The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-...The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-amine(PEA),to achieve a formaldehyde(FA)-sensitive and fluorescence-enhanced sensing film.Utilizing the specific Aza-Cope rearrangement reaction of allylamine of PEA and FA to generate a strong fluorescent product emitted at approximately 480 nm,we chose a PC whose blue band edge of stopband overlapped with the fluorescence emission wavelength.In virtue of the fluorescence enhancement property derived from slow photon effect of PC,FA was detected highly selectively and sensitively.The limit of detection(LoD)was calculated to be 1.38 nmol/L.Furthermore,the fast detection of FA(within 1 min)is realized due to the interconnected three-dimensional macroporous structure of the inverse opal PC and its high specific surface area.The prepared sensing film can be used for the detection of FA in air,aquatic products and living cells.The very close FA content in indoor air to the result from FA detector,the recovery rate of 101.5%for detecting FA in aquatic products and fast fluorescence imaging in 2 min for living cells demonstrate the reliability and accuracy of our method in practical applications.展开更多
Introduction: General physicians can play an important role in the early detection of diabetic nephropathy (DN). Purpose: To assess the levels of general physicians’ knowledge, attitude and practice in terms of early...Introduction: General physicians can play an important role in the early detection of diabetic nephropathy (DN). Purpose: To assess the levels of general physicians’ knowledge, attitude and practice in terms of early detection of DN in Cotonou. Method: It was a cross-sectional, analytical and descriptive study which was conducted from 1st March 2015 to 30th September 2015. Every general physician working in a health structure in Cotonou who consented to participate in the study was included. We did not included medical specialists and general physicians working in nephrology department. Data were collected through a survey form designed with a score to assess the various items such as: knowledge, attitude and practice. The significance threshold is set to below 0.05. Results: In total, 202 general physicians were included. The average age was 30.9 ± 6.9 years ranging from 24 to 68 years. A male predominance was observed with 2.2 sex ratio. The majority of respondent medical physicians had poor knowledge in 76.2% cases, bad attitudes (61%) and bad practices (64.9%) in terms of early detection of diabetic nephropathy. There was positive impact of continuing medical training focused on diabetic nephropathy on attitudes (p = 0.016) and practices (p = 0.001) of these physicians. Conclusion: Diabetic nephropathy requires particular attention. General physicians’ continuous training is a principal solution.展开更多
Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classification...Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classifications and mathematical methods of MSIF, a structural damage detection method based on MSIF is presented, which is to fuse two or more damage character vectors from different structural damage diagnosis methods on the character-level. In an experiment of concrete plates, modal information is measured and analyzed. The structural damage detection method based on MSIF is taken to localize cracks of concrete plates and it is proved to be effective. Results of damage detection by the method based on MSIF are compared with those from the modal strain energy method and the flexibility method. Damage, which can hardly be detected by using the single damage identification method, can be diagnosed by the damage detection method based on the character-level MSIF technique. Meanwhile multi-location damage can be identified by the method based on MSIF. This method is sensitive to structural damage and different mathematical methods for MSIF have different preconditions and applicabilities for diversified structures. How to choose mathematical methods for MSIF should be discussed in detail in health monitoring systems of actual structures.展开更多
Considering the nonlinear, multifunctional properties of double-flywheel with closed- loop control, a two-step method including clustering and principal component analysis is proposed to detect the two faults in the m...Considering the nonlinear, multifunctional properties of double-flywheel with closed- loop control, a two-step method including clustering and principal component analysis is proposed to detect the two faults in the multifunctional flywheels. At the first step of the proposed algorithm, clustering is taken as feature recognition to check the instructions of ＂integrated power and attitude control＂ system, such as attitude control, energy storage or energy discharge. These commands will ask the flywheel system to work in different operation modes. Therefore, the relationship of parameters in different operations can define the cluster structure of training data. Ordering points to identify the clustering structure （OPTICS） can automatically identify these clusters by the reachability-plot. K-means algorithm can divide the training data into the corresponding operations according to the teachability-plot. Finally, the last step of proposed model is used to define the rela- tionship of parameters in each operation through the principal component analysis （PCA） method. Compared with the PCA model, the proposed approach is capable of identifying the new clusters and learning the new behavior of incoming data. The simulation results show that it can effectively detect the faults in the multifunctional flywheels system.展开更多
Due to the consideration of safety,non-contact measurement methods are be-coming more acceptable.However,massive measurement will bring high labor-cost and low working efficiency.To address these limitations,this pape...Due to the consideration of safety,non-contact measurement methods are be-coming more acceptable.However,massive measurement will bring high labor-cost and low working efficiency.To address these limitations,this paper introduces a deep learning model for the antenna attitude parameter measurement,which can be divided into an an-tenna location phase and a calculation phase of the attitude parameter.In the first phase,a single shot multibox detector(SSD)is applied to automatically recognize and discover the antenna from pictures taken by drones.In the second phase,the located antennas’fea-ture lines are extracted and their attitude parameters are then calculated mathematically.Experiments show that the proposed algorithms outperform existing related works in effi-ciency and accuracy,and therefore can be effectively used in engineering applications.展开更多
