Showing papers in "International Journal of Engineering - Transactions C: Aspects in 2016"

Designing a Reliable Distribution Network with Facility Fortification and Transshipment under Partial and Complete Disruptions

Abstract: This paper designs a reliability distribution network with limited capacity under partial and complete facility disruptions. To increase the reliability of the distribution network, a new mixed integer linear programing model is developed by considering multiple mitigation strategies including diversification, fortification, and transshipment. The distribution network constitutes of reliable distribution centers are more expensive, always available and not affected by disruption, and unreliable distribution centers. Thus, they might be fortified at any level of reliability. Several numerical examples with sensitivity analyses are conducted to illustrate the usefulness of the proposed model. Results demonstrate that the transshipment strategy is more effective than the other mitigation strategies on distribution network reliability and cost.

A Hybrid Machine Learning Method for Intrusion Detection

Abstract: Data security is an important area of concern for every computer system owner. An intrusion detection system is a device or software application that monitors a network or systems for malicious activity or policy violations. Already various techniques of artificial intelligence have been used for intrusion detection. The main challenge in this area is the running speed of the available implementations. In this research work, we present a hybrid approach which is based on the “linear discernment analysis” and the “extreme learning machine” to build a tool for intrusion detection. In the proposed method, the linear discernment analysis is used to reduce the dimensions of data and the extreme learning machine neural network is used for data classification. This idea allowed us to benefit from the advantages of both methods. We implemented the proposed method on a microcomputer with core i5 1.6 GHz processor by using machine learning toolbox. In order to evaluate the performance of the proposed method, we run it on a comprehensive data set concerning intrusion detection. The data set is called KDD, which is a version of the data set DARPA presented by MIT Lincoln Labs. The experimental results were organized in related tables and charts. Analysis of the results show meaningful improvements in intrusion detection. In general, compared to the existing methods, the proposed approach works faster with higher accuracy.

Energy Resources Consumption Performance in Iranian Manufacturing Industries by Using Cost/Revenue Efficiency Model

Abstract: Industries are one of the main sources of pollution in the world. Besides, the levels of energy resources consumption including water, electricity, and fossil fuel are very different among industries. On the other hand, Iranian government pays a large amount of energy subsidy to manufacturing units. Because of it, the government wants to know which of manufacturing industries are efficient, produce less environmental pollutions, and hence, must be supported. Besides, manufacturing industries are classified into various groups. In this paper, the conventional data envelopment analysis (DEA) model has been extended to multi-group state for evaluating manufacturing systems. The main feature of the proposed model is that it takes into consideration inputs/outputs prices (cost/revenue). In the other words, we propose a linear multi-group cost/revenue efficiency model. The data of 59 Iranian manufacturing industries are grouped under 23 classes to demonstrate the model. The inputs are energy resources such as the amount of fossil fuel, water and electricity consumption as well as a non-energy resources such as the number of employees. The results show that the efficiency scores and energy consumption performance are greatly changed when each industry is evaluated in its own group.

A Self-starting Control Chart for Simultaneous Monitoring of Mean and Variance of Simple Linear Profiles

Abstract: In many processes in real practice at the start-up stages the process parameters are not known a priori and there are no initial samples or data for executing Phase I monitoring and estimating the process parameters. In addition, the practitioners are interested in using one control chart instead of two or more for monitoring location and variability of processes. In this paper, we consider a simple linear profile in which the relationship between a response variable and one explanatory characterizes the quality of a process. We proposed a self-starting Max-CUSUM control chart based on recursive residuals to monitor mean vector (including intercept and slope) and variability (variance of error term) of a simple linear profile simultaneously from the start-up stages of the process. We developed Max-CUSUM control chart to monitor simple linear profile in Phase II. Then, we compared our proposed control charts with the best one in the literature through simulation studies. The simulation results showed that our proposed control charts have better performance compared to competitive control charts under moderate and large shifts in terms of out-of-control (OC) ARLs. Finally, the application of the proposed self-starting control chart is illustrated through a real case in the leather industry.

