Search results for: optimal dispatch

Authors: Feng-Sheng Wang, Wu-Hsiung Wu, Kai-Cheng Hsu

Abstract:

The optimization of biological systems, which is a branch of metabolic engineering, has generated a lot of industrial and academic interest for a long time. In the last decade, metabolic engineering approaches based on mathematical optimizations have been used extensively for the analysis and manipulation of metabolic networks. In practical optimization of metabolic reaction networks, designers have to manage the nature of uncertainty resulting from qualitative characters of metabolic reactions, e.g., the possibility of enzyme effects. A deterministic approach does not give an adequate representation for metabolic reaction networks with uncertain characters. Fuzzy optimization formulations can be applied to cope with this problem. A fuzzy multi-objective optimization problem can be introduced for finding the optimal engineering interventions on metabolic network systems considering the resilience phenomenon and cell viability constraints. The accuracy of optimization results depends heavily on the development of essential kinetic models of metabolic networks. Kinetic models can quantitatively capture the experimentally observed regulation data of metabolic systems and are often used to find the optimal manipulation of external inputs. To address the issues of optimizing the regulatory structure of metabolic networks, it is necessary to consider qualitative effects, e.g., the resilience phenomena and cell viability constraints. Combining the qualitative and quantitative descriptions for metabolic networks makes it possible to design a viable strain and accurately predict the maximum possible flux rates of desired products. Considering the resilience phenomena in metabolic networks can improve the predictions of gene intervention and maximum synthesis rates in metabolic engineering. Two case studies will present in the conference to illustrate the phenomena.

Keywords: Fuzzy multi-objective optimization problem, kinetic model, metabolic engineering.

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Authors: M. N. Zelenin, V. S. Mikhailov, R. P. Zhivotovsky

Abstract:

It is known that residual welding deformations give negative effect to processability and operational quality of welded structures, complicating their assembly and reducing strength. Therefore, selection of optimal technology, ensuring minimum welding deformations, is one of the main goals in developing a technology for manufacturing of welded structures. Through years, JSC SSTC has been developing a theory for estimation of welding deformations and practical activities for reducing and compensating such deformations during welding process. During long time a methodology was used, based on analytic dependence. This methodology allowed defining volumetric changes of metal due to welding heating and subsequent cooling. However, dependences for definition of structures deformations, arising as a result of volumetric changes of metal in the weld area, allowed performing calculations only for simple structures, such as units, flat sections and sections with small curvature. In case of complex 3D structures, estimations on the base of analytic dependences gave significant errors. To eliminate this shortage, it was suggested to use finite elements method for resolving of deformation problem. Here, one shall first calculate volumes of longitudinal and transversal shortenings of welding joints using method of analytic dependences and further, with obtained shortenings, calculate forces, which action is equivalent to the action of active welding stresses. Further, a finiteelements model of the structure is developed and equivalent forces are added to this model. Having results of calculations, an optimal sequence of assembly and welding is selected and special measures to reduce and compensate welding deformations are developed and taken.

Keywords: Finite elements method, modeling, expected welding deformations, welding, assembling.

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Authors: S. V. Kasmir Raja, P. Herbert Raj

Abstract:

The overlay approach has been widely used by many service providers for Traffic Engineering (TE) in large Internet backbones. In the overlay approach, logical connections are set up between edge nodes to form a full mesh virtual network on top of the physical topology. IP routing is then run over the virtual network. Traffic engineering objectives are achieved through carefully routing logical connections over the physical links. Although the overlay approach has been implemented in many operational networks, it has a number of well-known scaling issues. This paper proposes a new approach to achieve traffic engineering without full-mesh overlaying with the help of integrated approach and equal subset split method. Traffic engineering needs to determine the optimal routing of traffic over the existing network infrastructure by efficiently allocating resource in order to optimize traffic performance on an IP network. Even though constraint-based routing [1] of Multi-Protocol Label Switching (MPLS) is developed to address this need, since it is not widely tested or debugged, Internet Service Providers (ISPs) resort to TE methods under Open Shortest Path First (OSPF), which is the most commonly used intra-domain routing protocol. Determining OSPF link weights for optimal network performance is an NP-hard problem. As it is not possible to solve this problem, we present a subset split method to improve the efficiency and performance by minimizing the maximum link utilization in the network via a small number of link weight modifications. The results of this method are compared against results of MPLS architecture [9] and other heuristic methods.

