Abstract: In this paper, a scalable augmented reality framework for handheld devices is presented. The presented framework is enabled by using a server-client data communication structure, in which the search for tracking targets among a database of images is performed on the server-side while pixel-wise 3D tracking is performed on the client-side, which, in this case, is a handheld mobile device. Image search on the server-side adopts a residual-enhanced image descriptors representation that gives the framework a scalability property. The tracking algorithm on the client-side is based on a gravity-aligned feature descriptor which takes the advantage of a sensor-equipped mobile device and an optimized intensity-based image alignment approach that ensures the accuracy of 3D tracking. Automatic content streaming is achieved by using a key-frame selection algorithm, client working phase monitoring and standardized rules for content communication between the server and client. The recognition accuracy test performed on a standard dataset shows that the method adopted in the presented framework outperforms the Bag-of-Words (BoW) method that has been used in some of the previous systems. Experimental test conducted on a set of video sequences indicated the real-time performance of the tracking system with a frame rate at 15-30 frames per second. The presented framework is exposed to be functional in practical situations with a demonstration application on a campus walk-around.
Abstract: Kuosheng nuclear power plant (NPP) is a BWR/6 type
NPP and located on the northern coast of Taiwan. First, Kuosheng
NPP TRACE model were developed in this research. In order to assess
the system response of Kuosheng NPP TRACE model, startup tests
data were used to evaluate Kuosheng NPP TRACE model. Second, the
overpressurization transient analysis of Kuosheng NPP TRACE model
was performed. Besides, in order to confirm the mechanical property
and integrity of fuel rods, FRAPTRAN analysis was also performed in
this study.
Abstract: This analysis of Kuosheng nuclear power plant (NPP)
was performed mainly by TRACE, assisted with FRAPTRAN and
FRAPCON. SNAP v2.2.1 and TRACE v5.0p3 are used to develop the
Kuosheng NPP SPU TRACE model which can simulate the turbine
trip without bypass transient. From the analysis of TRACE, the
important parameters such as dome pressure, coolant temperature and
pressure can be determined. Through these parameters, comparing
with the criteria which were formulated by United States Nuclear
Regulatory Commission (U.S. NRC), we can determine whether the
Kuoshengnuclear power plant failed or not in the accident analysis.
However, from the data of TRACE, the fuel rods status cannot be
determined. With the information from TRACE and burn-up analysis
obtained from FRAPCON, FRAPTRAN analyzes more details about
the fuel rods in this transient. Besides, through the SNAP interface, the
data results can be presented as an animation. From the animation, the
TRACE and FRAPTRAN data can be merged together that may be
realized by the readers more easily. In this research, TRACE showed
that the maximum dome pressure of the reactor reaches to 8.32 MPa,
which is lower than the acceptance limit 9.58 MPa. Furthermore,
FRAPTRAN revels that the maximum strain is about 0.00165, which
is below the criteria 0.01. In addition, cladding enthalpy is 52.44 cal/g
which is lower than 170 cal/g specified by the USNRC NUREG-0800
Standard Review Plan.