At present,developing high-efficiency microwave absorption materials with properties including lightweight,thin thickness,strong absorbing intensity and broad bandwidth is an urgent demand to solve the electromagnetic...At present,developing high-efficiency microwave absorption materials with properties including lightweight,thin thickness,strong absorbing intensity and broad bandwidth is an urgent demand to solve the electromagnetic pollution issues.An ideal microwave absorber should have excellent dielectric and magnetic loss capabilities,thereby inducing attenuation and absorption of incident electromagnetic radiation.Recently,various carbon/magnetic metal composites have been developed and expected to become promising candidates for high-performance microwave absorbers.In this review,we introduce the mechanisms of microwave absorption and summarize the recent advances in carbon/magnetic metal composites.Preparation methods and microwave absorption properties of carbon/magnetic metal composites with different components,morphologies and microstructures are discussed in detail.Finally,the challenges and future prospects of carbon/magnetic metal absorbing materials are also proposed,which will be useful to develop high-performance microwave absorption materials.展开更多
An ultrasensitive magnetic field sensor based on a compact in-fiber Mach–Zehnder interferometer(MZI) created in twin-core fiber(TCF) is proposed, and its performance is experimentally demonstrated. A section of TCF w...An ultrasensitive magnetic field sensor based on a compact in-fiber Mach–Zehnder interferometer(MZI) created in twin-core fiber(TCF) is proposed, and its performance is experimentally demonstrated. A section of TCF was spliced between two sections of standard single-mode fibers, and then a microchannel was drilled through one core of the TCF by means of femtosecond laser micromachining. The TCF with one microchannel was then immersed in a water-based Fe_3O_4 magnetic fluid(MF), forming a direct component of the light propagation path,and then sealed in a capillary tube, achieving a magnetic sensing element, which merges the advantages of an MZI with an MF. Experiments were conducted to investigate the magnetic response of the proposed sensor. The developed magnetic field sensor exhibits a linear response within a measurement range from 5 to 9.5 m T and an ultrahigh sensitivity of 20.8 nm/m T, which, to our best knowledge, is 2 orders of magnitude greater than other previously reported magnetic sensors. The proposed sensor is expected to offer significant potential for detecting weak magnetic fields.展开更多
Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries.Magnetic resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain images.MRI plays a cru...Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries.Magnetic resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain images.MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders.Typically,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention.However,early diagnosis of brain tumors is intricate,necessitating the use of computerized methods.This research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain tumors.The proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third step.The final step involves classification using the Support Vector Machine(SVM)classifier.The classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)database.The proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of 0.957.Additionally,this method exhibits a shorter processing time of 0.44 s compared to existing approaches.The performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and DSC.To enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classification method.展开更多
The vector transformation and pole reduction from the total-field anomaly are signifi cant for the interpretation.We examined these industry-standard processing procedures in the Fourier domain.We propose a novel iter...The vector transformation and pole reduction from the total-field anomaly are signifi cant for the interpretation.We examined these industry-standard processing procedures in the Fourier domain.We propose a novel iteration algorithm for regional magnetic anomalies transformations to derive the vertical-component data from the total-field measurements with the variation in the core-fi eld direction over the region.Additionally,we use the same algorithm to convert the calculated vertical-component data into the corresponding data at the pole and realize the processing of diff erential reduction to the pole(DRTP).Unlike Arkani-Hamed’s DRTP method,the two types of iterative algorithms have the same forms,and DRTP is realized by implementing this algorithm twice.The synthetic model’s calculation results show that the method has high accuracy,and the fi eld data processing confi rms its practicality.展开更多
Based on analysis of NMR T2 spectral characteristics,a new method for identifying fluid properties by decomposing T2 spectrum through signal analysis has been proposed.Because T2 spectrum satisfies lognormal distribut...Based on analysis of NMR T2 spectral characteristics,a new method for identifying fluid properties by decomposing T2 spectrum through signal analysis has been proposed.Because T2 spectrum satisfies lognormal distribution on transverse relaxation time axis,the T2 spectrum can be decomposed into 2 to 5 independent component spectra by fitting the T2 spectrum with Gauss functions.By analyzing the free relaxation response characteristics of crude oil and formation water,the dynamic response characteristics of the core mutual drive between oil and water,the petrophysical significance of each component spectrum is clarified.T2 spectrum can be decomposed into clay bound water component spectrum,capillary bound fluid component spectrum,micropores fluid component spectrum and macropores fluid component spectrum.According to the nature of crude oil in the target area,the distribution range of T2 component spectral peaks of oil-bearing reservoir is 165-500 ms on T2 time axis.This range can be used to accurately identify fluid properties.This method has high adaptability in identifying complex oil and water layers in low porosity and permeability reservoirs.展开更多
基金financially supported by the National Science and Technology Major Project(No.2017-VI-0008-0078)the Joint Fund of the National Natural Science Foundation of China and Baosteel Group Corporation(No.U1560106)+1 种基金the Aeronautical Science Foundation of China(No.2016ZF51050)the Scientific Research Foundation for the Returned Overseas Chinese Scholars(State Education Ministry)。
文摘At present,developing high-efficiency microwave absorption materials with properties including lightweight,thin thickness,strong absorbing intensity and broad bandwidth is an urgent demand to solve the electromagnetic pollution issues.An ideal microwave absorber should have excellent dielectric and magnetic loss capabilities,thereby inducing attenuation and absorption of incident electromagnetic radiation.Recently,various carbon/magnetic metal composites have been developed and expected to become promising candidates for high-performance microwave absorbers.In this review,we introduce the mechanisms of microwave absorption and summarize the recent advances in carbon/magnetic metal composites.Preparation methods and microwave absorption properties of carbon/magnetic metal composites with different components,morphologies and microstructures are discussed in detail.Finally,the challenges and future prospects of carbon/magnetic metal absorbing materials are also proposed,which will be useful to develop high-performance microwave absorption materials.
