Global Village as Virtual Community (On Writing, Thinking, and Teacher Education).

ERIC Educational Resources Information Center

Polin, Linda

1993-01-01

Describes virtual communities known as Multi-User Simulated Environment (MUSE) or Multi-User Object Oriented environment (MOO), text-based computer "communities" whose inhabitants are a combination of the real people and constructed objects that people agree to treat as real. Describes their uses in the classroom. (SR)

Combining Multiobjective Optimization and Cluster Analysis to Study Vocal Fold Functional Morphology

PubMed Central

Palaparthi, Anil; Riede, Tobias

2017-01-01

Morphological design and the relationship between form and function have great influence on the functionality of a biological organ. However, the simultaneous investigation of morphological diversity and function is difficult in complex natural systems. We have developed a multiobjective optimization (MOO) approach in association with cluster analysis to study the form-function relation in vocal folds. An evolutionary algorithm (NSGA-II) was used to integrate MOO with an existing finite element model of the laryngeal sound source. Vocal fold morphology parameters served as decision variables and acoustic requirements (fundamental frequency, sound pressure level) as objective functions. A two-layer and a three-layer vocal fold configuration were explored to produce the targeted acoustic requirements. The mutation and crossover parameters of the NSGA-II algorithm were chosen to maximize a hypervolume indicator. The results were expressed using cluster analysis and were validated against a brute force method. Results from the MOO and the brute force approaches were comparable. The MOO approach demonstrated greater resolution in the exploration of the morphological space. In association with cluster analysis, MOO can efficiently explore vocal fold functional morphology. PMID:24771563

Fish swarm intelligent to optimize real time monitoring of chips drying using machine vision

NASA Astrophysics Data System (ADS)

Hendrawan, Y.; Hawa, L. C.; Damayanti, R.

2018-03-01

This study attempted to apply machine vision-based chips drying monitoring system which is able to optimise the drying process of cassava chips. The objective of this study is to propose fish swarm intelligent (FSI) optimization algorithms to find the most significant set of image features suitable for predicting water content of cassava chips during drying process using artificial neural network model (ANN). Feature selection entails choosing the feature subset that maximizes the prediction accuracy of ANN. Multi-Objective Optimization (MOO) was used in this study which consisted of prediction accuracy maximization and feature-subset size minimization. The results showed that the best feature subset i.e. grey mean, L(Lab) Mean, a(Lab) energy, red entropy, hue contrast, and grey homogeneity. The best feature subset has been tested successfully in ANN model to describe the relationship between image features and water content of cassava chips during drying process with R2 of real and predicted data was equal to 0.9.

Optimized positioning of autonomous surgical lamps

NASA Astrophysics Data System (ADS)

Teuber, Jörn; Weller, Rene; Kikinis, Ron; Oldhafer, Karl-Jürgen; Lipp, Michael J.; Zachmann, Gabriel

2017-03-01

We consider the problem of finding automatically optimal positions of surgical lamps throughout the whole surgical procedure, where we assume that future lamps could be robotized. We propose a two-tiered optimization technique for the real-time autonomous positioning of those robotized surgical lamps. Typically, finding optimal positions for surgical lamps is a multi-dimensional problem with several, in part conflicting, objectives, such as optimal lighting conditions at every point in time while minimizing the movement of the lamps in order to avoid distractions of the surgeon. Consequently, we use multi-objective optimization (MOO) to find optimal positions in real-time during the entire surgery. Due to the conflicting objectives, there is usually not a single optimal solution for such kinds of problems, but a set of solutions that realizes a Pareto-front. When our algorithm selects a solution from this set it additionally has to consider the individual preferences of the surgeon. This is a highly non-trivial task because the relationship between the solution and the parameters is not obvious. We have developed a novel meta-optimization that considers exactly this challenge. It delivers an easy to understand set of presets for the parameters and allows a balance between the lamp movement and lamp obstruction. This metaoptimization can be pre-computed for different kinds of operations and it then used by our online optimization for the selection of the appropriate Pareto solution. Both optimization approaches use data obtained by a depth camera that captures the surgical site but also the environment around the operating table. We have evaluated our algorithms with data recorded during a real open abdominal surgery. It is available for use for scientific purposes. The results show that our meta-optimization produces viable parameter sets for different parts of an intervention even when trained on a small portion of it.