The Study of Organic Removal Efficiency and Membrane Fouling in a Submerged Membrane Bioreactor Treating Vegetable Oil Wastewater

Abstract: The characterizations of vegetable oil wastewater (VOW) are unpleasant odor, dark color, and high organic contents, including large amounts of oil and grease (O&G), chemical oxygen demand (COD), fatty acids and lipids. Therefore, VOWs should be treated efficiently to avoid the environment pollution. The aim of present study was the investigation of VOW biological treatment using membrane bioreactor (MBR) in terms of organic pollutant removal performance and membrane fouling. During the 30 days MBR operation at hydraulic retention time and solid retention time of 48 h and 20 days, respectively, there was a consistently low turbidity (

A Nonlinear Autoregressive Model with Exogenous Variables Neural Network for Stock Market Timing: The Candlestick Technical Analysis

Abstract: In this paper, the nonlinear autoregressive model with exogenous variables as a new neural network is used for timing of the stock markets on the basis of the technical analysis of Japanese Candlestick. In this model, the “nonlinear autoregressive model with exogenous variables” is an analyzer. For a more reliable comparison, here (like the literature) two approaches of Raw-based and Signal-based are devised to generate the input data of the model. The correct predictions percentages for periods of 1- 6 days with the total number of buy and sell signals are considered. The result proves that to some extent the approaches have similar performances while apparently, they are superior to a feed-forward static neural network. The created network is evaluated by the measure of Mean of Squared Error and the proposed model accuracy is calculated to be extremely high.

Bi-objectives Approach for a Multi-period Two Echelons Perishable Product Inventory-routing Problem with Production and Lateral Transshipment

Abstract: In this study, a two echelons supply chain system in which a supplier is producing perishable product and distribute it to multiple customers is considered. By allowing lateral transshipment mechanism, it is also possible to deliver products to some customers in some periods in bulk, then customers using their own vehicle to transship goods between each other seeking further reduction in the overall cost. The aim here is minimizing the production, inventory carrying cost, and distribution as the first objective, and transshipment cost as the second objective, which is contrary objectives, without facing any shortage anywhere in the chain during the planning horizon. This problem is formulated as a bi-objectives mixed integer programming (BOMIP), and then a proper Pareto front as a set of multiple decision alternatives is provided using NSGAII and NRGA approach. Novelty of this research is providing a bi-objectives mathematical modeling of perishable product inventory routing with production and transshipment (BO-P-PIRPT) that help the decision maker to choose the best mixture of routing and transshipment.

Solving the Dynamic Job Shop Scheduling Problem using Bottleneck and Intelligent Agents based on Genetic Algorithm

Abstract: The problem of Dynamic Job Shop (DJS) scheduling is one of the most complex problems of machine scheduling. This problem is one of NP-Hard problems for solving which numerous heuristic and metaheuristic methods have so far been presented. Genetic Algorithms (GA) are one of these methods which are successfully applied to these problems. In these approaches, of course, better quality of solutions, avoiding premature convergence, and robustness of solutions is still among the challenging arguments and in the researchers’ center of attention. The investigations in real manufacturing environments indicate that many of these systems have Bottleneck Recourses (BR). According to the concepts of the Theory of Constraint (TOC), the output of manufacturing systems is limited based on BR capacity. Hence, for improving system performance, one must find and investigate the improvement of BR effects in different levels of decision-making. Moreover, the capacity of these resources should be improved to the maximum possible limit. In this study, for detecting and using BR in shop floor decisions (e.g. scheduling), a new detection method of bottleneck for DJS problems known as bottleneck detection method based on Taguchi Approach (TA-DJS) has been developed. On the other hand, the adapting of GA operators in amount and range of coverage can operate as an efficient approach in improving its operation and effectiveness. In this way, the adapting for operators prevents its premature convergence and the adapting in the range of coverage causes maximum use of the problem’s important resources and better performance of the algorithm in each step of its run. In the proposed GA (GAIA), initially the adapting in the amount of algorithm’s operators based on the solutions’ tangent rate for premature convergence is done, then the adapting in the range of coverage of operators’ algorithm, in first step, happens by operators convergence on BR (which was detected initially) and, in the next step, occurs by operators convergence on the elite solutions so that the search process focuses on more probable ranges than the whole range of solution. Comparing the problem results in the static state with the results of other available methods in the literature indicated high efficiency of the proposed method.

Novel Automated Method for Minirhizotron Image Analysis: Root Detection using Curvelet Transform

Abstract: A B S T R A C T In this article a new method is introduced for distinguishing roots and background based on their digital curvelet transform in minirhizotron images. In the proposed method, the nonlinear mapping is applied to sub-band curvelet components followed by boundary detection using energy optimization concept. The curvelet transform has the excellent capability in detecting roots with different orientations and contrasts, thanks to its better sparse representation and more directionality feature than existing approaches. Furthermore, adapting the parameters of the mapping function due to curvelet coefficients is very beneficial for magnifying weak ridges as well as better compatibility with different minirhizotron images. Performance of the proposed method is evaluated on several minirhizotron images in two different scenarios. In the first scenario, images contain several roots, while the second scenario belongs to no-root images, which increases the chance of false detections. The results show that the detection rate of the proposed method is 4 to 27 percent better than its alternatives, in presence of zero false detection. Furthermore, it is shown that better characterization of roots by proposed algorithm does not lead to extract more false objects compared to the results of the other examined algorithms.

