Tomislav Rolich

Minimizing environmental electromagnetic field pollution adjusting transmitter parameters using genetic algorithm

Paper describes method for finding transmitter parameters (location and power) for optimal electromagnetic radiation distribution in observed area. Constrains are protected areas inside observed area where strength of electric field is limited because of permanent people presence. In observed area one wish to obtain strength of electric field which is higher than lower limit value to cover the area with enough reception signal strength. On the other side electric field inside protected areas has to be lower than prescribed upper limit. Those two conditions are contradictory. In here described investigation genetic algorithm is used to find transmitter parameters (location and power) constrained by above mentioned conditions. Main purpose of investigations is examining applicability of procedure. Initial investigation is limited to rectangular planar observing area with one transmitter and with different formation and size of protected areas (areas in which strength of electric field is limited because of permanent people presence). Procedure has been repeated and results statistically analyzed. Based on those results it can be concluded that procedure is applicable and it is justified to continue investigations for more complex and more realistic situations e.g. more transmitters with directed radiations and for three dimensional spaces.

Micromechanical Tensile Model of Woven Fabric and Parameter Optimization for Fit with KES Data

Nonlinear tensile behavior of woven fabric is simulated using a simplified model of the basic cell in which tensile and bending resistances of the yarn are geometrically discretized. Geometry of the basic cell, which undergoes changes in the deformation process, is represented by the slope of the yarn. Geometric nonlinearity is accounted for by writing static equations in current configuration, and the corresponding nonlinear set of equations is solved incrementally. In order to compare the computed tensile curve with the one experimentally recorded in KES procedure of textile evaluation, residual deformation in unloading needs to be accounted for. This is done by introducing elastoplastic tensile response of the yarn into the nonlinear micromechanical model. In that way, the model covers both geometric and material nonlinearities in fabric deformation with a limited set of basic parameters. In order to refine the initial estimate of the parameters and make up for neglected secondary phenomena, the set of model parameters is optimized for best fit of computed and recorded tensile curves. The optimization is done using genetic algorithm. Very good accuracy is obtained, which seems promising for future engineering of woven fabrics.

Evolutionary algorithms aided textile design

Fabric design has always been a matter of inspiration, depending mostly upon mans ingenuity and creativity. The authors suggest that creation of fabric patterns using evolution algorithms, which can not only help in improving design created by man, but also make the procedure semi‐automatic, meaning much less dependent upon the designer himself/herself. Evolution algorithm based software offers a wide range of fabric patterns and is also able to create new ones based on the users choice. The procedure can also be of considerable help to professional designers, as it can offer patterns they would not or could not create themselves. The system described is inexpensive and can be used on IBM compatible personal computers. It is user‐friendly and can be implemented with no previous preparation on the part of the user. The results of preliminary investigations suggest a practical applicability of the software.

Algorithms for 2D nesting problem based on the no-fit polygon

Strip-packing problem is apparent in textile industry where a set of items, i.e. cutting parts (2D convex or non-convex polygons) need to be placed on a rectangular container (fabric with an mxn area) so that cutting parts do not overlap and do not exceed the boundaries of the container. The goal is to find a placement that utilizes the area of a container. In this paper three methods (random search, greedy algorithm and genetic algorithm) are tested on sets of regular (convex polygons) and irregular (cutting parts) items. The goal is to find the optimal items placement that minimizes the cover area. In this paper a no-fit polygon (NFP) is used to assure two items touch without overlapping. NFP is constructed by rotating polygon B around a static polygon A in a way their edges always touch and never overlap. The result is a polygonal area enclosed by trajectory of rotating polygons reference point which represents the overlapping area of A and B. Items touch if polygon B is placed on the NFPs border. Non-convex cutting parts are approximated with their convex hull since a NFP version for convex polygons is used in this paper.

Use of genetic algorithms and artificial neural networks to predict the resolution of amino acids in thin-layer chromatography

A novel method is proposed for optimization of simultaneous thinlayer chromatographic separation of seven amino acids. For this purpose we used a useful combination of genetic algorithms (GA) with artificial neural networks (ANN). Methods investigated in this work were successfully used for prediction of resolution (RS) and optimization of the thin-layer chromatographic separation of model solutions containing the seven compound. Very good correlation was achieved between predicted and calculated RS data, and low absolute and relative errors were obtained.

