In this study, a new two-stage learning based reduction approach for fuzzy cognitive maps (FCM) is introduced in order to reduce the number of concepts. FCM is a graphical modeling technique that follows a reasoning approach similar to the human reasoning and the decision-making process. The FCM model incorporates the available knowledge and expertise in the form of concepts and in the direction and strength of the interactions among concepts. One of the modeling problems of FCMs is that oversized FCM models suffer from interpretability problems. An oversized FCM may contain concepts that are semantically similar and affect the other concepts in a similar way. This new study introduces a two-stage model reduction approach, and both static and dynamic analyses are considered without losing essential information. In the first stage, the number of concepts is reduced by merging similar concepts into clusters, whereas in the second stage the transformation function parameters of concepts are optimized. In order to show the benefit of using the proposed reduction approach, two sets of studies are conducted. First, a huge set of synthetic FCMs are generated, and the results of these statistical analyses are presented via various tables and figures. Subsequently, suggestions to the decision makers are given. Second, experimental studies are also presented to show the decision parameters and procedure for the proposed approach. The results show that after using the concept reduction approach presented in this study, the interpretability of FCM increases with an acceptable amount of information loss in the output concepts.

Diagnosing the density profile at the edge of high temperature fusion plasmas by an accelerated lithium beam is a known technique since decades. By knowledge of the relevant atomic physics rate coefficients, the plasma electron density profile can be calculated from the relatively calibrated light profile along the beam. Several additional possibilities have already been demonstrated: Charge Exchange Resonance Spectroscopy (CXRS) for ion temperature/flow and Zeeman polarimetry for edge plasma current; therefore the Li-beam diagnostic offers a wealth of information at the plasma edge. The weaknesses of the method are the relatively faint light signal, background light, and technical difficulties of the beam injector which usually seriously limit the applicability. In this talk, we present systematic developments in alkali-beam diagnostics (Li, Na) for the injector and the observation system and detectors which resulted in strongly increased capabilities. Advanced systems have been built, and microsecond scale density profile, turbulence, and zonal flow measurement have been demonstrated. A novel edge current measurement technique has also been designed, and components have been tested with potential microsecond-scale time resolution. Additional possibilities of these advanced systems for spectral measurements (CXRS and various Zeeman schemes) are also discussed.

First a method for comparing profiles given in the following considerations. The profile comparisons have to be carried out in such position, that their average distance should be the smallest possible. Helmert transform is used for fitting measured points sets to one another in geodesy so that the least square distance of the measured points would be the smallest. However, for Helmert transform corresponding point pairs are to be found, i.e. for each point of one of the profiles has to be found a nearest one on the other profile. Multiple approaches are studied for finding corresponding point to a measured point in a point set. The effectiveness of the Helmert transform and the results of profile comparison are given for profiles of rounded triangle wave shaped regular roughness standards in the most used mechanical roughness range taken by contact stylus and confocal white light optical scanner. As an application, a method for qualifying replica materials is also presented. Our method uses roughness standards of at least three different roughnesses and takes replicas of them. The inverted scanned profiles of the replicas are compared with the scanned profiles of the original surfaces. The comparison uses Helmert transform to fit the two corresponding profiles. The average distance of one of the profile point sets from the other one is calculated by determining the distance of all the points in one profile from the circles given by their three nearest measured points in the other profile. The method is also used for comparing the less noisy stylus scans and the more sensitive optical scans of the original surface in order to determine, whether the noise in the replica scans are due to the replica taking process or to the scanning.