Optimized fuzzy logic for nonlinear vibration control of aircraft semi-active shock absorber with input constraint (technical note)

Abstract: Landing impact and runway unevenness have proximate consequence on performance of landing gear system and conduce to discomfort of passengers and reduction of the pilot’s capability to control aircraft. Finally, vibrations caused by them result in structure fatigue. Fuzzy logic controller is used frequently in different applications because of simplicity in design and implementation. In the present paper, this control approach is performed by minimum error criteria procedure and bees algorithm as the optimization technique for the model of semi-active suspension system that chooses damping performance of shock absorber at touchdown to be the purpose of control on landing gear and its efficiency is evaluated with the competence of passive control. Results of numerical simulation by matlab/simulink software indicate that the force induced to body and the vertical vibration of fuselage have important improvement )60% and 50%( for fuzzy intelligent method optimized by bees algorithm compared to passive approach which lead to increase in quality of landing, easiness of passengers and structure’s fatigue life in various operation conditions.

Prediction and Optimization of Mechanical Properties of St52 in Gas Metal Arc Weld Using Response Surface Methodology and ANOVA

Abstract: Many researchers have developed algorithms to predict welding parameters. The variety of welding types is broad because the confine mixture of pressure and temperature could be selected. This paper introduces a response surface methodology (RSM) for optimization and prediction of the influence of Ar and CO2 gases and electrical current on tensile strength of St52’s gas metal arc weld (GMAW) line. After doing experiments the optimum levels of input variables for achieving high tensile strength and contribution of parameters have been obtained by RSM and ANOVA; respectively. In this study the maximum error is 0.44%. Thus it can be concluded that, RSM is one of the best methods and can be used to predict the output parameters and save the time and cost of additional experiments.

Uranium Ions Removal Using Amberlite CG-400 Anion Exchanger Resin in the Presence of Sulfate Anions

Abstract: Uptake of uranium (VI) ions using an anion exchanger resin, namely amberlite CG-400, was studied through this work in the presence of sulfate anions. Impact of various parameters including pH, contact time, initial concentration of uranium ions and intrusion with different anion contents (phosphate, chloride,sulfate, nitrate and fluoride) was examined on the sorption performance of the resin. An estimation of sorption process was also performed using isotherm and kinetics equations, which led to conformity of Freundlich and pseudo second-order models with experimental data, respectively. The maximum adsorption capacity (qmax) was found to be 63.29 mg g-1. Presence of various anions species in solution show that uranium sorption using amberlite CG-400 anion exchange resin could strongly be affected.

The Design and Realization of a Gait Rehabilitation Training Robot with Body Supporting Mechanism

Abstract: With the increasing number of people who have problems with their walking, a new type of gait rehabilitation training robot has been put forward and designed. In order to meet the requirements of the gait rehabilitation training, the whole mechanical structure and control system have been designed, and the model machine for gait rehabilitation training robot has been made. Using the human gait analysis system of the INSENCO, a large number of experiments on human bodies have been carried out, and human gait parameters have been measured and recorded. As has been shown in the experiments, the designed robot has achieved the goal of free movement and weight-reducing; the weight-reducing device is flexible in height and pull, which has accomplished the aim of rehabilitation training. doi: 10.5829/idosi.ije.2016.29.09c.18

Integration of Multi-period Vehicle Routing Problem and Economic Selection of Customers with the Objective of Optimizing Distribution, Sales and Discounts Planning

Abstract: Making decisions about the economic selection of the customers plays a significant role in the sale and logistic management of the companies. Furthermore, another issue affecting the relationship between the suppliers and the customers is the proper and timely distribution of the products as well as the optimum mixing of the distribution routes to reduce transportation costs. In this paper, the issues of the integration of routing, and the economic selection of customers has been explored aiming at minimizing the transportation, maintenance, and discount costs and maximizing the products selling profits. In addition to the amount of customer's purchasing, costumers' collection period is effective to offer them discounts. The economic customers are selected due to three factors: the discount on the price of the product, the marginal profit of the requested products, and the distance from the supplier's warehouse. Games software was used for the exact solution of the model, and Simulated annealing algorithm was used to solve the model in larger dimension. The efficiency and applicability of the proposed model was approved comparing the optimum results with a high preciseness as 98.91 percent. Moreover, in the case study of Kalleh Company, the results revealed 14% increase in pure profits.

