This paper presents a power conversion system for an environmental radiation monitoring system (ERMS) with high efficiency and high reliability. To enhance the system reliability, the proposed configuration for ERMS has three power sources, and the power conversion system is responsible for the interface with ERMS and the sources. The proposed power conversion system is designed to have high efficiency characteristics by minimizing the number of the power conversion process, and its circuit structure is designed to be suitable for small power application such as the ERMS. So the proposed power conversion system consists of a bidirectional flyback converter capable of a single power conversion and a buck converter with maximum power point tracking (MPPT) function. To ensure the feasibility of the proposed conversion system, the optimized control strategy is introduced. Finally, for the proposed power conversion system, a prototype was built, and its performance and validity were evaluated based on the experimental results.

This paper presents a high efficiency power conversion system for a mobile air radiation monitoring system (RMS). The proposed system consists of a two-stage dc-ac converter and a step-down converter. The two-stage dc-ac converter is used for the pump speed control of the mobile air RMS. For the dc-dc stage, by employing a soft-switching technique to all semiconductor devices, high efficiency and high power density are achieved. Meanwhile, the inverter stage controls the flow rate with the information provided by the air radiation monitor with variable voltage variable frequency (VVVF) algorithm; this enables active system control and energy saving. Considering that a battery, which is a finite energy source, is used in the system, the high efficiency characteristic gives the feasibility of the proposed power conversion system. The step-down converter is used to supply power to the electronics in the mobile air RMS. Finally, for the proposed power conversion system, a prototype is implemented, and its performance and validity are evaluated based on experimental results.

Along with the development of high-speed Internet and smart devices, various attack methods were emerged, and attack traffic has also changed into various and complex forms. In order to provide reliable services and efficient management of network resources, it is essential to detect and analyze the attack traffic. While various application and attack traffic detection or classification methods have been studied, but signature-based methods are still mainstream of the most. In this paper, we propose the seed based sequential grouping model for attack traffic detection. Model consists of two main indices, which are similarity and connectivity index. In addition to model, we define the set of optimal thresholds of each index by using our balancing algorithm and define it as Guideline. By applying the proposed model to the actual attack traffic, we demonstrate that the model has high detection accuracy and completeness.

Emergence of high-speed Internet and ubiquitous environment has led to a rapid increase of applications on the Internet and network traffic complexity. In order to provide reliable services and efficient management of network resources, it is essential to classify traffic with specific units. While various traffic classification methods are being studied, there is no single method to classify traffic completely yet. In this paper, we define the correlation model of network flow and propose a traffic grouping method based on it. The proposed correlation model of network flow for traffic grouping consists of the Similarity model and the Connectivity model. We define the Similarity model guideline and the Connectivity model guideline for the purpose of applying the proposed method effectively. By applying the proposed method to the actual application traffic classification, we demonstrate that the method has high accuracy and completeness.

Today, as the network environment increases, various types of traffic patterns generated for each application and service are generated, and traffic analysis methods that can classify traffic applications and services are being studied. In particular, Skype is a VoIP service that is serviced by Microsoft and is currently the most widely used internationally. For this reason, the importance of Skype traffic detection is growing in terms of network management. In order to overcome the limitations of signature and machine learning based detection methods and to more accurately analyze and detect the current Skype traffic pattern, this paper presents a comprehensive Skype traffic detection system that combines pattern, list and signature based application detection methods. The proposed system is applied to various Skype traffic collected through campus network to verify accuracy and detection rate.

Applications are becoming more complicated and diverse as the network environment grows day by day. So, it is important to classify application traffic accurately. Although there are many ways to classify applications traffic, machine learning based approaches are becoming more efficient in nowadays. This is because machine learning methods are more appropriate than existing methods for accurate and efficient applications traffic classification. Payload signature methods have limitations to deal with various patterns and increasing application traffic complexity. In this paper, we propose a method for extracting flow features and a system for classifying applications traffic based on Machine Learning.

