background.
基金Supported by the National Natural Science Foundation of China under Grant No 61671142the Fundamental Research Funds for the Central Universities under Grant No 02190022117021
文摘Ensemble learning for anomaly detection of data structured into a complex network has been barely studied due to the inconsistent performance of complex network characteristics and the lack of inherent objective function. We propose the intuitionistic fuzzy set（IFS）-based anomaly detection, a new two-phase ensemble method for anomaly detection based on IFS, and apply it to the abnormal behavior detection problem in temporal complex networks.Firstly, it constructs the IFS of a single network characteristic, which quantifies the degree of membership,non-membership and hesitation of each network characteristic to the defined linguistic variables so that makes the unuseful or noise characteristics become part of the detection. To build an objective intuitionistic fuzzy relationship, we propose a Gaussian distribution-based membership function which gives a variable hesitation degree. Then, for the fuzzification of multiple network characteristics, the intuitionistic fuzzy weighted geometric operator is adopted to fuse multiple IFSs and to avoid the inconsistence of multiple characteristics. Finally, the score function and precision function are used to sort the fused IFS. Finally, we carry out extensive experiments on several complex network datasets for anomaly detection, and the results demonstrate the superiority of our method to state-of-the-art approaches, validating the effectiveness of our method.
文摘This paper reports statistical results of Seismo-Ionospheric Anomalies(SIAs) of the Total Electron Content(TEC) in the Global Ionosphere Map(GIM) associated with 56 M≥6.0 earthquakes in China during 1998—2012.To detect SIA,a quartile-based(i.e.median-based) process is performed.TEC anomalies for the period of earthquakes without being led by magnetic storms about 10 days are further isolated and examined to confirm the SIP existence.Results show that SIA is the TEC significantly decrease in the afternoon period 2—9 days before the earthquakes in China,which is in a good agreement with the SIA appearing before the 12 May 2008 M 8.0 Wenchuan earthquake.
基金financed by the National Natural Science Foundation of China (Grant No. 41775067)
文摘Low visibility episodes (visibility < 1000 m) were studied by applying the anomaly-based weather analysis method. A regional episode of low visibility associated with a coastal fog that occurred from 27 to 28 January 2016 over Ningbo- Zhoushan Port, Zhejiang Province, East China, was first examined. Some basic features from the anomalous weather analysis for this case were identified:(1) the process of low visibility mainly caused by coastal fog was a direct response to anomalous temperature inversion in the lower troposphere, with a warm center around the 925 hPa level, which was formed by a positive geopotential height (GPH) anomaly in the upper troposphere and a negative GPH anomaly near the surface;(2) the positive humidity anomaly was conducive to the formation of coastal fog and rain;(3) regional coastal fog formed at the moment when the southwesterly wind anomalies transferred to northeasterly wind anomalies. Other cases confirmed that the low visibility associated with coastal fog depends upon low-level inversion, a positive humidity anomaly, and a change of wind anomalies from southwesterly to northeasterly, rain and stratus cloud amount. The correlation coefficients of six-hourly inversion, 850?925-hPa-averaged temperature, GPH and humidity anomalies against visibility are ?0.31, 0.40 and ?0.48, respectively, reaching the 99% confidence level in the first half-years of 2015 and 2016. By applying the anomaly-based weather analysis method to medium-range model output products, such as ensemble prediction systems, the anomalous temperature?pressure pattern and humidity?wind pattern can be used to predict the process of low visibility associated with coastal fog at several days in advance.
文摘The Global Geopotential Models (GGMs) of GOCE (Gravity Recovery and steady- state Ocean Circulation Explorer) differ globally as well as regionally in their accuracy and resolution based on the maximum degree and order (d/o) of the fully normalized spherical harmonic (SH) coefficients, which express each GGM. The main idea of this study is to compare the free-air gravity anomalies and quasi geoid heights determined from several recent GOCE-based GGMs with the corresponding ones from the Earth Gravitational Model 2008 (EGM2008) over Egypt on the one hand and with ground-based measurements on the other hand. The results regarding to the comparison of GOCE-based GGMs with terrestrial gravity and GPS/levelling data provide better improvement with respect to EGM2008. The 4th release GOCE-based GGM developed with the use of space-wise solution strategy (SPW_R4) approximates the gravity field well over the Egyptian region. The SPW_R4 model is accordingly suggested as a reference model for recovering the long wavelength (up to SH d/o 200) components of quasi geoid heights when modelling the gravimetric quasi-geoid over the Egypt. Finally, three types of transformation models: Four-, Five- and Seven-parameter transformations have been applied to reduce the data biases and to provide a better fitting of quasi geoid heights obtained from the studied GOCE-based GGMs to those from GPS/levelling data. These models reveal that the standard deviation of vertical datum over Egypt is at the level of about 32 cm.
