مجله فناوری اطلاعات در طراحی مهندسی
سال پنجم شماره 2 (بهار و تابستان 1391)

Task Scheduling (TS) poses a challenging problem in distributed systems from multiple perspectives such as the uncertainty in resource capacity and topology, heterogeneity of processors, computational complexity as well as theoretical performance analysis. To reach timely solutions, current approaches, whether classic approaches, which are based on list scheduling, or intelligent approaches, which are generally based on evolutionary algorithms, either impose extra constraints or ignore some aspects of the reality of this problem. Furthermore, they generally depend on numerical performance evaluation and lack the ability to reach clear theoretical conclusions. Here, we address the problem of theoretical analysis by proposing a new paradigm based on system engineering. This new modeling paradigm is promising due to its extensive theoretical developments. In its general form, TS is inherently nonlinear because of its many nonlinear constraints. In this paper, we demonstrate how TS can be mapped via nonlinear state space and, through theoretical analysis, show stability of the resulting system. Then, a suitable transformation is devised to convert this model to linear switching state space with some nonlinear constraints. It is shown that the resulting model can suitably represent uncertainty in resource capacity. We then present a systematic method to determine control vectors based on this model. Finally, the proposed method is compared against HEFT (heterogeneous earliest finish time) scheme on several random experiments and demonstrate comparative performance.

Wireless Sensor Network (WSN) is created based on a collaborative effort of a large number of nodes. One of the most important components of the sensor nodes is the power unit. It has low limited power and is usually renewable. To decreasing the consumption power of sensors and toincrease the scalability of a network, one can use unequal clustering mechanism. Toconfront the clustering overhead, in this paper we introduce Energy Efficient Multihop routing for Cluster-based wireless sensor networks using Ant Colony Optimization (EEMCA) protocol that prevents reclustering of sensor nodes and decides on the cluster heads based on the measuring of power of sensors in clusters. In EEMCA protocol, Ant Colony Optimization (ACO)method is used for creating energy efficient multihop routes from cluster heads to sink. EEMCA is compared to two protocols; ACALEACHand EEUC. Simulation results show that EEMCA protocol outperforms the other two protocols with an increase in network lifetime.

In recent years, the application of protein nanoparticles has become of more and more interest to the pharmaceutical industry. In this study,Human Serum Albumin (HSA) was used for the synthesis of nanoparticles by desolvation method for application in drug delivery system because of biodegradablity, owing to the high capacity of drug loading and nontoxiticty. Moreover, the effects of different parameters i.e. pH, HSA concentration, agitation speed, glutaraldehyde concentration, organic solvent adding rate, the ratio of organic solvent/HSA solution were examined in this research and Taguchi method with L16 orthogonal array was implemented to optimize experimental conditions. The best parameters for nanobioparticles production were obtained at pH=9, HSA concentration: 75 mg.ml-1, ethanol adding rate: 1.5 ml.min-1 and the ratio of organic solvent/HSA solution: 4. Under these conditions, the software predicted the 46.625 nm for HSA nanoparticle size, which, in experiment 53 nm, was achieved for the nanoparticles. With respect to our study, the synthesis of human serum albumin nanoparticles was carried out for the first time and to the best of our knowledge, no reference has been found in the literature about using Taguchi method for optimization of HSA nanoparticles up to now.

This paper presents an application of fuzzy control to determine the supplementary signal of static VAR compensator (SVC) for improvement of power system stability and enhancement of low frequency oscillations damping. The proposed controller for the SVC is based on fuzzy logic (FL) and is optimized by particle swarm optimization algorithm (PSO). The input signals for the fuzzy logic controller are chosen as deviation in generator speedand active power. Since the SVC is in a distance from the generator, it is assumed that the proper input signals are measured and transmitted to the SVC location using WAMS technology and PMUs. The output of SVC is changed based on these two signals. PSO optimizes scaling factors for the input signals as well as the output signal. The effectiveness and performance of the proposed controller is demonstrated by the simulation of a single machine infinite bus test system.

Inductive loop detectors are widely used on highways, streets and intersections in many countries. They are proven technologies for traffic count, but high error rates can affect their data quality. Accurate real-time knowledge of traffic volume is a critical issue in adaptive signal control systems. The aim of this research is to evaluate the data accuracy of inductive loop detectors installed in adaptive signalized intersections equipped with SCATS in Mashhad, Iran. This study also aims to understand the relationship between error ratios of loop detectors and traffic volume. Results theorize a parabolic relationship but its exact calibration of it depends upon prevailing conditions, which vary from location to location and even over time at the same locations. It is confirmed that traffic composition affects the data accuracy of loop detectors. The research reported in this paper also addresses the most common reasons for improper operation of inductive loop detectors in Mashhad’s signalized intersections; this is highly related to the driver’s behavior.

Genetic Algorithm is a powerful algorithm for solving optimization problems. Its performance is affected by appropriate selection of its parameters and operators such as size of population, type of crossover, mutation, etc. Different methods have been suggested to enhance the performance of standard Genetic Algorithm (sGA). In this paper, we propose a new method based on one dimensional Cellular Automata (1D CA) to enhance the performance of sGA. The proposed hybrid CA-GA algorithm then has been used to find the minimum of five well-known test functions (with 5 and 10 dimension). Results showed that the hybrid CA-GA algorithm has a greater accuracy compared with sGA in finding the best minimum of test function. Also, the convergence speed of hybrid CA-GA algorithm to the exact global minimum is clearly more than sGA.

In recent years, there are explosive photo sharing websites such as Flickr that users can upload and annotate images with arbitrary keywords called tags. Consequently users have been overwhelmed by the huge numbers of images that effective retrieval technology for this volume of images is needed. In tag-based image retrieval most existing methods use tags or visual characteristics and do not consider users information. In this paper we propose a system for tag-based image retrieval on Flickr. Base on extracted information from ternary relationships between users, images and tags in folksonomy and simultaneously visual features that extracted from images, the similarity between images and tags derive and use for tag-based image retrieval. A folksonomy can be viewed as a three-dimensional space of users, tags, and images; consequently this three-dimensional space can be projected onto three two-dimensional matrices user-tag, user-image and tag-image. We then extract Tag-Image similarity from tag-item matrix. To calculate similarity between images based on visual features we use cosine similarity between local features. To validate the effectiveness of our proposed approach on real-world web image datasets, we conduct extensive experiments on the image dataset NUS-WIDE. Since the NUS-WIDE dataset do not contain user information and only contain visual features and tag information, we use Flickr API to access users tag assignments. Experimental evaluations demonstrate that using simultaneously folksonomy and image visual features can improve results of tag-based image retrieval than using separated folksonomy information or image visual features.