Home About us Contact
|
Home About us Contact | ||
|
|
||
Optimization Techniques (optimization + techniques)
Kinds of Optimization Techniques Selected AbstractsEffektiver Algorithmus zur Lösung von inversen Aufgabenstellungen , Anwendung in der GeomechanikBAUTECHNIK, Issue 7 2006Jörg Meier Dipl.-Ing. Durch den Einsatz von numerischen Modellen für ingenieurtechnische Problemstellungen, wie z. B. der FEM oder der FDM, können zunehmend komplexere Berechnungen in immer kürzerer Zeit bewältigt werden. Gleichzeitig ergibt sich jedoch bei dem Einsatz dieser Werkzeuge der Bedarf an Werten für die verschiedenen Modellparameter, von rein konstitutiven Kennwerten bis hin zu geometrischen Angaben, für deren Bestimmung zunehmend inverse Verfahren Anwendung finden. Bei der Nutzung dieser Methoden ist jedoch , insbesondere bei komplizierten Simulationen , mit sehr langen Berechnungszeiten zu rechnen. Gegenstand dieses Beitrags ist die Vorstellung einer Verfahrensklasse, die eine Abschätzung der Lösung solcher inverser Aufgaben auf der Basis von relativ wenigen Stützstellen ermöglicht. An die Verteilung der Stützstellen werden geringste Anforderungen gestellt, so daß diese wahlweise aus vorhergehenden Simulationen oder auch aus alternativen Quellen stammen können. Im Rahmen dieses Beitrags soll ausgehend von einer Einführung in den theoretischen Ansatz eine Strategie zur Beschleunigung der Lösung von inversen Problemstellungen und Optimierungsaufgaben an einem Beispiel aus dem Gebiet der Geomechanik vorgestellt werden. Effective algorithm for solving inverse problems , geomechanical application. When working with numerical models, it is essential to determine model parameters which are as realistic as possible. Optimization techniques are being employed more and more frequently for solving this task. However, using these methods may lead to very high time costs , in particular, if rather complicated forward calculations are involved. In this paper, we present a class of methods that allows estimating the solution of this kind of optimization problems based on relatively few sampling points. We put very weak constraints on the sampling point distribution; hence, they may be taken from previous forward calculations as well as from alternative sources. Starting from an introduction into the theoretical approach, a strategy for speeding up inverse optimization problems is introduced which is illustrated by an example geomechanics. [source] Application of the Radial Basis Neural Network to Optimization of a MicromixerCHEMICAL ENGINEERING & TECHNOLOGY (CET), Issue 7 2007A. Ansari Abstract The radial basis neural network (RBNN) method has been applied to shape optimization of a staggered herringbone groove micromixer using three-dimensional Navier-Stokes analysis. The calculation of the variance of the mass fraction at various nodes on a plane in the channel is used to quantify the mixing. Optimization techniques based on the RBNN method are used to optimize the shape of the grooves on a single wall of the channel. Three design variables, i.e., the ratio of the groove depth to channel height, the ratio of the groove width to groove pitch, and the angle of the groove, are selected for optimization. The mixing index at the end of the patterned groove is employed as the objective function. The dependence of the objective function on the design variables is analyzed. The RBNN method is successfully applied to improve the degree of mixing with modification of the groove shape. [source] Comparison of Two Evolutionary Algorithms for Optimization of Bridge Deck RepairsCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 8 2006Hatem Elbehairy These decisions, however, represent complex optimization problems that traditional optimization techniques are often unable to solve. This article introduces an integrated model for bridge deck repairs with detailed life cycle costs of network-level and project-level decisions. Two evolutionary-based optimization techniques that are capable of handling large-size problems, namely Genetic Algorithms and Shuffled Frog Leaping, are then applied on the model to optimize maintenance and repair decisions. Results of both techniques are compared on case study problems with different numbers of bridges. Based on the results, the benefits of the bridge deck management system are illustrated along with various strategies to improve optimization performance. [source] Reconciling Financial Information at Varied Levels of Aggregation,CONTEMPORARY ACCOUNTING RESEARCH, Issue 2 2004ANIL ARYA Abstract Financial statements summarize a firm's fiscal position using only a limited number of accounts. Readers often interpret financial statements in conjunction with other information, some of which may be aggregated in a different way (or not at all). This paper exploits properties of the double-entry accounting system to provide a systematic approach to reconciling diverse financial data. The key is