International Journal of Engineering
Volume:27 Issue: 7, July 2014

This study focuses on the performance of H-shaped steel beams to CFT column rigid connections. To this end, the effects of internal and external stiffeners on hysteretic behavior of connections were studied. Comparative numerical analyses were carried out on eight different connections. To this end,finite element models were prepared using ANSYS and nonlinear cyclic analyses were carried out. Results of this study revealed that external stiffeners are key elements to increase the ductility and strength of steel beam to CFT column connections. The connections with internal stiffeners, without any external stiffeners did not exhibit observable hysteretic behavior. On the other hands T-shaped stiffened connections exhibited stable hysteresis loops. Furthermore the capacities of T stiffened connections were about 1.75 times greater than that of models with horizontal or vertical stiffeners.

It is often needed to label electroencephalogram(EEG) signals by segments of similar characteristics that are particularly meaningful to clinicians and for assessment by neurophysiologists. Within each segment, the signals are considered statistically stationary, usually with similar characteristics such as amplitude and/or frequency. In order to detect the segment boundaries of a signal, we propose an improved method using time-varying autoregressive (TVAR) model, integral, basic generalized likelihood ratio (GLR) and new particle swarm optimization (NPSO) which is a powerful intelligent optimizer. Since autoregressive (AR) model for the GLR method is valid for only stationary signals, the TVAR as a valuable and powerful tool for non-stationary signals is suggested. Moreover, to improve the performance of the basic GLR and increase the speed of that, we propose to use moving steps formore than one sample for successive windows in the basic GLR method. The purpose of using NPSO is finding two parameters used in this approach. By using synthetic and real EEG data, the proposed method is compared with the conventional ones, i.e. the GLR and wavelet GLR (WGLR). The simulation results indicate the absolute advantages of the proposed method.

In this paper, we propose a new method for kernel-based object tracking. Definition of union image blob and mapping it to a new representation which we named as potential pixels matrix are the main part of tracking algorithm. The union image blob is constructed by expanding the previous object region based on the histogram feature. The potential pixels matrix of union blob is used to obtain an algebraic equation for tracking the location of the kernel. We demonstrate that tracking accuracy is independent of the previous object region and it takes effect only from the expanded area of the union blob. To eliminate the background information, we propose a new method which is performed in two stages. At first, the effect of background of expanding part is reduced using a threshold distance. Then, the expanding part is divided into two parts. Tracking equations and a similarity criterion are used for each part to detect the background and target regions. We demonstrate thatthe background removing of proposed method has better performance than the mean shift tracking. Also, for better cancellation of background, the segmentation of object is used. We demonstrate the capability of the proposed method for several image sequences.

There are two types of digital filters including Infinite Impulse Response (IIR) and Finite Impulse Response (FIR). IIR filters attract more attention as they can decrease the filter order significantly compared to FIR filters. Owing to multi-modal error surface, simple powerful optimization techniques should be utilized in designing IIR digital filters to avoid local minimum. Imperialist competitive algorithm is an evolutionary algorithm used in solving optimization problems in recent years. It can find global optimum response in a nonlinear searching space. In this paper, performance of chaotic orthogonal imperialist competitive algorithm has been improved through some variations in it. Then, this modified algorithm has been applied in designing IIR digital filters and their performance has been compared to some evolutionary algorithms.

In this paper, a novel technique based on fuzzy method is presented for chaotic nonlinear time series prediction. Fuzzy approach with the gradient learning algorithm and methods constitutes the main components of this method. The learning process in this method is similar to the conventional gradient descent learning process, except that the input patterns and parameters are stored in memory as a look-up table after upgrade. In the testing phase according to input patterns, the nearest neighbors and the weights corresponding to the test pattern, similar patterns are extracted from memory. Eventually, by extracted weights and input pattern, prediction is performed. In order to validate the proposed method for predicting, the Mackey-Glass, Lorenz and biological Heart Rate Variability (HRV) time series is used. Finally, the results of proposed method with the conventional methods of time-series prediction are also compared. The results demonstrate the capability of proposed method in chaotic time series prediction.

The undesired operation of zone-3 distance relay may be one of the most conspicuous protective relay due to the incorrect operations of this relay that have been conducive to the severity of blackouts. Therefore, determining the accurate zone-3 setting of the distance relay is deemed to be necessary. This paper focuses on the development of a new technique for calculating zone-3 setting of distance relays. The proposed technique utilizes impedance seen by distance relays in order to compute zone-3 setting of the relays when faults are modeled on the reach of zone-2 of primary distance relays for themaximum and minimum generation outputs of the power system. The new technique is also improved to be used in an adaptive protection system. The technique and its adaptive version are applied to the IEEE 30-bus test system under different operating circumstances to reveal its robust performance. System simulation studies show that the proposed scheme is able to increase the reach of zone-3 relays without causing mis-coordination problems. Therefore, the two main requirements of the protection system, namely security (without causing coordination) and simultaneity, would be satisfied.

