Current Issue : April - June Volume : 2012 Issue Number : 2 Articles : 7 Articles
A significant attention of researchers has been drawn by automated textile inspection systems in order to replace manual\ninspection, which is time consuming and not accurate enough. Automated textile inspection systems mainly involve two\nchallenging problems, one of which is defect classification. The amount of research done to solve the defect classification problem\nis inadequate. Scene analysis and feature selection play a very important role in the classification process. Inadequate scene analysis\nresults in an inappropriate set of features. Selection of an inappropriate feature set increases the complexities of the subsequent\nsteps and makes the classification task harder. By taking into account this observation, we present a possibly appropriate set of\ngeometric features in order to address the problem of neural network-based textile defect classification. We justify the features\nfrom the point of view of discriminatory quality and feature extraction difficulty.We conduct some experiments in order to show\nthe utility of the features. Our proposed feature set has obtained classification accuracy of more than 98%, which appears to be\nbetter than reported results to date....
Experts predict that in the next 10 to 100 years scientists will succeed in creating human-level artificial general intelligence.While it\nis most likely that this task will be accomplished by a government agency or a large corporation, the possibility remains that it will\nbe done by a single inventor or a small team of researchers. In this paper, we address the question of safeguarding a discovery which\ncould without hesitation be said to be worth trillions of dollars. Specifically, we propose a method based on the combination of\nzero knowledge proofs and provably AI-complete CAPTCHA problems to show that a superintelligent system has been constructed\nwithout having to reveal the system itself....
A novel structure of fuzzy logic controller is presented for trajectory tracking and vibration control of a flexible joint manipulator.\r\nThe rule base of fuzzy controller is divided into two sections. Each section includes two variables. The variables of first section\r\nare the error of tip angular position and the error of deflection angle, while the variables of second section are derivatives of\r\nmentioned errors. Using these structures, it would be possible to reduce the number of rules. Advantages of proposed fuzzy logic\r\nare low computational complexity, high interpretability of rules, and convenience in fuzzy controller. Implementing of the fuzzy\r\nlogic controller on Quanser flexible joint reveals efficiency of proposed controller. To show the efficiency of this method, the results\r\nare compared with LQR method. In this paper, experimental validation of proposed method is presented....
We analyze the convergence time of particle swarm optimization (PSO) on the facet of particle interaction. We firstly introduce\r\na statistical interpretation of social-only PSO in order to capture the essence of particle interaction, which is one of the key\r\nmechanisms of PSO. We then use the statistical model to obtain theoretical results on the convergence time. Since the theoretical\r\nanalysis is conducted on the social-only model of PSO, instead of on common models in practice, to verify the validity of our\r\nresults, numerical experiments are executed on benchmark functions with a regular PSO program....
Clustering involves grouping data points together according to some measure of similarity. Clustering is one of the most significant\r\nunsupervised learning problems and do not need any labeled data. There are many clustering algorithms, among which fuzzy cmeans\r\n(FCM) is one of the most popular approaches. FCM has an objective function based on Euclidean distance. Some improved\r\nversions of FCM with rather different objective functions are proposed in recent years. Generalized Improved fuzzy partitions\r\nFCM (GIFP-FCM) is one of them, which uses Lp norm distance measure and competitive learning and outperforms the previous\r\nalgorithms in this field. In this paper, we present a novel FCM clustering method with improved fuzzy partitions that utilizes\r\nshadowed sets and try to improve GIFP-FCM in noisy data sets. It enhances the efficiency of GIFP-FCM and improves the clustering\r\nresults by correctly eliminating most outliers during steps of clustering.We name the novel fuzzy clustering method shadowed setbased\r\nGIFP-FCM (SGIFP-FCM). Several experiments on vessel segmentation in retinal images of DRIVE database illustrate the\r\nefficiency of the proposed method....
Humor processing is still a less studied issue, both in NLP and AI. In this paper we contribute to this field. In our previous research\r\nwe showed that adding a simple pun generator to a chatterbot can significantly improve its performance. The pun generator we\r\nused generated only puns based on words (not phrases). In this paper we introduce the next stage of the systemââ?¬â?¢s developmentââ?¬â?\r\nan algorithm allowing generation of phrasal pun candidates. We show that by using only the Internet (without any hand-made\r\nhumor-oriented lexicons), it is possible to generate puns based on complex phrases. As the output list is often excessively long,\r\nwe also propose a method for reducing the number of candidates by comparing two web-query-based rankings. The evaluation\r\nexperiment showed that the system achieved an accuracy of 72.5% for finding proper candidates in general, and the reduction\r\nmethod allowed us to significantly shorten the candidates list. The parameters of the reduction algorithm are variable, so that the\r\nbalance between the number of candidates and the quality of output can be manipulated according to needs....
Research in Artificial Immune Systems (AIS) for optimization has attracted attention in recent years. Exploration and adoption of the inspired immune theories in clonal selection, immune network, negative selection and Quantum Inspired Clonal Algorithm is becoming a popular basis for algorithm design for solving optimization problems, especially on multi objective optimizations. Novel algorithms are design, bench marked and applied to real life applications. This paper aims to review and outlook on the latest development of AIS-based algorithms in the recent decade. An analysis of the AIS applications is also discussed....
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