Based on the most recent Tensor Product model transformation solutions, the paper presents an improved control performance for the most recent version of the three Degree of Freedom aeroelastic wing section model including Stribeck friction, according to signals pitch, plunge, trailing edge and control value, based on practical engineering criteria such as overshoot, undershoot, signal end values and settling time. This is achieved through proposing a novel two dimensional parametric convex hull manipulation based method for Tensor Product model transformation based Control Design Frameworks. The approach provides two TP model representations for the different requirements of the controller and observer of a given model, opening the possibility to utilize the TP model transformation's convex hull manipulation potential in control performance optimization for a separate optimization of the two TP model representations. Numerical simulation results are provided to illustrate the control performance improvements of the aeroelastic wing section model through the proposed 2D parametric convex hull manipulation based design method.

Unsupervised learning methods play an essential role in many deep learning approaches because the training of complex models with several parameters is an extremely data-hungry process. The execution of such a training process in a fully supervised manner requires numerous labeled examples. Since the labeling of the training samples is very time-consuming, learning approaches that require less or no labeled examples are sought. Unsupervised learning can be used to extract meaningful information on the structure and hierarchies in the data, relying only on the data samples without any ground truth provided. The extracted knowledge representation can be used as a basis for a deep model that requires less labeled examples, as it already has a good understanding of the hidden nature of the data and should be only fine-tuned for the specific task. The trend for deep learning applications most likely leads to substituting as much portion of supervised learning methods with unsupervised learning as possible. Regarding this consideration, our survey aims to give a brief description of the unsupervised clustering methods that can be leveraged in case of deep learning applications.

About twenty years ago, the research activities aiming at the development of the optimal maintenance and rehabilitation strategies (models) of roads and bridges started in several countries, including Hungary. In the first foreign models, the deterioration depending on time and other parameters was given by Markov transition probability matrices. Due to the inaccuracies and inconsistencies of earlier models, a continuous model upgrading could have been carried out by many researchers world-wide. Besides, basically new models appeared in the literature, which are able to describe the actual processes more reliably. The research work of the authors of the paper has concentrated on Pavement Management Systems (PMSs) and Bridge Management Systems (BMSs). Since a common financing of roads and bridges is typical, a combined model of road pavement and bridge managements was developed by the authors increasing considerably the efficient use of available funds.

Order picking is the most labor-intensive and costly activities in many warehouses by consuming ca. 60 % of the total operating expenses. Order picking development strategies mostly concentrate on warehouse layout, storage assignment policy, routing, zoning and on batching methods, while the physical parameters of the products - which are hardly ever taken into account - do also have a significant impact on the processes. Researchers of the pallet-loading problem could provide a wider horizon on considerable parameters, but their results are rarely implemented into order picking processes. In order to design a successful and optimal order picking system, considering all influential parameters is inevitable, since all of them are strongly connected to each other. This paper introduces an interactive training and modeling tool, which allows us to model, test, analyze and to evaluate order picking algorithms by taking numerous influencing factors into consideration. We describe an application of the simulation environment designed for highlighting the importance of physical product parameters in order picking performance.

The determination of ordered weighted averaging (OWA) operator weights is a very important issue of applying the OWA operator for decision making. One of the first approaches, suggested by O’Hagan, determines a special class of OWA operators having maximal entropy of the OWA weights for a given level of orness; algorithmically it is based on the solution of a constrained optimization problem. In 2001, using the method of Lagrange multipliers, Fullér and Majlender solved this constrained optimization problem analytically and determined the optimal weighting vector. In 2003 Fullér and Majlender computed the exact minimal variability weighting vector for any level of orness using the Karush-Kuhn-Tucker second-order sufficiency conditions for optimality. The problem of maximizing an OWA aggregation of a group of variables that are interrelated and constrained by a collection of linear inequalities was first considered by Yager in 1996, where he showed how this problem can be modeled as a mixed integer linear programming problem. In 2003 Carlsson, Fullér and Majlender derived an algorithm for solving the constrained OWA aggregation problem under a simple linear constraint: the sum of the variables is less than or equal to one. In this paper we give a short survey of numerous later works which extend and develop these models.

At some VVER-type pressurized water nuclear reactors (Russian-type PWR) different versions of the so-called AP-CITROX method have been widely used for the chemical decontamination of the heat exchanger tubes of steam generators. In the period of 2000-2007, within the frame of a joint-project dealing with the comprehensive investigation of the corrosion state of the steam generators of the Paks Nuclear Power Plant, Hungary, effects of the AP-CITROX chemical decontamination procedure on the corrosion and surface characteristics of the heat exchanger tubes have been studied. These studies provide evidences that some adverse features (formation of a "hybrid" layer with accelerated corrosion rate and great mobility) can be detected after 1-3 years of applying the AP-CITROX procedure. The present work is a continuation of the above program and focused on the long-term trends in the corrosion state and structure of protective oxide-layer grown on the decontaminated surfaces. The results of electrochemical (voltammetric), surface analytical (SEM-EDX, CEMS) and mobility (ICP-OES) studies have revealed that (1) some beneficial changes in the corrosion characteristics, mobility and chemical composition of the inner surfaces of decontaminated heat exchanger tubes can be observed in the long run, and (2) the passivity of the oxide-layers formed on decontaminated surfaces of steel tubes exhibits favorable tendency after 4-7 years under normal operation conditions. © 2009 Elsevier Ltd. All rights reserved.