Keywords: Constraint based routing, Link Utilization, Subsetsplit method and Traffic Engineering.

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Authors: Taekyoon Park, Seok Goo Lee, Sung Ho Kim, Ung Lee, Jong Min Lee, Chonghun Han

Abstract:

CO₂ capture and storage/sequestration (CCS) is a key technology for addressing the global warming issue. This paper proposes an integrated model for the whole chain of CCS, from a power plant to a reservoir. The integrated model is further utilized to determine optimal operating conditions and study responses to various changes in input variables.

Keywords: CCS, Caron Dioxide, Carbon Capture and Storage, Simulation, Optimization.

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Authors: M. Almutairi, S. Hadjiloucas

Abstract:

The harmonic distortion of voltage is important in relation to power quality due to the interaction between the large diffusion of non-linear and time-varying single-phase and three-phase loads with power supply systems. However, harmonic distortion levels can be reduced by improving the design of polluting loads or by applying arrangements and adding filters. The application of passive filters is an effective solution that can be used to achieve harmonic mitigation mainly because filters offer high efficiency, simplicity, and are economical. Additionally, possible different frequency response characteristics can work to achieve certain required harmonic filtering targets. With these ideas in mind, the objective of this paper is to determine what size single tuned passive filters work in distribution networks best, in order to economically limit violations caused at a given point of common coupling (PCC). This article suggests that a single tuned passive filter could be employed in typical industrial power systems. Furthermore, constrained optimization can be used to find the optimal sizing of the passive filter in order to reduce both harmonic voltage and harmonic currents in the power system to an acceptable level, and, thus, improve the load power factor. The optimization technique works to minimize voltage total harmonic distortions (VTHD) and current total harmonic distortions (ITHD), where maintaining a given power factor at a specified range is desired. According to the IEEE Standard 519, both indices are viewed as constraints for the optimal passive filter design problem. The performance of this technique will be discussed using numerical examples taken from previous publications.

Keywords: Harmonics, passive filter, power factor, power quality.

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Authors: Mohammed A. Alolfe, Abou-Bakr M. Youssef, Yasser M. Kadah

Abstract:

The proper design of RF pulses in magnetic resonance imaging (MRI) has a direct impact on the quality of acquired images, and is needed for many applications. Several techniques have been proposed to obtain the RF pulse envelope given the desired slice profile. Unfortunately, these techniques do not take into account the limitations of practical implementation such as limited amplitude resolution. Moreover, implementing constraints for special RF pulses on most techniques is not possible. In this work, we propose to develop an approach for designing optimal RF pulses under theoretically any constraints. The new technique will pose the RF pulse design problem as a combinatorial optimization problem and uses efficient techniques from this area such as genetic algorithms (GA) to solve this problem. In particular, an objective function will be proposed as the norm of the difference between the desired profile and the one obtained from solving the Bloch equations for the current RF pulse design values. The proposed approach will be verified using analytical solution based RF simulations and compared to previous methods such as Shinnar-Le Roux (SLR) method, and analysis, selected, and tested the options and parameters that control the Genetic Algorithm (GA) can significantly affect its performance to get the best improved results and compared to previous works in this field. The results show a significant improvement over conventional design techniques, select the best options and parameters for GA to get most improvement over the previous works, and suggest the practicality of using of the new technique for most important applications as slice selection for large flip angles, in the area of unconventional spatial encoding, and another clinical use.

Keywords: Selective excitation, magnetic resonance imaging, combinatorial optimization, pulse design.