the ability to represent the double-entry system by network flows and, thereby, access well-recognized network optimization techniques. Two specific uses are investigated: the reconciliation of audit evidence with management-prepared financial statements, and the creation of transaction-level financial ratios. [source] Particle swarm optimization of TMD by non-stationary base excitation during earthquakeEARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 9 2008A. Y. T. Leung Abstract There are many traditional methods to find the optimum parameters of a tuned mass damper (TMD) subject to stationary base excitations. It is very difficult to obtain the optimum parameters of a TMD subject to non-stationary base excitations using these traditional optimization techniques. In this paper, by applying particle swarm optimization (PSO) algorithm as a novel evolutionary algorithm, the optimum parameters including the optimum mass ratio, damper damping and tuning frequency of the TMD system attached to a viscously damped single-degree-of-freedom main system subject to non-stationary excitation can be obtained when taking either the displacement or the acceleration mean square response, as well as their combination, as the cost function. For simplicity of presentation, the non-stationary excitation is modeled by an evolutionary stationary process in the paper. By means of three numerical examples for different types of non-stationary ground acceleration models, the results indicate that PSO can be used to find the optimum mass ratio, damper damping and tuning frequency of the non-stationary TMD system, and it is quite easy to be programmed for practical engineering applications. Copyright © 2008 John Wiley & Sons, Ltd. [source] Solving resource constrained multiple project scheduling problems by random key-based genetic algorithmELECTRONICS & COMMUNICATIONS IN JAPAN, Issue 8 2009Ikutaro Okada Abstract In this paper, we propose a hybrid genetic algorithm with fuzzy logic controller (flc-rkGA) to solve the resource-constrained multiple project scheduling problem (rc-mPSP) which is well known as an NP-hard problem and the objective in this paper is to minimize total complete time in the project. It is difficult to treat the rc-mPSP problems with traditional optimization techniques. The new approach proposed is based on the hybrid genetic algorithm (flc-rkGA) with fuzzy logic controller (FLC) and random-key encoding. For these rc-mPSP problems, we demonstrate that the proposed flc-rkGA to solve the rc-mPSP problem yields better results than several heuristic genetic algorithms presented in the computation result. © 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 92(8): 25,35, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10101 [source] Medical association rule mining using genetic network programmingELECTRONICS & COMMUNICATIONS IN JAPAN, Issue 2 2008Kaoru Shimada Abstract An efficient algorithm for building a classifier is proposed based on an important association rule mining using genetic network programming (GNP). The proposed method measures the significance of the association via the chi-squared test. Users can define the conditions of important association rules for building a classifier flexibly. The definition can include not only the minimum threshold chi-squared value, but also the number of attributes in the association rules. Therefore, all the extracted important rules can be used for classification directly. GNP is one of the evolutionary optimization techniques, which uses the directed graph structure as genes. Instead of generating a large number of candidate rules, our method can obtain a sufficient number of important association rules for classification. In addition, our method suits association rule mining from dense databases such as medical datasets, where many frequently occurring items are found in each tuple. In this paper, we describe an algorithm for classification using important association rules extracted by GNP with acquisition mechanisms and present some experimental results of medical datasets. © 2008 Wiley Periodicals, Inc. Electron Comm Jpn, 91(2): 46,54, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10022 [source] Identification of optimal poultry litter biorefinery location in Alabama through minimization of feedstock transportation costENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY, Issue 4 2008Burak Aksoy Abstract The estimated amount of poultry litter produced annually in Alabama is more than 1,250,000 tons. This large amount results in significant litter management challenges. Currently, poultry producers are facing many regulatory issues and challenges with respect to environmental impacts of litter management. Commercialization and implementation of environmentally benign biorefinery technologies have the potential to generate electric power (including on-site power) and heat as well as transportation fuels, hydrogen, valuable