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Recently, through the development of IT and mobile technology, spatial information plays a role of infrastructure of the people life and the national economy. Many kinds of applications including SNS and social commerce is to leverage the spatial information for their services. In the case of domestic, spatial open platform that can provide national spatial data infrastructure services in a stable manner has been released. And many people have been interested to the open platform services. However, the open platform currently has many difficulties to analyze its service status and load in real time, because it does not hold a real-time monitoring system. Therefore, we propose a method that can analyze the real-time service status of the open platform using the analysis of the web server log information. In particular, we propose the results of the analysis as follows: amount of data transferred, network bandwidth, number of visitors, hit count, contents usage, and connection path. We think the results presented in this study is insufficient to understand the perfect service status of the open platform. However, it is expected to be utilized as the basic data for understanding of the service status and for system expansion of the open platform, every year.

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The assessment of the saphenofemoral junction (SFJ) is important in the diagnosis and treatment of venous reflux of the great saphenous vein (GSV). In the clinical practice of venous medicine, the SFJ is used to represent the region at which the saphenous arch connects with the common femoral vein (CFV). A number of notable variations of the SFJ have been documented, and rare variable courses of the GSV have been described recently. Our case study reports two unusual GSV terminations. In both cases, the SFJ was located below the confluence of the profunda femoris vein (PFV) with the femoral vein (FV). Case 1 showed the SFJ was formed by the GSV and FV; whereas case 2 showed the PFV was joined by the GSV after a transposition with the FV. Anatomical variations of the SFJ are rare; however, they are increasingly diagnosed with the use of duplex ultrasound. The identification of SFJ variants warrants a safe endovenous procedure and prevents surgical complications.

A fruit monitoring system based on image processing technology and multi-layer neural network is proposed. The advantage of the proposed fruit monitoring system allows it to be remotely controlled by PCs and the graphical user interface (GUI) program by LabVIEW which has been designed for more intuitive and convenient operation of this system. In addition, the neural network can reduce nonlinearity of the system compared to the calculation based system. Therefore, experienced workers and novices can easily judge the ripeness of the fruits using the GUI program without necessarily going to the orchards. In this study, the color is used as a criterion to judge the maturity of tomatoes. Ripe tomatoes will appear to be red, while the unripe tomatoes will be green in color. The region of interest (ROI) function and Canny edge detection are applied to crop the image and remove the background, then the pixel data obtained are to supply the use of neural network. After that the maturity level of tomatoes is judged by the neural network. In laboratory test, 50 experiments have been down, 48 of which were successful, 2 of which failed, so the recognition rate was 96%. The experiments of this fruit monitoring system in the greenhouse on real growing tomatoes has been conducted. Therefore, 10 experiments on the red and green tomatoes has been conducted, respectively. As a result, the recognition rate of the red tomatoes is 100%, and recognition rate of the green tomatoes is 90%. The experimental results show that the proposed mobile fruit monitoring system has a very high recognition rate of accuracy.