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Authors: Y. Z. Wu, Z. Dong, S. K. You

Abstract:

Global approximation using metamodel for complex mathematical function or computer model over a large variable domain is often needed in sensibility analysis, computer simulation, optimal control, and global design optimization of complex, multiphysics systems. To overcome the limitations of the existing response surface (RS), surrogate or metamodel modeling methods for complex models over large variable domain, a new adaptive and regressive RS modeling method using quadratic functions and local area model improvement schemes is introduced. The method applies an iterative and Latin hypercube sampling based RS update process, divides the entire domain of design variables into multiple cells, identifies rougher cells with large modeling error, and further divides these cells along the roughest dimension direction. A small number of additional sampling points from the original, expensive model are added over the small and isolated rough cells to improve the RS model locally until the model accuracy criteria are satisfied. The method then combines local RS cells to regenerate the global RS model with satisfactory accuracy. An effective RS cells sorting algorithm is also introduced to improve the efficiency of model evaluation. Benchmark tests are presented and use of the new metamodeling method to replace complex hybrid electrical vehicle powertrain performance model in vehicle design optimization and optimal control are discussed.

Keywords: Global approximation, polynomial response surface, domain decomposition, domain combination, multiphysics modeling, hybrid powertrain optimization

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Authors: Christopher C. Ihueze, Constance C. Obiuto, Christian E. Okafor, Charles C. Okpala

Abstract:

This paper presents a methodical approach for designing and optimizing process parameters in oil blending industries. Twenty seven replicated experiments were conducted for production of A-Z crown super oil (SAE20W/50) employing L9 orthogonal array to establish process response parameters. Power law model was fitted to experimental data and the obtained model was optimized applying the central composite design (CCD) of response surface methodology (RSM). Quadratic model was found to be significant for production of A-Z crown supper oil. The study recognized and specified four new lubricant formulations that conform to ISO oil standard in the course of analyzing the batch productions of A-Z crown supper oil as: L1: KV = 21.8293Cst, BS200 = 9430.00Litres, Ad102=11024.00Litres, PVI = 2520 Litres, L2: KV = 22.513Cst, BS200 = 12430.00 Litres, Ad102 = 11024.00 Litres, PVI = 2520 Litres, L3: KV = 22.1671Cst, BS200 = 9430.00 Litres, Ad102 = 10481.00 Litres, PVI= 2520 Litres, L4: KV = 22.8605Cst, BS200 = 12430.00 Litres, Ad102 = 10481.00 Litres, PVI = 2520 Litres. The analysis of variance showed that quadratic model is significant for kinematic viscosity production while the R-sq value statistic of 0.99936 showed that the variation of kinematic viscosity is due to its relationship with the control factors. This study therefore resulted to appropriate blending proportions of lubricants base oil and additives and recommends the optimal kinematic viscosity of A-Z crown super oil (SAE20W/50) to be 22.86Cst.

Keywords: Additives, control factors, kinematic viscosity, lubricant, orthogonal array, process parameter.

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Authors: T. Holden, G. Tremberger, Jr, E. Cheung, R. Subramaniam, R. Sullivan, N. Gadura, P. Schneider, P. Marchese, A. Flamholz, T. Cheung, D. Lieberman