chemicals, and fertilizer from poultry litter economically while addressing environmental problems caused by traditional disposal practices. In this study, poultry litter generated annually in northern and southern Alabama was documented on the basis of published literature, and transportation cost of poultry litter is minimized for both north and south Alabama by the selection of the best large-scale biorefining facility location and optimal feedstock allocation using mathematical optimization techniques. The available portion of the existing poultry litter feedstock for a large scale biorefinery is found to be an important factor in determining transportation cost. Transportation cost increases several fold as the local feedstock availability for biorefining reduces from 100 to 50%. Optimum facility locations for both north and south Alabama were found within a 10 mile radius for three different poultry litter feedstock availabilities. © 2008 American Institute of Chemical Engineers Environ Prog, 2008 [source] Colon segmentation and colonic polyp detection using cellular neural networks and three-dimensional template matchingEXPERT SYSTEMS, Issue 5 2009Niyazi Kilic Abstract: In this study, an automatic three-dimensional computer-aided detection system for colonic polyps was developed. Computer-aided detection for computed tomography colonography aims at facilitating the detection of colonic polyps. First, the colon regions of whole computed tomography images were carefully segmented to reduce computational burden and prevent false positive detection. In this process, the colon regions were extracted by using a cellular neural network and then the regions of interest were determined. In order to improve the segmentation performance of the study, weights in the cellular neural network were calculated by three heuristic optimization techniques, namely genetic algorithm, differential evaluation and artificial immune system. Afterwards, a three-dimensional polyp template model was constructed to detect polyps on the segmented regions of interest. At the end of the template matching process, the volumes geometrically similar to the template were emhanced. [source] Memetic evolutionary training for recurrent neural networks: an application to time-series predictionEXPERT SYSTEMS, Issue 2 2006M. Delgado Abstract: Artificial neural networks are bio-inspired mathematical models that have been widely used to solve complex problems. The training of a neural network is an important issue to deal with, since traditional gradient-based algorithms become easily trapped in local optimal solutions, therefore increasing the time taken in the experimental step. This problem is greater in recurrent neural networks, where the gradient propagation across the recurrence makes the training difficult for long-term dependences. On the other hand, evolutionary algorithms are search and optimization techniques which have been proved to solve many problems effectively. In the case of recurrent neural networks, the training using evolutionary algorithms has provided promising results. In this work, we propose two hybrid evolutionary algorithms as an alternative to improve the training of dynamic recurrent neural networks. The experimental section makes a comparative study of the algorithms proposed, to train Elman recurrent neural networks in time-series prediction problems. [source] Application and comparison of metaheuristic techniques to reactive power planning problemIEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, Issue 6 2008Mehdi Eghbal Non-Member Abstract This paper presents the application and comparison of metaheuristic techniques to reactive power planning (RPP) problem which involves optimal allocation and combination of to-be-installed VAr sources to satisfy voltage constraints during normal and contingency states for multiple load levels. The main objective of the proposed RPP problem is to minimize the investment cost through balanced installation of SCs and SVCs while keeping a specified security level and minimizing the amount of load shedding. The problem is formulated as a large scale mixed integer nonlinear programming problem, which is a nonsmooth and nondifferentiable optimization problem using conventional optimization techniques and induces lots of local minima. Among the metaheuristic techniques, genetic algorithm (GA), particle swarm optimization (PSO) and evolutionary particle swarm optimization (EPSO) are applied to solve the RPP problem. To investigate the effectiveness of the metaheuristic techniques, the proposed approaches have been successfully tested on IEEE-14 buses, as well as IEEE-57 buses test system. The results obtained are compared and the effectiveness of each technique has been illustrated. Copyright © 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source] Index tracking with constrained portfoliosINTELLIGENT SYSTEMS IN ACCOUNTING, FINANCE & MANAGEMENT, Issue 