This paper investigates a new integrated diagnostic system for islanding detection by means of a neural network approach for distributed generation. Islanding is an important concern for grid connected distributed resources due to personnel and equipment safety. Several methods based on passive and active detection scheme have been proposed. While passive schemes have a large non detection zone (NDZ); concern has been raised on active method due to its degrading power quality effect. Reliably detecting this condition is regarded as an ongoing challenges in existing methods are not totaly satisfactory. The main emphasis of the proposed scheme is to reduce the NDZ to as close as possible and to keep the output power quality unchanged. In addition, this technique can also overcome the problem of setting the detection thresholds inherent in the existing techniques. In this study, we propose to use a radial basis neural network for islanding detection.The proposed algorithm is compared with the widely used rate of change of frequency relays (ROCOF) and was found to work effectively in situations where ROCOF fails. This approach utilizes rate of change of frequency at the target distributed generation location and was fed to the radial basis neural network for intelligent islanding detection. Hence a better reliability is provided. This approach utilizes the artificial neural network (ANN) as a machine learning technology for processing and analyzing the large data sets provided from network simulations using MATLAB software. To validate the feasibility of this approach, the method has been validated through several conditions and different loadings, switching operations, and network conditions. Simulation studies showed that the RBNN-based algorithm detects islanding situation more accurately than other islanding detection algorithms.

In this manuscript, a new method is proposed to provide a perfect tracking of the supercavitation system based on a new two-state model. The tracking of the pitch rate and angle of attack for fin and cavitator input is our aim. The pitch rate of the supercavitation with respect to fin angle is found as a non-minimum phase behavior. This effect reduces the speed of command pitch rate. Control of such non-minimum phase in a specific time interval and improving the speed response with respect to fin control reaction is still an open problem. To overcome the problem a feed-forward control is proposed to apply on the cavitator as a control in the feed-forward configuration. The idea of this paper is to provide a certain signal for the cavitator in order to improve the tracking performance in presence of uncertainty using iterative learning control. Moreover, this paper proposes a new method based on parameter-optimal iterative learning control to solve a perfect tracking problem of systems for indefinite (not sign-definite) system. This technique provides an updating control law through applying adaptive Lyapunov gain for monotonic zero convergence of tracking error in sense of 2norm. The simulation results verify performance and robustness of the proposed modification of iterative learning control in comparison with classical controller of the supercavitating vehicle.

In recent researches, vendor managed inventory (VMI) policy is rarely considered in the case of deteriorating items. This study considers the supply chain partner’s collaboration via a VMI system and provides an EOQ model for a two-level supply chain (single supplier-single retailer) with stockdependent demand and constant deterioration and backlogging rates, to examine the inventory management proceedings for VMI and non-VMI supply chains. The equations are simplified using a new approach in modeling. A comparison between the costs of two mentioned cases is done. The results of analytical examination of inventory costs with and without VMI show that VMI system in our proposed model always has the ability to reduce total costs of the supply chain and is always better than the traditional one. Some numerical examples and sensitivity analysis are also given to illustrate the application of the proposed model.

The present paper extends the idea of job shop scheduling problem with resting constraints to the train scheduling problem with the Muslim praying considerations. For this purpose, after proposing the new mathematical model, a heuristic algorithm based on the Electromagnetism-Like algorithm (EM) which is well adjusted to scheduling problems is employed to solve the large-size practical cases. The effectiveness of the proposed algorithm is then validated by comparing with optimum solution using small-size instances and simulated annealing algorithm, and Particle swarm Optimization (PSO) using medium and large-size instances. At the end, a practical case from Iranian railway network is studied and the results are reported. The results indicate that in the case of considering the Muslim praying constraint, the ratios of total tardiness of trains, and the total praying times are 14.5%, and 3.5%, respectively, while in the case of relaxing this constraint; the first ratio reduces to 12.3%. This result demonstrates that the proposed algorithm is able to schedule the praying times so that in many cases the trains with different directions meet each other during the praying times.

In this paper, an optimal iterative decision rule for minimizing total cost in designing a sampling plan for machine replacement problem is presented using the approach of dynamic programming and Bayesian inferences. Cost of replacing the machine and cost of produced defectives havebeen considered in model. Concept of control threshold policy has been applied for decision making. If the probability of producing a defective was more than a control threshold then the machine is replaced otherwise its performenace will be accepted and continues its production. A numerical example along with sensitivity analysis is performed to show the application of proposed methodology.