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A nucleotide sequence can be expressed as a numerical sequence when each nucleotide is assigned its proton number. A resulting gene numerical sequence can be investigated for its fractal dimension in terms of evolution and chemical properties for comparative studies. We have investigated such nucleotide fluctuation in the 16S rRNA gene of archaea thermophiles. The studied archaea thermophiles were archaeoglobus fulgidus, methanothermobacter thermautotrophicus, methanocaldococcus jannaschii, pyrococcus horikoshii, and thermoplasma acidophilum. The studied five archaea-euryarchaeota thermophiles have fractal dimension values ranging from 1.93 to 1.97. Computer simulation shows that random sequences would have an average of about 2 with a standard deviation about 0.015. The fractal dimension was found to correlate (negative correlation) with the thermophile-s optimal growth temperature with R2 value of 0.90 (N =5). The inclusion of two aracheae-crenarchaeota thermophiles reduces the R2 value to 0.66 (N = 7). Further inclusion of two bacterial thermophiles reduces the R2 value to 0.50 (N =9). The fractal dimension is correlated (positive) to the sequence GC content with an R2 value of 0.89 for the five archaea-euryarchaeota thermophiles (and 0.74 for the entire set of N = 9), although computer simulation shows little correlation. The highest correlation (positive) was found to be between the fractal dimension and di-nucleotide Shannon entropy. However Shannon entropy and sequence GC content were observed to correlate with optimal growth temperature having an R2 of 0.8 (negative), and 0.88 (positive), respectively, for the entire set of 9 thermophiles; thus the correlation lacks species specificity. Together with another correlation study of bacterial radiation dosage with RecA repair gene sequence fractal dimension, it is postulated that fractal dimension analysis is a sensitive tool for studying the relationship between genotype and phenotype among closely related sequences.

Keywords: Fractal dimension, archaea thermophiles, Shannon entropy, GC content

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Authors: Omolola A. Tajelawi, Hari L. Garbharran

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Revenue leakages are one of the major challenges manufacturers face in production processes, as most of the input materials that should emanate as products from the lines are lost as waste. Rather than generating income from material input which is meant to end-up as products, losses are further incurred as costs in order to manage waste generated. In addition, due to the lack of a clear view of the flow of resources on the lines from input to output stage, acquiring information on the true cost of waste generated have become a challenge. This has therefore given birth to the conceptualization and implementation of waste minimization strategies by several manufacturing industries. This paper reviews the principles and applications of three environmental management accounting tools namely Activity-based Costing (ABC), Life-Cycle Assessment (LCA) and Material Flow Cost Accounting (MFCA) in the manufacturing industry and their effectiveness in curbing revenue leakages. The paper unveils the strengths and limitations of each of the tools; beaming a searchlight on the tool that could allow for optimal resource utilization, transparency in production process as well as improved cost efficiency. Findings from this review reveal that MFCA may offer superior advantages with regards to the provision of more detailed information (both in physical and monetary terms) on the flow of material inputs throughout the production process compared to the other environmental accounting tools. This paper therefore makes a case for the adoption of MFCA as a viable technique for the identification and reduction of waste in production processes, and also for effective decision making by production managers, financial advisors and other relevant stakeholders.

Keywords: MFCA, environmental management accounting, resource efficiency, waste reduction, revenue losses.

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Authors: Mehdi Ghayoumi

Abstract:

We have applied new accelerated algorithm for linear discriminate analysis (LDA) in face recognition with support vector machine. The new algorithm has the advantage of optimal selection of the step size. The gradient descent method and new algorithm has been implemented in software and evaluated on the Yale face database B. The eigenfaces of these approaches have been used to training a KNN. Recognition rate with new algorithm is compared with gradient.

Keywords: lda, adaptive, svm, face recognition.

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Authors: Yujun Zheng, Qin Song, Haihe Shi, and Jinyun Xue

Abstract:

There are a number of different cars for transferring hundreds of close contacts of swine influenza patients to hospital, and we need to carefully assign the passengers to those cars in order to minimize the risk of influenza spreading during transportation. The paper presents an approach to straightforward obtain the optimal solution of the relaxed problems, and develops two iterative improvement algorithms to effectively tackle the general problem.

