Optimization of neural classifiers based on bayesian decision boundaries and idle neurons pruning

In this article we describe a feature extraction algorithm for pattern classification based on Bayesian Decision Boundaries and Pruning techniques. The proposed method is capable of optimizing MLP neural classifiers by retaining those neurons in the hidden layer that realy contribute to correct classification. Also in this article we proposed a method which defines a plausible number of neurons in the hidden layer based on the stem-and-leaf graphics of training samples. Experimental investigation reveals the efficiency of the proposed method. © 2002 IEEE.

A new implementation of Population Based Incremental Learning method for optimization studies in electromagnetics

To enhance the global search ability of Population Based Incremental Learning (PBIL) methods, It Is proposed that multiple probability vectors are to be Included on available PBIL algorithms. As a result, the strategy for updating those probability vectors and the negative learning and mutation operators are redefined as reported. Numerical examples are reported to demonstrate the pros and cons of the newly Implemented algorithm. ©2006 IEEE.

Self-optimization of dense wireless sensor networks based on simulated annealing

Wireless sensor network (WSN) Is a technology that can be used to monitor and actuate on environments in a non-intrusive way. The main difference from WSN and traditional sensor networks is the low dependability of WSN nodes. In this way, WSN solutions are based on a huge number of cheap tiny nodes that can present faults in hardware, software and wireless communication. The deployment of hundreds of nodes can overcome the low dependability of individual nodes, however this strategy introduces a lot of challenges regarding network management, real-time requirements and self-optimization. In this paper we present a simulated annealing approach that self-optimize large scale WSN. Simulation results indicate that our approach can achieve self-optimization characteristics in a dynamic WSN. © 2012 IEEE.

Crop optimization and pre-steps standardization to get a Bipolaris euphorbiae-based bioherbicide

Making bioproducts available to the market requires finding appropriate processes for mass production and formulation of biological agents. This study aimed at evaluating the Bipolaris euphorbiae production in a solid medium (fermentation in solid substrate) and in a biphasic system (growth in a liquid medium followed by growth in a solid medium), as well as determining the processes for collecting and drying conidia, under laboratory conditions. The influence of the incubation period and inoculum quantity were also investigated. The conidia were dried by using an oven (30ºC, 35ºC, 40ºC, 45ºC, 50ºC, 55ºC and 60ºC), and laminar flow, continuous air flow and aseptic chamber at room temperature. Dry conidia were obtained by sieving and grinding in a ball mill, hammer mill or grain grinder. The conidia viability and sporulation efficiency were evaluated in the solid medium and in the biphasic system. For growth period, the best sporulation on solid medium was obtained after 10 days of incubation, reaching 8.3 x 10(7) conidia g-1 of substrate. The biphasic system did not increase the B. euphorbiae sporulation (4.5 x 10(7) conidia g-1 of substrate), after 14 days, and the amount of liquid inoculum used in this system was not an important factor for increasing its production. The continuous air flow and laminar flow preserved the conidial viability (94.6% and 99.1%, respectively), while promoting a great moisture loss (62.6% and 54.0%, respectively). All the grinding processes reduced the conidia germination (86.2%, 10.5% and 12%, respectively), while sieving allowed the collecting of powdered conidia with high viability (94.8%).

Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments

Optical flow methods are accurate algorithms for estimating the displacement and velocity fields of objects in a wide variety of applications, being their performance dependent on the configuration of a set of parameters. Since there is a lack of research that aims to automatically tune such parameters, in this work we have proposed an evolutionary-based framework for such task, thus introducing three techniques for such purpose: Particle Swarm Optimization, Harmony Search and Social-Spider Optimization. The proposed framework has been compared against with the well-known Large Displacement Optical Flow approach, obtaining the best results in three out eight image sequences provided by a public dataset. Additionally, the proposed framework can be used with any other optimization technique.

Optimization of Amplifier Placements in Switch-Based Optical Networks

Wavelength division multiplexing (WDM) offers a solution to the problem of exploiting the large bandwidth on optical links; it is the current favorite multiplexing technology for optical communication networks. Due to the high cost of an optical amplifier, it is desirable to strategically place the amplifiers throughout the network in a way that guarantees that all the signals are adequately amplified while minimizing the total number amplifiers being used. Previous studies all consider a star-based network. This paper demonstrates an original approach for solving the problem in switch-based WDM optical network assuming the traffic matrix is always the permutation of the nodes. First we formulate the problem by choosing typical permutations which can maximize traffic load on individual links; then a GA (Genetic Algorithm) is used to search for feasible amplifier placements. Finally, by setting up all the lightpaths without violating the power constaints we confirm the feasibility of the solution.

