A novel distance measure on q-rung picture fuzzy sets and its application to decision making and classification problems

Artificial Intelligence Review - In recent years, different higher order fuzzy sets have been introduced to better handle the uncertainty in many practical decision making and data mining problems....     

Perpetual Robot Swarm: Long-Term Autonomy of Mobile Robots Using On-the-fly Inductive Charging

Swarm robotics studies the intelligent collective behaviour emerging from long-term interactions of large number of simple robots. However, maintaining a large number of robots operational for long time periods requires significant battery capacity, which is an issue for small robots. Therefore, re-charging systems such as automated battery-swapping stations have been implemented. These systems require that the robots interrupt, albeit shortly, their activity, which influences the swarm behaviour. In this paper, a low-cost on-the-fly wireless charging system, composed of several charging cells, is proposed for use in swarm robotic research studies. To determine the system’s ability to support perpetual swarm operation, a probabilistic model that takes into account the swarm size, robot behaviour and charging area configuration, is outlined. Based on the model, a prototype system with 12 charging cells and a small mobile robot, Mona, was developed. A series of long-term experiments with different arenas and behavioural configurations indicated the model’s accuracy and demonstrated the system’s ability to support perpetual operation of multi-robotic system.     

First application of symbiotic organisms search algorithm to off-line optimization of PI parameters for DSP-based DC motor drives

To enhance the performance and dynamics of a direct current (DC) motor drive, this paper proposes a new alternative based on recently introduced powerful symbiotic organisms search (SOS) algorithm for tuning proportional integral parameters. While imitating the symbiotic behavior that is seen among organisms in an ecosystem, SOS has important features such that it does not require tuning parameters, and its implementation is very easy with efficient three phases. After obtaining the optimized values of K _p  − K _i pair within the accurately prepared simulation software, they are used in real time. By managing the DC motor speed-controlled system with DSP of TMS320F28335, several simulations and experimental results confirming the performance of our proposal are presented along with comparisons against those of particle swarm optimization (PSO), genetic algorithm (GA), and Ziegler–Nichols (Z–N) tuning method. Results explicitly show that SOS is the pioneer in yielding better tracking performance and load disturbance rejection capability of the concerned drive system, which is followed by PSO, GA, and Z–N method, respectively. This has been achieved due to the fact that the gains obtained by SOS are more performant than those obtained by other applied methods.

     

A Geometric Model of Multi-scale Orientation Preference Maps via Gabor Functions

In this paper we present a new model for the generation of orientation preference maps in the primary visual cortex (V1), considering both orientation and scale features. First we undertake to model the functional architecture of V1 by interpreting it as a principal fiber bundle over the 2-dimensional retinal plane by introducing intrinsic variables orientation and scale. The intrinsic variables constitute a fiber on each point of the retinal plane and the set of receptive profiles of simple cells is located on the fiber. Each receptive profile on the fiber is mathematically interpreted as a rotated Gabor function derived from an uncertainty principle. The visual stimulus is lifted in a 4-dimensional space, characterized by coordinate variables, position, orientation and scale, through a linear filtering of the stimulus with Gabor functions. Orientation preference maps are then obtained by mapping the orientation value found from the lifting of a noise stimulus onto the 2-dimensional retinal plane. This corresponds to a Bargmann transform in the reducible representation of the \(\text {SE}(2)=\mathbb {R}^2\times S^1\) group. A comparison will be provided with a previous model based on the Bargmann transform in the irreducible representation of the \(\text {SE}(2)\) group, outlining that the new model is more physiologically motivated. Then, we present simulation results related to the construction of the orientation preference map by using Gabor filters with different scales and compare those results to the relevant neurophysiological findings in the literature.     