Keywords: Influenza spread, discrete optimization, stationary point, iterative improvement

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Authors: Emanuel Teixeira, Anderson Ramos, Marisa Lourenço, Fernando J. Velez, Jon M. Peha

Abstract:

This article discusses the benefit cost analysis aspects of millimetre wavebands (mmWaves) and Super High Frequency (SHF). The devaluation along the distance of the carrier-to-noise-plus-interference ratio with the coverage distance is assessed by considering two different path loss models, the two-slope urban micro Line-of-Sight (UMiLoS) for the SHF band and the modified Friis propagation model, for frequencies above 24 GHz. The equivalent supported throughput is estimated at the 5.62, 28, 38, 60 and 73 GHz frequency bands and the influence of carrier-to-noise-plus-interference ratio in the radio and network optimization process is explored. Mostly owing to the lessening caused by the behaviour of the two-slope propagation model for SHF band, the supported throughput at this band is higher than at the millimetre wavebands only for the longest cell lengths. The benefit cost analysis of these pico-cellular networks was analysed for regular cellular topologies, by considering the unlicensed spectrum. For shortest distances, we can distinguish an optimal of the revenue in percentage terms for values of the cell length, R ≈ 10 m for the millimeter wavebands and for longest distances an optimal of the revenue can be observed at R ≈ 550 m for the 5.62 GHz. It is possible to observe that, for the 5.62 GHz band, the profit is slightly inferior than for millimetre wavebands, for the shortest Rs, and starts to increase for cell lengths approximately equal to the ratio between the break-point distance and the co-channel reuse factor, achieving a maximum for values of R approximately equal to 550 m.

Keywords: 5G, millimetre wavebands, super high-frequency band, SINR, signal-to-interference-plus-noise ratio, cost benefit analysis.

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Authors: A. Ghanbari Mardasi, N. Wu, C. Wu

Abstract:

In this study, a spatial wavelet-based crack localization technique for a thick beam is presented. Wavelet scale in spatial wavelet transformation is optimized to enhance crack detection sensitivity. A windowing function is also employed to erase the edge effect of the wavelet transformation, which enables the method to detect and localize cracks near the beam/measurement boundaries. Theoretical model and vibration analysis considering the crack effect are first proposed and performed in MATLAB based on the Timoshenko beam model. Gabor wavelet family is applied to the beam vibration mode shapes derived from the theoretical beam model to magnify the crack effect so as to locate the crack. Relative wavelet coefficient is obtained for sensitivity analysis by comparing the coefficient values at different positions of the beam with the lowest value in the intact area of the beam. Afterward, the optimal wavelet scale corresponding to the highest relative wavelet coefficient at the crack position is obtained for each vibration mode, through numerical simulations. The same procedure is performed for cracks with different sizes and positions in order to find the optimal scale range for the Gabor wavelet family. Finally, Hanning window is applied to different vibration mode shapes in order to overcome the edge effect problem of wavelet transformation and its effect on the localization of crack close to the measurement boundaries. Comparison of the wavelet coefficients distribution of windowed and initial mode shapes demonstrates that window function eases the identification of the cracks close to the boundaries.

Keywords: Edge effect, scale optimization, small crack locating, spatial wavelet.

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Authors: S. Rozhkova, O. Rozhkova, A. Harlova, V. Lasukov

Abstract:

For optimal unbiased filter as mean-square and in the case of functioning anomalous noises in the observation memory channel, we have proved insensitivity of filter to inaccurate knowledge of the anomalous noise intensity matrix and its equivalence to truncated filter plotted only by non anomalous components of an observation vector.

Keywords: Mathematical expectation, filtration, anomalous noise, memory.

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Authors: Yongxin Yuan

Abstract:

