Closed-loop control of hydraulic telescopic handler

Automatization is supposed to improve working conditions and safety in the construction industry, as it already did in manufacturing industries. This paper presents the development of a robotic control system for a commercially available hydraulic telescopic handler. The target application for the telescopic handler is semi-automated assembly of facade panels. The base handler was upgraded with two additional hydraulic axes, position sensors and closed-loop control system, while the original handler safety assurance mechanisms were preserved. The control approach is based on a PI controller with velocity feedforward and valve overlap compensation. The direct and inverse kinematic models of handler mechanism were developed to enable control of end-effector motion along a straight line in the Cartesian coordinate system. The motion performances were evaluated following the ISO 9283 standard with payload of 2000 kg. Results show the repeatability of positioning bellow 7.0 mm and the straight line tracking error smaller than 63 mm.     

Waveform diversity via mutual information

A novel criterion for waveform diversity in radar systems is presented that is based on the information theoretic concept of Shannon mutual information (MI). In general, waveform diversity refers to adaptively changing a transmitted waveform based on the target and interference environment. MI is a measure of the information in a random variable or vector about another random variable or vector. It is shown herein that an MI framework provides the basis for a general and powerful criterion for evaluation of candidate waveforms, as well as design of waveforms. The criterion inherently includes radar system and scenario statistical information. In addition, the criterion reduces to a simple analytical function of a set of scalar parameters for the case of Gaussian-distributed proper random processes. It is shown that those scalar parameters are also the canonical correlations of the random variables in the MI expression that involves the received radar signal at two distinct time instants. This property suggests the possibility of new insights into waveform diversity using MI as a criterion. The concept is presented assuming a SAR system configuration, but can be extended to other radar modes and functions, as well as to other sensing applications.     

Extension of domains of states

We deal with categorical aspects of the extensions of generalized probability measures. In particular, we study various domains of fuzzy sets, describe the relationships between σ-fields of crisp sets and generated Łukasiewicz tribes of measurable functions, and mention some probabilistic aspects. D-posets and sequential continuity play an important role. This work was supported by the Slovak Research and Development Agency under the contract No. APVV-0071-06, Protocol for Scientific and Technological Collaboration between the Republic of Italy and Slovak Republic, Project No. 15: Algebraic and Logical Systems of Soft Computing, and VEGA 2/6088/26.     

On the number of Dejean words over alphabets of 5, 6, 7, 8, 9 and 10 letters

We give lower bounds on the growth rate of Dejean words, i.e. minimally repetitive words, over a k-letter alphabet, for 5≤k≤10. Put together with the known upper bounds, we estimate these growth rates with the precision of 0.005. As a consequence, we establish the exponential growth of the number of Dejean words over a k-letter alphabet, for 5≤k≤10.     

Selection of a representative value function in robust multiple criteria sorting

We introduce the concept of a representative value function in robust ordinal regression applied to multiple criteria sorting problems. The proposed approach can be seen as an extension of UTADIS^GMS, a new multiple criteria sorting method that aims at assigning actions to p pre-defined and ordered classes. The preference information supplied by the decision maker (DM) is composed of desired assignments of some reference actions to one or several contiguous classes—they are called assignment examples. The robust ordinal regression builds a set of general additive value functions compatible with the assignment examples and results in two assignments: necessary and possible. The necessary assignment specifies the range of classes to which the action can be assigned considering all compatible value functions simultaneously. The possible assignment specifies, in turn, the range of classes to which the action can be assigned considering any compatible value function individually. In this paper, we propose a way of selecting a representative value function among the set of compatible ones. We identify a few targets which build on results of the robust ordinal regression and could be attained by a representative value function. They concern enhancement of differences between possible assignments of two actions. In this way, the selected function highlights the most stable part of the robust sorting, and can be perceived as representative in the sense of robustness preoccupation. We envisage two possible uses of the representative value function in decision support systems. The first one is an explicit exhibition of the function along with the results of the UTADIS^GMS method, in order to help the DM to understand the robust sorting. The other is an autonomous use, in order to supply the DM with sorting obtained by an example-based procedure driven by the chosen function. Three case studies illustrating the use of a representative value function in real-world decision problems are presented. One of those studies is devoted to the comparison of the introduced concept of representativeness with alternative procedures for determining a single value function, which we adapted to sorting problems, because they were originally proposed for ranking problems.     

Comparison of the Adomian decomposition method and the variational iteration method in solving the moving boundary problem

In this paper, a comparison between two methods: the Adomian decomposition method and the variational iteration method, used for solving the moving boundary problem, is presented. Both of the methods consist in constructing the appropriate iterative or recurrence formulas, on the basis of the equation considered and additional conditions, enabling one to determine the successive elements of a series or sequence approximating the function sought. The precision and speed of convergence of the procedures compared are verified with an example.     

Weighted thin-plate spline image denoising

This note aims to present new denoising method based on thin-plate splines (TPS). The proposed approach is based on the general TPS denoising Berman [2], however, its unfavorable smoothing of edges and details is suppressed by introduction of a weighting approach applied locally. The performance of the method is shown and compared to the original TPS denoising.     

Modified progressive random walk with chaotic PRNG

Abstract

In this paper, two modifications are proposed to the Progressive Random Walk (PRW) algorithm in order to address its potentially insufficient search space coverage. The first modification replaces the Pseudo-Random Number Generator (PRNG) with the uniform distribution by the chaotic map based PRNG for generating of the offset values and the second modification is called direction switching and is based on experiment observation. The modifications are implemented into the PRW and the resulting algorithm is called modified Progressive Random Walk. The search space coverage of the two algorithms is compared. Both algorithms are used in macro ruggedness estimation of the CEC2015 benchmark set and the results are discussed.     

Formulating the template ILP consistency problem as a constraint satisfaction problem

Inductive Logic Programming (ILP) deals with the problem of finding a hypothesis covering positive examples and excluding negative examples, where both hypotheses and examples are expressed in first-order logic. In this paper we employ constraint satisfaction techniques to model and solve a problem known as template ILP consistency, which assumes that the structure of a hypothesis is known and the task is to find unification of the contained variables. In particular, we present a constraint model with index variables accompanied by a Boolean model to strengthen inference and hence improve efficiency. The efficiency of models is demonstrated experimentally.