The objective of this paper is to develop a neural network-based residual generator to detect the fault in the actuators for a specific communication satellite in its attitude control system （ACS）. First, a dynamic ...The objective of this paper is to develop a neural network-based residual generator to detect the fault in the actuators for a specific communication satellite in its attitude control system （ACS）. First, a dynamic multilayer perceptron network with dynamic neurons is used, these neurons correspond to a second order linear Infinite Impulse Response （IIR） filter and a nonlinear activation function with adjustable parameters. Second, the parameters from the network are adjusted to minimize a performance index specified by the output estimated error, with the given input-output data collected from the specific ACS. Then, the proposed dynamic neural network is trained and applied for detecting the faults injected to the wheel, which is the main actuator in the normal mode for the communication satellite. Then the performance and capabilities of the proposed network were tested and compared with a conventional model-based observer residual, showing the differences between these two methods, and indicating the benefit of the proposed algorithm to know the real status of the momentum wheel. Finally, the application of the methods in a satellite ground station is discussed.展开更多
This study explores the potential of Artificial Intelligence(AI)in early screening and prognosis of Dry Eye Disease(DED),aiming to enhance the accuracy of therapeutic approaches for eye-care practitioners.Despite the ...This study explores the potential of Artificial Intelligence(AI)in early screening and prognosis of Dry Eye Disease(DED),aiming to enhance the accuracy of therapeutic approaches for eye-care practitioners.Despite the promising opportunities,challenges such as diverse diagnostic evidence,complex etiology,and interdisciplinary knowledge integration impede the interpretability,reliability,and applicability of AI-based DED detection methods.The research conducts a comprehensive review of datasets,diagnostic evidence,and standards,as well as advanced algorithms in AI-based DED detection over the past five years.The DED diagnostic methods are categorized into three groups based on their relationship with AI techniques:(1)those with ground truth and/or comparable standards,(2)potential AI-based methods with significant advantages,and(3)supplementary methods for AI-based DED detection.The study proposes suggested DED detection standards,the combination of multiple diagnostic evidence,and future research directions to guide further investigations.Ultimately,the research contributes to the advancement of ophthalmic disease detection by providing insights into knowledge foundations,advanced methods,challenges,and potential future perspectives,emphasizing the significant role of AI in both academic and practical aspects of ophthalmology.展开更多
基金supported by the National Natural Science Foundation of China(21663032 and 22061041)the Open Sharing Platform for Scientific and Technological Resources of Shaanxi Province(2021PT-004)the National Innovation and Entrepreneurship Training Program for College Students of China(S202110719044)。
文摘The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-amine(PEA),to achieve a formaldehyde(FA)-sensitive and fluorescence-enhanced sensing film.Utilizing the specific Aza-Cope rearrangement reaction of allylamine of PEA and FA to generate a strong fluorescent product emitted at approximately 480 nm,we chose a PC whose blue band edge of stopband overlapped with the fluorescence emission wavelength.In virtue of the fluorescence enhancement property derived from slow photon effect of PC,FA was detected highly selectively and sensitively.The limit of detection(LoD)was calculated to be 1.38 nmol/L.Furthermore,the fast detection of FA(within 1 min)is realized due to the interconnected three-dimensional macroporous structure of the inverse opal PC and its high specific surface area.The prepared sensing film can be used for the detection of FA in air,aquatic products and living cells.The very close FA content in indoor air to the result from FA detector,the recovery rate of 101.5%for detecting FA in aquatic products and fast fluorescence imaging in 2 min for living cells demonstrate the reliability and accuracy of our method in practical applications.
文摘Introduction: General physicians can play an important role in the early detection of diabetic nephropathy (DN). Purpose: To assess the levels of general physicians’ knowledge, attitude and practice in terms of early detection of DN in Cotonou. Method: It was a cross-sectional, analytical and descriptive study which was conducted from 1st March 2015 to 30th September 2015. Every general physician working in a health structure in Cotonou who consented to participate in the study was included. We did not included medical specialists and general physicians working in nephrology department. Data were collected through a survey form designed with a score to assess the various items such as: knowledge, attitude and practice. The significance threshold is set to below 0.05. Results: In total, 202 general physicians were included. The average age was 30.9 ± 6.9 years ranging from 24 to 68 years. A male predominance was observed with 2.2 sex ratio. The majority of respondent medical physicians had poor knowledge in 76.2% cases, bad attitudes (61%) and bad practices (64.9%) in terms of early detection of diabetic nephropathy. There was positive impact of continuing medical training focused on diabetic nephropathy on attitudes (p = 0.016) and practices (p = 0.001) of these physicians. Conclusion: Diabetic nephropathy requires particular attention. General physicians’ continuous training is a principal solution.