文摘The gravity field and steady-state ocean circulation explorer (GOCE) satellite mission has provided numerous Global Geopotential Models (GGMs) with different processing methodologies and model accuracies. In the current contribution, the latest releases of GOCE-based GGMs are evaluated on the regional scale using the available terrestrial GPS/Levelling and gravity data collected over Egypt. To overcome the spectral inconsistency between the GOCE-based GGMs and the ground-based data, the spectral enhancement method (SEM) is applied. Five of GOCE-based GGMs have been used, namely GOSG01S, IGGT_R1, IfE_GOCE05s_ GO_CONS_GCF_2_SPW_R5 (SPW_R5 in the following) and NULP-02. The evaluation process of GOCE-based GGMs with the available ground data over Egypt considering the SEM method shows remarkable improvements obtained from the SPW_R5 model. The model provides lower differences of the standard deviations with respect to the EGM2008 and the other applied geopotential gravity models as well as the applied ground-based gravity and GPS/Levelling data. The findings regarding the ground-based data show obvious reductions of about 15.16% and 32.22% achieved by the GOCE-based model in term of standard deviations differences of gravity anomalies and geoid heights, respectively. Therefore, the SPW_R5 model is recommended to be applied as a reference model for compensating the long-to-short wavelength (up to spherical harmonics degree/order 280) components when modelling the gravimetric geoid over Egypt.
基金The project supported by IAEA under the Contract No.5295/RBby China National Nuclear Corporation.
文摘The positron annihilation lifetime and Doppler broadened line-shapeparameter have been measured between 77 and 300 K for Bi<sub>1.8</sub>Pb<sub>0.1</sub>Sb<sub>0.1</sub>Sr<sub>2</sub>Ca<sub>2</sub>Cu<sub>3</sub>O<sub>x</sub>,Bi<sub>1.8</sub>Sb<sub>0.2</sub>Sr<sub>2</sub>Ca<sub>2</sub>Cu<sub>3</sub>O<sub>x</sub> and Bi<sub>1.7</sub>Pb<sub>0.2</sub>Sb<sub>0.1</sub>Sr<sub>2</sub>Ca<sub>2</sub>Cu<sub>2</sub>O<sub>x</sub>. The charge transfer from Cu-Olayers to Bi-O layers has been observed across T<sub>c</sub>s for all samples.Three samples allshow two normal state anomalies around 160 K and 240 K,respectively.The anomalyaround 160 K is attributed to the structural instability and that around 240 Kpresumably to the displacement phase transition.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFB1713300)the Guizhou Provincial Colleges and Universities Talent Training Base Project(Grant No.[2020]009)+3 种基金the Guizhou Province Science and Technology Plan Project(Grant Nos.[2015]4011,[2017]5788)the Guizhou Provincial Department of Education Youth Science and Technology Talent Growth Project(Grant No.[2022]142)the Scientific Research Project for Introducing Talents from Guizhou University(Grant No.(2021)74)the Guizhou Province Higher Education Integrated Research Platform Project(Grant No.[2020]005)。
文摘Anomaly detection is crucial to the flight safety and maintenance of unmanned aerial vehicles(UAVs)and has attracted extensive attention from scholars.Knowledge-based approaches rely on prior knowledge,while model-based approaches are challenging for constructing accurate and complex physical models of unmanned aerial systems(UASs).Although data-driven methods do not require extensive prior knowledge and accurate physical UAS models,they often lack parameter selection and are limited by the cost of labeling anomalous data.Furthermore,flight data with random noise pose a significant challenge for anomaly detection.This work proposes a spatiotemporal correlation based on long short-term memory and autoencoder(STCLSTM-AE)neural network data-driven method for unsupervised anomaly detection and recovery of UAV flight data.First,UAV flight data are preprocessed by combining the Savitzky-Golay filter data processing technique to mitigate the effect of noise in the original historical flight data on the model.Correlation-based feature subset selection is subsequently performed to reduce the reliance on expert knowledge.Then,the extracted features are used as the input of the designed LSTM-AE model to achieve the anomaly detection and recovery of UAV flight data in an unsupervised manner.Finally,the method's effectiveness is validated on real UAV flight data.
