This project intends to develop an effective educational media that is not only rich in cultural content but also feasible in the museum setting. We want to introduce the Mao-Kung Ting, one of the most valuable collections of the National Palace Museum, to the public in two key aspects—its aesthetic beauty as an antique bronze cauldron, and its historical significance of carrying the longest bronze inscriptions ever discovered among unearthed bronze in China, which has made it plays an important role in the evolution of Chinese characters.  Our mission is to develop an interactive installation that could help the audiences to understand this critical cultural heritage with ease. The major techniques that have been employed to facilitate this process include intuitive interactive interface, computer graphics animation, as well as an immersive environment with audio and video. Finally, two case studies were presented to show how the use of multimedia technology is helping to enhance visitors’ experience while the key challenges of the contemporary curatorial tasks are being discussed.

With the aim to improve the divisibility of the features extracted by wavelet transformation in P300 detection, we researched the P300 frequency domain of event related potentials and the influence of mother wavelet selection towards the divisibility of extracted features, and then a novel P300 feature extraction method based on wavelet transform and Fisher distance. This can select features dynamically for a particular subject and thereby overcome the drawbacks of no systematic feature selection method during traditional P300 feature extraction based on wavelet transform. In this paper, both the BCI Competition 2003 and the BCI Competition 2005 data sets of P300 were used for validation, the experiment results showed that the proposed method can increase the divisibility by 121.8% of the features extracted by wavelet transformation, and the classification results showed that the proposed method can increase the classification accuracy by 1.2% while reduce 73.5% of the classification time. At the same time, integration of multi-domain algorithm is proposed based on the research of EEG feature extraction algorithm, and can be utilized in EEG preprocessing and feature extraction, even classification.

During the operation of a heated oil transportation pipeline, the shutdown was caused by some pipeline accidents and repairing. In order to ensure the safety operation of the pipeline, determine the temperature drop and restart pressure was needed at different shutdown is important. This paper builds the heated oil pipeline temperature drop model after shutdown based on the unsteady heat transfer theory and restart model based on fluid transient flow theory. In order to solve the models, MATLAB is adopted a code for the models solving program. VB and MATLAB hybrid programming method which is on the basis of COM technical is utilized to develop a friendly man-machine interface for the heated oil pipeline shutdown and restart simulation software. The practical application shows using of VB and MATLAB hybrid programming method can reduce the work of algorithm developing and enhance the reliability of heated oil pipeline shutdown and restart simulation software.

Smaller time loss and smoother communication pattern is the urgent pursuit in the software development enterprise. However, communication is difficult to control and manage and demands on technical support, due to the uncertainty and complex structure of data appeared in communication. Data mining is a well established framework aiming at intelligently discovering knowledge and principles hidden in massive amounts of original data. Data mining technology together with shared repositories results in an intelligent way to analyze data of communication in software development environment. We propose a data mining based algorithm to tackle the problem, adopting a co-training styled algorithm to discover pattern in software development environment. Decision tree is trained as based learners and a majority voting procedure is then launched to determine labels of unlabeled data. Based learners are then trained again with newly labeled data and such iteration stops when a consistent state is reached. Our method is naturally semi-supervised which can improve generalization ability by making use of unlabeled data. Experimental results on data set gathered from productive environment indicate that the proposed algorithm is effective and outperforms traditional supervised algorithms.

In many problems of classification, the performances of a classifier are often evaluated by a factor (rate of error).the factor is not well adapted for the complex real problems, in particular the problems multiclass. Our contribution consists in adapting an evolutionary method for optimization of this factor. Among the methods of optimization used we chose the method PSO (Particle Swarm Optimization) which makes it possible to optimize the performance of classifier SVM (Separating with Vast Margin). The experiments are carried out on corpus TIMIT. The results obtained show that approach PSO-SVM gives a better classification in terms of accuracy even though the execution time is increased.

Energy and environment conservation is one of hotspots all around world now. Light emitting diode (LED) road illumination based on Wireless Sensor Network (WSN) technology can not only reduces complexity for arrangement of cables and route construction process, but also show advantage of LED illumination well. Meanwhile, it is good for energy and environment conservation. Route construction is a crucial issue when we use WSN technology. In this paper, we present two network structures, introduce the route construction process of illumination area and derive total transmit power (TTP) of WSN. Then we analyse impact of some factors on normalized TTP and do computer simulation to evaluate them.

With the development of future Internet, it is of great significance to study how to realize unified management information modeling, in order to avoid a lot of repetitive work and standardize information modeling in network management domain. This paper discusses the problem from the ontology point of view and introduces the theory of concept lattices into the research on semantic management information modeling, which includes a) establishing an ontology-driven framework for semantic management information modeling, b) building unified management information modeling ontology based on concept lattices, and c) generating semantic models for network management information modeling using the theory of concept lattices.

An axial force estimation is a crucial problem in the design of a deep well pump. According to the cost of time and financial resources by experimental measurement and low precision and applicability of using experiential formulas, the effects of solid modeling, mesh generation, residual convergence precision, turbulence model and numerical solutions on the accuracy of numerical simulation were investigated. And the best scheme was applied in the numerical simulation to predict the axial thrust of the deep well pump. The simulation values of axial force are in agreement with the testing values, and the maximum error is less than 10%.