An adaptive support vector regressor controller for nonlinear systems

In this study, a novel online support vector regressor (SVR) controller based on system model estimated by a separate online SVR is proposed. The main idea is to obtain an SVR controller based on an estimated model of the system by optimizing the margin between reference input and system output. For this purpose, “closed-loop margin” which depends on tracking error is defined, then the parameters of the SVR controller are optimized so as to optimize the closed-loop margin and minimize the tracking error. In order to construct the closed-loop margin, the model of the system estimated by an online SVR is utilized. The parameters of the SVR controller are adjusted via the SVR model of system. The stability of the closed-loop system has also been analyzed. The performance of the proposed method has been evaluated by simulations carried out on a continuously stirred tank reactor (CSTR) and a bioreactor, and the results show that SVR model and SVR controller attain good modeling and control performances.     

Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation

Skyline computation in databases has been a hot topic in the literature because of its interesting applications. The basic idea is to find non-dominated values within a database. The task is mainly a multi-objective optimization process as described in this paper. This motivated for our approach that employs a multi-objective genetic algorithm based clustering approach to find the pareto-optimal front which allows us to locate skylines within a given data. To tackle large data, we simply split the data into manageable subsets and concentrate our analysis on the subsets instead of the whole data at once. The proposed approach produced interesting results as demonstrated by the outcome from the conducted experiments.     

Design of a decision support system for machine tool selection based on machine characteristics and performance tests

Economic globalization, together with heightened market competition and increasingly short product life cycles are motivating companies to use advanced manufacturing technologies. Use of high speed machining is increasingly widespread; however, as the technology is relatively new, it lacks a deep-rooted knowledge base which would facilitate implementation. One of the most frequent problems facing companies wishing to adopt this technology is selecting the most appropriate machine tool for the product in question and own enterprise characteristics. This paper presents a decision support system for high speed milling machine tool selection based on machine characteristics and performance tests. Profile machining tests are designed and conducted in participating machining centers. The decision support system is based on product dimension accuracy, process parameters such as feed rate and interpolation scheme used by CNC and machine characteristics such as machine accuracy and cost. Experimental data for process error and cycle operation time are obtained from profile machining tests with different geometrical feature zones that are often used in manufacturing of discrete parts or die/moulds. All those input parameters have direct impact on productivity and manufacturing cost. Artificial neural network models are utilized for decision support system with reasonable prediction capability.     

Trend Assessment by the Innovative-Şen Method

Hydro-meteorological time series may include trend components mostly due to climate change since about three to four decades. Trend detection and identification in a better and refined manner are among the major current research topics in water resources domain. Even though different methodologies can be found for trend detection in literature, two well-known procedures are the Mann-Kendall (MK) trend test and recently innovative-?en trend method, which provides different aspects of the trend. The theoretical basis and application of these two methods are completely different. The MK test gives a holistic monotonic trend without any categorization of the time series into a set of clusters, but the innovative-?en method is based on cluster and provides categorical trend behavior in a given time series. The main purpose of this paper is to provide important differences between these two approaches and their possible similarities. The applications of the two approaches are given for hydro-meteorological records including relative humidity, temperature, precipitation and runoff from Ergene drainage basin in the north-western part of Turkey. It is observed that although MK trend test does not show significant trend almost in all the cases, the innovative-?en approach yields trend categorizations as “very low”, “low”, “medium” “high” and “very high”, which should be taken into consideration in future flood (“very high”) and drought (“very low”) studies.     

Reordering for improving global Arnoldi–Tikhonov method in image restoration problems

This paper discusses the solution of large-scale linear discrete ill-posed problems arising from image restoration problems. Since the scale of the problem is usually very large, the computations with the blurring matrix can be very expensive. In this regard, we consider problems in which the coefficient matrix is the sum of Kronecker products of matrices to benefit the computation. Here, we present an alternative approach based on reordering of the image approximations obtained with the global Arnoldi–Tikhonov method. The ordering of the intensities is such that it makes the image approximation monotonic and thus minimizes the finite differences norm. We present theoretical properties of the method and numerical experiments on image restoration.     

Co-authorship networks in social sciences: The case of Turkey

We analyse the co-authorship networks of researchers affiliated at universities in Turkey by using two databases: the international SSCI database and the Turkish ULAKBIM database. We find that co-authorship networks are composed largely of isolated groups and there is little intersection between the two databases, permitting little knowledge diffusion. There seems to be two disparate populations of researchers. While some scholars publish mostly in the international journals, others target the national audience, and there is very little intersection between the two populations. The same observation is valid for universities, among which there is very little collaboration. Our results point out that while Turkish social sciences and humanities publications have been growing impressively in the last decade, domestic networks to ensure the dissemination of knowledge and of research output are very weak and should be supported by domestic policies.     

One-pot two-step lipase-catalyzed synthesis of α,ω-thiophene-capped poly(ε-caprolactone) macromonomers and their use in electropolymerization

Macromonomers based on poly(ε-caprolactone) (PCL) with α,ω-thiophene functional end groups were prepared in bulk by enzymatic polymerization using immobilized Candida Antarctica lipase B (Novozym 435) as the catalyst. In the synthesis strategy, 3-thiophenemethanol was used to initiate the enzyme-assisted ring-opening polymerization of ε-caprolactone (ε-CL) to yield PCL with α-thiophene end group (initiation reaction, ThPCL) and then 3-thiopheneacetic acid was added to prepolymerized ε-CL to introduce ω-thiophene functionality in termination step (ThPCLTh). Macromonomers were characterized by ¹H and ¹³C NMR, FTIR, and GPC. Moreover, the obtained macromonomers were employed in electropolymerization experiments and copolymers with thiophene or pyrrole were synthesized through their end thiophene groups. These polymers were characterized by cyclic voltammetry (CV), FTIR, and scanning electron microscopy (SEM). Conductivity measurements were carried out by the four-probe technique.     

Investigation of thermoluminescence properties of metal oxide doped lithium triborate

In this study, lithium triborate (LiB₃O₅) doped with different metal oxides were investigated to explore its thermoluminescence properties. Solid-state reaction method was employed for the synthesis of the desired materials. The formation of the produced phases was confirmed by Powder X-Ray Diffraction (XRD), Infrared (IR), Differential Thermal Analysis (DTA) and Scanning Electron Microscopy (SEM) examinations. It was found that, CuO and Al₂O₃ doped lithium triborate samples exhibit very significant thermoluminescence glow curves to be promising dosimetric material.     

Surface Effect on Spatial Length-Scales in a Two-Gap Superconductor

The healing of two-band superconductivity near its interface is studied. It is demonstrated that the restoration of superconductivity gaps in the immediate vicinity of the interface is governed by two length scales: the first one diverges at critical temperature T _c , while the second one diverges at T _c+<T _c . By moving away from the boundary, the temperature dependencies of characteristic lengths change so that singularity at T _c+ becomes removed in the bulk by arbitrary weak interband coupling. The asymptotes for the spatial behavior of gaps have been found analytically near the surface and approaching the bulk state.