The second part of the paper deals with the Jung-Kerényi -Pauli dominantly unconscious 'cooperation' on the basis of the synchronistic background in the years 1954-57.The main patterns of the 'especial communication' is based also on the structure and meaning of the 'Eleusinian mysteries' together with the Perseus mythologem and its possible relation to the 'Leviathan myth' exploring as an extended 'dynamic hermeneutical system'.

Small tokamaks may significantly contribute to the better understanding of phenomena in a wide range of fields such as plasma confiement and energy transport; plasma stability in different magnetic configurations; plasma turbulence and its impact on local and global plasma parameters; processes at the plasma edge and plasmaawall interaction; scenarios of additional heating and nonainductive current drive; new methods of plasma profile and parameter control; development of novel plasma diagnostics; benchmarking of new numerical codes and so on. Furthermore, due to the compactness, flexibility, low operation costs and high skill of their personnel small tokamaks are very convenient to develop and test new materials and technologies. Small tokamaks are suitable and important for broad international cooperation, providing the necessary environment and manpower to conduct dedicated joint research programmes. In addition, the experimental work on small tokamaks is very appropriate for the education of students, scientific activities of postagraduate students and for the training of personnel for large tokamaks. The first Joint (Host Laboratory) Experiment (JE1) has been carried out in 2005 on the CASTOR tokamak at the IPP Prague, Czech Republic. It was jointly organized by the IPPaASCR and KFKI HAC, Budapest, involved 20 scientists from 7 countries and was supported through the IAEA and the ICTP, Trieste. The objective of JE1 was to perform studies of plasma edge turbulence and plasma confinement. Following the success of JE1, JE2 has been performed on Ta10 at RRC "Kurchatov Institute" in Moscow; 30 scientists from 13 countries participated in this experiment. This experiment aimed to continue JE1 turbulence studies, now extending them to the plasma core. Results of JE1 and JE2 will be overviewed and compared. © 2008 American Institute of Physics.

Introduction Landslides and sinkholes damage social, economic, and natural infrastructure. These processes have direct and indirect impacts on important infrastructure, including residential areas, and influence land use change and migration from rural to urban areas. Sinkholes and landslides occur when parts of a soil collapse mainly in more gentle or steeper slopes, which are often triggered by intensive rainfall. One of the main goals in sustainable land management is the identification and control of natural disasters, which on the one hand leads to the quantitative and qualitative improvement of production in the long term, and on the other hand, maintains the quality of the soil and prevents soil degradation. In order to manage better and more stable, it seems necessary to know how to change and identify different forms of erosion such as sinkholes and landslides. Sinkholes and landslides occur when parts of a soil collapse mainly in more gentle or steeper slopes, which are often triggered by intensive rainfall. Materials and methods Recent advances in acquiring images from unmanned aerial vehicles (UAV) (UAV) and deep learning (DL) methods inherited from computer vision have made it feasible to propose semi-automated soil landform detection methodologies for large areas at an unprecedented spatial resolution. In this study, we evaluate the potential of two cutting-edge DL deep learning segmentation models, the vanilla U-Net model, and the Attention Deep Supervision Multi-Scale U-Net model, applied to UAV-derived products, to map landslides and sinkholes in a semi-arid environment, the “Golestan Province” (north-east Iran). Results and discussion Landslides: The performance of the U-Net model shows that it has fewer false positives, but at the same time, it has missed many landslide cells. Meanwhile, the ADSMS U-Net model has performed better in detecting landslide cells, but it attributed many cases to incorrect predictions (which is explained by the low accuracy score). The best F1 score achieved for the ADSMS U-Net model is 0.68. Sinkholes: For all band combinations, the performances of ADSMS U-Net are better than those of the traditional U-Net model. The best overall scores by ADSMS U-Net were obtained when trained on the ALL data. Regarding the effectiveness of the various combinations evaluated in this study, we can observe the contradictory behaviors of the models. The traditional U-Net achieves the best performance using the RGB optical combination, while the ADSMS U-Net can leverage topographic derivative information and optical data, showing the best results with the ALL combination. Moreover, it is evident that the DSHC data alone provides the worst results for both models. In overall, the results show that the ability of ADSMS U-Net to predict landslides is closer to the ground reality compared to U-Net. This model identifies most of the landslides in the test sections. Also, for all combinations of sinkhole bands, ADSMS U-Net performs better than the U-Net model. The best overall scores were obtained by ADSMS U-Net when trained on ALL data. Conclusions Since this kind of soil erosion is the main origin of some major soil erosion including gully initiation and extension, applying new technology namely, UAV and deep learning is highly important and recommended. Our framework can successfully map landslides in a challenging environment (with an F1-score of 69 %), and topographical derivates from UAV-derived DSM decrease the capacity of mapping sinkholes and landslides of the models calibrated with optical data. Future research could explore the use of such an approach to map landslides and sinkholes over time to assess time-based changes in the formation and spread of natural hazards.