14419943
Information granulation and granular relationships
As an emerging research method to deal with information and knowledge processing, various topics of granular computing have recently received more attention by researchers. The foundations of granular computing involves general principles of many disciplines that are explored for many years such as divide and conquer, interval computing, fuzzy sets, rough sets and so on. Granules, granulations and relationships are some of the key issues in the study of granular computing. This paper aims to understand mainly granular computing theory from the perspective of information granulation and granular relationships.
12580116
Supporting Research with Weblogs: A Study on Web-Based Research Support Systems
Research environments have been changing with the availability of new technologies for decades. Researchers are benefited from digital libraries, online databases, and Web search engines recently. It is expected that the Web-based research systems play an important role in supporting research activities. Blogs, a new form of mainstream personal communication and a new voice for traditional media, recently gained much attention from the public, industrial and academic. This article studies issues on Web-based research support systems with blogs
18829938
An Enhanced Support Vector Machine Model for Intrusion Detection
Design and implementation of intrusion detection systems remain an important research issue in order to maintain proper network security. Support Vector Machines (SVM) as a classical pattern recognition tool have been widely used for intrusion detection. However, conventional SVM methods do not concern different characteristics of features in building an intrusion detection system. We propose an enhanced SVM model with a weighted kernel function based on features of the training data for intrusion detection. Rough set theory is adopted to perform a feature ranking and selection task of the new model. We evaluate the new model with the KDD dataset and the UNM dataset. It is suggested that the proposed model outperformed the conventional SVM in precision, computation time, and false negative rate
5263048
On Web-based Support Systems
Web-based Support Systems (WSS) is viewed as a multidisciplinary research that focuses on supporting human activities in specific domains or fields based on computer science, information technology, and Web technology. Research on WSS is motivated by the challenges and opportunities of the Web and the recent advancements of computer and Web technologies which make the implementation of WSS feasible. Fundamental issues of WSS are examined, a framework of WSS is presented, and research on WSS is discussed in this article. Some concrete examples such as Web-based research support systems (WRSS) and Web-based information retrieval support systems (WIRSS) are also discussed. It is expected that researchers will pay more attention on the new research area on WSS.
14229561
Time dependent directional profit model for financial time series forecasting
Goodness-of-fit is the most popular criterion for neural network time series forecasting. In the context of financial time series forecasting, we are not only concerned at how good the forecasts fit their targets, but we are more interested in profits. In order to increase the forecastability in terms of profit earning, we propose a profit based adjusted weight factor for backpropagation network training. Instead of using the traditional least squares error, we add a factor which contains the profit, direction, and time information to the error function. The results show that this new approach does improve the forecastability of neural network models, for the financial application domain.
821925
Knowledge Based Descriptive Neural Networks
This paper presents a study of knowledge based descriptive neural networks (DNN). DNN is a neural network that incorporates rules extracted from trained neural networks. One of the major drawbacks of neural network models is that they could not explain what they have done. Extracting rules from trained neural networks is one of the solutions. However, how to effectively use extracted rules has been paid little attention. This paper addresses issues of effective ways of using these extracted rules. With the introduction of DNN, we not only keep the good feature of nonlinearity in neural networks but also have explanation of underlying reasoning mechanisms, for instance, how prediction is made.
8612227
Mixed-State Markov Random Fields for Motion Texture Modeling and Segmentation
The aim of this work is to model the apparent motion in image sequences depicting natural dynamic scenes. We adopt the mixed-state Markov random fields (MRF) models recently introduced to represent so-called motion textures. The approach consists in describing the spatial distribution of some motion measurements which exhibit mixed-state nature: a discrete component related to the absence of motion and a continuous part for measurements different from zero. We propose several significative extensions to this model. We define an original motion texture segmentation method which does not assume conditional independence of the observations for each texture and normalizing factors are properly handled. Results on real examples demonstrate the accuracy and efficiency of our method.
10944826
Recognition of Dynamic Video Contents With Global Probabilistic Models of Visual Motion
The exploitation of video data requires methods able to extract high-level information from the images. Video summarization, video retrieval, or video surveillance are examples of applications. In this paper, we tackle the challenging problem of recognizing dynamic video contents from low-level motion features. We adopt a statistical approach involving modeling, (supervised) learning, and classification issues. Because of the diversity of video content (even for a given class of events), we have to design appropriate models of visual motion and learn them from videos. We have defined original parsimonious global probabilistic motion models, both for the dominant image motion (assumed to be due to the camera motion) and the residual image motion (related to scene motion). Motion measurements include affine motion models to capture the camera motion and low-level local motion features to account for scene motion. Motion learning and recognition are solved using maximum likelihood criteria. To validate the interest of the proposed motion modeling and recognition framework, we report dynamic content recognition results on sports videos
18387908
Extraction of Semantic Dynamic Content from Videos with Probabilistic Motion Models
The exploitation of video data requires to extract information at a rather semantic level, and then, methods able to infer “concepts” from low-level video features. We adopt a statistical approach and we focus on motion information. Because of the diversity of dynamic video content (even for a given type of events), we have to design appropriate motion models and learn them from videos. We have defined original and parsimonious probabilistic motion models, both for the dominant image motion (camera motion) and the residual image motion (scene motion). These models are learnt off-line. Motion measurements include affine motion models to capture the camera motion, and local motion features for scene motion. The two-step event detection scheme consists in pre-selecting the video segments of potential interest, and then in recognizing the specified events among the pre-selected segments, the recognition being stated as a classification problem. We report accurate results on several sports videos.
10942933
Estimation of 2D Displacement Field Based on Affine Geometric Invariance and Scene Constraints
In the paper, a novel approach using affine transfer and scene constraint for estimation of 2D displacement field was developed. In this approach, we derived a system of 5 linear equations for computing corresponding point of any image point via the utilisation of affine invariant. Subsequently, the characteristics of such a linear system was thoroughly studied, and the way to obtain a reliable solution of the system via robust least squares (RLS) approach, in conjunction with total least squares technique, was proposed. In addition, through the interpretation of the approach from both geometric point of view and numerical point of view, we gave the limitation of the algorithm. The limitation was then relaxed to a certain extent by using full fundamental matrix. These findings were further verified through the experimental results.
41338736
An approach to identification of variances for radar tracking systems
An approach for the identification of the variances of the process noise W(k) and the measurement noise V(k) for radar tracking systems is presented. In the proposed algorithm, the variances are expressed as a linear combination of the covariances of the third-order difference of the measurement data. The variances can then be estimated by estimating the covariances of the third-order difference and solving the matrix equation. The two cases for the measurement noise being white and color are considered in this algorithm. Simulation results show that for both cases a steady estimation is reached after 20 samples.