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41.
Network-on-chip-based multiprocessor systems-on-chip are considered as future embedded systems platforms. One of the steps in mapping an application onto such a parallel platform involves scheduling the communication on the network-on-chip. This paper presents different scheduling strategies that minimize resource usage by exploiting all scheduling freedom offered by networks-on-chip. It also introduces a technique to take the dynamism in applications into account when scheduling the communication of an application on the network-on-chip while minimizing the resource usage. Our experiments show that resource-utilization is improved when compared to existing techniques. 相似文献
42.
Alireza Rahimi-Vahed Ali Hossein Mirzaei 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2008,12(5):435-452
Flow shop problems as a typical manufacturing challenge have gained wide attention in academic fields. In this paper, we consider
a bi-criteria permutation flow shop scheduling problem, where the weighted mean completion time and the weighted mean tardiness
are to be minimized simultaneously. Due to the complexity of the problem, it is very difficult to obtain optimum solution
for this kind of problems by means of traditional approaches. Therefore, a new multi-objective shuffled frog-leaping algorithm
(MOSFLA) is introduced for the first time to search locally Pareto-optimal frontier for the given problem. To prove the efficiency
of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison
metrics, is compared with three distinguished multi-objective genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The
computational results show that the proposed MOSFLA performs better than the above genetic algorithms, especially for the
large-sized problems. 相似文献
43.
Seyed Ali Modarres Najafabadi József Kövecses Jorge Angeles 《Multibody System Dynamics》2008,20(2):163-176
This paper outlines a novel approach to the modeling and analysis of impact involving multibody systems. This approach is
based on an analysis of energy absorption and restitution during impact, using a decomposition of the kinetic energy, which
decouples the parts associated with the spaces of admissible and constrained motions of the underlying unilateral constraints.
Such a decomposition turns out to be useful in the analysis of energy dissipation during impact, and leads to a generalized
definition of the energetic coefficient of restitution, which targets particularly collisions in multibody systems. The applicability
of the approach reported is investigated by conducting an experimental study on a robotic testbed. It is shown that impact
between multibody systems is considerably affected not only by the local dynamics characteristics of the interacting bodies,
but also the configuration of the whole multibody system. The results reported here show that our decomposition can offer
a sound characterization of impact in several problems of multibody systems. 相似文献
44.
Alasl MK Khosravi M Hosseini M Pazuki GR Nezakati Esmail Zadeh R 《Water science and technology》2012,66(9):1962-1967
Physico-chemical water quality parameters and nutrient levels such as water temperature, turbidity, saturated oxygen, dissolved oxygen, pH, chlorophyll-a, salinity, conductivity, total nitrogen and total phosphorus, were measured from April to September 2011 in the Karaj dam area, Iran. Total nitrogen in water was modelled using an artificial neural network system. In the proposed system, water temperature, depth, saturated oxygen, dissolved oxygen, pH, chlorophyll-a, salinity, turbidity and conductivity were considered as input data, and the total nitrogen in water was considered as output. The weights and biases for various systems were obtained by the quick propagation, batch back propagation, incremental back propagation, genetic and Levenberg-Marquardt algorithms. The proposed system uses 144 experimental data points; 70% of the experimental data are randomly selected for training the network and 30% of the data are used for testing. The best network topology was obtained as (9-5-1) using the quick propagation method with tangent transform function. The average absolute deviation percentages (AAD%) are 2.329 and 2.301 for training and testing processes, respectively. It is emphasized that the results of the artificial neural network (ANN) model are compatible with the experimental data. 相似文献
45.
Ata Allah Taleizadeh Seyed Taghi Akhavan Niaki Seyed Mohammad Haji Seyedjavadi 《Journal of Manufacturing Systems》2012
In this paper, a multiproduct inventory control problem is considered in which the periods between two replenishments of the products are assumed independent random variables, and increasing and decreasing functions are assumed to model the dynamic demands of each product. Furthermore, the quantities of the orders are assumed integer-type, space and budget are constraints, the service-level is a chance-constraint, and that the partial back-ordering policy is taken into account for the shortages. The costs of the problem are holding, purchasing, and shortage. We show the model of this problem is an integer nonlinear programming type and to solve it, a harmony search approach is used. At the end, three numerical examples of different sizes are given to demonstrate the applicability of the proposed methodology in real world inventory control problems, to validate the results obtained, and to compare its performances with the ones of both a genetic and a particle swarm optimization algorithms. 相似文献
46.
