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Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: The deformation measurement of long-span bridge alignment is of great significance to the assessment of bridge health and safety. The traditional deformation measurement methods of longspan bridge alignment, such as sensor and close range vision measurement, have shortcomings such as high measurement cost and limited measurement frequency, which are difficult to meet the requirements of highprecision measurement of deformation of long-span bridge alignment. Methods: Combined with dynamic precision engineering measurement and machine vision technology, this paper proposes a method for measuring the deformation of long-span bridge alignment based on inertial cameras. The camera is used to take high-frequency images of bridge measurement targets. At the same time, the inertial sensors are used to correct the measurement errors caused by camera movement. The deformation of long-span bridge alignment is calculated through inertial cameras in series mode, and the corresponding measuring equipment is developed and applied to the health monitoring system of a super large bridge. Results: The precision experiment results show that root mean square error of the proposed method is 0.38 mm. In engineering applications, the deformation of bridge alignment trend measured by the inertial camera is highly consistent with the static leveling results. The correlation coefficient is better than 99.90%, and the mutual difference is 4.94 mm. Conclusions: The proposed method can measure the linear deformation of large span bridges with high accuracy and real-time, and has good engineering value. [ABSTRACT FROM AUTHOR]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: The fine spatial distribution of urban villages is important for urban planning and urban renewal. However, since urban villages are high-level semantic geo-objects and have obscure remote sensing characteristics, it is difficult to obtain fine spatial distribution with good precision from high-density cities using traditional methods. Methods: We propose a novel hierarchical recognition method for urban villages that fuses remote sensing images and social sensing data to finely recognize the urban villages. The method combines the advantages of remote sensing images and social perception data in features. Large and small-scale information are both considered into the process by using the hierarchical framework. Re⁃ sults: The method provides a new idea for a comprehensive understanding of urban villages at a fine scale. A case study has been implemented in Shenzhen. An urban village distribution with a spatial resolution of 2.5 m is obtained. The accuracy assessment shows that the overall accuracy and Kappa coefficient reach 98.68% and 0.807， respectively, indicating the excellent performance of the method. In addition, the gain effects of the hierarchical framework and the fusion of remote sensing images and social perception data are demonstrated, respectively. Conclusions: The results show that both the hierarchical framework and the multi-source spatial data are effective in improving the accuracy of the urban village recognition method. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development of technologies in sensor, computer and robotics, the trends of multi-sensor integration and intelligent applications are shown in surveying field. Engineering surveying has developed in the direction of automation, dynamism and intelligence, and is widely used for high-precision measurement of large bridges, water conservancy hubs, high-speed rail subways, highways and other projects, as well as precision industrial measurement in aerospace, aviation, intelligent manufacturing and other fields. The expansion of application fields also puts forward new requirements for surveying tasks.The surveying data processing is no longer limited to the traditional pure geometric parameter estimation, but gradually expands to the generalized measurement data processing with both geometric parameters and feature information. We review the development process from classical measurement data processing to generalized measurement data processing, summarize the processing and analysis logic of multiple types of measurement data, and present the challenges of measurement data processing in the era of big data. The basic ideas and strategies of generalized measurement data processing are elaborated and illustrated with several typical cases. [ABSTRACT FROM AUTHOR]

To satisfy the widely demand of 3D real scene reconstruction, a novel un‐ manned aerial vehicle (UAV) photogrammetric technique approach, named optimized views photogrammetry, is presented. Methods: In terms of the 3D rough model of the scene which obtained in various ways, optimized views photogrammetry generates initial views via stereoscopic observation sampling and selects optimal views under the constraint of observability measure, and then forms the aerial photogrammetry planning path for quadcopters. For the contradiction between large-scale application and limited endurance of UAV system, optimized views photogrammetry provides a multi-UAV collaborative survey solution with its specific path division function in the planning end. A practical application verification was performed in urban central district of Qingdao, to assess the performance of multi-UAV cooperative optimized views photogrammetry in relatively large range complicated urban scene. Results: The output results of real 3D re‐ construction postprocessing and the detailed comparison with the model generated from oblique photogrammetry verify that, on the premise of high-quality real scene 3D output, the technical approach realizes the real scene 3D acquisition in a large range of complex space with light and small aerial photography equipment, as meanwhile and improves the efficiency through collaborative operation. Conclusions: With the key support of optimized views photogrammetry, it could also make an extension to the new technology application mode of ubiquitous real 3D data acquisition for multiple technical directions and application fields. [ABSTRACT FROM AUTHOR]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Feed Research is the property of Editorial Board of Feed Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Clinical Neurosurgery / Linchuang Shenjingwaike Zazhi is the property of Editorial Board of Journal of Clinical Neurosurgery and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: Flatness inspection during construction period is critical to ensure the floor perform its desired function after completion. There are some shortages in traditional floor flatness measurement methods based on straight edge or levelling instrument, for example, not all locations in area can be measured and the measurement speed is slow, so that these methods can not fit the fast and accurate flatness measuring requirement of oversized floor during construction. Methods: According to the features of target floor, which has large area and is not entirely solid, we present a rapid measuring and calculating method for floor flatness based on inertial navigation system and total station. First, we collect inertial data by inertial measurement sled, and observe the coordinates of the sled by total station. Second, we use Kalman filter to fuse inertial data and the unit̓s coordinates to solve locations and attitudes of trajectory points. Third, we calculate the flatness indicator according to the elevation of trajectory points. Results: Experimental results show that the proposed method has nearly equal accuracy comparing to levelling instrument, with significant improvement in speed performance. Conclusions: The proposed method can not only make flatness quality assessment for floor under construction, but also detect and locate the flatness abnormalities so that provide direction for floor grinding. This method has great practical value and a wide application prospect. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives：The sewer pipeline network is an essential urban infrastructure. It is easy to be with faults because of the complicated circumstance under the ground, as well as undertaking the transportation and circulation of water. To avoid serious accidents caused by the faults, periodic and systematical inspection devices and methods are necessary. While current methods exist a variety of limitations, especially for large‐scale and hard‐inspected urban sewer pipe networks. Thus, we proposed a novel collaborative inspection method with high efficiency and availability, and low cost for urban sewer pipe networks.Methods： Firstly,we invent a novel device named pipe capsule equipped with video cameras,lights,and a data storage facility. It will be dropped into pipes, move along with water flow, and record the videos of the inner walls until salvaged by the staff from the downstream. Such that, the video is captured to inspect the underground pipe. Multiple capsules are cooperated to complete the inspection task. A collaborative inspection method is designed through well design practice plan by maximizing the inspection range and degree, as well as minimizing the traveling length and time of the workers. Simulated annealing with a simple neighborhood search strategy is used to find the best practice plan.Results：Taking the sewer pipe network at Shenzhen University, China an example, an experiment was conducted to verify the performance of the proposed approach. The results demonstrate that the collaborative inspection method achieves the same inspection with a shorter working time. Conclusion：This study presented a collaborative inspection approach to detect the large ‐ scale and complicated urban sewer networks with machine vision. It will benefit the inspection and operation of the urban underground pipe network. [ABSTRACT FROM AUTHOR]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Feed Research is the property of Editorial Board of Feed Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ancient mural paintings are often suffered from damages such as color degradation, pigment peeling and even large-area shedding. Image inpainting techniques are widely used to virtually repair these damages. Firstly, we utilize the human-computer interaction techniques to complete missing structure information in the damaged areas according to the line drawings. Secondly, according to the classification of patches into textures and structures, a novel patch selection scheme from texture patch to structure patch is designed. Then, the order of patch structure complexity and the global randomly-selected strategy increase the inpainting accuracy and the efficiency. Finally, the sparse linear combination of candidate patches is constructed to sharply estimate the selected patch to be filled in a framework of sparse representation. Experimental results show the superior performance of the proposed method on damaged Dunhuang mural images. [ABSTRACT FROM AUTHOR]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Efficient and automatic road pavement defect detection technology has become increasingly important given the transition of China's highway development from the large-scale construction stage to the large-scale conservation stage. We briefly review the study of road surface defect detection over the past two decades, and present a new method for road surface defect detection using 3D laser profiling techniques. Road surface defect detection involves the use of optical sensors such as CCD cameras and laser scanners to collect the information of road surface, and pattern recognition and machine learning algorithms to automatically locate the defects and identify the categories of defects. Comparative experiments in the laboratory and test practices on the real roads show that the road surface defect detection technology using 3D laser profiling has stronger environmental adaptability and higher recognition precision than the traditional technology based on 2D visible light imaging. [ABSTRACT FROM AUTHOR]

In order to fully understand the working principle and behavior characteristics，The integrated experiment platform of micro - computer relay protection is developed，which can help students understand the working principle and behavior characteristics of microcomputer relay protection consisting of the Matlab the simulation platformof Matlab and the DSP hardware platforof DSP. By using the Matlab software. According to the actual parameters of the network components and the required protection types，the power system model and the microcomputer relay protection module are built，modeling the power system model and the microcomputer relay protection module according to the actual parameters of the network components and the required protection types for simulation testby using the Matlab software. . The operation data of grid model in different conditions are transferred to the DSP hardware platfor by using the Matlab the serial port and then put to practical testis transfeed to hardware platform of DSP by the serial port of Matlab. . At the same time，the microcomputer relay protection testing device is employed to can make an integrated whole test for the hardware platform. The result shows that Transfer Practice shows that， the development and application of the platform have a good effect to the teaching for those majoring electrical engineering and for training staff engaged in relay protection in electric power system. [ABSTRACT FROM AUTHOR]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the era of new urbanization, industrialization and informatization, China’s cities confront complex problems on the way to asustainable economy, society and ecology. Now, applications integrating Geomatics, Internet, and cloud computing technologies generate big urban data, becoming a strategic resource for the smart city. Urban informatics, a new direction in surveying, mapping and geoinformation, is developing out of the cross-fusion of Geomatics, Informatics, and urban science. Based on the unified spatial-temporal datum, urban informatics is to capture, process, analyze urban big data, and make smart decision with the help of information technology, aiming to realize the green, low carbon , and sustainable city. We discuss the relationship between Geomatics and urban informatics, and the five characteristics of urban informatics, includingthe dynamic evolution, datadriven, crowdsourced learning, collaborative decision-support, and cross-disciplinary nature of this field. Finally, we summarize scientific questions and outlook for the future of urban informatics. [ABSTRACT FROM AUTHOR]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)