This paper proposes two novel approaches to estimate accurately mobile robot attitudes based on the fusion of low-cost accelerometers and gyroscopes. The first part of the paper demonstrates the use of a special test bench that both enables simulations of various dynamic behaviors of wheeled robots and measures their real attitude angles along with the raw sensor data. These measurements are applied in a simulation environment and we outline an offline optimization of Kalman filter parameters. The second part of the paper introduces a novel adaptive Kalman filter structure that modifies the noise covariance values according to the system dynamics. The instantaneous dynamics are characterized regarding the magnitudes of both the instantaneous vibration and the external acceleration. The proposed adaptive solution measures these magnitudes and utilizes fuzzy-logic to modify the filter parameters in real time. The results show that the adaptive filter improves the overall filter convergence by a remarkable 10.9% over using the optimized Kalman filter, thereby demonstrating its efficacy as an accurate and robust attitude filter. The proposed filter performances are also benchmarked against other common methods indicating that the flexibility of the developed adaptive filter allowed it to compete and even outperform the benchmark filters.

Introduction. An integrated assessment of the accounting and analytical supplying indicators for the sustainable development of agricultural enterprises is the basis and starting point for the development of methodical and methodological background for the compilation of non-financial or expanded financial reports supplemented by social and environmental data of agricultural enterprises' external and internal activities. The purpose of the article is to develop a cross-functional procedure for the integrated assessment of accounting indicators and analytical support of the sustainable development of agriculture at the micro and macro levels. Methods. As a methodological basis of the study, the authors use the provisions of the statistical and structural system along with comparative analysis, as well as theories of agriculture growth. Results. The need to use componential methodology for managing agricultural sustainable development is emphasised in the article. The relevant indicators are obtained in accordance with clear economic, environmental and social objectives and realistic requirements for data collection and calculations. After a detailed study of the methods for assessing the sustainable development of agriculture, we propose a diagnostic technique based on two methods. The first method involves the quantitative parameters of the development index and scores of economic, environmental and social factors. The second is based on the intuitive approach, in the situation where it is impossible to take into account impacts of many factors due to object complications, including social indicators of sustainable development. The authors of the article propose a number of indicators characterising the economic, ecological and social component of the management system for a comprehensive calculation of the agriculture sustainable development integral index. Conclusions. The results of our analysis allow elucidating the advantages of the method of indicator standardisation for the integral indicator of agriculture sustainable development. The choice and justification of the indicators to characterise certain elements of sustainable development, i.e. economic, social and environmental elements, is the basis for an integral assessment of the level of sustainable development of agriculture in Ukraine. The importance of this method lies in its simplicity, unification, harmonisation and universality, which is achieved by using two completely different levels and objects of research: for the local level - the level of agricultural enterprises, and the unrestricted level, which is the level of the district, region or country. The purpose of the developed methodology is to determine the integral indicator of agricultural sustainable development without attracting additional knowledge and skills from the researcher in the presence of the necessary primary data. The calculations show that Ukraine there are three relevant groups in Ukraine: most regions of Ukraine have a medium level of the integral indicator of agriculture sustainable development; three regions in the western part of Ukraine (Ivano-Frankivsk, Chernivtsi, Zakarpattia) have a low level, while a capital Kyiv region has a level which is higher than the average level.

The study represents an assessment of socio-economic integration consequences within the framework of the Eurasian Economic Union (EEU) for the participating countries - Armenia, Belarus, Kazakhstan, Kyrgyzstan and Russia. The authors implemented a personally developed approach of integral efficiency evaluation based on the calculation of the coefficients of state social and economic development indicators of growth as a main method for such assessment. These indicators allow us to characterise the following segments: national welfare, inflation, investment activity, labour market and the level of poverty, and the condition of the main economic sectors. The authors determined that Russia is the only export-oriented member of the Eurasian Economic Union (EEU). The worst consequences of integration in the cross-border trade sphere are observed in Belarus. For the analysed countries (excluding Kyrgyzstan), the first year of the existence of the EEU can be characterised as a period of economic recession (2015). According to the comparison of integral rates for the periods of 2005-2014 and 2015-2017, it was defined that the integration had a positive economic effect in the short term. By now, all the five participating countries have achieved the same level of social and economic development as in the pre-crisis period (2012-2013). In terms of the EEU membership, the calculated economic growth expands from 3% in Kyrgyzstan (by the integral index) up to 30% in the Republic of Belarus. Russia has also significantly strengthened its position (the growth rate of the index in 2015-17 was 25%).

