Artificial Intelligence

1304 Submissions

[9] viXra:1304.0155 [pdf] submitted on 2013-04-27 09:28:39

Foundations of Neutrosophic Logic and Set and Their Applications to Information Fusion

Authors: Florentin Smarandache
Comments: 101 Pages.

Neutrosophy, neutrosophic logic, neutrosophic set, and neutrosophic probability are presented. Also their applications to various scientific fields.
Category: Artificial Intelligence

[8] viXra:1304.0139 [pdf] submitted on 2013-04-25 07:02:03

A Novel Neutrosophic Logic SVM (N-SVM) and Its Application to Image Categorization

Authors: Wen Ju, H. D. Cheng
Comments: 14 Pages.

Neutrosophic logic is a relatively new logic that is a generalization of fuzzy logic. In this paper, for the first time, neutrosophic logic is applied to the field of classifiers where a support vector machine (SVM) is adopted as the example to validate its feasibility and effectiveness. The proposed neutrosophic set is integrated into a reformulated SVM, and the performance of the obtained classifier N-SVM is evaluated under a region-based image categorization system. Images are first segmented by a hierarchical two-stage self-organizing map (HSOM), using color and texture features. A novel approach is proposed to select the training samples of HSOM based on homogeneity properties. A diverse density support vector machine (DD-SVM) framework is then applied to viewing an image as a bag of instances corresponding to the regions obtained from image segmentation. Each bag is mapped to a point in the new bag space, and the categorization is transformed to a classification problem. Then, the proposed N-SVM is used as the classifier in the new bag space. N-SVM treats samples differently according to the weighting function, and it helps to reduce the effects of outliers. Experimental results have demonstrated the validity and effectiveness of the proposed method which may find wide applications in the related areas.
Category: Artificial Intelligence

[7] viXra:1304.0133 [pdf] replaced on 2013-05-02 12:13:40

An Indicator of Inclusion with Applications in Computer Vision

Authors: Ovidiu Ilie Şandru, Florentin Smarandache
Comments: 3 Pages.

In this paper we present an algorithmic process of necessary operations for the automatic movement of a predefined object from a video image in the target region of that image, intended to facilitate the implementation of specialized software applications in solving this kind of problems.
Category: Artificial Intelligence

[6] viXra:1304.0101 [pdf] submitted on 2013-04-20 11:05:18

Multicriteria Decision-Making Method Using the Correlation Coefficient Under Single-Valued Neutrosophic Environment

Authors: Jun Ye
Comments: 9 Pages.

The paper presents the correlation and correlation coefficient of single-valued neutrosophic sets (SVNSs) based on the extension of the correlation of intuitionistic fuzzy sets and demonstrates that the cosine similarity measure is a special case of the correlation coefficient in SVNS. Then a decision-making method is proposed by the use of the weighted correlation coefficient or the weighted cosine similarity measure of SVNSs, in which the evaluation information for alternatives with respect to criteria is carried out by truth-membership degree, indeterminacy-membership degree, and falsity-membership degree under single-valued neutrosophic environment. We utilize the weighted correlation coefficient or the weighted cosine similarity measure between each alternative and the ideal alternative to rank the alternatives and to determine the best one(s). Finally, an illustrative example demonstrates the application of the proposed decision-making method.
Category: Artificial Intelligence

[5] viXra:1304.0091 [pdf] submitted on 2013-04-19 06:08:36

Cryptography Using an Image

Authors: Yousuf Ibrahim Khan
Comments: 10 Pages.

