Nonlinear set-membership estimation: a support vector machine approach

In this paper a support vector machine (SVM) approach for characterizing the feasible parameter set (FPS) in non-linear set-membership estimation problems is presented. It iteratively solves a regression problem from which an approximation of the boundary of the FPS can be determined. To guarantee convergence to the boundary the procedure includes a no-derivative line search and for an appropriate coverage of points on the FPS boundary it is suggested to start with a sequential box pavement procedure. The SVM approach is illustrated on a simple sine and exponential model with two parameters and an agro-forestry simulation model.

UAS See-and-Avoid Strategy using a Fuzzy Logic Controller Optimized by Cross-Entropy in Scaling Factors and Membership Functions

This work aims to develop a novel Cross-Entropy (CE) optimization-based fuzzy controller for Unmanned Aerial Monocular Vision-IMU System (UAMVIS) to solve the seeand- avoid problem using its accurate autonomous localization information. The function of this fuzzy controller is regulating the heading of this system to avoid the obstacle, e.g. wall. In the Matlab Simulink-based training stages, the Scaling Factor (SF) is adjusted according to the specified task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the collison avoidance performance. After obtained the optimal SF and MF, 64% of rules has been reduced (from 125 rules to 45 rules), and a large number of real flight tests with a quadcopter have been done. The experimental results show that this approach precisely navigates the system to avoid the obstacle. To our best knowledge, this is the first work to present the optimized fuzzy controller for UAMVIS using Cross-Entropy method in Scaling Factors and Membership Functions optimization.

UAS See-and-Avoid Strategy using a Fuzzy Logic Controller Optimized by Cross-Entropy in Scaling Factors and Membership Functions.

This work aims to develop a novel Cross-Entropy (CE) optimization-based fuzzy controller for Unmanned Aerial Monocular Vision-IMU System (UAMVIS) to solve the seeand-avoid problem using its accurate autonomous localization information. The function of this fuzzy controller is regulating the heading of this system to avoid the obstacle, e.g. wall. In the Matlab Simulink-based training stages, the Scaling Factor (SF) is adjusted according to the specified task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the collison avoidance performance. After obtained the optimal SF and MF, 64% of rules has been reduced (from 125 rules to 45 rules), and a large number of real flight tests with a quadcopter have been done. The experimental results show that this approach precisely navigates the system to avoid the obstacle. To our best knowledge, this is the first work to present the optimized fuzzy controller for UAMVIS using Cross-Entropy method in Scaling Factors and Membership Functions optimization.

Demographics and Brexit: A deeper problem for the EU? CEPS Commentary, 8 July 2016

There has been much debate about the demographic factors that contributed to the outcome of the UK’s referendum on its membership of the EU. Two aspects command special attention. First, there are marked differences between young and old generations in the preferences shown for remaining in the EU. While the former predominantly voted to remain, a great majority of the latter voted to leave. On the other hand, voter turnout revealed the opposite trend: about 83% of UK citizens aged 65 and older voted, whereas only 36% of those aged 18-24 cast their vote (Figure 1).

Group membership salience and task performance

Purpose – Social loafing is described in the literature as a frequent problem reducing individuals' performance when working in groups. This paper aims to utilize the social identity approach and proposes that under conditions of heightened group salience social loafing can be reduced and turned into social laboring (i.e. increased performance). Design/methodology/approach – Two experimental studies are conducted to examine the impact of participant's group membership salience on task performance. In Study 1, school teachers work either in coactive or in collective working conditions on brainstorming tasks. In Study 2, participants perform both a brainstorming task and a motor task. Findings – The results show social laboring effects. As predicted, participants in the high salient group conditions outperform participants in the low salient group conditions and the coactive individual condition. Practical implications – The results indicate that rather than individuating group members or tasks to overcome social loafing, managers can increase group performance by focusing on group members' perceptions of their groups as important and salient. Originality/value – The studies presented in this paper show that social identity theory and self categorization theory can fruitfully be applied to the field of group performance. The message of these studies for applied settings is that collective work in groups must not necessarily negatively impact performance, i.e. social loafing. By heightening the salience of group memberships groups can even outperform coactively working individuals.

An alternative formulation for the fuzzy assignment problem

The existing assignment problems for assigning n jobs to n individuals are limited to the considerations of cost or profit measured as crisp. However, in many real applications, costs are not deterministic numbers. This paper develops a procedure based on Data Envelopment Analysis method to solve the assignment problems with fuzzy costs or fuzzy profits for each possible assignment. It aims to obtain the points with maximum membership values for the fuzzy parameters while maximizing the profit or minimizing the assignment cost. In this method, a discrete approach is presented to rank the fuzzy numbers first. Then, corresponding to each fuzzy number, we introduce a crisp number using the efficiency concept. A numerical example is used to illustrate the usefulness of this new method. © 2012 Operational Research Society Ltd. All rights reserved.

An Efficient and Verifiable Solution to the Millionaire Problem

A new solution to the millionaire problem is designed on the base of two new techniques: zero test and batch equation. Zero test is a technique used to test whether one or more ciphertext contains a zero without revealing other information. Batch equation is a technique used to test equality of multiple integers. Combination of these two techniques produces the only known solution to the millionaire problem that is correct, private, publicly verifiable and efficient at the same time.