Single machine scheduling with unequal release date using neuro-dominance rule

A neuro-dominance rule (NDR) for single machine total weighted tardiness problem with unequal release date is presented by the author. To obtain the NDR, backpropagation artificial neural network (BPANN) has been trained using 10,000 data and also tested using 10,000 another data. Inputs of the trained BPANN are starting date of the first job (t), processing times (p_i and p_j), due dates (d_i and d_j), weights of the jobs (w_i and w_j) and r_i and r_j release dates of the jobs. Output of the BPANN is a decision of which job should precede. Training set and test set have been obtained using Adjusted Pairwise Interchange method. The proposed NDR provides a sufficient condition for local optimality. It has been proved that if any sequence violates the NDR then violating jobs are switched according to the total weighted tardiness criterion. The proposed NDR is compared to a number of competing heuristics (ATC, COVERT, EDD, SPT, LPT, WDD, WSPT, WPD, CR, FCFS) and meta heuristics (simulated annealing and genetic algorithms) for a set of randomly generated problems. The problem sizes have been taken as 50, 70, 100. NDR is applied 270,000 randomly generated problems. Computational results indicate that the NDR dominates the heuristics and meta heuristics in all runs. Therefore, the NDR can improve the upper and lower bounding schemes.     

An ERP software selection process with using artificial neural network based on analytic network process approach

An enterprise resource planning (ERP) software selection is known to be multi attribute decision making (MADM) problem. This problem has been modeled according with analytic network process (ANP) method due to fact that it considers criteria and sub criteria relations and interrelations in selecting the software.Opinions of many experts are obtained while building ANP model for the selection ERP then opinions are reduced to one single value by methods like geometric means so as to get desired results. To use ANP model for the selection of ERP for a new organization, a new group of expert’s opinions are needed. In this case the same problem will be in counter. In the proposed model, when ANP and ANN models are setup, an ERP software selection can be made easily by the opinions of one single expert. In that case calculation of geometric mean of answers that obtained from many experts will be unnecessary. Additionally the effect of subjective opinion of one single decision maker will be avoided. In terms of difficulty, ANP has some difficulties due to eigenvalue and their limit value calculation.An ANN model has been designed and trained with using ANP results in order to calculate ERP software priority. The artificial neural network (ANN) model is trained by results obtained from ANP. It seems that there is no any major difficulty in order to predict software priorities with trained ANN model. By this results ANN model has been come suitable for using in the selection of ERP for another new decision.     

A neuro-genetic-simulated annealing approach to the inverse kinematics solution of robots: a simulation based study

In this study, a hybrid intelligent solution system including neural networks, genetic algorithms and simulated annealing has been proposed for the inverse kinematics solution of robotic manipulators. The main purpose of the proposed system is to decrease the end effector error of a neural network based inverse kinematics solution. In the designed hybrid intelligent system, simulated annealing algorithm has been used as a genetic operator to decrease the process time of the genetic algorithm to find the optimum solution. Obtained best solution from the neural network has been included in the initial solution of genetic algorithm with randomly produced solutions. The end effector error has been reduced micrometer levels after the implementation of the hybrid intelligent solution system.     

Mobile multi-view object image search

High user interaction capability of mobile devices can help improve the accuracy of mobile visual search systems. At query time, it is possible to capture multiple views of an object from different viewing angles and at different scales with the mobile device camera to obtain richer information about the object compared to a single view and hence return more accurate results. Motivated by this, we propose a new multi-view visual query model on multi-view object image databases for mobile visual search. Multi-view images of objects acquired by the mobile clients are processed and local features are sent to a server, which combines the query image representations with early/late fusion methods and returns the query results. We performed a comprehensive analysis of early and late fusion approaches using various similarity functions, on an existing single view and a new multi-view object image database. The experimental results show that multi-view search provides significantly better retrieval accuracy compared to traditional single view search.     

Vitamin A status of healthy children in Manisa,Turkey

Background  

Vitamin A deficiency is a major public health nutrition problem in the developing world. Even subclinical Vitamin A deficiency is associated with increased childhood mortality. Severe maternal vitamin A deficiency may cause increased mortality in the first months of life. There have been a limited number of studies regarding vitamin A status in Turkey. The aim of this study was to assess vitamin A status of healthy children in Manisa, Turkey.     

Mechanism of boron tribromide electroreduction

This study was carried out to investigate the electrochemical behavior of boron tribromide in dimethlyformamide. The reduction of the compound was found to follow a CE mechanism. The kinetic parameters and the diffusion coefficient were calculated by the use of ultramicrodisc electrodes and chronoamperometry. The number of electrons transferred was found to be 2 by rotating disc and ultramicro disc electrodes and 3 by coulometry. These results are in good accordance with those obtained from molten boron salts. This study is important in regard to electrochemical boronizing at low temperatures.     

