基于词干的蒙古语语音关键词检测方法的研究

To improve in-vocabulary performance in Mongolian speech keyword spotting task, we propose a Mongolian speech keyword spotting method by searching the stem according to the characteristic of Mongolian word-formation rule. First, Mongolian speech is decoded to lattice file by Segmentation-based LVCSR system, and this lattice file is converted to a confusion network. Then, we detect the keywords according to their stems among the confusion network. Experimental results show that the proposed method outperforms baselines based on word confusion network.     

A simple robust control for global asymptotic position stabilization of underactuated surface vessels

Global asymptotic stabilization of underactuated surface vessels is generally achieved only by designing complicated controllers. This paper proposes a very simple control law that globally asymptotically stabilizes the position of underactuated surface vessel to a desired constant location and its velocities to zero. The proposed controller is independent of velocity signals and is robust to model parameters. It neither includes an observer nor an adaptive/sliding‐mode law. Controller development and stability analysis rely on a novel Lyapunov function and LaSalle's theorem. Furthermore, by extending the proposed control strategy, a saturated control law is also obtained ensuring the semiglobal asymptotic stability of position error system. Effectiveness of the proposed control schemes is demonstrated by simulation examples. Copyright © 2017 John Wiley & Sons, Ltd.     

A Hybrid Heat Rate Forecasting Model Using Optimized LSSVM Based on Improved GSA

Heat rate value is considered as one of the most important thermal economic indicators, which determines the economic, efficient and safe operation of steam turbine unit. At the same time, an accurate heat rate forecasting is core task in the optimal operation of steam turbine unit. Recently, least squares support vector machine (LSSVM) is being proved an effective machine learning technique for solving nonlinear regression problem with a small sample set. However, it has also been proved that the prediction precision of LSSVM is highly dependent on its parameters, which are hardly choosing for the LSSVM. In the paper, an improved gravitational search algorithm (AC-GSA) is presented to further enhance optimal performance of GSA, and it is employed to serve as an approach for pre-selecting LSSVM parameters. Then, a novel soft computing method, based on LSSVM and AC-GSA, is therefore proposed to forecast heat rate of a 600 MW supercritical steam turbine unit. It combines the merits of the high accuracy of LSSVM and the fast convergence of GSA in order to build heat rate prediction model and obtain a well-generalized model. Results indicate that the developed AC-GSA–LSSVM model demonstrates better regression precision and generalization capability.     

Towards accurate intrusion detection based on improved clonal selection algorithm

Artificial immune system constructs a dynamic and adaptive information defense system through a function similar to the biological immune system. In order to resist the external invasion of useless and harmful information and ensure the effectiveness and the harmlessness of received information. Due to the low accuracy and the high false positive rate of the existing clonal selection algorithms applied to intrusion detection, in this paper, we propose an improved clonal selection algorithm. The improved method detects the intrusion behavior by selecting the best individual overall and cloning them. Experimental results show that the improved algorithm achieves very good performance when applied to intrusion detection. And it is shown that the algorithm is better than BP neural network with its 99.5 % accuracy and 0.1 % false positive rate.     

Iterative quadtree decomposition based automatic selection of the seed point for ultrasound breast tumor images

Based on seed region growing method, lesion segmentation for ultrasound breast tumor images often requires manual selection of the seed point, which is both time-consuming and laborious. To overcome this limit, this paper attempts to explore an automatic method for finding the seed point inside the tumor. Two criteria combining iterative quadtree decomposition (QTD) and the gray characteristics of the lesion are thus designed to locate the seed point. One is to seek the biggest homogenous region and the other is to select the seed region where the seed point is found. Furthermore, this study validates the proposed algorithm through 110 ultrasonic breast tumor images (including 58 malignant tumor images and 52 benign tumor images). According to the needs of the seed region growing algorithm, if the seed point is found inside the tumor, it means the proposed method is correct. Otherwise, it means that the method is a failure. As the quantitative experiment results show, the proposed method in this paper can automatically find the seed point inside the tumor with an accuracy rate of 97.27 %.     

The application of interactive dynamic virtual surgical simulation visualization method

In this paper, an interactive dynamic simulation method is proposed to solve computational models of soft tissue undergoing large deformation, collision detection, and volume conservation in medical surgical simulation visualization. During the process of implementation of the interactive dynamic simulation method, the point-based method is used to simulate the elastic solids undergoing large deformations and the position-based method is used to simulate the objects collision, friction and volume conservation. Numerical results demonstrate that the proposed method improves the efficiency and stability of the response of heterogeneous soft tissue undergoing contact or even the multi-organs interactions, and it can be extended to interactive biopsy and cutting simulation.

     

Multi-scale segmentation of very high resolution remote sensing image based on gravitational field and optimized region merging

Multimedia Tools and Applications - This paper proposes a multi-scale segmentation approach for high resolution remote sensing image (HRRSI) based on the gravitational field and region merging. In...     

Data hiding in AMBTC images using quantization level modification and perturbation technique

Multimedia Tools and Applications - A novel data hiding method for Absolute Moment Block Truncation Coding (AMBTC) compressed image based on quantization level modification is proposed. Blocks of...     

Dual graph regularized NMF model for social event detection from Flickr data

In this work, we aim to discover real-world events from Flickr data by devising a three-stage event detection framework. In the first stage, a multimodal fusion (MF) model is designed to deal with the heterogeneous feature modalities possessed by the user-shared data, which is advantageous in computation complexity. In the second stage, a dual graph regularized non-negative matrix factorization (DGNMF) model is proposed to learn compact feature representations. DGNMF incorporates Laplacian regularization terms for the data graph and base graph into the objective, keeping the geometry structures underlying the data samples and dictionary bases simultaneously. In the third stage, hybrid clustering algorithms are applied seamlessly to discover event clusters. Extensive experiments conducted on the real-world dataset reveal the MF-DGNMF-based approaches outperform the baselines.     

Finding users preferences from large-scale online reviews for personalized recommendation

Along with the growth of Internet and electronic commerce, online consumer reviews have become a prevalent and rich source of information for both consumers and merchants. Numerous reviews record massive consumers’ opinions on products or services, which offer valuable information about users’ preferences for various aspects of different entities. This paper proposes a novel approach to finding the user preferences from free-text online reviews, where a user-preference-based collaborative filtering approach, namely UPCF, is developed to discover important aspects to users, as well as to reflect users’ individual needs for different aspects for recommendation. Extensive experiments are conducted on the data from a real-world online review platform, with the results showing that the proposed approach outperforms other approaches in effectively predicting the overall ratings of entities to target users for personalized recommendations. It also demonstrates that the approach has an advantage in dealing with sparse data, and can provide the recommendation results with desirable understandability.