The present study has developed a remote crack-measuring device using an Android camera with the laser-positioning technique. Four laser points projecting on walls along with cracks are photographed and utilized as reference points to process the image ortho-rectification. Then, for the crack recognition, a crack-tracking method based on the edge-detecting algorithm is used and implemented in the system. Finally, a field experiment was conducted to validate the proposed remote crack-measuring system. The preliminary result shows that this system has a great potential for field applications.

This paper presents a three-dimensional earth representation system for visualization of Taiwan Ocean data, based on the mixed MySQL and HBase databases. The Client-Server architecture is used to provide Web GIS services in this system. In addition, the WebGIS and WebGL are adopted to enhance 3D visualization of the ocean data. In order to achieve better data-access performance, the architecture of HBase combined with MySQL is used to accommodate different data types and sizes in the back end of system. Finally, we implement the information of sea-going onboard survey, including concentrations of chlorophyll a salinity and temperature, shore-based surface current data and PM2.5 data, onto the 3D-GIS display platform for environmental decision making in the future.

The present study utilizes VirtualBox virtual environment technology to develop the personal and compact size of multi-node big data VM platform with Spark and Hadoop cluster that can effectively replicate and provides an environment for developers to easily design and implement Spark and Hadoop Map/Reduce programming. By using the multi-node Hadoop VM system, developers can conduct Map/Reduce programing completely the same as that in the real multi-node Hadoop cluster. To demonstrate its capability and applicability, this study performs the benchmark by using the big data VM platform and a physical Multi-Node Hadoop Cluster. Based on the standard WordCount benchmarking, the computing time of the physical multi-node Hadoop cluster is 3.7 times faster than that of VM Hadoop cluster. The benchmark results show that the big data VM platform is an ideal platform for the portal and Map/Reduce programming, Spark programming and testing purposes, and the physical Hadoop cluster is the most appropriate for production runs. In addition, the big data VM platform contains a web portal development module designed to support applications that implement big data computing services for the engineering and science users. Such applications are inherently complex, potentially accessing data from a variety of sources and distributing applications to a variety of clients. This portal development module can act as multiple roles in many projects such as personal portals, small business portals, enterprise portals, educational portal, infrastructure portal, and other types of portals. Finally, the big data VM platform, in term of a big data development platform, is ready for users to download. The first author of this paper would like to give a demonstration for the proposed multi-node big data VM platform.

The present study utilizes VirtualBox virtual environment technology to develop the Personal, small size, Big Data platform that can effectively replicate a VM Hadoop system and provides an environment for developers to easily design and implement Hadoop Map/Reduce programming. This study also performs the benchmark by using the VM Hadoop, small-cluster Hadoop, and NCHC's large-scale Hadoop cluster, Braavos. The benchmark results show that the VM Hadoop is an ideal platform for the Map/Reduce code development and testing purpose, and the Braavos Hadoop cluster is the most appropriate for production runs. Moreover, based on the standard WordCount example, the computing time of Braavos Hadoop cluster is 232 times faster than the small-cluster Hadoop. In addition, an engineering example, the image recognition of flow monitoring, is given to illustrate the way of big image data analytics in the Hadoop system. Finally, the VM Hadoop, in term of a Big Data development platform, is ready for users to download. The first author of this paper would like to give a demonstration for the proposed VM Hadoop system as well as an engineering application.

A portable particle image velocimetry (PIV) measurement system is developed for sensing flow surface velocities. Using the laser-based technique, four laser points are projected on the flow surface and utilized as reference points to simplify the image processing of orthorectification in PIV measurement. Furthermore, by combining a smart phone with laser modules, a portable PIV device has been developed that is capable of performing PIV measurement comprehensively. Two laboratory experiments were carried out to validate the proposed portable PIV measurement system. The experimental results show that this portable PIV system could be easily set up and measure the flow surface velocities immediately.

In the present study, a portable PIV device is developed for surface flow velocity measurement, which mainly integrates the laser-projecting module with the smart phone. With 4 laser points projected on the flow surface as reference scale, it can capture flow images, process image ortho-rectification, do PIV calculation and perform velocity vector all in the smart phone. Two cases are conducted to test its capability and applicability. In general, the preliminary test results are quite well.

Severe pier scour is usually the major cause of bridge failures. It is therefore crucial to develop various scour monitoring techniques and prediction models for real-time warning of bridge safety. In the present study, a multilens monitoring system for pier scour under laboratory conditions has been developed. By utilising a plurality of lenses, this system is capable of tracking scour images and obtaining real-time scour-depth variation through a series of image recognition processes. Laboratory experiments under unsteady flow conditions were carried out to validate the proposed monitoring method. Then, three time-dependent scour prediction models were employed for simulation and comparison with the measured data. In order to improve the scour prediction results, with the real-time scour monitoring data, a data assimilation scheme is proposed and applied to the scour model under clear-water scour conditions. The result shows that the accuracy of scour prediction for a lead time of 3 h can be improved significantly.

A scour monitoring system with a micro camera tracking the bed-level images is proposed in this study.Two image recognition algorithms have been developed to support the bed-level image tracking approach.Through the laboratory experiments of pier scour,this study demonstrates that the proposed system is able to accurately monitor the scour-depth evolution in real time.In addition,five commonly-used temporal scour models are employed to simulate scour-depth evolution and their results are compared with monitoring data.In general,the results indicate that the proposed scour monitoring system has the potential for further applications in the field.

In this paper, we present two types of the real-time water monitoring system using the image processing technology, the water level recognition and the surface velocity recognition. According to the bridge failure investigation, floods in the river often pose potential risk to bridges, and scouring could undermine the pier foundation and cause the structures to collapse. It is very important to develop monitoring techniques for bridge safety in the field. In this study, we installed two high-resolution cameras on the in-situ bridge site to get the real-time water level and surface velocity image. For the water level recognition, we use the image processing techniques of the image binarization, character recognition, and water line detection. For the surface velocity recognition, the proposed system apply the PIV(Particle Image Velocimetry, PIV) method to obtain the recognition of the water surface velocity by the cross correlation analysis. Finally, the proposed systems are used to record and measure the variations of the water level and surface velocity for a period of three days. The good results show that the proposed systems have potential to provide real-time information of water level and surface velocity during flood periods.

The present study utilizes VirtualBox virtual environment technology to develop the Personal, small size, Big Data platform that can effectively replicate a VM Hadoop system and provides an environment for developers to easily design and implement Hadoop Map/Reduce programming. This study also performs the benchmark by using the VM Hadoop, small-cluster Hadoop, and NCHC's large-scale Hadoop cluster, Braavos. The benchmark results show that the VM Hadoop is an ideal platform for the Map/Reduce code development and testing purpose, and the Braavos Hadoop cluster is the most appropriate for production runs. Moreover, based on the standard WordCount example, the computing time of Braavos Hadoop cluster is 232 times faster than the small-cluster Hadoop. In addition, an engineering example, the image recognition of flow monitoring, is given to illustrate the way of big image data analytics in the Hadoop system. Finally, the VM Hadoop, in term of a Big Data development platform, is ready for users to download. The first author of this paper would like to give a demonstration for the proposed VM Hadoop system as well as an engineering application.