基金National Natural Science Foundation of China(NSFC)(61425007,61377090,61575128)Guangdong Science and Technology Department(2014A030308007,2014B050504010,2015B010105007,2015A030313541)+1 种基金Science and Technology Innovation Commission of Shenzhen(ZDSYS20140430164957664,GJHZ20150313093755757,KQCX20140512172532195,JCYJ20150324141711576)Pearl River Scholar Fellowships
文摘An ultrasensitive magnetic field sensor based on a compact in-fiber Mach–Zehnder interferometer(MZI) created in twin-core fiber(TCF) is proposed, and its performance is experimentally demonstrated. A section of TCF was spliced between two sections of standard single-mode fibers, and then a microchannel was drilled through one core of the TCF by means of femtosecond laser micromachining. The TCF with one microchannel was then immersed in a water-based Fe_3O_4 magnetic fluid(MF), forming a direct component of the light propagation path,and then sealed in a capillary tube, achieving a magnetic sensing element, which merges the advantages of an MZI with an MF. Experiments were conducted to investigate the magnetic response of the proposed sensor. The developed magnetic field sensor exhibits a linear response within a measurement range from 5 to 9.5 m T and an ultrahigh sensitivity of 20.8 nm/m T, which, to our best knowledge, is 2 orders of magnitude greater than other previously reported magnetic sensors. The proposed sensor is expected to offer significant potential for detecting weak magnetic fields.
基金supported by the Deanship of Scientific Research,Najran University,Kingdom of Saudi Arabia,for funding this work under the Distinguished Research Funding Program Grant Code Number(NU/DRP/SERC/12/16).
文摘Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries.Magnetic resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain images.MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders.Typically,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention.However,early diagnosis of brain tumors is intricate,necessitating the use of computerized methods.This research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain tumors.The proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third step.The final step involves classification using the Support Vector Machine(SVM)classifier.The classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)database.The proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of 0.957.Additionally,this method exhibits a shorter processing time of 0.44 s compared to existing approaches.The performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and DSC.To enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classification method.
基金supported by the National Key R&D Program of China (No. 2017YFC0602000)the China Geological Survey Project (Nos. DD20191001 and DD20189410)。
文摘The vector transformation and pole reduction from the total-field anomaly are signifi cant for the interpretation.We examined these industry-standard processing procedures in the Fourier domain.We propose a novel iteration algorithm for regional magnetic anomalies transformations to derive the vertical-component data from the total-field measurements with the variation in the core-fi eld direction over the region.Additionally,we use the same algorithm to convert the calculated vertical-component data into the corresponding data at the pole and realize the processing of diff erential reduction to the pole(DRTP).Unlike Arkani-Hamed’s DRTP method,the two types of iterative algorithms have the same forms,and DRTP is realized by implementing this algorithm twice.The synthetic model’s calculation results show that the method has high accuracy,and the fi eld data processing confi rms its practicality.
基金Supported by the China National Science and Technology Major Project(2016ZX05050)
文摘Based on analysis of NMR T2 spectral characteristics,a new method for identifying fluid properties by decomposing T2 spectrum through signal analysis has been proposed.Because T2 spectrum satisfies lognormal distribution on transverse relaxation time axis,the T2 spectrum can be decomposed into 2 to 5 independent component spectra by fitting the T2 spectrum with Gauss functions.By analyzing the free relaxation response characteristics of crude oil and formation water,the dynamic response characteristics of the core mutual drive between oil and water,the petrophysical significance of each component spectrum is clarified.T2 spectrum can be decomposed into clay bound water component spectrum,capillary bound fluid component spectrum,micropores fluid component spectrum and macropores fluid component spectrum.According to the nature of crude oil in the target area,the distribution range of T2 component spectral peaks of oil-bearing reservoir is 165-500 ms on T2 time axis.This range can be used to accurately identify fluid properties.This method has high adaptability in identifying complex oil and water layers in low porosity and permeability reservoirs.