1-2 2007Dietmar Maringer Passive portfolio management strategies, such as index tracking, are popular in the industry, but so far little research has been done on the cardinality of such a portfolio, i.e. on how many different assets ought to be included in it. One reason for this is the computational complexity of the associated optimization problems. Traditional optimization techniques cannot deal appropriately with the discontinuities and the many local optima emerging from the introduction of explicit cardinality constraints. More recent approaches, such as heuristic methods, on the other hand, can overcome these hurdles. This paper demonstrates how one of these methods, differential evolution, can be used to solve the constrained index-tracking problem. We analyse the financial implication of cardinality constraints for a tracking portfolio using an empirical study of the Down Jones Industrial Average. We find that the index can be tracked satisfactorily with a subset of its components and, more important, that the deviation between computed actual tracking error and the theoretically achievable tracking error out of sample is negligibly affected by the portfolio's cardinality. Copyright © 2007 John Wiley & Sons, Ltd. [source] Detection and quantification of flaws in structures by the extended finite element method and genetic algorithmsINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 3 2010Haim Waisman Abstract This paper investigates the extended finite element method (XFEM)-GA detection algorithm proposed by Rabinovich et al. (Int. J. Numer. Meth. Engng 2007; 71(9):1051,1080; Int. J. Numer. Meth. Engng 2009; 77(3):337,359) on elastostatic problems with different types of flaws. This algorithm is designed for non-destructive assessment of structural components. Trial flaws are modeled using the XFEM as the forward problem and genetic algorithms (GAs) are employed as the optimization method to converge to the true flaw location and size. The main advantage of the approach is that XFEM alleviates the need for re-meshing the domain at every new iteration of the inverse solution process and GAs have proven to be robust and efficient optimization techniques in particular for this type of problems. In this paper the XFEM-GA methodology is applied to elastostatic problems where flaws are considered as straight cracks, circular holes and non-regular-shaped holes. Measurements are obtained from strain sensors that are attached to the surface of the structure at specific locations and provide the target solution to the GA. The results show convergence robustness and accuracy provided that a sufficient number of sensors are employed and sufficiently large flaws are considered. Copyright © 2009 John Wiley & Sons, Ltd. [source] Optimal flow control for Navier,Stokes equations: drag minimizationINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Issue 4 2007L. Dedè Abstract Optimal control and shape optimization techniques have an increasing role in Fluid Dynamics problems governed by partial differential equations (PDEs). In this paper, we consider the problem of drag minimization for a body in relative motion in a fluid by controlling the velocity through the body boundary. With this aim, we handle with an optimal control approach applied to the steady incompressible Navier,Stokes equations. We use the Lagrangian functional approach and we consider the Lagrangian multiplier method for the treatment of the Dirichlet boundary conditions, which include the control function itself. Moreover, we express the drag coefficient, which is the functional to be minimized, through the variational form of the Navier,Stokes equations. In this way, we can derive, in a straightforward manner, the adjoint and sensitivity equations associated with the optimal control problem, even in the presence of Dirichlet control functions. The problem is solved numerically by an iterative optimization procedure applied to state and adjoint PDEs which we approximate by the finite element method. Copyright © 2007 John Wiley & Sons, Ltd. [source] The state of the art of microwave CAD: EM-based optimization and modelingINTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING, Issue 5 2010Qingsha S. Cheng Abstract We briefly review the current state of the art of microwave CAD technologies. We look into the history of design optimization and CAD-oriented modeling of microwave circuits as well as electromagnetics-based optimization techniques. We emphasize certain direct approaches that utilize efficient sensitivity evaluations as well as surrogate-based optimization approaches that greatly enhance electromagnetics-based optimization performance. On the one hand, we review recent adjoint methodologies, on the other we focus on space mapping implementations, including the original, aggressive, implicit, output, tuning, and related developments. We illustrate our presentation with suitable examples and applications. