Existingfirefighting robots are focused on simple storage orfire sup-pression outside buildings rather than detection or recognition.Utilizing a large number of robots using expensive equipment is challenging.This study ...Existingfirefighting robots are focused on simple storage orfire sup-pression outside buildings rather than detection or recognition.Utilizing a large number of robots using expensive equipment is challenging.This study aims to increase the efficiency of search and rescue operations and the safety offirefigh-ters by detecting and identifying the disaster site by recognizing collapsed areas,obstacles,and rescuers on-site.A fusion algorithm combining a camera and three-dimension light detection and ranging(3D LiDAR)is proposed to detect and loca-lize the interiors of disaster sites.The algorithm detects obstacles by analyzingfloor segmentation and edge patterns using a mask regional convolutional neural network(mask R-CNN)features model based on the visual data collected from a parallelly connected camera and 3D LiDAR.People as objects are detected using you only look once version 4(YOLOv4)in the image data to localize persons requiring rescue.The point cloud data based on 3D LiDAR cluster the objects using the density-based spatial clustering of applications with noise(DBSCAN)clustering algorithm and estimate the distance to the actual object using the center point of the clustering result.The proposed artificial intelligence(AI)algorithm was verified based on individual sensors using a sensor-mounted robot in an actual building to detectfloor surfaces,atypical obstacles,and persons requiring rescue.Accordingly,the fused AI algorithm was comparatively verified.展开更多
Traditional modal parameter identifi cation methods have many disadvantages,especially when used for processing nonlinear and non-stationary signals.In addition,they are usually not able to accurately identify the dam...Traditional modal parameter identifi cation methods have many disadvantages,especially when used for processing nonlinear and non-stationary signals.In addition,they are usually not able to accurately identify the damping ratio and damage.In this study,methods based on the Hilbert-Huang transform(HHT) are investigated for structural modal parameter identifi cation and damage diagnosis.First,mirror extension and prediction via a radial basis function(RBF) neural network are used to restrain the troublesome end-effect issue in empirical mode decomposition(EMD),which is a crucial part of HHT.Then,the approaches based on HHT combined with other techniques,such as the random decrement technique(RDT),natural excitation technique(NExT) and stochastic subspace identifi cation(SSI),are proposed to identify modal parameters of structures.Furthermore,a damage diagnosis method based on the HHT is also proposed.Time-varying instantaneous frequency and instantaneous energy are used to identify the damage evolution of the structure.The relative amplitude of the Hilbert marginal spectrum is used to identify the damage location of the structure.Finally,acceleration records at gauge points from shaking table testing of a 12-story reinforced concrete frame model are taken to validate the proposed approaches.The results show that the proposed approaches based on HHT for modal parameter identifi cation and damage diagnosis are reliable and practical.展开更多
The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scatt...The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.展开更多
Nowadays, an increasing number of web applications require identification registration. However, the behavior of website registration has not ever been thoroughly studied. We use the database provided by the Chinese S...Nowadays, an increasing number of web applications require identification registration. However, the behavior of website registration has not ever been thoroughly studied. We use the database provided by the Chinese Software Develop Net (CSDN) to provide a complete perspective on this research point. We concentrate on the following three aspects: complexity, correlation, and preference. From these analyses, we draw the following conclusions: firstly, a considerable number of users have not realized the importance of identification and are using very simple identifications that can be attacked very easily. Secondly, there is a strong complexity correlation among the three parts of identification. Thirdly, the top three passwords that users like are 123456789, 12345678 and 11111111, and the top three email providers that they prefer are NETEASE, qq and sina. Further, we provide some suggestions to improve the quality of user passwords.展开更多
An computationally efficient damage identification technique for the planar and space truss structures is presented based on the force method and the micro ge-netic algorithm.For this purpose,the general equilibrium equ...An computationally efficient damage identification technique for the planar and space truss structures is presented based on the force method and the micro ge-netic algorithm.For this purpose,the general equilibrium equations and the kinematic relations in which the reaction forces and the displacements at nodes are take into ac-count,respectively,are formulated.The compatibility equations in terms of forces are explicitly presented using the singular value decomposition(SVD)technique.Then governing equations with unknown reaction forces and initial elongations are derived.Next,the micro genetic algorithm(MGA)is used to properly identify the site and ex-tent of multiple damage cases in truss structures.In order to verify the accuracy and the superiority of the proposed damage detection technique,the numerical solutions are presented for the planar and space truss models.The numerical results indicate that the combination of the force method and the MGA can provide a reliable tool to accurately and efficiently identify the multiple damages of the truss structures.展开更多
基金supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education(No.2020R1I1A3068274),Received by Junho Ahn.https://www.nrf.re.kr/supported by the Korea Agency for Infrastructure Technology Advancement(KAIA)by the Ministry of Land,Infrastructure and Transport under Grant(No.22QPWO-C152223-04),Received by Chulsu Kim.https://www.kaia.re.kr/.
