Statistical Determination of Decision-Making Regions for Branching Paths: An Algorithm With a Wheelchair Assistance Application

Typically, mobile vehicles follow the same paths repeatedly, resulting in a common path bounded with some variance. These paths are often punctuated by branches into other paths based on decision-making in the area around the branch. This work applies a statistical methodology to determine decision-making regions for branching paths. An average path is defined in the proposed algorithm, as well as boundaries representing variances along the path. The boundaries along each branching path intersect near the decision point; these intersections in path variances are used to determine path-branching locations. The resulting analysis provides decision points that are robust to typical path conditions, such as two paths that may not clearly diverge at a specific location. Additionally, the methodology defines decision region radii that encompass statistical memberships of a location relative to the branching paths. To validate the proposed technique, an off-line implementation of the decision-making region algorithm is applied to previously classified wheelchair path subsets. Results show robust detection of decision regions that intuitively agree with user decision-making in real-world path following. For the experimental situation of this study, approximately 70% of path locations were outside of decision regions and thus could be navigated with a significant reduction in user inputs.

Transportation Problem of LPP Involving Probability Density Function

<p>In Linear Programming Problem (LPP), Transportation Problem (TP) is an application which is used to optimize through the probability density function of statistical approach. The main objective of this paper is to reduce complexity in Maximization problem of LPP, by fulfilling the relation between the objective function and constraints with the largest value. Here, we used non-negative integer and complex number of linear combination of form x^me^λx. It has been decided with reasonably great probability, decision region, fundamental probabilities and Laplace Transform (LT). To obtain proposed results we applied probability density function over transportation problem. According to our proposed method we implemented mathematical model through the probability density function of statistical tools. Categorically, probability density function is an approach in our proposed method to obtain the feasible solution of transportation problem, which perform better than the existing methods.</p>

Bayesian Active Edge Evaluation on Expensive Graphs

We consider the problem of real-time motion planning that requires evaluating a minimal number of edges on a graph to quickly discover collision-free paths. Evaluating edges is expensive, both for robots with complex geometries like robot arms, and for robots sensing the world online like UAVs. Until now, this challenge has been addressed via laziness, i.e. deferring edge evaluation until absolutely necessary, with the hope that edges turn out to be valid. However, all edges are not alike in value - some have a lot of potentially good paths flowing through them, and some others encode the likelihood of neighbouring edges being valid. This leads to our key insight - instead of passive laziness, we can actively choose edges that reduce the uncertainty about the validity of paths. We show that this is equivalent to the Bayesian active learning paradigm of decision region determination (DRD). However, the DRD problem is not only combinatorially hard but also requires explicit enumeration of all possible worlds. We propose a novel framework that combines two DRD algorithms, DIRECT and BISECT, to overcome both issues. We show that our approach outperforms several state-of-the-art algorithms on a spectrum of planning problems for mobile robots, manipulators and autonomous helicopters.

An Novel Image Recognition method based on Three-way Decision

The traditional recognition method takes the low-level information of the image as the foundation. The image recognition center of gravity is biased towards the typical features, and achieves the effect of recognition by region-dependent segmentation. Because the general image segmentation is a regular rectangle, easily lead to the same target is divided into different sub-blocks, ignoring the image of the fuzzy part, so the image recognition is not complete. An image recognition algorithm based on threeway decision is proposed. It takes full advantage of effective information in the image, improving the image recognition accuracy. First, this method divided the image into three regions: positive region, negative region and delay decision region. Second, an iterative process is performed on the region of the delay decision. Final, image recognition is performed on the positive sample region. Based on the basic theory of the three-way decision, the more obvious the decision result is, the more iterations are, and the information is added to the classifier until the blurred part of image cannot be divided. Finally, to achieve the realize effective image recognition. This method simulates the process of human cognition effectively, and makes the utilization of the effective information reach the maximum in the recognition process. The results of the experimental analysis showed that the method is more concise and efficient, and the recognition accuracy is more accurate.

Caracterización de Señales EEG mediante Wavelet Packet y Entropía Difusa para Tareas de Imaginación Motora

Context: Clinical rhythm analysis on advanced signal processing methods is very important in medical areas such as brain disorder diagnostic, epilepsy, sleep analysis, anesthesia analysis, and more recently in brain-computer interfaces (BCI).Method: Wavelet transform package is used on this work to extract brain rhythms of electroencephalographic signals (EEG) related to motor imagination tasks. We used the Competition BCI 2008 database for this characterization. Using statistical functions we obtained features that characterizes brain rhythms, which are discriminated using different classifiers; they were evaluated using a 10-fold cross validation criteria.Results: The classification accuracy achieved 81.11% on average, with a degree of agreement of 61%, indicating a "suitable" concordance, as it has been reported in the literature. An analysis of relevance showed the concentration of characteristics provided in the nodes as a result of Wavelet decomposition, as well as the characteristics that more information content contribute to improve the separability decision region for the classification task.Conclusions: The proposed method can be used as a reference to support future studies focusing on characterizing EEG signals oriented to the imagination of left and right hand movement, considering that our results proved to compared favourably to those reported in the literature.Language: Spanish.

Distributed One-Class Support Vector Machine

This paper presents a novel distributed one-class classification approach based on an extension of the ν-SVM method, thus permitting its application to Big Data data sets. In our method we will consider several one-class classifiers, each one determined using a given local data partition on a processor, and the goal is to find a global model. The cornerstone of this method is the novel mathematical formulation that makes the optimization problem separable whilst avoiding some data points considered as outliers in the final solution. This is particularly interesting and important because the decision region generated by the method will be unaffected by the position of the outliers and the form of the data will fit more precisely. Another interesting property is that, although built in parallel, the classifiers exchange data during learning in order to improve their individual specialization. Experimental results using different datasets demonstrate the good performance in accuracy of the decision regions of the proposed method in comparison with other well-known classifiers while saving training time due to its distributed nature.