The problem of updating damped gyroscopic systems using measured modal data can be mathematically formulated as following two problems. Problem I: Given Ma ∈ Rn×n, Λ = diag{λ1, ··· , λp} ∈ Cp×p, X = [x1, ··· , xp] ∈ Cn×p, where p<n and both Λ and X are closed under complex conjugation in the sense that λ2j = λ¯2j−1 ∈ C, x2j = ¯x2j−1 ∈ Cn for j = 1, ··· , l, and λk ∈ R, xk ∈ Rn for k = 2l + 1, ··· , p, find real-valued symmetric matrices D,K and a real-valued skew-symmetric matrix G (that is, GT = −G) such that MaXΛ2 + (D + G)XΛ + KX = 0. Problem II: Given real-valued symmetric matrices Da, Ka ∈ Rn×n and a real-valued skew-symmetric matrix Ga, find (D, ˆ G, ˆ Kˆ ) ∈ SE such that Dˆ −Da2+Gˆ−Ga2+Kˆ −Ka2 = min(D,G,K)∈SE (D− Da2 + G − Ga2 + K − Ka2), where SE is the solution set of Problem I and · is the Frobenius norm. This paper presents an iterative algorithm to solve Problem I and Problem II. By using the proposed iterative method, a solution of Problem I can be obtained within finite iteration steps in the absence of roundoff errors, and the minimum Frobenius norm solution of Problem I can be obtained by choosing a special kind of initial matrices. Moreover, the optimal approximation solution (D, ˆ G, ˆ Kˆ ) of Problem II can be obtained by finding the minimum Frobenius norm solution of a changed Problem I. A numerical example shows that the introduced iterative algorithm is quite efficient.

Keywords: Model updating, iterative algorithm, gyroscopic system, partially prescribed spectral data, optimal approximation.

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Authors: N.Vahdatifar, Ss.Mortazavi, R.Kianinezhad

Abstract:

In this paper, a Neural Network based predictive DTC algorithm is proposed .This approach is used as an alternative to classical approaches .An appropriate riate Feed - forward network is chosen and based on its value of derivative electromagnetic torque ; optimal stator voltage vector is determined to be applied to the induction motor (by inverter). Moreover, an appropriate torque and flux observer is proposed.

Keywords: Neural Networks, Predictive DTC

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Authors: Stamatios A. Theocharis, George A. Tsihrintzis

Abstract:

e-Government is already in its second decade. Prerequisite for further development and adaptation to new realities is the optimal management of administrative information and knowledge production by those involved, i.e. the public sector, citizens and businesses. Nowadays, the amount of information displayed or distributed on the Internet has reached enormous dimensions, resulting in serious difficulties when extracting and managing knowledge. The semantic web is expected to play an important role in solving this problem and the technologies that support it. In this article, we address some relevant issues.

Keywords: e-Government, semantic web, ontologies

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Authors: A. R. Aswatha, T. Basavaraju, N. Bhaskara Rao

Abstract:

This paper presents a new technique for the optimum placement of processors to minimize the total effective communication load under multi-processor communication dominated environment. This is achieved by placing heavily loaded processors near each other and lightly loaded ones far away from one another in the physical grid locations. The results are mathematically proved for the Algorithms are described.

Keywords: Ascending Sort Index Vector, EffectiveCommunication Load, Effective Distance Matrix, OptimalPlacement, Sorting Order.

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Authors: P. Sowa

Abstract:

The new idea of analyze of power system failure with use of artificial neural network is proposed. An analysis of the possibility of simulating phenomena accompanying system faults and restitution is described. It was indicated that the universal model for the simulation of phenomena in whole analyzed range does not exist. The main classic method of search of optimal structure and parameter identification are described shortly. The example with results of calculation is shown.

Keywords: Dynamic equivalents, Network reduction, Neural networks, Power system analysis.

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Authors: Nadia Bensiali, Erik Etien, Amar Omeiri, Gerard Champenois

Abstract:

Adaptive observers used in sensorless control of induction motors suffer from instability especally in regenerating mode. In this paper, an optimal feed back gain design is proposed, it can reduce the instability region in the torque speed plane .

Keywords: Induction motor drive, adaptive observer, regenerating mode, stabilizing design.

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Authors: Anatoli Torokhti, Stan Miklavcic

Abstract:

We study a new technique for optimal data compression subject to conditions of causality and different types of memory. The technique is based on the assumption that some information about compressed data can be obtained from a solution of the associated problem without constraints of causality and memory. This allows us to consider two separate problem related to compression and decompression subject to those constraints. Their solutions are given and the analysis of the associated errors is provided.

Keywords: stochastic signals, optimization problems in signal processing.

