비만아 부모의 돌봄 경험: q 방법론

Mobile robots are increasingly used in automated industrial environments. There are also other applications like planet exploration, surveillance, landmine detection, etc. In all these applications, in order that the mobile robots perform their tasks, collision-free path planning is a prerequisite. This article provides an overview of the research progress in path planning of a mobile robot for off-line as well as on-line environments. Commonly used classic and evolutionary approaches of path planning of mobile robots have been addressed. Review shows that evolutionary optimization algorithms are computationally efficient and hence are increasingly being used in tandem with classic approaches while handling Non-deterministic Polynomial time hard (NP-hard) problems. Also, challenges involved in developing a computationally efficient path planning algorithm are addressed.

	 

	Key words: Path planning, mobile robot, off-line environment, on-line environment, classic, evolutionary algorithms.

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Optimal path planning of mobile robots: A review

Marc Dacier's topic is: intrusion detection vs. detection of errors caused by intentionally malicious faults. Although research on intrusion detection has been carried out for more than two decades, it has recently received increased attention due to the success of the Internet. A recent survey conducted by the IBM Global Security Laboratory indicates that more than 20 intrusion-detection products are now available on the market, whereas two years ago there were only three. Despite this growth of product offerings, intrusion-detection solutions are still in their infancy. Not only is there a lack of understanding of what an intrusion really is, but also how it should be handled. Moreover, from a technical point of view, many critical issues remain unsolved.In this talk, Marc presents what has been done in the intrusion domain in the past, and highlights new research directions that need to be addressed. He will highlight the relationship between intrusion detection and fault tolerance, drawing on the body of knowledge that has been developed within the traditional dependability community, and noting the opportunities for these two communities to work together to solve this important problem.Sami Saydjari's topic is: the detection of novel, previously unseen attacks. Although intrusion detection is a field still in its infancy, two broad approaches have evolved: pattern-based detection and anomaly-based detection. Pattern-based detection, sometimes called misuse-based detection, relies on matching known patterns of attacks already suffered. Anomaly-based detection, on the other hand, relies on detecting behaviors that are abnormal with respect to some normal standard. An example is that of a masquerader trying to hide behind someone else's login; unless the masquerader is clever indeed, his activities will stand out as anomalous against a victim's profile of normal behavior. Anomaly-based detection techniques appear to hold the best hope of detecting new variants of attacks.Sami emphasizes that although there exist some low-level sensors that can detect known attacks, the research community must move quickly in learning how to detect novel attacks at much higher detection rates (state of the art is around 80%) while keeping the false positive rates very low (0.1% or better). Detecting novel attacks will require better anomaly detection algorithms. Achieving a 99.9% detection rate will require gaining a firm understanding of the "sweet spots" of various detection algorithms, as well as an understanding of how to fuse the results of the best of the best. There is also a need to better represent knowledge of attack patterns in a canonical form, to be able to share that knowledge across multiple detection tools, and to be able to judge tools on the basis of how effectively they use knowledge, as opposed to which tool can include the largest corpus of precompiled (pattern-based) knowledge into its on-line database.The session concludes with an open discussion of how research communities can work together to reduce undependability caused by intentionally malicious faults.

Intrusion Detection

Dynamic path planning of mobile robots with improved genetic algorithm

This paper presents a parallel elite genetic algorithm (PEGA) and its application to global path planning for autonomous mobile robots navigating in structured environments. This PEGA, consisting of two parallel EGAs along with a migration operator, takes advantages of maintaining better population diversity, inhibiting premature convergence, and keeping parallelism in comparison with conventional GAs. This initial feasible path generated from the PEGA planner is then smoothed using the cubic B-spline technique, in order to construct a near-optimal collision-free continuous path. Both global path planner and smoother are implemented in one field-programmable gate array chip utilizing the system-on-a-programmable-chip technology and the pipelined hardware implementation scheme, thus significantly expediting computation speed. Simulations and experimental results are conducted to show the merit of the proposed PEGA path planner and smoother for global path planning of autonomous mobile robots.

Parallel Elite Genetic Algorithm and Its Application to Global Path Planning for Autonomous Robot Navigation

In this study we present our initial idea for using genetic algorithms to help a controllable mobile robot to find an optimal path between a starting and ending point in a grid environment. The mobile robot has to find the optimal path which reduces the number of steps to be taken between the starting point and the target ending point. GAs can overcome many problems encountered by traditional search techniques such as the gradient based methods. The proposed controlling algorithm allows four-neighbor movements, so that path-planning can adapt with complicated search spaces with low complexities. The results are promising.