An information is a message which is received and understood. Information can be sent one person to another over a long range but the process of sending information must be done in a secure way especially in case of a private message. Mathematicians and Engineers have historically relied on different algorithmic techniques to secure messages and signals. Cryptography, to most people, is concerned with keeping communications private. Indeed, the protection of sensitive communications has been the emphasis of cryptography throughout much of its history. Sometimes it is safer to send a message using an image and thus cryptography can also be done using images during an emergency. The need to extract information from images and interpret their contents has been one of the driving factors in the development of image processing and cryptography during the past decades. In this paper, a simple cryptographic method was used to decode a message which was in an image and it was done using a popular computational software.
Category: Artificial Intelligence

[4] viXra:1304.0085 [pdf] submitted on 2013-04-18 02:30:46

Artificial Neural Network based Short Term Load Forecasting of Power System

Authors: Salman Quaiyum, Yousuf Ibrahim Khan, Saidur Rahman, Parijat Barman
Comments: 7 Pages.

Load forecasting is the prediction of future loads of a power system. It is an important component for power system energy management. Precise load forecasting helps to make unit commitment decisions, reduce spinning reserve capacity and schedule device maintenance plan properly. Besides playing a key role in reducing the generation cost, it is also essential to the reliability of power systems. By forecasting, experts can have an idea of the loads in the future and accordingly can make vital decisions for the system. This work presents a study of short-term hourly load forecasting using different types of Artificial Neural Networks.
Category: Artificial Intelligence

[3] viXra:1304.0084 [pdf] submitted on 2013-04-18 02:35:31

Application of Computational Intelligence in Motor Modeling

Authors: Yousuf Ibrahim Khan
Comments: 8 Pages.

Modeling is very important in the field of science and engineering. Modeling gives us an abstract and mathematical description of a particular system and describes its behavior. Once we get the model of a system then we can work with that in various applications without using the original system repeatedly. Computational Intelligence method like Artificial Neural Network is very sophisticated tool for modeling and data fitting problems. Modeling of Electrical motors can also be done using ANN. The Neural network that will represent the model of the motor will be a useful tool for future use especially in digital control systems. The parallel structure of a neural network makes it potentially fast for the computation of certain tasks. The same feature makes a neural network well suited for implementation in VLSI technology. Hardware realization of a Neural Network (NN), to a large extent depends on the efficient implementation of a single neuron. In this paper only a motor model is presented along with some neural networks those will mimic the motor behavior acquiring data from the original motor output.
Category: Artificial Intelligence

[2] viXra:1304.0064 [pdf] submitted on 2013-04-14 07:10:21

A Study of Non-Linear Control for Energy Storage Systems

Authors: Chenwen Zheng, Margaret Jenkins
Comments: 4 Pages.

This paper presents an overall solution consisting of a wind plant with a Smart Storage Modular System (SSMS). The SSMS consists in a Short Time Storage Module (STSM based on a flywheel with induction motor) and a Medium/Long Time Storage Module (MLTSM based on a Vanadium Redox flow Battery). The aim of this paper is to provide a nonlinear sensorless control solution for the induction motor (IM) within the inertial storage system based on flywheel. To this related one, computer simulations and laboratory tests are accomplished.
Category: Artificial Intelligence

[1] viXra:1304.0011 [pdf] submitted on 2013-04-02 21:49:41

Lie Algebrized Gaussians for Image Representation

Authors: Liyu Gong, Meng Chen, Chunlong Hu
Comments: 8 Pages.

We present an image representation method which is derived from analyzing Gaussian probability density function (\emph{pdf}) space using Lie group theory. In our proposed method, images are modeled by Gaussian mixture models (GMMs) which are adapted from a globally trained GMM called universal background model (UBM). Then we vectorize the GMMs based on two facts: (1) components of image-specific GMMs are closely grouped together around their corresponding component of the UBM due to the characteristic of the UBM adaption procedure; (2) Gaussian \emph{pdf}s form a Lie group, which is a differentiable manifold rather than a vector space. We map each Gaussian component to the tangent vector space (named Lie algebra) of Lie group at the manifold position of UBM. The final feature vector, named Lie algebrized Gaussians (LAG) is then constructed by combining the Lie algebrized Gaussian components with mixture weights. We apply LAG features to scene category recognition problem and observe state-of-the-art performance on 15Scenes benchmark.
Category: Artificial Intelligence