MOOving around the Net: The Educational Potential of MOOs.

ERIC Educational Resources Information Center

1996

The use of MOOs, multi-user simulated environments in education examined in three papers include: "MOOving around the Net: The Educational Potential of MOOs: A Point of View" (Daniel Ingvarson); "MOOing in a Foreign Language: How, Why and Who?" (Lonnie Turbee) and "MOOving around the Net: Real to Virtual and Back…

Variable fidelity robust optimization of pulsed laser orbital debris removal under epistemic uncertainty

NASA Astrophysics Data System (ADS)

Hou, Liqiang; Cai, Yuanli; Liu, Jin; Hou, Chongyuan

2016-04-01

A variable fidelity robust optimization method for pulsed laser orbital debris removal (LODR) under uncertainty is proposed. Dempster-shafer theory of evidence (DST), which merges interval-based and probabilistic uncertainty modeling, is used in the robust optimization. The robust optimization method optimizes the performance while at the same time maximizing its belief value. A population based multi-objective optimization (MOO) algorithm based on a steepest descent like strategy with proper orthogonal decomposition (POD) is used to search robust Pareto solutions. Analytical and numerical lifetime predictors are used to evaluate the debris lifetime after the laser pulses. Trust region based fidelity management is designed to reduce the computational cost caused by the expensive model. When the solutions fall into the trust region, the analytical model is used to reduce the computational cost. The proposed robust optimization method is first tested on a set of standard problems and then applied to the removal of Iridium 33 with pulsed lasers. It will be shown that the proposed approach can identify the most robust solutions with minimum lifetime under uncertainty.

MOOville: The Writing Project's Own "Private Idaho".

ERIC Educational Resources Information Center

Conlon, Michael

1997-01-01

Describes how a computerized environment supplemented traditional undergraduate courses in English literature and composition at the University of Florida, and was developed with a grant from IBM. Highlights include the use of MOO (multi-user, object-oriented) space; student assignments; the client-server setting; and student and teacher…

Three Community Building Strategies and Their Impacts in an On-Line Course.

ERIC Educational Resources Information Center

Egbert, Joy; Chao, Chin-Chi; Ngeow, Karen

This paper describes three instructional strategies designed to support community building in an online graduate teacher education course: (1) MOO (Multi-User Dimensions Object Oriented) field trips, in which participants are introduced to text-based virtual environments on the Internet through metaphoric online "field trips"; (2)…

"Mooving" to a Virtual Curriculum.

ERIC Educational Resources Information Center

LaRoe, R. John

Three writing classes at the University of Missouri (freshman, sophomore, and senior) spent much or most of the semester on the virtual campus of the Diversity University (DU) MOO (multi-user object oriented). The freshman class wrote one paper on Internet exploration, another on their favorite Internet destination, and for the third were given a…

PAPR-Constrained Pareto-Optimal Waveform Design for OFDM-STAP Radar

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sen, Satyabrata

We propose a peak-to-average power ratio (PAPR) constrained Pareto-optimal waveform design approach for an orthogonal frequency division multiplexing (OFDM) radar signal to detect a target using the space-time adaptive processing (STAP) technique. The use of an OFDM signal does not only increase the frequency diversity of our system, but also enables us to adaptively design the OFDM coefficients in order to further improve the system performance. First, we develop a parametric OFDM-STAP measurement model by considering the effects of signaldependent clutter and colored noise. Then, we observe that the resulting STAP-performance can be improved by maximizing the output signal-to-interference-plus-noise ratiomore » (SINR) with respect to the signal parameters. However, in practical scenarios, the computation of output SINR depends on the estimated values of the spatial and temporal frequencies and target scattering responses. Therefore, we formulate a PAPR-constrained multi-objective optimization (MOO) problem to design the OFDM spectral parameters by simultaneously optimizing four objective functions: maximizing the output SINR, minimizing two separate Cramer-Rao bounds (CRBs) on the normalized spatial and temporal frequencies, and minimizing the trace of CRB matrix on the target scattering coefficients estimations. We present several numerical examples to demonstrate the achieved performance improvement due to the adaptive waveform design.« less

