Influence of pulse electrodeposition parameters on microhardness,grain size and surface morphology of Ni–Co/SiO<Subscript>2</Subscript> nanocomposite coating

Ni–Co/SiO ₂ nanocomposite coatings and Ni–Co alloy coatings were prepared on steel substrate using direct and pulse electrodeposition methods. X-ray diffraction (XRD), field emission scanning electron microscope (FESEM), X-ray map and energy dispersive X-ray spectroscopy (EDX) were employed to investigate the phase structure, surface morphology, and elemental analysis of coatings, respectively. In high discharge rates, the surface morphology was rough, disordered and gross globular; on the contrary, in the low rates, it was smoother, more ordered and fine globular. Also, effect of electrodeposition parameters such as average current density, pulse frequency and duty cycle on the microhardness and grain size of nanocomposite coatings that produced through the pulse current electrodeposition method have been investigated. By amplifying both duty cycles up to 50% and average current density from 2 to 6 A dm^?2, microhardness increased, while the grain size decreased. But when duty cycle mounted on more than 50% and the average current density went up to 8 A dm^?2, microhardness lessened, while the grain size rose. The optimum value for pulse frequency was about 25 Hz. Results showed that microhardness of nanocomposite coatings which were produced by pulse current method was higher than that of produced by direct current method.     

Combining stochastic programming and optimal control to decompose multistage stochastic optimization problems

The paper suggests a possible cooperation between stochastic programming and optimal control for the solution of multistage stochastic optimization problems. We propose a decomposition approach for a class of multistage stochastic programming problems in arborescent form (i.e. formulated with implicit non-anticipativity constraints on a scenario tree). The objective function of the problem can be either linear or nonlinear, while we require that the constraints are linear and involve only variables from two adjacent periods (current and lag 1). The approach is built on the following steps. First, reformulate the stochastic programming problem into an optimal control one. Second, apply a discrete version of Pontryagin maximum principle to obtain optimality conditions. Third, discuss and rearrange these conditions to obtain a decomposition that acts both at a time stage level and at a nodal level. To obtain the solution of the original problem we aggregate the solutions of subproblems through an enhanced mean valued fixed point iterative scheme.     

A non-autonomous optimal control model of renewable energy production under the aspect of fluctuating supply and learning by doing

Given the constantly raising world-wide energy demand and the accompanying increase in greenhouse gas emissions that pushes the progression of climate change, the possibly most important task in future is to find a carbon-low energy supply that finds the right balance between sustainability and energy security. For renewable energy generation, however, especially the second aspect turns out to be difficult as the supply of renewable sources underlies strong volatility. Further on, investment costs for new technologies are so high that competitiveness with conventional energy forms is hard to achieve. To address this issue, we analyze in this paper a non-autonomous optimal control model considering the optimal composition of a portfolio that consists of fossil and renewable energy and which is used to cover the energy demand of a small country. While fossil energy is assumed to be constantly available, the supply of the renewable resource fluctuates seasonally. We further on include learning effects for the renewable energy technology, which will underline the importance of considering the whole life span of such a technology for long-term energy planning decisions.     

A single phase photovoltaic inverter control for grid connected system

This paper presents a control scheme for single phase grid connected photovoltaic (PV) system operating under both grid connected and isolated grid mode. The control techniques include voltage and current control of grid-tie PV inverter. During grid connected mode, grid controls the amplitude and frequency of the PV inverter output voltage, and the inverter operates in a current controlled mode. The current controller for grid connected mode fulfills two requirements – namely, (i) during light load condition the excess energy generated from the PV inverter is fed to the grid and (ii) during an overload condition or in case of unfavorable atmospheric conditions the load demand is met by both PV inverter and the grid. In order to synchronize the PV inverter with the grid a dual transport delay based phase locked loop (PLL) is used. On the other hand, during isolated grid operation the PV inverter operates in voltage-controlled mode to maintain a constant amplitude and frequency of the voltage across the load. For the optimum use of the PV module, a modified P&O based maximum power point tracking (MPPT) controller is used which enables the maximum power extraction under varying irradiation and temperature conditions. The validity of the proposed system is verified through simulation as well as hardware implementation.     

Block truncation coding with color clumps: A novel feature extraction technique for content based image classification

The paper has explored principle of block truncation coding (BTC) as a means to perform feature extraction for content based image classification. A variation of block truncation coding, named BTC with color clumps has been implemented in this work to generate feature vectors. Classification performance with the proposed technique of feature extraction has been compared to existing techniques. Two widely used public dataset named Wang dataset and Caltech dataset have been used for analyses and comparisons of classification performances based on four different metrics. The study has established BTC with color clumps as an effective alternative for feature extraction compared to existing methods. The experiments were carried out in RGB color space. Two different categories of classifiers viz. K Nearest Neighbor (KNN) Classifier and RIDOR Classifier were used to measure the classification performances. A paired t test was conducted to establish the statistical significance of the findings. Evaluation of classifier algorithms were done in receiver operating characteristic (ROC) space.     