Internet traffic identification is an essential preliminary step for stable service provision and efficient network management. The payload signature-based-classification is considered as a reliable method for Internet traffic identification. But its performance is highly dependent on the number and the structure of signatures. If the numbers and structural complexity of signatures are not proper, the performance of payload signature-based-classification easily deteriorates. Therefore, in order to improve the performance of the identification system, it is necessary to regulate the numbers of the signature. In this paper, we propose a novel signature quality evaluation method to decide which signature is highly efficient for Internet traffic identification. We newly define the signature quality evaluation criteria and find the highly efficient signature through the method. Quality evaluation is performed in three different perspectives and the weight of each signature is computed through those perspectives values. And we construct the signature map(S-MAP) to find the highly efficient signature. The proposed method achieved an approximately fourfold increased efficiency in application traffic identification.

Internet traffic classification is an essential step for stable service provision and efficient network management. The payload signature-based-classifier is considered as a reliable method for Internet traffic classification, but is prohibitively and computationally expensive for real-time handling of large amounts of traffic on high-speed network. To solve this problem, most studies focused on the pattern matching algorithm or hardware-based approaches such as FPGA and network processor. However, in order to improve the performance of the classification system, It is also necessary to consider the classification criteria and signature model in accordance with the characteristics of various application protocols. In this paper, we newly define the classification criteria and signature model, and propose an optimized classification architecture in perspective of input data minimization and complexity of pattern matching algorithm to improve the processing speed of classification system. Each of them can be applied individually, or in any combination. The proposed method achieved an approximately 5-fold increase in processing speed over existing baseline classification system.

Abstract We propose theprimitive tree, a novel and compact space‐partition method that samples and reconstructs a distance field with high accuracy, even for regions far from the surfaces. The primitive tree is based on the octree and stores the indices of the nearest primitives in its leaf nodes. Most previous approaches have involved a trade‐off between accuracy and speed in distance queries, but our method can improve both aspects simultaneously. In addition, our method can sample unsigned distance fields effectively, even for self‐intersecting and nonmanifold models. We present test results showing that our method can sample and represent large scenes, with more than ten million triangles, rapidly and accurately.

We address the computational resource requirements of 3D example-based synthesis with an adaptive synthesis technique that uses a tree-based synthesis map. A signed-distance field (SDF) is determined for the 3D exemplars, and then new models can be synthesized as SDFs by neighborhood matching. Unlike voxel synthesis approach, our input is posed in the real domain to preserve maximum detail. In comparison to straightforward extensions to the existing volume texture synthesis approach, we made several improvements in terms of memory requirements, computation times, and synthesis quality. The inherent parallelism in this method makes it suitable for a multicore CPU. Results show that computation times and memory requirements are very much reduced, and large synthesized scenes exhibit fine details which mimic the exemplars.

This paper presents analytic solutions to the integral moving least squares (MLS) equations originally proposed by Shen et al. by choosing another specific weighting function that renders the numerator in the MLS equation unitless. In addition, we analyze the original method to show that their approximation surfaces (i.e., enveloping surfaces with nonzero values in the weighting function) often form zero isosurfaces near concavities behind the triangle-soup models. This paper also presents error terms for the integral MLS formulations against signed distance fields. Based on our analytic solutions, we show that our method provides both interpolation and approximation surfaces faster and more efficiently. Because our method computes solutions for integral MLS equations directly, it does not rely on numerical steps that might have numerical-accuracy issues. In particular, unlike the original method that deals with incorrect approximation surfaces by iteratively adjusting parameters, this paper proposes faster and more efficient approximations to surfaces without needing iterative routines. We also present computational efficiency comparisons, in which our method is 15-fold faster in computing integrations, even with conservative assumptions. Finally, we show that the surface normal vectors on the implicit surfaces formed by our analytic solutions are identical to the angle-weighted pseudonormal vectors.

MediaGIS is a concept of tightly integrated multimedia with spatial information. VideoGIS is an example of MediaGIS focused on the integration and interaction of video and spatial information. Our suggested GeoVideo, a new concept of interactive geographic video has its key feature in interactiveness. In GeoVideo, the geographic tasks such as browsing, searching, querying, and spatial analysis can be performed based on video itself. GeoVideo can have the meaning of paradigm shift from artificial, static, abstracted, and graphical paradigm to natural, dynamic, real, and image-based paradigm. We discuss about the integration of video and geography and also suggest the GeoVideo system design. Studies on the systematic architecture utilizing the video stream and MPEG must follow with the inter-disciplinary studies especially on the computer vision and virtual reality.