基金The National High Technology Research and Develop-ment Program of China(863Program)(No.2006AA04Z416)the Na-tional Science Fund for Distinguished Young Scholars(No.50725828)the Excellent Dissertation Program for Doctoral Degree of Southeast University(No.0705)
文摘Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classifications and mathematical methods of MSIF, a structural damage detection method based on MSIF is presented, which is to fuse two or more damage character vectors from different structural damage diagnosis methods on the character-level. In an experiment of concrete plates, modal information is measured and analyzed. The structural damage detection method based on MSIF is taken to localize cracks of concrete plates and it is proved to be effective. Results of damage detection by the method based on MSIF are compared with those from the modal strain energy method and the flexibility method. Damage, which can hardly be detected by using the single damage identification method, can be diagnosed by the damage detection method based on the character-level MSIF technique. Meanwhile multi-location damage can be identified by the method based on MSIF. This method is sensitive to structural damage and different mathematical methods for MSIF have different preconditions and applicabilities for diversified structures. How to choose mathematical methods for MSIF should be discussed in detail in health monitoring systems of actual structures.
基金supported by the National Basic Research Program of China(No.2012CB720003)
文摘Considering the nonlinear, multifunctional properties of double-flywheel with closed- loop control, a two-step method including clustering and principal component analysis is proposed to detect the two faults in the multifunctional flywheels. At the first step of the proposed algorithm, clustering is taken as feature recognition to check the instructions of ＂integrated power and attitude control＂ system, such as attitude control, energy storage or energy discharge. These commands will ask the flywheel system to work in different operation modes. Therefore, the relationship of parameters in different operations can define the cluster structure of training data. Ordering points to identify the clustering structure （OPTICS） can automatically identify these clusters by the reachability-plot. K-means algorithm can divide the training data into the corresponding operations according to the teachability-plot. Finally, the last step of proposed model is used to define the rela- tionship of parameters in each operation through the principal component analysis （PCA） method. Compared with the PCA model, the proposed approach is capable of identifying the new clusters and learning the new behavior of incoming data. The simulation results show that it can effectively detect the faults in the multifunctional flywheels system.
文摘Due to the consideration of safety,non-contact measurement methods are be-coming more acceptable.However,massive measurement will bring high labor-cost and low working efficiency.To address these limitations,this paper introduces a deep learning model for the antenna attitude parameter measurement,which can be divided into an an-tenna location phase and a calculation phase of the attitude parameter.In the first phase,a single shot multibox detector(SSD)is applied to automatically recognize and discover the antenna from pictures taken by drones.In the second phase,the located antennas’fea-ture lines are extracted and their attitude parameters are then calculated mathematically.Experiments show that the proposed algorithms outperform existing related works in effi-ciency and accuracy,and therefore can be effectively used in engineering applications.
文摘The objective of this paper is to develop a neural network-based residual generator to detect the fault in the actuators for a specific communication satellite in its attitude control system （ACS）. First, a dynamic multilayer perceptron network with dynamic neurons is used, these neurons correspond to a second order linear Infinite Impulse Response （IIR） filter and a nonlinear activation function with adjustable parameters. Second, the parameters from the network are adjusted to minimize a performance index specified by the output estimated error, with the given input-output data collected from the specific ACS. Then, the proposed dynamic neural network is trained and applied for detecting the faults injected to the wheel, which is the main actuator in the normal mode for the communication satellite. Then the performance and capabilities of the proposed network were tested and compared with a conventional model-based observer residual, showing the differences between these two methods, and indicating the benefit of the proposed algorithm to know the real status of the momentum wheel. Finally, the application of the methods in a satellite ground station is discussed.
基金funded by the National Natural Science Foundation of China Natural(Nos.U22A2041,82071915,and 62372047)the Shenzhen Key Laboratory of Intelligent Bioinformatics(No.ZDSYS20220422103800001)+5 种基金the Shenzhen Science and Technology Program(No.KQTD20200820113106007)the Guangdong Basic and Applied Basic Research Foundation(No.2022A1515220015)the Zhuhai Technology and Research Foundation(Nos.ZH22036201210034PWC,2220004000131,and 2220004002412)the Project of Humanities and Social Science of MOE(Ministry of Education in China)(No.22YJCZH213)the Science and Technology Research Program of Chongqing Municipal Education Commission(Nos.KJZD-K202203601,KJQN0202203605,and KJQN202203607)the Natural Science Foundation of Chongqing China(No.cstc2021jcyj-msxmX1108).
文摘This study explores the potential of Artificial Intelligence(AI)in early screening and prognosis of Dry Eye Disease(DED),aiming to enhance the accuracy of therapeutic approaches for eye-care practitioners.Despite the promising opportunities,challenges such as diverse diagnostic evidence,complex etiology,and interdisciplinary knowledge integration impede the interpretability,reliability,and applicability of AI-based DED detection methods.The research conducts a comprehensive review of datasets,diagnostic evidence,and standards,as well as advanced algorithms in AI-based DED detection over the past five years.The DED diagnostic methods are categorized into three groups based on their relationship with AI techniques:(1)those with ground truth and/or comparable standards,(2)potential AI-based methods with significant advantages,and(3)supplementary methods for AI-based DED detection.The study proposes suggested DED detection standards,the combination of multiple diagnostic evidence,and future research directions to guide further investigations.Ultimately,the research contributes to the advancement of ophthalmic disease detection by providing insights into knowledge foundations,advanced methods,challenges,and potential future perspectives,emphasizing the significant role of AI in both academic and practical aspects of ophthalmology.