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010. [source] Robust fault detection and isolation for parameter-dependent LFT systemsINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 7 2010Xuejing Cai Abstract In this paper, we consider robust fault detection and isolation (FDI) problems for faulty linear systems with linear fractional transformation (LFT) parameter dependency and propose an observer-based solution by using multiobjective optimization techniques. To simplify the design process, a general faulty LFT system will be constructed from the standard LFT description by converting actuator/system component faults into sensor faults first. Then a bank of parameter-dependent FDI filters will be designed to identify each fault. Each FDI filter will generate a residual signal to track an individual fault with minimum error and to suppress the effects of disturbances, time-varying parameters and other fault signals. The design of LFT parameter-dependent FDI filters, as a multiobjective optimization problem, will be formulated in terms of linear matrix inequalities (LMIs) and can be solved efficiently. A numerical example is used to demonstrate the proposed fault detection and isolation approach for LFT systems with different parametric structures. Copyright © 2009 John Wiley & Sons, Ltd. [source] Robust fault estimation of uncertain systems using an LMI-based approachINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 18 2008Euripedes G. Nobrega Abstract General recent techniques in fault detection and isolation (FDI) are based on H, optimization methods to address the issue of robustness in the presence of disturbances, uncertainties and modeling errors. Recently developed linear matrix inequality (LMI) optimization methods are currently used to design controllers and filters, which present several advantages over the Riccati equation-based design methods. This article presents an LMI formulation to design full-order and reduced-order robust H, FDI filters to estimate the faulty input signals in the presence of uncertainty and model errors. Several cases are examined for nominal and uncertain plants, which consider a weight function for the disturbance and a reference model for the faults. The FDI LMI synthesis conditions are obtained based on the bounded real lemma for the nominal case and on a sufficient extension for the uncertain case. The conditions for the existence of a feasible solution form a convex problem for the full-order filter, which may be solved via recently developed LMI optimization techniques. For the reduced-order FDI filter, the inequalities include a non-convex constraint, and an alternating projections method is presented to address this case. The examples presented in this paper compare the simulated results of a structural model for the nominal and uncertain cases and show that a degree of conservatism exists in the robust fault estimation; however, more reliable solutions are achieved than the nominal design. Copyright © 2008 John Wiley & Sons, Ltd. [source] Non-smooth structured control design with application to PID loop-shaping of a processINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 14 2007Pierre Apkarian Abstract Feedback controllers with specific structure arise frequently in applications because they are easily apprehended by design engineers and facilitate on-board implementations and re-tuning. This work is dedicated to H, synthesis with structured controllers. In this context, straightforward application of traditional synthesis techniques fails, which explains why only a few ad hoc methods have been developed over the years. In response, we propose a more systematic way to design H, optimal controllers with fixed structure using local optimization techniques. Our approach addresses in principle all those controller structures which can be built into mathematical programming constraints. We apply non-smooth optimization techniques to compute locally optimal solutions, and provide practical tests for descent and optimality. In the experimental part we apply our technique to H, loop-shaping proportional integral derivative (PID) controllers for MIMO systems and demonstrate its use for PID control of a chemical process. Copyright © 2007 John Wiley & Sons, Ltd. [source] A robust integrated controller/diagnosis aircraft applicationINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 12 2005Andrés Marcos Abstract In this paper, an application of the robust integrated control/diagnosis approach using ,, -optimization techniques to the nonlinear longitudinal dynamics of a Boeing 747-100/200 aircraft is presented. The integrated approach allows to address directly the trade-off between the conflicting controller and fault diagnosis objectives. The integrated design formulation (interconnection and weight selection) is defined using five LTI plants obtained through out the Up-and-Away flight envelope. Linear and nonlinear