Machine loading problem in a flexible manufacturing system (FMS) encompasses various types of flexibility aspects pertaining to part selection and operation assignments. The evolution of flexible manufacturing systems offers great potential for increasing flexibility by ensuring both cost-effectiveness and customized manufacturing at the same time. This paper proposes a linear mathematical programming model with both continuous and zero-one variables for job selection and operation allocation problems in an FMS to maximize profitability and utilization of system. The proposed model assigns operations to different machines considering capacity of machines, batch-sizes, processing time of operations, machine costs, tool requirements, and capacity of tool magazine. A genetic algorithm (GA) is then proposed to solve the formulated problem. Performance of the proposed GA is evaluated based on some benchmark problems adopted from the literature. A statistical test is conducted which implies that the proposed algorithm is robust in finding near-optimal solutions. Comparison of the results with those published in the literature indicates supremacy of the solutions obtained by the proposed algorithm for attempted model. 相似文献
47.
Seyed Jafar Sadjadi Seyed Ahmad Yazdian Kamran Shahanaghi 《Computers & Industrial Engineering》2012,62(1):349-358
In the classical economic production quantity (EPQ) problem demand is considered to be known in advance. However, in the real-world, demand of a product is a function of factors such as product’s price, its quality, and marketing expenditures for promoting the product. Quality level of the product and specifications of the adopted manufacturing process also affect the unit product’s cost. Therefore, in this paper we consider a profit maximizing firm who wants to jointly determine the optimal lot-sizing, pricing, and marketing decisions along with manufacturing requirements in terms of flexibility and reliability of the process. Geometric programming (GP) technique is proposed to address the resulting nonlinear optimization problem. Using recent advances in optimization techniques we are able to optimally solve the developed, highly nonlinear, mathematical model. Finally, using numerical examples, we illustrate the solution approach and analyze the solution under different conditions. 相似文献
48.
Moussa Sofiane Karoui Yannick Deville Shahram Hosseini Abdelaziz Ouamri 《Pattern recognition》2012,45(12):4263-4278
Remote sensing has become an unavoidable tool for better managing our environment, generally by realizing maps of land cover using classification techniques. Traditional classification techniques assign only one class (e.g., water, soil, grass) to each pixel of remote sensing images. However, the area covered by one pixel contains more than one surface component and results in the mixture of these surface components. In such situations, classical classification is not acceptable for many major applications, such as environmental monitoring, agriculture, mineral exploration and mining, etc. Most methods proposed for treating this problem have been developed for hyperspectral images. On the contrary, there are very few automatic techniques suited to multispectral images. In this paper, we propose new unsupervised spatial methods (called 2D-Corr-NLS and 2D-Corr-NMF) in order to unmix each pixel of a multispectral image for better recognizing the surface components constituting the observed scene. These methods are related to the blind source separation (BSS) problem, and are based on sparse component analysis (SCA), clustering and non-negativity constraints. Our approach consists in first identifying the mixing matrix involved in this BSS problem, by using the first stage of a spatial correlation-based SCA method with very limited source sparsity constraints, combined with clustering. Non-negative least squares (NLS) or non-negative matrix factorization (NMF) methods are then used to extract spatial sources. An important advantage of our proposed methods is their applicability to the possibly globally underdetermined, but locally (over)determined BSS model in multispectral remote sensing images. Experiments based on realistic synthetic mixtures and real multispectral images collected by the Landsat ETM+ and the Formosat-2 sensors are performed to evaluate the performance of the proposed approach. We also show that our methods significantly outperform the sequential maximum angle convex cone (SMACC) method. 相似文献
49.