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Authors: T. C. Manjunath, C. Ardil

Abstract:

This paper presents an efficient method of obtaining a straight-line motion in the tool configuration space using an articulated robot between two specified points. The simulation results & the implementation results show the effectiveness of the method.

Keywords: Bounded deviation algorithm, Straight line motion, Tool configuration space, Joint space, TCV.

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Authors: Thomas Jin-Chee Liu, Yu-Shen Chen, Hsi-Yang Ho, Jyun-Ting Liu, Chih-Chun Lee

Abstract:

This paper discusses some notes on the vibration design for the piezoelectric cooling fan. After reviewing the fundamental formulas of the cantilever Euler beam, it is not easy to find the optimal design of the piezoelectric fan. The experiments also show the complicated results of the vibration behavior and air flow.

Keywords: Piezoelectric cooling fan, vibration, cantilever Euler beam, air flow.

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Authors: Robin Gremlich, Andreas Hamfelt, Héctor de Pereda, Vladislav Valkovsky

Abstract:

By introducing the concept of Oracle we propose an approach for improving the performance of genetic algorithms for large-scale asymmetric Traveling Salesman Problems. The results have shown that the proposed approach allows overcoming some traditional problems for creating efficient genetic algorithms.

Keywords: Genetic algorithms, Traveling Salesman Problem, optimal decision distribution, oracle.

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Authors: Thomas Jin-Chee Liu, Jin-Wei Liang, Wei-Long Chen, Teng-Hui Chen

Abstract:

The finite element analysis is adopted in this primary study. Using the Tsai-Wu criterion and delamination criterion, the stacking sequence [45/0₄/-45₄/90₄]_s is the final optimal design for the wheelchair frame. On the contrary, the uni-directional laminates, i.e. [90₁₃]_s, [45₁₃]_s and [-45₁₃]_s, are bad designs due to the higher failure indexes.

Keywords: Wheelchair frame, stacking sequence, failure index, finite element.

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Authors: S. Bougrine, S. Ouchraa, B. Ahiod, A. A. El Imrani

Abstract:

Ant colony optimization is an ant algorithm framework that took inspiration from foraging behavior of ant colonies. Indeed, ACO algorithms use a chemical communication, represented by pheromone trails, to build good solutions. However, ants involve different communication channels to interact. Thus, this paper introduces the acoustic communication between ants while they are foraging. This process allows fine and local exploration of search space and permits optimal solution to be improved.

Keywords: Acoustic Communication, Ant Colony Optimization, Local Search, Traveling Salesman Problem.

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Authors: Wang Haiyan

Abstract:

This paper attempts to explore the phenomenon of metaphorization in English newspaper headlines from the perspective of pragmatic investigation. With relevance theory as the guideline, this paper makes an explanation of the processing of metaphor with a pragmatic approach and points that metaphor is the stimulus adopted by journalists to achieve optimal relevance in this ostensive communication, as well as the strategy to fulfill their writing purpose.

Keywords: Metaphorization, newspaper, headlines, relevance theory.

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Authors: Minghui Wang, Luping Xu, Juntao Zhang

Abstract:

Based on the conjugate gradient (CG) algorithm, the constrained matrix equation AXB=C and the associate optimal approximation problem are considered for the symmetric arrowhead matrix solutions in the premise of consistency. The convergence results of the method are presented. At last, a numerical example is given to illustrate the efficiency of this method.

Keywords: Iterative method, symmetric arrowhead matrix, conjugate gradient algorithm.

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Authors: Donglan Zha, Jiansong Si

Abstract:

Because of its flexibility, CES attracts much interest in economic growth and programming models, and the macroeconomics or micro-macro models. This paper focuses on the development, estimating methods of CES production function considering energy as an input. We leave for future research work of relaxing the assumption of constant returns to scale, the introduction of potential input factors, and the generalization method of the optimal nested form of multi-factor production functions.

Keywords: Bias of technical change, CES production function, elasticity of substitution, energy input.

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