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A Mobile Robot Path Planning Using Genetic Algorithm in Static Environment

Feature selection (FS) is considered to be a hard optimization problem in data mining and some artificial intelligence fields. It is a process where rather than studying all of the features of a whole dataset, some associated features of a problem are selected, the aim of which is to increase classification accuracy and reduce computational time. In this paper, a recent optimization algorithm, the monarch butterfly optimization (MBO) algorithm, is implemented with a wrapper FS method that uses the k-nearest neighbor (KNN) classifier. Experiments were implemented on 18 benchmark datasets. The results showed that, in comparison with four metaheuristic algorithms (WOASAT, ALO, GA and PSO), MBO was superior, giving a high rate of classification accuracy of, on average, 93% for all datasets as well as reducing the selection size significantly. Therefore, the use of the MBO to solve the FS problems has been proven through the results obtained to be effective and highly efficient in this field, and the results have also proven the strength of the balance between global and local search of MBO.

The monarch butterfly optimization algorithm for solving feature selection problems

In recent years, classifier ensemble techniques have drawn the attention of many researchers in the machine learning research community. The ultimate goal of these researches is to improve the accuracy of the ensemble compared to the individual classifiers. In this paper, a novel algorithm for building ensembles called dynamic programming-based ensemble design algorithm (DPED) is introduced and studied in detail. The underlying theory behind DPED is based on cooperative game theory in the first phase and applying a dynamic programming approach in the second phase. The main objective of DPED is to reduce the size of the ensemble while encouraging extra diversity in order to improve the accuracy. The performance of the DPED algorithm is compared empirically with the classical ensemble model and with a well-known algorithm called “the most diverse.” The experiments were carried out with 13 datasets from UCI and three ensemble models. Each ensemble model is constructed from 15 different base classifiers. The experimental results demonstrate that DPED outperforms the classical ensembles on all datasets in terms of both accuracy and size of the ensemble. Regarding the comparison with the most diverse algorithm, the number of selected classifiers by DPED across all datasets and all domains is less than or equal to the number selected by the most diverse algorithm. Experiment on blog spam dataset, for instance, shows that DPED provides an accuracy of 96.47 compared to 93.87 obtained by the most diverse using 40% training size. Finally, the experimental results verify the reliability, stability, and effectiveness of the proposed DPED algorithm.

An optimal pruning algorithm of classifier ensembles: dynamic programming approach

Hybridizes the firefly algorithm with simulated annealing, where simulated annealing is applied to control the randomness step inside the firefly algorithm.A Levy flight is embedded within the firefly algorithm to better explore the search space.A combination of firefly, Levy flight and simulated annealing is investigated to further improve the solution. Classification is one of the important tasks in data mining. The probabilistic neural network (PNN) is a well-known and efficient approach for classification. The objective of the work presented in this paper is to build on this approach to develop an effective method for classification problems that can find high-quality solutions (with respect to classification accuracy) at a high convergence speed. To achieve this objective, we propose a method that hybridizes the firefly algorithm with simulated annealing (denoted as SFA), where simulated annealing is applied to control the randomness step inside the firefly algorithm while optimizing the weights of the standard PNN model. We also extend our work by investigating the effectiveness of using Levy flight within the firefly algorithm (denoted as LFA) to better explore the search space and by integrating SFA with Levy flight (denoted as LSFA) in order to improve the performance of the PNN. The algorithms were tested on 11 standard benchmark datasets. Experimental results indicate that the LSFA shows better performance than the SFA and LFA. Moreover, when compared with other algorithms in the literature, the LSFA is able to obtain better results in terms of classification accuracy.

Hybridizing firefly algorithms with a probabilistic neural network for solving classification problems

Time series classification is a supervised learning method maps the input to the output using historical data. The primary objective is to discover interesting patterns hidden in the data. For the purpose of solving time series classification problems used the multi-layered perceptrons Artificial Neural Networks (ANN). The weights in the ANN are modified to provide the output values of the net, which are much closer to the values of the preferred output. For this reason, several algorithms had been proposed to train the parameters of the neural network for time series classification problems. This study attempts to hybrid the Firefly Algorithm (FA) with the ANN in order to minimize the error rate of classification (coded as FA-ANN). The FA is employed to optimize the weights of the ANN model based on the processes. The proposed FA-ANN algorithm was tested on 6 benchmark UCR time series data sets. The experimental results have revealed that the proposed FA-ANN can effectively solve time series classification problems.

Mohammed Alweshah

Papers

A Mobile Robot Path Planning Using Genetic Algorithm in Static Environment

The monarch butterfly optimization algorithm for solving feature selection problems

An optimal pruning algorithm of classifier ensembles: dynamic programming approach

Hybridizing firefly algorithms with a probabilistic neural network for solving classification problems

Firefly Algorithm with Artificial Neural Network for Time Series Problems