Advanced Materials Research Vols. 532-533

Abstract: This paper mainly studies application of formal design for security protocols in digital campus. With a comprehensive analysis on security requirements of information transmission in digital campus, an information transmission model is built from the viewpoint of information security. Based on this model, a new security protocol, called DCIT for short, is designed. The formal analysis shows that secrecy, integrity, availability, controllability, non-repudiation and identifiability of information during transmission could be insured by DCIT, which could be used as a direction for development of various application systems in digital campus, from the viewpoint of information security.

Abstract: Multiprocessor Scheduling is a problem of discrete optimization. The strong optimization capacity of Ant Colony Algorithm (ACA) on solving the discrete optimization suggests the feasibility to solve the multiprocessor scheduling problem using the ACA algorithm. Contrary to the shortcomings of traditional ant colony algorithm, this is easy to fall into the local convergence, In order to improve the calculation accuracy. combined of multiprocessor scheduling problem, propose a more efficient ant colony optimization algorithm and present a new state transition rule, at the same time use dynamically update the strategy of ant pheromones and the optimal parameter selection. it can find a better scheduling strategy in a short time, and it has excellent global optimization properties, The simulation results show that the credibility and the validity of the improved ant colony optimization algorithm for multiprocessor scheduling problem.

Abstract: Testing of the text categorization and comparison testing is carried out based on small-scaled dataset. In case of lack of trained set, without training, the indexed text keywords are used to categorize the expert subject terms, with large categorization accuracy amounted to 0.82. In case of less trained set, after training, the characteristics vectors acquired from the training are added into experts’ subject terms and are categorized, with large accuracy amounted to 0.94, the level-3 accuracy amounted to 0.73, so the results are satisfying.

Abstract: A collocation dictionary is a useful component to many natural language and spoken language processing application, such as grammar checking, text-speech conversion and machine translation. Currently the collocation dictionary is constructed by human. Firstly, it may not be updated frequently and many lexicon entries may be not available. Secondly, to construct such a dictionary may need lots of human resources. In this paper, a data-mining approach for constructing a collocation dictionary is surveyed. The main purpose is to enable cheap and quick acquisition of a collocation dictionary from a large-scale text corpus. Experimental results show the approach is effective and suitability.

Abstract: Nowadays the number of the users of the network data storage is becoming more and more. One of the problems we are confronted with is that how to construct our network data storage in the new period. Several typical network data storage is analyzed in the paper and the problems are analyzed .Some advice is put forward about the construction of the network data storage .We hope that the construction of our country’s network data storage can be push forward constantly through our effort.

Abstract: Speech can be broadly categorized into voiceless, voiced, and mute signal, in which voiced speech can be further classified into vowel and voiced consonant. With the ever increasing demand of the speech synthesis applications, it is urgent to develop an effective classification method to differentiate vowel and voiced consonant signal since they are two distinct components that affect the naturalness of the synthetic speech signal. State-of-the-arts algorithms for speech signal classification are effective in classifying voiceless, voiced and mute speech signal, however, not effective in further classifying the voiced signal. In view of the issue, a new algorithm for speech classification based on Gaussian Mixture Model (GMM) is proposed, which can directly classify a speech into voiceless, voiced consonant, vowel and mute signal. Specifically, a new speech feature is proposed, and the GMM is also modified for speech classification. Simulation results demonstrate that the proposed algorithm is effective even under the noisy environments.

Abstract: The objective of this work is multiple objects detection in remote sensing images. Many classifiers have been proposed to detect military objects. In this paper, we demonstrate that linear combination of kernels can get a better classification precision than product of kernels. Starting with base kernels, we obtain different weights for each class through learning. Experiment on Caltech-101 dataset shows the learnt kernels yields superior classification results compared with single-kernel SVM. While such a powerful classifier act as a sliding-window detector to search planes in images collected from Google Earth, results shows the effectiveness of using MKL detector to locate military objects in remote sensing images.

Abstract: This paper presented a new method to construct semantic web of three-dimension model database based on ontology. Firstly we build ontology of three-dimension model database, according the model to extract classes, objects and attributes. Secondly utilize WordNet which is an English ontology to expand original ontology node to semantic extension node, including synonym, hypernym, hyponym and holonym. Experiment result shows that this method not only effectively expands the semantic vocabularies of a 3D model database, but also keeps good semantic relevance of the expanded vocabularies to the original ones, so as to achieve semantic based 3D model retrieval effectively.

Abstract: Weather services closely linked with the geographic data. To some extent, weather data are geographic information. Because the wind speed, temperature, pressure, etc. are all relative to the specific space domain and time domain. Weather data is very ambitious. The climate observatory needs to be updated daily hundreds of weather data, together with accumulated decades of climate history data. The data amount is very large. With the development of science and technology, weather data are growing very quickly. As for these characteristics, the weather services have a wide range of applications of the GIS system. A good weather information query system allows forecasters the most intuitive and rapid control of various weather information. It plays an important role in decision making.

Abstract: This paper presents a model by combining BP neural network and DS evidential reasoning, which not only achieves the feature level fusion of all subjective and objective evidences in various domains and layers, but also makes distinct models complement each other. By the experiment, this method improves classification precision by 7.9 percent and reduces the time complexity of algorithm. The model solves the problems such as high complexity of algorithms and low accuracy rate of classifications lie in the flood prediction using single models.