Inventory models in which the demand rate depended on the stock are based on the common real- life observation that greater product availability tends to stimulate more sales. In this study, we develop an inventory model to determine an optimal ordering policy for quantity dependent demand rate and time dependent holding cost items with delay in payments permitted by the supplier under inflation and time discounting. Mathematical models have been derived under two situations. These situations are: Case I: cycle time greater than or equal to permissible delay period and Case II: cycle time less than permissible delay period. Finally, numerical example is given to illustrate the proposed model.

In cross docking strategy, arrived products are immediately classified, sorted and organized with respect to their destination. Among all the problems related to this strategy, the vehicle routing problem (VRP) is very important and of special attention in modern technology. This paper addresses the particular type of VRP, called VRPCDTW, considering a time limitation for each customer/retailer. This problem is known as NP-hard problem. Two meta-heuristic algorithms based on the Tabu search (TS) algorithm and variable neighborhood search (VNS) are proposed for its solution. These algorithms are designed for real-world cases and can be generalized to the more complex models such as those which deliveries can be specified in a split form. The proposed TS algorithm also offers a candidate list strategy which has no limitation for the number of nodes and vehicles. A computational experiment is performed to verify our presented algorithms. Through computational experiments, it is indicated that the proposed TS algorithm performs better than VNS algorithm in both aspects of the total cost and computation time.

In this investigation, physical vapor deposited Ti-Cr-N coatings were coated on tool steel substrates using reactive arc evaporation. Microstructure and mechanical properties of coatings such as roughness, thickness, phase composition, hardness, Young’s modulus and coefficient of friction were studied. Phase compositions were investigated by X-ray diffraction method. Surface microstructure and morphology were studied using scanning electron microscope (SEM) and coatingcompositions were determined by energy dispersive spectroscopy (EDS). Mechanical properties were measured by nano indentation. The friction behavior of the coatings were investigated using ball-on disc tests under normal loads of 7 N. The results showed that (Ti, Cr)N and TiN coatings consisted of only one cubic phase solid solution, while CrN coatings consisted of hexagonal Cr2N and cubic CrN phases. Average grain size of the coatings was found to be in the range of 14 to 20 nm. Mechanical behavior of the (Ti, Cr)N coatings was related to the Ti content, i.e. hardness decreased with decreasing Ti content.

In this work, a comparative study of hybrid solar cells based on P3HT and TiO2 nanofibers was accomplished. Electrospinning, a low cost production method for large area nanofibrous films, was employed to fabricate the organic-inorganic hybrid solar cells based on poly (3 hexylthiophene) and TiO2 nanofibers. The performance of the hybrid solar cells was analyzed for four density levels of TiO2 nanofibers which resulted in the average power conversion efficiency of about 0.0134% under AM 1.5 simulated illuminations (100 mWcm-2). It is found that the higher densities of TiO2 lead to more interface area and generating exciton, so the power conversion efficiency will be increased till the active layer thickness is about 400 nm; but, with increasing thickness more than 400 nm the increasing density did not have positive effect.

In this paper, the impacts of the uncertainty of fuel heating value as well as the burner efficiency on performance functions of a turbofan engine are studied. The mean value and variance curves for thrust, thrust specific fuel consumption as well as propulsive, thermal and overall efficiencies are drawn and analyzed, considering the aforementioned uncertainties based on various Mach numbersat a number of flying altitudes in order to yield a more accurate prediction of values of performance functions. The results of this study can be of essential significance for an optimal and robust design of turbofan engines. This study is done employing Monte Carlo Simulation method which is a probabilistic analysis method.

This paper reports the thermal performance of a new cylindrical solar collector based on an experimental investigation with the difference that instead of the collector tube with absorbent coating, coil into a helical copper tube is placed in the center of the collector. Because of the helical shape of the tube, without any change in the heat transfer area, heat transfer increases. In this case, the centrifugal forces generated secondary flow, increases the coefficient of the convection heat transfer.Another advantage of this type of collector with respect to the flat plate collector, is its circular shape that is constantly exposed to sunlight. Inlet and outlet temperatures of the cylindrical solar collector was measured, during September 6-8, 2012 from 10 AM to 14 PM, and the thermal efficiency was between 43.5 and 58.7 percent. The results of this article can be useful for analysis of the solar energy systems.

Hydrodynamic deep drawing assisted by radial pressure is one of the main types of hydroforming process. In this paper, forming of cylindrical cups is investigated numerically and experimentally in hydrodynamic deep drawing assisted by radial pressure. The effects of significant parameters such as pressure path, punch corner radius and die profile radius are studied on thickness distribution and punch force. Results showed that increasing maximum pressure affects the thickness reduction of the cup in critical regions. Applying pressure above a certain value does not have a considerable effect on thickness of the part. On the other hand, increasing maximum pressure increases the force required for forming a cylindrical cup. Also, it was found that by increasing the punch corner radius, thickness distribution will be more uniform while maximum punch force does not change. Increasing the die profile radius has a positive effect on the thickness distribution and the forming force.