closed-loop time simulations are carried out under a realistic turbulence and noise environment. A comparison drawn with the non-integrated design of a controller and a diagnosis filter with the same objectives shows that the integrated case results in similar diagnosis characteristics but improved fault tolerant performance and ease of design. Copyright © 2005 John Wiley & Sons, Ltd. [source] Fault estimation,a standard problem approachINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 8 2002J. Stoustrup This paper presents a range of optimization based approaches to fault diagnosis. A variety of fault diagnosis problems are reformulated in the so-called standard problem set-up introduced in the literature on robust control. Once the standard problem formulations are given, the fault diagnosis problems can be solved by standard optimization techniques. The proposed methods include (1) fault diagnosis (fault estimation, (FE)) for systems with model uncertainties; FE for systems with parametric faults, and FE for a class of nonlinear systems. Copyright © 2002 John Wiley & Sons, Ltd. [source] An affordable modular mobile robotic platform with fuzzy logic control and evolutionary artificial neural networksJOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 8 2004Maurice Tedder Autonomous robotics projects encompass the rich nature of integrated systems that includes mechanical, electrical, and computational software components. The availability of smaller and cheaper hardware components has helped make possible a new dimension in operational autonomy. This paper describes a mobile robotic platform consisting of several integrated modules including a laptop computer that serves as the main control module, microcontroller-based motion control module, a vision processing module, a sensor interface module, and a navigation module. The laptop computer module contains the main software development environment with a user interface to access and control all other modules. Programming language independence is achieved by using standard input/output computer interfaces including RS-232 serial port, USB, networking, audio input and output, and parallel port devices. However, with the same hardware technology available to all, the distinguishing factor in most cases for intelligent systems becomes the software design. The software for autonomous robots must intelligently control the hardware so that it functions in unstructured, dynamic, and uncertain environments while maintaining an autonomous adaptability. This paper describes how we introduced fuzzy logic control to one robot platform in order to solve the 2003 Intelligent Ground Vehicle Competition (IGVC) Autonomous Challenge problem. This paper also describes the introduction of hybrid software design that utilizes Fuzzy Evolutionary Artificial Neural Network techniques. In this design, rather than using a control program that is directly coded, the robot's artificial neural net is first trained with a training data set using evolutionary optimization techniques to adjust weight values between neurons. The trained neural network with a weight average defuzzification method was able to make correct decisions to unseen vision patterns for the IGVC Autonomous Challenge. A comparison of the Lawrence Technological University robot designs and the design of the other competing schools shows that our platforms were the most affordable robot systems to use as tools for computer science and engineering education. © 2004 Wiley Periodicals, Inc. [source] Neural network approach to firm grip in the presence of small slipsJOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 6 2001A. M. Al-Fahed Nuseirat This paper presents a two stage method for constructing a firm grip that can tolerate small slips of the fingertips. The fingers are assumed to be of frictionless contact type. The first stage was to formulate the interaction in the gripper,object system as a linear complementarity problem (LCP). Then it was solved using a special neural network to find minimal fingers forces. The second stage was to use the obtained results in the first stage as a static mapping in training another neural network. The second neural network training included emulating the slips by random noise in the form of changes in the positions of the contact points relative to the reference coordinate system. This noisy training increased robustness against unexpected changes in fingers positions. Genetic algorithms were used in training the second neural network as global optimization techniques. The resulting neural network is a robust, reliable, and stable controller for rigid bodies that can be handled by a robot gripper. © 2001 John Wiley & Sons, Inc. [source] Sequential Quadratic Programming for Development of a New Probiotic Dairy Tofu with Glucono-,-LactoneJOURNAL OF FOOD SCIENCE, Issue 7 2004M.