Hyper-Heuristic Approach for Improving Marker Efficiency

Abstract Marker planning is an optimization arrangement problem, where a set of cutting parts need to be placed on a thin paper without overlapping to create a marker – an exact diagram of cutting parts that will be cut from a single spread. An optimal marker that utilizes the length of textile material has to be obtained. The aim of this research was to develop novel algorithms for obtaining an efficient marker that would achieve competitive results and optimize the garment production in terms of improving the utilization of textile material. In this research, a novel Grid heuristic was introduced for obtaining a marker, alongside its improvement methods: Grid-BLP and Grid-Shaking. These heuristics were hybridized with genetic algorithm that determined the placement order of cutting parts using the newly introduced All Equal First (AEF) placement order. A novel individual representation for genetic algorithm was designed that was composed of order sequence, rotation detection and the choice of placement algorithm (hyper-heuristic). Experiments were conducted to determine the best marker making method, and hyper-heuristic efficiency. The implementation and experiments were conducted in MATLAB using GEATbx toolbox on five datasets from the garment industry: ALBANO, DAGLI, MAO, MARQUES and MAN SHIRT. Marker efficiency in percentage was recorded with best results: 84.50%, 80.13%, 79.54%, 84.67% and 86.02% obtained for the datasets respectively. The most efficient heuristic was Grid-Shaking. Hyper-heuristic applied Grid-Shaking in 88% of times. The created algorithm is independent of cutting parts’ shape. It can produce markers of arbitrary shape and is flexible in terms of expansion to new instances from the garment industry (leather nesting, avoiding damaged areas of material, marker making with materials with patterns).

Bottom-left and sequence pair for solving packing problems

Nesting problem is a combinatorial optimization problem with a goal of placing a set of polygons within container without overlapping. If polygons in shape of cutting patterns are used, and the container has rectangular shape with a fixed width, the problem is defined as a strip packing problem. In this paper two methodologies are described: Bottom-Left (BL) and Sequence Pair (SP). Bottom-left is order based strategy which can solve strip packing problem. It moves each polygon as far as possible to the bottom of the temporary layout and then as far as possible to the left until a stable position is found. In this paper BLDi version was used with genetic algorithm. Sequence pair is an abstract representation method used for nesting problem that creates a relationship model between rectangular approximations of polygons instead of direct placement. The goal of the research is to obtain maximum packing density for described methodologies. Experiments have been conducted on benchmark datasets: Albano (24 parts), Dagli (30 parts), Mao (20 parts) and Marques (24 parts) in MATLAB environment, with GA-BLDi performing 78.23, 70.15, 70.70 and 77.63 percent respectively (in average), and SP-BLi performing 76.28, 63.10, 68.52 and 75.60 percent respectively (in average).

Durability Assessment of Functionally Printed Knitted Fabrics for T-Shirts

Knitted shirts are an appropriate carrier for easily readable information, and therefore, in recent years, QR Codes are very often printed on T-shirts. With the purpose of investigating QR Code fastness printed on textiles, in the paper a methodology was proposed for durability assessment of functionally printed knitted fabrics that includes the implementation of long-term care and wearing simulation - which the T-shirts are exposed to over the lifetime. Dimensional changes after multiple washing and drying cycles were determined for non-printed and functionally printed white knitting ; abrasion resistance and tendency to surface pilling after multiple rubbing cycles. The effect of usage simulation on functional prints durability was defined, as well as color fastness, readability and aesthetic properties. It was found that structure of knitted fabrics, their dimensional stability, the number of care and wear cycles performed, the printing technique and size of black modules in QR Code all have an influence on the results obtained.

Genetic algorithm based reduction of electromagnetic field pollution

Paper describes a method for finding transmitter parameters (location, power and horizontal direction) for optimal electromagnetic radiation distribution in the observed area. Constraints are protected areas inside the observed area where strength of electric field is limited because of a permanent people presence. In the observed area one wishes to obtain the strength of electric field which is higher than the lower limit value to cover the area with enough reception signal power. On the other hand electric field inside protected areas has to be lower than the prescribed upper limit determined by a health hazard. These two conditions are contradictory. In this investigation a genetic algorithm is used to find transmitter parameters (location, power and horizontal direction) constrained by above mentioned conditions. Investigation is limited to a rectangular planar observing area with one transmitter and with different formation and size of protected areas. The procedure has been repeated for 15 times and the results are analyzed statistically. Based on these results it can be concluded that the procedure is applicable and it is justified to continue investigations for more complex and more realistic situations, e.g. more than one transmitter with directed radiations and for 3D spaces.