Search results

The mischievous nodes that defy the standard corrupt the exhibition of good nodes considerably. Therefore, an intrusion discovery mechanism should be included to the mobile ad-hoc network (MANET). In this paper, worm-hole and other destructive malignant attacks are propelled in MANET.

A wireless ad-hoc network also called as mobile ad-hoc network (MANET) is a gathering of hubs that utilizes a wireless channel to exchange information and coordinate together to establish information exchange among any pair of hubs, without any centralized structure. The security issue is a major difficulty while employing MANETs.

Consequently, the attacks due to the malicious node activity are detected using Hybrid Reactive Search and Bat (HRSB) mechanism to prevent the mischievous nodes from entering the network beneath the untruthful information. Moreover, the attack detection rate and node energy are predicted for determining the lifetime of the node.

The simulation outcomes of the proposed HRSB technique are evaluated with the prevailing methods. The comparison studies have proven the efficacy of the current research model by attaining high attack detection rate and achieving more network lifetime.

This paper aims to deal with the problem of the global stabilization for a class of tension leg platform (TLP) nonlinear control systems.

It is well-known that, in general, the global asymptotic stability of the TLP subsystems does not imply the global asymptotic stability of the composite closed-loop system.

An effective approach is proposed to control chaos via the combination of fuzzy controllers, fuzzy observers and dithers.

If a fuzzy controller and a fuzzy observer cannot stabilize the chaotic system, a dither, as an auxiliary of the controller and the observer, is simultaneously introduced to asymptotically stabilize the chaotic system.

Thus, the behavior of the closed-loop dithered chaotic system can be rigorously predicted by establishing that of the closed-loop fuzzy relaxed system.

The purpose of this paper is to provide an overview of the wide range of biomimetic sensor technology and innovations.

The reader is introduced to biomimetic sensors, their types, their advantages and how they are different from traditional sensors. Background information is also provided regarding sensor design, inspiration and innovation.

There are two approaches to sensor design, which lead to diverse advantages and innovations. Classification of biomimetic sensors indicated which natural senses are underutilized by sensor designers and researchers.

The paper provides information of value for those seeking innovative sensor designs and research information for those who want to research in this area.

This paper seeks to argue that there are two distinct problems of ignorance: a problem of size and a problem of type. Both are more pressing today than ever before, given the extraordinary expansion of collective human knowledge, and both pertain to epistemic limitations intrinsic to evolved cognitive systems. After delineating these problems in detail, one possible way of overcoming “relative” and “absolute” ignorance about the universe – enhancement technologies – is to be examined. The paper then aims to argue that, given one's epistemic situation, resources currently being spent on normal research would be far better spent on developing cognition‐enhancing technologies – technologies that promise to help solve the size and type problems previously sketched.

The paper identifies two important limitations on human knowledge, one deriving from the size or complexity of certain problems and the other from one's inability to access specific concepts necessary to understand them. It suggests that cognitive enhancements offer the best chance at overcoming these two limitations.

There are both strong practical and moral reasons for diverting more resources into the development of cognitive enhancement technologies.

No author has yet elaborated on the distinction, which is taken to be important, between the problems of “size” and “type.” Furthermore, no author has yet explored how cognitive enhancements may address the problem that Colin McGinn calls “cognitive closure” (the problem of type). Thus, cognitive enhancements may offer the only possibility of solving conundrums like conscious experience and free will.

This paper aims to address the robust controller design problem for a class of fuzzy C-means clustering algorithm that is robust against both the plant parameter perturbations and controller gain variations. Based on Takagi–Sugeno (T-S) fuzzy model description, the stability and control problems of nonlinear systems are studied.

A recently proposed integral inequality is selected based on the free-weight matrix, and the less conservative stability criterion is given in the form of linear matrix inequalities (LMIs).

Under the premise that the controller and the system share the same, the method does not require the number of membership functions and rules.

Furthermore, the modified controller in a large-scale nonlinear system is utilized as a stability criterion for a closed-loop T-S fuzzy system obtained by LMI, and is rearranged by a machine learning membership function.

The closed-loop controller criterion is derived by energy functions to guarantee the stability of systems. Finally, an example is given to demonstrate the results.

Metaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a new hybrid optimization algorithm that combines the characteristics of biogeography-based optimization (BBO), invasive weed optimization (IWO) and genetic algorithms (GAs).

The significant difference between the new algorithm and original optimizers is a periodic selection scheme for offspring. The selection criterion is a function of cyclic discharge and the fitness of populations. It differs from traditional optimization methods where the elite always gains advantages. With this method, fitter populations may still be rejected, while poorer ones might be likely retained. The selection scheme is applied to help escape from local optima and maintain solution diversity.

The efficiency of the proposed method is tested on 13 high-dimensional, nonlinear benchmark functions and a homogenous slope stability problem. The results of the benchmark function show that the new method performs well in terms of accuracy and solution diversity. The algorithm converges with a magnitude of 10-4, compared to 102 in BBO and 10-2 in IWO. In the slope stability problem, the safety factor acquired by the analogy of slope erosion (ASE) is closer to the recommended value.