Designing piezoresistive plate-based sensors with distribution of piezoresistive material using topology optimization

Piezoresistive sensors are commonly made of a piezoresistive membrane attached to a flexible substrate, a plate. They have been widely studied and used in several applications. It has been found that the size, position and geometry of the piezoresistive membrane may affect the performance of the sensors. Based on this remark, in this work, a topology optimization methodology for the design of piezoresistive plate-based sensors, for which both the piezoresistive membrane and the flexible substrate disposition can be optimized, is evaluated. Perfect coupling conditions between the substrate and the membrane based on the `layerwise' theory for laminated plates, and a material model for the piezoresistive membrane based on the solid isotropic material with penalization model, are employed. The design goal is to obtain the configuration of material that maximizes the sensor sensitivity to external loading, as well as the stiffness of the sensor to particular loads, which depend on the case (application) studied. The proposed approach is evaluated by studying two distinct examples: the optimization of an atomic force microscope probe and a pressure sensor. The results suggest that the performance of the sensors can be improved by using the proposed approach.

A comparison of deterministic, reliability-based and risk-based structural optimization under uncertainty

In this paper, the effects of uncertainty and expected costs of failure on optimum structural design are investigated, by comparing three distinct formulations of structural optimization problems. Deterministic Design Optimization (DDO) allows one the find the shape or configuration of a structure that is optimum in terms of mechanics, but the formulation grossly neglects parameter uncertainty and its effects on structural safety. Reliability-based Design Optimization (RBDO) has emerged as an alternative to properly model the safety-under-uncertainty part of the problem. With RBDO, one can ensure that a minimum (and measurable) level of safety is achieved by the optimum structure. However, results are dependent on the failure probabilities used as constraints in the analysis. Risk optimization (RO) increases the scope of the problem by addressing the compromising goals of economy and safety. This is accomplished by quantifying the monetary consequences of failure, as well as the costs associated with construction, operation and maintenance. RO yields the optimum topology and the optimum point of balance between economy and safety. Results are compared for some example problems. The broader RO solution is found first, and optimum results are used as constraints in DDO and RBDO. Results show that even when optimum safety coefficients are used as constraints in DDO, the formulation leads to configurations which respect these design constraints, reduce manufacturing costs but increase total expected costs (including expected costs of failure). When (optimum) system failure probability is used as a constraint in RBDO, this solution also reduces manufacturing costs but by increasing total expected costs. This happens when the costs associated with different failure modes are distinct. Hence, a general equivalence between the formulations cannot be established. Optimum structural design considering expected costs of failure cannot be controlled solely by safety factors nor by failure probability constraints, but will depend on actual structural configuration. (c) 2011 Elsevier Ltd. All rights reserved.

Non-Dominated Sorting Genetic Algorithm Based on Reinforcement Learning to Optimization of Broad-Band Reflector Antennas Satellite

This paper aims to provide an improved NSGA-II (Non-Dominated Sorting Genetic Algorithm-version II) which incorporates a parameter-free self-tuning approach by reinforcement learning technique, called Non-Dominated Sorting Genetic Algorithm Based on Reinforcement Learning (NSGA-RL). The proposed method is particularly compared with the classical NSGA-II when applied to a satellite coverage problem. Furthermore, not only the optimization results are compared with results obtained by other multiobjective optimization methods, but also guarantee the advantage of no time-spending and complex parameter tuning.

Development and optimization of biodegradable films based on achira flour

The influence of glycerol concentration (C-g), process temperature (T-p), drying temperature (T-s), and relative humidity (RH) on the properties of achira flour films was initially assessed. The optimized process conditions were C-g of 17g glycerol/100g flour, T-p of 90 degrees C, T-s of 44.8 degrees C, and RH of 36.4%. The films produced under these conditions displayed high mechanical strength (7.0 MPa), low solubility (38.3%). and satisfactory elongation values (14.6%). This study showed that achira flour is a promising source for the development of biodegradable films with good mechanical properties, low water vapor permeability, and solubility compared to films based on other tubers. (c) 2011 Elsevier Ltd. All rights reserved.

