One of the most promising advantages of Web service technology is the possibility of creating value-added services by combining existing ones. A major challenge is how to discover and select concrete service according...
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One of the most promising advantages of Web service technology is the possibility of creating value-added services by combining existing ones. A major challenge is how to discover and select concrete service according to user requirements. This paper addresses the topic of service discovery composite Web services. The main feature is that we take the process model as well as service profile into account. Firstly, the process models of Web services are translated into Petri nets. Based on this, we propose a service matchmaking algorithm, via comparing the functionality compatibility and process consistency, thus leading to more accurate matchmaking.
In this paper, we perform Chinese text classification using n-gram text representation on TanCorp which is a new large corpus special for Chinese text classification more than 14,000 texts divided into 12 classes. We ...
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According to the conventional hypothesis, the velocity of target in the coherent integration time (CIT) is invariable in the application of utilizing high frequency surface wave radar to detect target. First, this pap...
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ISBN:
(纸本)9788372077578
According to the conventional hypothesis, the velocity of target in the coherent integration time (CIT) is invariable in the application of utilizing high frequency surface wave radar to detect target. First, this paper proposes a more proper hypothesis in which the velocity and acceleration of target are treated as time-varying. However the acceleration of target can be regarded as invariable within the small fraction of coherent integration time. Then the model of echo signal in the hypothesis is analyzed. Considering the disadvangtages of some conventional methods of time-frequency analysis such as short-time Fourier transform (STFT) and Wigner-Ville distribution (WVD), the paper proposes the method of time-frequency analysis based on chirplets signal decomposition which can improve the time and frequency resolution simultaneously and solve the cross-term problem in WVD method. Firstly, we process the small fraction of coherent integration time with the chirplets signal decomposition. Then according to the differences between target signal and ocean clutter echo, the BP neural network classifier can be exploited to suppress ocean clutter. The different fractions of coherent integration time are composed together with the method of the nearest correlation. Lastly, the feasibility of the method for detecting targets of variable acceleration is proved by its processing the actual data.
In this paper, we present a pedestrian detection approach using spatial histograms of oriented gradients feature. As spatial histograms of oriented gradients consist of marginal distributions of an image over local an...
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In this paper, we present a pedestrian detection approach using spatial histograms of oriented gradients feature. As spatial histograms of oriented gradients consist of marginal distributions of an image over local and global patches, they can preserve shape and contour of a pedestrian simultaneously. There are two main contributions in this paper. First of all, we expand the histograms of oriented gradients features from single-size to variable-size which can capture local and global feature of pedestrian automatically. We call theses feature as the "spatial histograms of oriented gradients". Secondly, we employ histogram similarity and Fisher criterion to measure discriminability of features and select some discriminative features to identify the pedestrian. SVM classifier is constructed to train the selected features from target and surrounding background. The proposed algorithm is tested on some public database. Experimental results show that the proposed approach is efficient and rapid in pedestrian detection.
Short-term forecasting of travel time is essential for the success of intelligent transportation system. In this paper, we review the state-of-art of short-term traffic forecasting models and outline their basic ideas...
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Short-term forecasting of travel time is essential for the success of intelligent transportation system. In this paper, we review the state-of-art of short-term traffic forecasting models and outline their basic ideas, related works, advantages and disadvantages of each model. An improved adaptive exponential smoothing (IAES) model is also proposed to overcome the drawbacks of the previous adaptive exponential smoothing model. Then, comparing experiments are carried out under normal traffic condition and abnormal traffic condition to evaluate the performance of four main branches of forecasting models on direct travel time data obtained by license plate matching (LPM). The results of experiments show each model seems to have its own strength and weakness. The forecasting performance of IASE is superior to other models in shorter forecasting horizon (one and two step forecasting) and the IASE is capable of dealing with all kind of traffic conditions.
A novel trust model for P2P networks, namely RETM, a Recommendation Evidence based Trust Model, is presented in this paper. It solves some problems, for instance, not invalidly aggregating incompatible recommendation ...
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A novel trust model for P2P networks, namely RETM, a Recommendation Evidence based Trust Model, is presented in this paper. It solves some problems, for instance, not invalidly aggregating incompatible recommendation information and dealing with uncertainty of information in the reputation-based P2P trust model. Before combining the evidences, RETM will filter out noisy recommendation information, and moreover the method makes RETM more robust. In addition a feedback-based probabilistic searching algorithm is proposed to find the recommendation information, which improves the searching success rate and lowers the network traffic. Theoretical analyses and experimental results show that, compared to the current some trust models, the proposed model RETM has advantages in modeling dynamic trust relationship and aggregating recommendation information, moreover, is more robust on trust security problems and more advanced in successful transaction rate.
Recently, dynamic Bayesian network (DBN) based speech recognition has aroused an increasing interest, because of its interpretability, factorization and extensibility, which hidden Markov models (HMMs) lack. Although ...
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Recently, dynamic Bayesian network (DBN) based speech recognition has aroused an increasing interest, because of its interpretability, factorization and extensibility, which hidden Markov models (HMMs) lack. Although a huge success of the introduction of DBNs into speech recognition in many areas and DBNs has been presented with promising potential to overcome inherent limitations of HMMs in speech recognition, previous work on DBN based speech recognition mainly focuses on isolated word speech recognition, and the frameworks and recognition algorithms for DBN based continuous speech recognition are not as mature and flexible as those for HMM based one. This paper is trying to address the problems of flexibility and extensibility in DBN based continuous speech recognition. To achieve this purpose, the token passing model, which works very well to address the above problems for HMM based continuous speech recognition, is adapted for DBN based one, and a general framework based on it is proposed. In this framework, the advantages of both token passing model and DBN are combined. A novel recognition algorithm independent of the upper layer language model is proposed under this framework, and a toolkit DTK for building DBN based speech recognition under this framework is developed.
For Dubois rough fuzzy sets, the membership of the lower or upper approximations is defined as the elements memberships' infimum or supsmum in equivalent class. As a result of not considering the elements whose me...
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For Dubois rough fuzzy sets, the membership of the lower or upper approximations is defined as the elements memberships' infimum or supsmum in equivalent class. As a result of not considering the elements whose memberships are between the minimum and maximum, some useful information of these elements may be lost in the information processing. The paper presents a new operator of rough fuzzy sets that every element's membership in equivalent class is taken into account. Based on the new operator, algebra properties are put forward and rough fuzzy membership is defined. Moreover, the paper presents accurate degree, classified quality, dependence degree and attribute reduction algorithm. At last, an example proves that the algorithm is efficient.
The development of VLSI technology results in the dramatically improvement of the performance of integrated circuits. However, it brings more challenges to the aspect of reliability. Integrated circuits become more su...
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The development of VLSI technology results in the dramatically improvement of the performance of integrated circuits. However, it brings more challenges to the aspect of reliability. Integrated circuits become more susceptible to soft errors. Therefore, it is imperative to study the reliability of circuits under the soft error. This paper implements three probabilistic methods (two pass, error propagation probability, and probabilistic transfer matrix) for estimating gate-level circuit reliability on PC. The functions and performance of these methods are compared by experiments using ISCAS85 and 74-series circuits.
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