This paper introduces a periodic selection strategy and constructs a hybrid optimizer, which enhances the global exploration capacity of metaheuristic algorithms.

Breast cancer (BC) is one of the leading cancer in the world, BC risk has been there for women of the middle age also, it is the malignant tumor. However, identifying BC in the early stage will save most of the women’s life. As there is an advancement in the technology research used Machine Learning (ML) algorithm Random Forest for ranking the feature, Support Vector Machine (SVM), and Naïve Bayes (NB) supervised classifiers for selection of best optimized features and prediction of BC accuracy. The estimation of prediction accuracy has been done by using the dataset Wisconsin Breast Cancer Data from University of California Irvine (UCI) ML repository. To perform all these operation, Anaconda one of the open source distribution of Python has been used. The proposed work resulted in extemporize improvement in the NB and SVM classifier accuracy. The performance evaluation of the proposed model is estimated by using classification accuracy, confusion matrix, mean, standard deviation, variance, and root mean-squared error.

The experimental results shows that 70-30 data split will result in best accuracy. SVM acts as a feature optimizer of 12 best features with the result of 97.66% accuracy and improvement of 1.17% after feature reduction. NB results with feature optimizer 17 of best features with the result of 96.49% accuracy and improvement of 1.17% after feature reduction.

The study shows that proposal model works very effectively as compare to the existing models with respect to accuracy measures.

In this paper, improvements in reducing transmitted accelerations in a full vehicle are obtained by optimizing the gain parameters of an active control in a roughness road profile.

For a classically designed linear quadratic regulator (LQR) control, the vibration attenuation performance will depend on weighting matrices Q and R. A methodology is proposed in this work to determine the optimal elements of these matrices by using a genetic algorithm method to get enhanced controller performance. The active control is implemented in an eight degrees of freedom (8-DOF) vehicle suspension model, subjected to a standard ISO road profile. The control performance is compared against a controlled system with few Q and R parameters, an active system without optimized gain matrices, and an optimized passive system.

The control with 12 optimized parameters for Q and R provided the best vibration attenuation, reducing significantly the Root Mean Square (RMS) accelerations at the driver’s seat and car body.

The research has positive implications in a wide class of active control systems, especially those based on a LQR, which was verified by the multibody dynamic systems tested in the paper.

Better active control gains can be devised to improve performance in vibration attenuation.

The main contribution proposed in this work is the improvement of the Q and R parameters simultaneously, in a full 8-DOF vehicle model, which minimizes the driver’s seat acceleration and, at the same time, guarantees vehicle safety.

The purpose of this paper is to demonstrate how commercially‐off‐the‐shelf sensors and stimulators, such as infrared rangers and vibrators, can be retrofitted as a useful assistive technology in real and virtual environments.

The paper describes how a wearable range‐vibrotactile device is designed and tested in the real‐world setting, as well as thorough evaluations in a virtual environment for complicated navigation tasks and neuroscience studies.

In the real‐world setting, a person with normal vision who has to navigate their way around a room with their eyes closed will quickly rely on their arms and hands to explore the room. The authors’ device allows a person to “feel” their environment without touching it. Due to inherent difficulties in testing human subjects when navigating a real environment, a virtual environment affords us an opportunity to scientifically and extensively test the prototype before deploying the device in the real‐world.

This project serves as a starting‐point for further research in benchmarking assistive technology for the visually impaired and to eventually develop a man‐machine sensorimotor model that will improve current state‐of‐the‐art technology, as well as a better understanding of neural coding in the human brain.

Based on 2012 World Health Organization, there are 39 million blind people. This project will have a direct impact on this community.

The paper demonstrates a low cost design of assistive technology that has been tested and evaluated in real and virtual environments, as well as integration of sensor designs and neuroscience.

The purpose of this paper is to obtain an optimal mobile robot path planning by the hybrid algorithm, which is developed by two nature inspired meta-heuristic algorithms, namely, cuckoo-search and bat algorithm (BA) in an unknown or partially known environment. The cuckoo-search algorithm is based on the parasitic behavior of the cuckoo, and the BA is based on the echolocation behavior of the bats.

The developed algorithm starts by sensing the obstacles in the environment using ultrasonic sensor. If there are any obstacles in the path, the authors apply the developed algorithm to find the optimal path otherwise reach the target point directly through diagonal distance.

The developed algorithm is implemented in MATLAB for the simulation to test the efficiency of the algorithm for different environments. The same path is considered to implement the experiment in the real-world environment. The ARDUINO microcontroller along with the ultrasonic sensor is considered to obtain the path length and time of travel of the robot to reach the goal point.

In this paper, a new hybrid algorithm has been developed to find the optimal path of the mobile robot using cuckoo search and BAs. The developed algorithm is tested with the real-world environment using the mobile robot.