Comparing the Effects of Nb,Pb, Y,and La Replacement on the Structural,Electrical, and Magnetic Characteristics of Bi-Based Superconductors

In this study, the effects of Pb, Nb, La, and Y replacements were investigated on Bi-based superconducting materials. In preparing the samples, we used a method called solid-state reaction method. The patterns of the X-ray diffraction of all samples indicated presence of Bi-2212 and Bi-2223 phases. The results obtained from XRD revealed that with increase of the melting point of substation elements, the Bi-2223 phase decreased while the Bi-2212 phase and impurity phases of samples grew. From the electrical resistivity measurements using the four-probe method, it was found that sample A with Pb and sample B with La replacements had the maximum and minimum critical temperatures of 111.4 and 81.6 K, respectively. Based on hysteresis loop (M–H) measurement using Bean’s model, estimation of critical current density (J_c) showed that sample A with Pb and sample B with La substitution had the maximum and minimum values respectively. These results may be due to the melting point of these elements with values of 888, 1512, 2315, and 2425 ^°C for PbO, Nb₂O₅, La₂O₃, and Y₂O₃, respectively. These elements were replaced by Bi₂O₃ with a melting point of 817 ^° C. Further, the samples were prepared at the temperature of 845 ^°C. It seems at this temperature, these elements not only dissolve within the main matrix and participate in the formation of the Bi-2212 phase during the sintering process but they also participate in the development of the variety of the impurity phases as confirmed by XRD results.     

The Effect of Y<Subscript>2</Subscript>O<Subscript>3</Subscript> Buffer Layer on the Magnetic Properties of Melt-Processed YBCO Superconductor

In this study, two kinds of melt-processed YBCO samples (Y1050-1 and Y1050-2) grown on a buffer layer of Y₂O₃ addition were fabricated. The compacted powders were located on a crucible with Y₂O₃ powder freely poured and a buffer layer of pressed Y₂O₃ to avoid liquid to spread on the furnace plate. The superconducting transition temperature was determined by a standard four-point method at temperatures between 40 and 100 K. The transition temperatures of Y1050-1 and Y1050-2 were found to be 91.6 and 92.9 K at zero magnetic fields, respectively. The magnetization measurements were performed using a vibrating sample magnetometer (VSM) on the same system at 5, 40, and 77 K. The critical current densities for Y1050-1 and Y1050-2 samples were determined to be 6.5×10³ and 5.6×10³ A/cm² at 77 K in zero magnetic fields, respectively. The trapped magnetic field measurements of the samples were performed by using a Hall probe scanning device and a Fe–Nd–B permanent magnet at 77 K. It was found that the trapped magnetic field of the Y1050-1 sample is higher than that of the Y1050-2 sample.     

Artificial Neural Network Channel Estimation Based on Levenberg-Marquardt for OFDM Systems

The many advantages responsible for the widespread application of orthogonal frequency division multiplexing (OFDM) systems are limited by the multipath fading. In OFDM systems, channel estimation is carried out by transmitting pilot symbols generally. In this paper, we propose an artificial neural network (ANN) channel estimation technique based on levenberg-marquardt training algorithm as an alternative to pilot based channel estimation technique for OFDM systems over Rayleigh fading channels. In proposed technique, there are no pilot symbols which added to OFDM. Therefore, this technique is more bandwidth efficient compared to pilot-based channel estimation techniques. Also, this technique is making full use of the learning property of neural network. By using this feature, there is no need of any matrix computation and the proposed technique is less complex than the pilot based techniques. Simulation results show that ANN based channel estimator gives better results compared to the pilot based channel estimator for OFDM systems over Rayleigh fading channel.     

WSANFlow: An Interface Protocol Between SDN Controller and End Devices for SDN-Oriented WSAN

When dealing with a Wireless Sensor and Actuator Network (WSAN) structure, one of the challenging problems is lack of flexibility in such network operations as establishment, management, and configuration. Software-defined Networking (SDN) is a promising technology for a simpler, more flexible, and less overworked network structure. Integration of SDN as a solution into the existing WSAN structures seems to be a strong candidate of deployment solutions for next generation WSAN systems. In order to get enhanced performance results for WSAN systems, we proposed an interface protocol, referred to as WSANFlow, which is responsible for all the communications between SDN controller (SDNC) and SDN-oriented end devices. The SDNC in this approach has the network intelligence and is capable of handling all the control and management operations related to the network. Thus, advanced communication operations can be managed and efficiently optimized efficiently by the SDN controller and then, subsequently, corresponding instructions can be delivered to end devices using the proposed WSANFlow protocol. In the study, we analyzed the proposed framework performance, in terms of power consumption ratio, throughput, and end to end delay metrics. Then, we compared the results with those of a ZigBee-based counterpart for different workloads such as; light, heavy and heavier load which modelizes a video stream of mild parameters. The results show that not only has the overall performance of the existing WSAN system been enhanced, but also control and management operations have been simplified by the proposed model.