Automated test data generation for branch testing using incremental genetic algorithm

Cost of software testing can be reduced by automated test data generation to find a minimal set of data that has maximum coverage. Search-based software testing (SBST) is one of the techniques recently used for automated testing task. SBST makes use of control flow graph (CFG) and meta-heuristic search algorithms to accomplish the process. This paper focuses on test data generation for branch coverage. A major drawback in using meta-heuristic techniques is that the CFG paths have to be traversed from the starting node to end node for each automated test data. This kind of traversal could be improved by branch ordering, together with elitism. But still the population size and the number of iterations are maintained as the same to keep all the branches alive. In this paper, we present an incremental genetic algorithm (IGA) for branch coverage testing. Initially, a classical genetic algorithm (GA) is used to construct the population with the best parents for each branch node, and the IGA is started with these parents as the initial population. Hence, it is not necessary to maintain a huge population size and large number of iterations to cover all the branches. The performance is analyzed with five benchmark programs studied from the literature. The experimental results indicate that the proposed IGA search technique outperforms the other meta-heuristic search techniques in terms of memory usage and scalability.     

Object Recognition in X-ray Testing Using Adaptive Sparse Representations

In recent years, X-ray screening systems have been used to safeguard environments in which access control is of paramount importance. Security checkpoints have been placed at the entrances to many public places to detect prohibited items such as handguns and explosives. Human operators complete these tasks because automated recognition in baggage inspection is far from perfect. Research and development on X-ray testing is, however, ongoing into new approaches that can be used to aid human operators. This paper attempts to make a contribution to the field of object recognition by proposing a new approach called Adaptive Sparse Representation (XASR+). It consists of two stages: learning and testing. In the learning stage, for each object of training dataset, several patches are extracted from its X-ray images in order to construct representative dictionaries. A stop-list is used to remove very common words of the dictionaries. In the testing stage, test patches of the test image are extracted, and for each test patch a dictionary is built concatenating the ‘best’ representative dictionary of each object. Using this adapted dictionary, each test patch is classified following the Sparse Representation Classification methodology. Finally, the test image is classified by patch voting. Thus, our approach is able to deal with less constrained conditions including some contrast variability, pose, intra-class variability, size of the image and focal distance. We tested the effectiveness of our method for the detection of four different objects. In our experiments, the recognition rate was more than 97 % in each class, and more than 94 % if the object is occluded less than 15 %. Results show that XASR+ deals well with unconstrained conditions, outperforming various representative methods in the literature.     

Noncontact Sonic NDE and Defect Imaging Via Local Defect Resonance

A selective acoustic activation of defects based on the concept of local defect resonance enables to enhance considerably the intensity of defect vibrations and makes it possible to reduce the input acoustic powers to the levels permissible for noncontact nondestructive inspection. Since for cm-size defects in composite materials, the LDR frequencies lie in the low kHz-range, the resonant noncontact activation shifts to an audible frequency range and can be provided by conventional sonic equipment. In this paper, the feasibility of the resonant noncontact inspection is validated for the most “problematic” methodologies of nonlinear, thermosonic and shearosonic NDE that usually require an elevated acoustic power and, therefore, a reliable contact between the specimen and the transducer. In contrast, the noncontact versions developed employ commercial loudspeakers which can simultaneously insonify large areas and be applied for a contactless sonic inspection of different materials and various scale components.     

Manifold Learning Using Linear Local Tangent Space Alignment (LLTSA) Algorithm for Noise Removal in Wavelet Filtered Vibration Signal

A denoising procedure is proposed to remove both out-band and in-band noise for extraction of weak bursts in signal obtained from defective bearing. Energy of continuous wavelet scalogram is computed and the band having higher energy is selected to remove the out-band noise. Signals of selected band are brought together to form a high-dimensional waveform feature space. Further, low dimensional waveform manifold is formed using linear local tangent space alignment (LLTSA) algorithm to remove in-band noise. A criterion, entitled as frequency factor is also proposed to determine the optimum neighbour size of LLTSA. The two complicated conditions are chosen to demonstrate the effectiveness of the technique in the extraction of bursts in the noisy situations. A significant improvement in the signal to noise ratio is observed when in-band noise is removed using manifold learning by LLTSA algorithm. The experimental result reveals the success of the proposed denoising procedure in extraction of defect features, even in the case of noisy condition.     

Efficiency of surface modified Ti coated with copper nanoparticles to control marine bacterial adhesion under laboratory simulated conditions

Titanium (Ti) used as condenser material in nuclear power plants encounter severe biofouling in marine environment which in turn affects the efficiency of the metal. To reduce the biofouling by marine microorganisms, surface modification of the Ti was carried out by anodization process to obtain nanotubes (TiO₂-NTs). The electrolyte solution containing 1% of ammonium fluoride resulted in uniform growth of TiO₂-NTs. TiO₂-NTs were further coated with chemically synthesized copper nanoparticles (NT-CuNP) using 3-amino propyl triethoxy silane as a coupling agent. NT-CuNP was characterized by field-emission scanning electron microscopy (FE-SEM), energy-dispersive spectroscopy and X-ray diffraction. The stability of the coating was determined by the amount of Cu⁺ ions released into the surrounding using AAS. The microbial adhesion on the surface of Ti, TiO₂-NTs and NT-CuNP coupons were evaluated by sea water exposure studies using total viable count method and also characterized by FE-SEM for any morphological changes. The NT-CuNP coupons show a 60% reduction in microbial adhesion when compared to control Ti coupons.