文摘Existingfirefighting robots are focused on simple storage orfire sup-pression outside buildings rather than detection or recognition.Utilizing a large number of robots using expensive equipment is challenging.This study aims to increase the efficiency of search and rescue operations and the safety offirefigh-ters by detecting and identifying the disaster site by recognizing collapsed areas,obstacles,and rescuers on-site.A fusion algorithm combining a camera and three-dimension light detection and ranging(3D LiDAR)is proposed to detect and loca-lize the interiors of disaster sites.The algorithm detects obstacles by analyzingfloor segmentation and edge patterns using a mask regional convolutional neural network(mask R-CNN)features model based on the visual data collected from a parallelly connected camera and 3D LiDAR.People as objects are detected using you only look once version 4(YOLOv4)in the image data to localize persons requiring rescue.The point cloud data based on 3D LiDAR cluster the objects using the density-based spatial clustering of applications with noise(DBSCAN)clustering algorithm and estimate the distance to the actual object using the center point of the clustering result.The proposed artificial intelligence(AI)algorithm was verified based on individual sensors using a sensor-mounted robot in an actual building to detectfloor surfaces,atypical obstacles,and persons requiring rescue.Accordingly,the fused AI algorithm was comparatively verified.
基金Gansu Science and Technology Key Project under Grant No.2GS057-A52-008
文摘Traditional modal parameter identifi cation methods have many disadvantages,especially when used for processing nonlinear and non-stationary signals.In addition,they are usually not able to accurately identify the damping ratio and damage.In this study,methods based on the Hilbert-Huang transform(HHT) are investigated for structural modal parameter identifi cation and damage diagnosis.First,mirror extension and prediction via a radial basis function(RBF) neural network are used to restrain the troublesome end-effect issue in empirical mode decomposition(EMD),which is a crucial part of HHT.Then,the approaches based on HHT combined with other techniques,such as the random decrement technique(RDT),natural excitation technique(NExT) and stochastic subspace identifi cation(SSI),are proposed to identify modal parameters of structures.Furthermore,a damage diagnosis method based on the HHT is also proposed.Time-varying instantaneous frequency and instantaneous energy are used to identify the damage evolution of the structure.The relative amplitude of the Hilbert marginal spectrum is used to identify the damage location of the structure.Finally,acceleration records at gauge points from shaking table testing of a 12-story reinforced concrete frame model are taken to validate the proposed approaches.The results show that the proposed approaches based on HHT for modal parameter identifi cation and damage diagnosis are reliable and practical.
基金supported by the National Key Research and Development Program of China(No.2018YFA0702800)the National Natural Science Foundation of China(No.12072056)supported by National Defense Fundamental Scientific Research Project(XXXX2018204BXXX).
文摘The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.
基金supported by the Foundation for Key Program of Ministry of Education, China under Grant No.311007National Science Foundation Project of China under Grants No. 61202079, No.61170225, No.61271199+1 种基金the Fundamental Research Funds for the Central Universities under Grant No.FRF-TP-09-015Athe Fundamental Research Funds in Beijing Jiaotong University under Grant No.W11JB00630
文摘Nowadays, an increasing number of web applications require identification registration. However, the behavior of website registration has not ever been thoroughly studied. We use the database provided by the Chinese Software Develop Net (CSDN) to provide a complete perspective on this research point. We concentrate on the following three aspects: complexity, correlation, and preference. From these analyses, we draw the following conclusions: firstly, a considerable number of users have not realized the importance of identification and are using very simple identifications that can be attacked very easily. Secondly, there is a strong complexity correlation among the three parts of identification. Thirdly, the top three passwords that users like are 123456789, 12345678 and 11111111, and the top three email providers that they prefer are NETEASE, qq and sina. Further, we provide some suggestions to improve the quality of user passwords.
基金This researchwas supported by a grant(14CTAP-C077285-01-000000)from Infrastructure and transportation technology promotion research Program funded by MOLIT(Min-istry of Land,Infrastructure and Transport)of Korean government and a grant(2013-R1A12058208)from NRF(National Research Foundation of Korea)funded by MEST(Ministry of Education and Science Technology)of Korean government.
文摘An computationally efficient damage identification technique for the planar and space truss structures is presented based on the force method and the micro ge-netic algorithm.For this purpose,the general equilibrium equations and the kinematic relations in which the reaction forces and the displacements at nodes are take into ac-count,respectively,are formulated.The compatibility equations in terms of forces are explicitly presented using the singular value decomposition(SVD)technique.Then governing equations with unknown reaction forces and initial elongations are derived.Next,the micro genetic algorithm(MGA)is used to properly identify the site and ex-tent of multiple damage cases in truss structures.In order to verify the accuracy and the superiority of the proposed damage detection technique,the numerical solutions are presented for the planar and space truss models.The numerical results indicate that the combination of the force method and the MGA can provide a reliable tool to accurately and efficiently identify the multiple damages of the truss structures.
