Modeling a ground-coupled heat pump system by a support vector machine

This paper reports on a modeling study of ground coupled heat pump (GCHP) system performance (COP) by using a support vector machine (SVM) method. A GCHP system is a multi-variable system that is hard to model by conventional methods. As regards the SVM, it has a superior capability for generalization, and this capability is independent of the dimensionality of the input data. In this study, a SVM based method was intended to adopt GCHP system for efficient modeling. The Lin-kernel SVM method was quite efficient in modeling purposes and did not require a pre-knowledge about the system. The performance of the proposed methodology was evaluated by using several statistical validation parameters. It is found that the root-mean squared (RMS) value is 0.002722, the coefficient of multiple determinations (R²) value is 0.999999, coefficient of variation (cov) value is 0.077295, and mean error function (MEF) value is 0.507437 for the proposed Lin-kernel SVM method. The optimum parameters of the SVM method were determined by using a greedy search algorithm. This search algorithm was effective for obtaining the optimum parameters.The simulation results show that the SVM is a good method for prediction of the COP of the GCHP system. The computation of SVM model is faster compared with other machine learning techniques (artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS)); because there are fewer free parameters and only support vectors (only a fraction of all data) are used in the generalization process.     

Economics of nuclear power stations in Russia: Increasing the market competitiveness of producers of electrical power

An analysis is made of the role and the place of Russian nuclear power stations on the federal (all-Russia) wholesale market for electrical energy and power, the structure of the costs of producing electricity at these stations, the competitiveness of nuclear energy sources with other producers of electricity. Possible ways are discussed for reducing the cost of producing electricity at nuclear power stations. Journal version of the plenary paper “From the first nuclear power station in the world to the power engineering of the twenty first century” (Annual Conference of Nuclear Society, Obninsk, June 28 to July 2, 1999). Rosénergoatm Concern. Translated from Atomnaya énergiya, Vol. 87, No. 2, pp. 92–101, August, 1999.     

Reduced Collocation Methods: Reduced Basis Methods in the Collocation Framework

In this paper, we present the first reduced basis method well-suited for the collocation framework. Two fundamentally different algorithms are presented: the so-called Least Squares Reduced Collocation Method (LSRCM) and Empirical Reduced Collocation Method (ERCM). This work provides a reduced basis strategy to practitioners who prefer a collocation, rather than Galerkin, approach. Furthermore, the empirical reduced collocation method eliminates a potentially costly online procedure that is needed for non-affine problems with Galerkin approach. Numerical results demonstrate the high efficiency and accuracy of the reduced collocation methods, which match or exceed that of the traditional reduced basis method in the Galerkin framework.     

Deviation of Fiber Tracts in the Vicinity of Brain Lesions: Evaluation by Diffusion Tensor Imaging

Diffusion Tensor Imaging (DTI) is used to characterize the diffusion properties of deviated white matter caused by brain tumors. DTI was recently shown to be very helpful in delineating white matter both within brain lesions and surrounding them. Displacement of white matter fibers may be one of the consequences of tumor growth adjacent to white matter. The combination of white matter mapping with DTI and gray matter mapping using functional MRI, in some cases, facilitated assessment of the relation between the shifted cortical areas and the corresponding white matter tracts. We found that the fractional anisotropy extracted from DTI is increased by 38% in areas of non-edematous shifted white matter fibers. By contrast, trace apparent diffusion coefficient (ADC) values in those areas were found to be similar to contralateral side and normal control values. Analysis of the three diffusion tensor eigenvalues revealed that the increase in the fractional anisotropy is a result of two processes. The first is the increase in the diffusion parallel to the fibers—λ₁ (by 18%), and the second is the decrease in the diffusion perpendicular to fibers—λ₃ (by 34%) as compared with the contralateral side. These opposing changes cause an increase in the diffusion anisotropy but no change in the trace ADC. It is suggested that the pressure caused by the tumor may lead to an increase in white matter fiber tension, thus causing an increase in λ₁. On the other hand, the same pressure causes increased fiber density per unit area, leading to a higher degree of restricted diffusion in the extracellular space and, hence, a reduction in λ₃.     

A novel anti-slip control approach for railway vehicles with traction based on adhesion estimation with swarm intelligence

Anti-slip control systems are essential for railway vehicle systems with traction. In order to propose an effective anti-slip control system, adhesion information between wheel and rail can be useful. However, direct measurement or observation of adhesion condition for a railway vehicle in operation is quite demanding. Therefore, a proportional–integral controller, which operates simultaneously with a recently proposed swarm intelligence-based adhesion estimation algorithm, is proposed in this study. This approach provides determination of the adhesion optimum on the adhesion-slip curve so that a reference slip value for the controller can be determined according to the adhesion conditions between wheel and rail. To validate the methodology, a tram wheel test stand with an independently rotating wheel, which is a model of some low floor trams produced in Czechia, is considered. Results reveal that this new approach is more effective than a conventional controller without adhesion condition estimation.

     

Optimising the production of energy from coblended food waste and biosolids using batch reactor studies

Energy production from a coblended mixture of biosolids and food waste was optimised for hydrogen and methane production. Four different blends were prepared by varying the carbohydrate : protein (carb : pro) ratios. The biosolids contained a low carbohydrate fraction and so was not suitable for hydrogen production when used alone. However coblending this waste with a carbohydrate‐enriched food waste produced a greater hydrogen yield, making this option viable. Batch studies showed that the optimised mix had a biosolids concentration of 25.7% (w/w). The largest hydrogen yield of 198.5 mL/gVS_removed was observed when the carb : pro was 2.78, and this was threefold higher than the other carb : pro ratios evaluated in this study. The digestate recovered after hydrogen recovery had a C : N of 17.5, which is in the ideal range for methane production. The biochemical methane potential test showed a methane yield of 239 mL/gVS_removed, and the total volatile solids destruction following two‐phase hydrogen and methane production was 93%.     

Electromagnetic and thermal models of emission of a cooled directional for local endocavity hyperthermia

Possibilities of creation of controlled temperature fields in deep-seated biological tissue with the use of a microwave endocavity applicator with directed emission and surface cooling are explored. Mathematical models are proposed and calculated that make it possible to construct electromagnetic and thermal fields in biotissue depending on the specific thermophysical and ultrasound characteristics of the medium being irradiated, and to reveal situations and effects which should be achieved to solve problems of practical medicine in the field of local microwave hyperthermia of tissues. Kiev Polytechnical Institute, Kiev, Ukraine. Translated from Inzhenerno-Fizicheskii Zhurnal, Vol. 69, No. 5, pp. 785–789, September–October, 1996.     

Genetic programming based formulation of rotation capacity of wide flange beams

This study is a pioneer work that proposes genetic programming (GP) as a new approach for the explicit formulation of available rotation capacity of wide-flange beams which is an important phenomenon that determines the plastic behaviour of steel structures. The database for the GP formulation is based on extensive experimental results from literature. The results of the GP-based formulation are compared with numerical results obtained by a specialized computer program and existing analytical equations. The results indicate that the proposed GP formulation performs quite well compared to numerical results and existing analytical equations and is quite practical for use.     

The prefactor in the expression of the decay rate of a metastable state

We compute the prefactor for the decay rate of a metastable state in dissipative systems. The result, for small values of viscosity, is given in terms of integrals over an extremal trajectory.Supported by NSERC under Grant NA7901.