Order picking is the most labour and capital intensive warehousing operation whose primary development field is routing optimisation due to its time consuming nature. The Order Picking Routing Problem is a special case of the vehicle routing problem with loading constraints, when the operator visits picking positions and collects items to build transport unit. Where the stacking and stability challenges are relevant during the picking of ordered items and exact routing algorithms are not available, the order picking operators have huge challenges to sequence the order picking list. They should take into consideration several factors by themselves, such as product properties, order picking list characteristics, and order picking system properties. The goal of the proposed research is to support the order picking operators in order to make more objective decisions in decreasing the order picking lead time, building stable transport units, and avoiding product damages, when industrially relevant, but rarely discussed, order picking sequencing based on stacking property is necessary. The paper defines the Order Picking Routing Problem based on Pallet Loading Feature (OPRP-PLF) and presents Bacterial Memetic Algorithm (BMA) based solutions for it, which is compared to Simulated Annealing (SA) algorithms. BMA has already been applied for Travelling Salesman Problem (TSP) but never used for the defined OPRP-PLF. The paper describes several BMA operators, most of them have an alternative which can be completed with SA based decisions. Using the BMA operators with SA methodology is a novelty of the proposed algorithms, which might support a quicker approximation to the global optimum. The possible combination of BMA operators will be evaluated with shorter and longer order picking lists and compared to SA algorithms on the same basis. The simulation results highlight, that allowing unit load reconstruction could decrease the order picking lead time and the developed BMA algorithms are more effective for OPRP-PLF than the SA algorithms. The paper concludes that the SA combined BMA operators are more effective than the SA-less operators in the case of shorter (less than about 20 records) order picking lists. While the shorter lists are the most commonly occurring order picking lists of warehouses, the SA combined BMA operators can increase the effectiveness of the OPRP-PLF optimisation.

In this paper we present a novel universal metaheuristic, Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA), which is based on the combination of the Bacterial Evolutionary Algorithm and local search techniques, used for solving NP-hard optimization problems. The algorithm was tested on a series of symmetric Traveling Salesman Problems (TSP) and Traveling Salesman Problem with time windows (TSPTW) benchmarks. The size of the symmetric TSP benchmarks went up to 5 000 cities. In all cases the DBMEA algorithm produced optimal or near-optimal solutions and the difference from the known best values was within 0.16%. While for large size problems it was much faster than the Concorde solver, it was found to be slower compared to the Helsgaun-Lin-Kernighan heuristic, which is the most efficient TSP solver method. With some slight modifications the same algorithm was also tested on TSP with time windows (TSPTW) benchmark instances. In most cases the DBMEA procedure found the known best solutions, and it was again the second fastest method compared with the state-of-the-art techniques for the TSPTW. DBMEA is called efficacious because it is a universal method. It can be efficiently applied to various NP-hard optimization problems and, as in all cases, it results in the optimal or a very near-optimal solutions, while its runtime is very predictable in terms of the size of the problem, and the topology of the instance does not affect its runtime significantly. Even though heuristics developed for a particular type of problem might perform better for that restricted class, our novel method proposed here is universally applicable and may be deployed successfully for optimizing other discreet NP-hard graph search and optimization problems as well.

Perturbed Runge–Kutta methods (also referred to as downwind Runge–Kutta methods) can guarantee monotonicity preservation under larger step sizes relative to their traditional Runge–Kutta counterparts. In this paper we study the question of how to optimally perturb a given method in order to increase the radius of absolute monotonicity (a.m.). We prove that for methods with zero radius of a.m., it is always possible to give a perturbation with positive radius. We first study methods for linear problems and then methods for nonlinear problems. In each case, we prove upper bounds on the radius of a.m., and provide algorithms to compute optimal perturbations. We also provide optimal perturbations for many known methods.