Reliability-based design optimization strategies based on FORM: a review

In deterministic optimization, the uncertainties of the structural system (i.e. dimension, model, material, loads, etc) are not explicitly taken into account. Hence, resulting optimal solutions may lead to reduced reliability levels. The objective of reliability based design optimization (RBDO) is to optimize structures guaranteeing that a minimum level of reliability, chosen a priori by the designer, is maintained. Since reliability analysis using the First Order Reliability Method (FORM) is an optimization procedure itself, RBDO (in its classical version) is a double-loop strategy: the reliability analysis (inner loop) and the structural optimization (outer loop). The coupling of these two loops leads to very high computational costs. To reduce the computational burden of RBDO based on FORM, several authors propose decoupling the structural optimization and the reliability analysis. These procedures may be divided in two groups: (i) serial single loop methods and (ii) unilevel methods. The basic idea of serial single loop methods is to decouple the two loops and solve them sequentially, until some convergence criterion is achieved. On the other hand, uni-level methods employ different strategies to obtain a single loop of optimization to solve the RBDO problem. This paper presents a review of such RBDO strategies. A comparison of the performance (computational cost) of the main strategies is presented for several variants of two benchmark problems from the literature and for a structure modeled using the finite element method.

T-spline parameterization of 2D geometries based on the meccano method with a new T-mesh optimization algorithm

[EN]We present a new method, based on the idea of the meccano method and a novel T-mesh optimization procedure, to construct a T-spline parameterization of 2D geometries for the application of isogeometric analysis. The proposed method only demands a boundary representation of the geometry as input data. The algorithm obtains, as a result, high quality parametric transformation between 2D objects and the parametric domain, the unit square. First, we define a parametric mapping between the input boundary of the object and the boundary of the parametric domain. Then, we build a T-mesh adapted to the geometric singularities of the domain in order to preserve the features of the object boundary with a desired tolerance…

Optimization of RNA-based transgene expression by targeting Protein Kinase R

The aim of this thesis was to establish a method for repeated transfection of in vitro transcribed RNA (IVT-RNA) leading to a sustained protein expression lasting for days or even weeks. Once transfected cells recognize IVT-RNA as "non-self" and initiate defense pathways leading to an upregulated interferon (IFN) response and stalled translation. In this work Protein Kinase R (PKR) was identified as the main effector molecule mediating this cellular response. We assessed four strategies to inhibit PKR and the IFN response: A small molecule PKR inhibitor enhanced protein expression and hampered the induction of IFN-transcripts, but had to be excluded due to cytotoxicity. A siRNA mediated PKR knockdown and the overexpression of a kinase inactive PKR mutant elevated the protein expression, but the down-regulation of the IFN response was insufficient. The co-transfer of the viral inhibitors of PKR and the IFN response was most successful. The use of E3, K3 and B18R co-transfection enabled repeated IVT-RNA-based transfection of human fibroblasts. Thus, the developed protocol allows a continuous IVT-RNA encoded protein expression of proteins, which could be the basis for the generation of induced pluripotent stem cells (iPS) for several therapeutic applications in regenerative medicine or drug research.

Modelling, simulation and optimization of maintenance considerations on condition based maintenance

Globalization has increased the pressure on organizations and companies to operate in the most efficient and economic way. This tendency promotes that companies concentrate more and more on their core businesses, outsource less profitable departments and services to reduce costs. By contrast to earlier times, companies are highly specialized and have a low real net output ratio. For being able to provide the consumers with the right products, those companies have to collaborate with other suppliers and form large supply chains. An effect of large supply chains is the deficiency of high stocks and stockholding costs. This fact has lead to the rapid spread of Just-in-Time logistic concepts aimed minimizing stock by simultaneous high availability of products. Those concurring goals, minimizing stock by simultaneous high product availability, claim for high availability of the production systems in the way that an incoming order can immediately processed. Besides of design aspects and the quality of the production system, maintenance has a strong impact on production system availability. In the last decades, there has been many attempts to create maintenance models for availability optimization. Most of them concentrated on the availability aspect only without incorporating further aspects as logistics and profitability of the overall system. However, production system operator’s main intention is to optimize the profitability of the production system and not the availability of the production system. Thus, classic models, limited to represent and optimize maintenance strategies under the light of availability, fail. A novel approach, incorporating all financial impacting processes of and around a production system, is needed. The proposed model is subdivided into three parts, maintenance module, production module and connection module. This subdivision provides easy maintainability and simple extendability. Within those modules, all aspect of production process are modeled. Main part of the work lies in the extended maintenance and failure module that offers a representation of different maintenance strategies but also incorporates the effect of over-maintaining and failed maintenance (maintenance induced failures). Order release and seizing of the production system are modeled in the production part. Due to computational power limitation, it was not possible to run the simulation and the optimization with the fully developed production model. Thus, the production model was reduced to a black-box without higher degree of details.