A novel classification-based shilling attack detection approach for multi-criteria recommender systems

Recommender systems are emerging techniques guiding individuals with provided referrals by considering their past rating behaviors. By collecting multi-criteria preferences concentrating on distinguishing perspectives of the items, a new extension of traditional recommenders, multi-criteria recommender systems reveal how much a user likes an item and why user likes it; thus, they can improve predictive accuracy. However, these systems might be more vulnerable to malicious attacks than traditional ones, as they expose multiple dimensions of user opinions on items. Attackers might try to inject fake profiles into these systems to skew the recommendation results in favor of some particular items or to bring the system into discredit. Although several methods exist to defend systems against such attacks for traditional recommenders, achieving robust systems by capturing shill profiles remains elusive for multi-criteria rating-based ones. Therefore, in this study, we first consider a prominent and novel attack type, that is, the power-item attack model, and introduce its four distinct variants adapted for multi-criteria data collections. Then, we propose a classification method detecting shill profiles based on various generic and model-based user attributes, most of which are new features usually related to item popularity and distribution of rating values. The experiments conducted on three benchmark datasets conclude that the proposed method successfully detects attack profiles from genuine users even with a small selected size and attack size. The empirical outcomes also demonstrate that item popularity and user characteristics based on their rating profiles are highly beneficial features in capturing shilling attack profiles.     

PatrIoT: practical and agile threat research for IoT

The Internet of things (IoT) products, which have been widely adopted, still pose challenges in the modern cybersecurity landscape. Many IoT devices are resource-constrained and almost constantly online. Furthermore, the security features of these devices are less often of concern, and fewer methods, standards, and guidelines are available for testing them. Although a few approaches are available to assess the security posture of IoT products, the ones in use are mostly based on traditional non-IoT-focused techniques and generally lack the attackers’ perspective. This study provides a four-stage IoT vulnerability research methodology built on top of four key elements: logical attack surface decomposition, compilation of top 100 weaknesses, lightweight risk scoring, and step-by-step penetration testing guidelines. Our proposed methodology is evaluated with multiple IoT products. The results indicate that PatrIoT allows cyber security practitioners without much experience to advance vulnerability research activities quickly and reduces the risk of critical IoT penetration testing steps being overlooked.

     

Mean shift based gradient vector flow for image segmentation

In recent years, gradient vector flow (GVF) based algorithms have been successfully used to segment a variety of 2-D and 3-D imagery. However, due to the compromise of internal and external energy forces within the resulting partial differential equations, these methods may lead to biased segmentation results. In this paper, we propose MSGVF, a mean shift based GVF segmentation algorithm that can successfully locate the correct borders. MSGVF is developed so that when the contour reaches equilibrium, the various forces resulting from the different energy terms are balanced. In addition, the smoothness constraint of image pixels is kept so that over- or under-segmentation can be reduced. Experimental results on publicly accessible datasets of dermoscopic and optic disc images demonstrate that the proposed method effectively detects the borders of the objects of interest.     

ITU Tailless UAV Design

Nowadays, mini Unmanned Aerial Vehicles (UAVs) are utilized in a wide range of reconnaissance and surveillance missions with an ever increasing need for endurance and range. Thus, a slight improvement on these two primary performance parameters is considered as a competitive advantage. In this work, a multi-functional tailless UAV concept and its design process is presented. In comparison to existing conventional UAV designs, the concept is shown to have superior aerodynamic and flight performance characteristics. In addition the detachable wing and body concept provides the much needed flexibility and multi-functionality for using the UAV for a range of operation concepts, in which each concept requires different payloads of distinct weight and size.     

On coordination under random yield and random demand

This paper analyzes coordination in a supply chain with random yield and random demand (SCRYRD). We study wholesale price, buy-back, revenue share, quantity discount and quantity flexibility contracts. We show that the randomness in the yield does not change the coordination ability of the contracts but affects the values of the contract parameters. In particular, all contracts are shown to coordinate the supply chain under voluntary compliance except the wholesale price contract. We also provide a sensitivity analysis of the optimal policy parameters to the cost and demand parameters.     

Personnel selection based on intuitionistic fuzzy sets

One of the most important activities carried out by human resource management is personnel selection, concerned with identifying an individual from a pool of candidates suitable for a vacant position. Traditionally, personnel selection is a group decision‐making problem under multiple criteria containing subjectivity, imprecision, and vagueness, which are best represented with fuzzy data. In this article, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method extended to intuitionistic fuzzy environments is proposed to select appropriate personnel among candidates. An intuitionistic fuzzy set (IFS), which is characterized by membership function, nonmembership function, and hesitation margin, is a more suitable way to deal with vagueness when compared to a fuzzy set. To demonstrate the applicability and effectiveness of the intuitionistic fuzzy TOPSIS method, a numerical example of personnel selection in a manufacturing company for a sales manager position is given. © 2011 Wiley Periodicals, Inc.