-J. Chen ABSTRACT: The purpose of this research was to evaluate the effects of various concentrations of glucono-,-lactone (GDL) and skim milk powder, as well as the addition of prebiotics, on the rheology and probiotic viabilities of dairy tofu. Additionally, modern optimization techniques were applied to attempt to determine the optimal processing conditions and growth rate for the selected probiotics (Lactobacillus. acidophilus, L. casei, Bifidobacteria bifidum, and B. longum). There were 2 stages in this research to accomplish the goal. The 1st stage was to derive surface models using response surface methodology (RSM); the 2nd stage performed optimization on the models using sequential quadratic programming (SQP) techniques. The results were demonstrated to be effective. The most favorable production conditions of dairy tofu were 1% GDL, 0% peptides, 3% isomaltooligosaccharides (IMO), and 18% milk, as confirmed by subsequent verification experiments. Analysis of the sensory evaluation results revealed no significant difference between the probiotic dairy tofu and the GDL analog in terms of texture and appearance (P < 0.05). The viable numbers of probiotics were well above the recommended limit of 106 CFU/g for the probiotic dairy tofu throughout the tested storage period. [source] Frequency-multiplier design using negative-image device modelsMICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 11 2010Nam-Tae Kim Abstract This article presents a novel design methodology for wireless frequency multipliers using negative-image device models applicable to nonlinear devices. Negative-image device models of nonlinear devices are generated by incorporating optimization techniques into a hypothetical negative-image multiplier model. The negative-image device-modeling methodology provides the following advantages over previously developed techniques: (1) It can predict achievable multiplier performance in the device-modeling stage and (2) it provides an accurate starting point for the synthesis of impedance-matching networks. The negative-image device-modeling method is described, and its application to the design of a field-effect transistor (FET) frequency multiplier is presented. Results of an experimental implementation of the multiplier demonstrate the effectiveness of the proposed methodology. © 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52:2544,2548, 2010; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.25521 [source] Protein crystallization for genomics: towards high-throughput optimization techniquesACTA CRYSTALLOGRAPHICA SECTION D, Issue 6-2 2002Naomi E. Chayen Protein crystallization has gained a new strategic and commercial relevance in the next phase of the genome projects, in which X-ray crystallography will play a major role. Considerable advances have been made in the automation of protein preparation and also in the X-ray analysis and bioinformatics stages once diffraction-quality crystals are available. These advances have not yet been matched by equally good methods for the crystallization process itself. In the area of crystallization, the main effort and resources are currently being invested into the automation of screening procedures to identify potential crystallization conditions. However, in spite of the ability to generate numerous trials, so far only a small percentage of the proteins produced have led to structure determinations. This is because screening in itself is not usually enough; it has to be complemented by an equally important procedure in crystal production, namely crystal optimization. In the rush towards structural genomics, optimization techniques have been somewhat neglected, mainly because it was hoped that large-scale screening alone would produce the desired results. In addition, optimization has relied on particular individual methods that are often difficult to automate and to adapt to high throughput. This article addresses a major gap in the field of structural genomics by describing practical ways of automating individual optimization methods in order to adapt them to high-throughput techniques. [source] Mass Transport in Multilayer Porous Metallic Membranes , Diagnosis, Identification and ValidationCHEMICAL ENGINEERING & TECHNOLOGY (CET), Issue 4 2009V. Edreva Abstract For a reliable description of mass transfer in membrane reactors the multilayer structure of the membrane is essential. This paper discusses methods which are sufficient to distinguish between homogeneous and composite membranes, and some others which are not. Different mass transport experiments (single gas permeation, isobaric diffusion, transient diffusion) with a porous metallic membrane consisting of two layers and the dusty gas model were used for this purpose. Simultaneous identification of mass transport parameters of both layers was achieved by modern optimization techniques on single gas permeation data. These parameters were validated by isobaric or transient diffusion measurements. [source] | ||||||||||||||||||||||||||||