Dechlorination of Polychlorinated Biphenyls in Real Waste Transformer Oil Using a Modified Household Microwave Oven in Presence of Sodium Hydroxide and Polyethylene Glycol

Abstract: This research was carried out to assess the dechlorination and decomposition of Polychlorinated biphenyls (PCBs) in the real waste transformer oil using polyethylene glycol 1000/NaOH through a modified household microwave oven. To do so, the influence of polyethylene glycol (PEG) (1.5-7.5 g) and NaOH (0.3-1.5 g) under microwave (MW) power of 500 W on the dechlorination efficiency of PCBs, existed in real waste transformer oil, was investigated. The results showed that by increasing the PEG 1000 (1.5- 7.5g) and NaOH (0.3-1.5g) amounts, the dechlorination efficiency of PCBs increased from 35 to 99.99% under MV power of 500 W for the reaction time of 6 min. The optimum amounts of PEG and NaOH were 5 g and 1 g, respectively. The results indicated that the PCBs dechlorination rate followed first-order kinetic (k = 0.019, R2 = 0.91), and more than 90% of total PCBs were dechlorinated at the first 90 seconds of the reaction. After 90 seconds, the dechlorination rate decreased. Accordingly, results showed that MW has extraordinary influence on PCBs decomposition from waste transformer oil, and the reaction time substantially decreased compared to conventional heating.

Nonlocal Effect on Buckling of Triangular Nano-composite Plates

Abstract: In the present study, small scale effect on critical buckling loads of triangular nano- composite plates under uniform in-plane compression is studied. Since at nano-scale the structure of the plate is discrete, the size dependent nonlocal elasticity theory is employed to develop an equivalent continuum plate model for this nanostructure incorporating the changes in its mechanical behavior. The two-parameter, Winkler-Pasternak, elastic medium is used for precisely modelling the behavior of matrix surrounding the nano-plate. The governing stability equations are then derived using the classical plate theory and the principle of virtual work for a perfect uniform triangular nano-plate composite. The well-known numerical Galerkin method in conjunction with the areal coordinates system is used as the basis for the solution. The solution procedure views the entire nano-composite plate as a single super-continuum element. Effects of nonlocal parameter, length, aspect ratio, mode number, anisotropy, edge supports and elastic medium on the buckling loads are rigorously investigated. All of these parameters are seen to have significant effect on the stability of nano-composite plate. It is shown that the results depend obviously on the non-locality of buckled nano-composite plate, especially at very small dimensions, small aspect ratios, higher mode numbers, higher anisotropy degree, stiffer edge supports and softer elastic mediums. Also, it is seen that the classical continuum mechanics overestimates the buckling results which can lead to deficient design and analysis of these widely used nanostructures.

Robust Fuzzy Content Based Regularization Technique in Super Resolution Imaging

Abstract: Super-resolution (SR) aims to overcome the ill-posed conditions of image acquisition. SR facilitates scene recognition from low-resolution image(s). Generally assumes that high and low resolution images share similar intrinsic geometries. Various approaches have tried to aggregate the informative details of multiple low-resolution images into a high-resolution one. In this paper, we present a new robust fuzzy super resolution approach. Our approach, firstly registers two input image using SIFT-BP-RANSAC registration. Secondly, due to the importance of information gain ratio in the SR outcomes, the fuzzy regularization scheme uses the prior knowledge about the low-resolution image to add the amount of lost details of the input images to the registered one using the common linear observation model. Due to this fact, our approach iteratively tries to make a prediction of the high-resolution image based on the predefined regularization rules. Afterwards the low-resolution image have made out of the new high-resolution image. Minimizing the difference between the resulted low-resolution image and the input low-resolution image will justify our regularization rules. Flexible characteristics of fuzzy regularization behave adaptively on edges, detailed segments, and flat regions of local segments within the image. General information gain ratio also should grow during the regularization. Our fuzzy regularization indicates independence from the acquisition model. Consequently, robustness of our method on different ill-posed capturing conditions and against registration error noise compensates the shortcomings of same regularization approaches in the literature. Our final results indicate reduced aliasing achievements in comparison with similar recent state of the art works.

Nonlinear Dynamics of the Rotational Slender Axially Moving String with Simply Supported Conditions

Abstract: In this research, dynamic analysis of the rotational slender axially moving string is investigated. String assumed as Euler Bernoulli beam. The axial motion of the string, gyroscopic force and mass eccentricity were considered in the study. Equations of motion are derived using Hamilton’s principle, resulting in two partial differential equations for the transverse motions. The equations are changed to non-dimensional form and discretized via Galerkin’ method. The bifurcation diagrams and Poincare' portraits are represented in the case that the mean axial speed, the speed fluctuation and the mass eccentricity are respectively varied. The dynamical behaviors are numerically identified based on the Poincare' portraits. Numerical simulations indicate that quasi-periodic motion occurs in the transverse vibrations of the string by variation of axial speed and mass eccentricity.