Multiobjective evolutionary algorithm with many tables for purely ab initio protein structure prediction.

PubMed

Brasil, Christiane Regina Soares; Delbem, Alexandre Claudio Botazzo; da Silva, Fernando Luís Barroso

2013-07-30

This article focuses on the development of an approach for ab initio protein structure prediction (PSP) without using any earlier knowledge from similar protein structures, as fragment-based statistics or inference of secondary structures. Such an approach is called purely ab initio prediction. The article shows that well-designed multiobjective evolutionary algorithms can predict relevant protein structures in a purely ab initio way. One challenge for purely ab initio PSP is the prediction of structures with β-sheets. To work with such proteins, this research has also developed procedures to efficiently estimate hydrogen bond and solvation contribution energies. Considering van der Waals, electrostatic, hydrogen bond, and solvation contribution energies, the PSP is a problem with four energetic terms to be minimized. Each interaction energy term can be considered an objective of an optimization method. Combinatorial problems with four objectives have been considered too complex for the available multiobjective optimization (MOO) methods. The proposed approach, called "Multiobjective evolutionary algorithms with many tables" (MEAMT), can efficiently deal with four objectives through the combination thereof, performing a more adequate sampling of the objective space. Therefore, this method can better map the promising regions in this space, predicting structures in a purely ab initio way. In other words, MEAMT is an efficient optimization method for MOO, which explores simultaneously the search space as well as the objective space. MEAMT can predict structures with one or two domains with RMSDs comparable to values obtained by recently developed ab initio methods (GAPFCG , I-PAES, and Quark) that use different levels of earlier knowledge. Copyright © 2013 Wiley Periodicals, Inc.

EdMOO: One Approach to a Multimedia Collaborative Environment.

ERIC Educational Resources Information Center

Holkner, Bernard

The nature of the multiuser object oriented (MOO) environment lends itself to flexible and rich interactive collaboration space providing interactive discussion, mail, mailing list, and news features to its virtual denizens. EdMOO (HREF1) was created in mid-1995 as an environment for teachers to experience the text based virtual reality…

MoO x thin films deposited by magnetron sputtering as an anode for aqueous micro-supercapacitors.

PubMed

Liu, Can; Li, Zhengcao; Zhang, Zhengjun

2013-12-01

In order to examine the potential application of non-stoichiometric molybdenum oxide as anode materials for aqueous micro-supercapacitors, conductive MoO x films (2 ⩽ x ⩽ 2.3) deposited via RF magnetron sputtering at different temperatures were systematically studied for composition, structure and electrochemical properties in an aqueous solution of Li 2 SO 4 . The MoO x ( x ≈ 2.3) film deposited at 150 °C exhibited a higher areal capacitance (31 mF cm -2 measured at 5 mV s -1 ), best rate capability and excellent stability at potentials below -0.1 V versus saturated calomel electrode, compared to the films deposited at room temperature and at higher temperatures. These superior properties were attributed to the multi-valence composition and mixed-phase microstructure, i.e., the coexistence of MoO 2 nanocrystals and amorphous MoO x (2.3 < x ⩽ 3). A mechanism combining Mo(IV) oxidation/reduction on the hydrated MoO 2 grain surfaces and cation intercalation/extrusion is proposed to illustrate the pseudo-capacitive process.

Adaptive surrogate model based multi-objective transfer trajectory optimization between different libration points

NASA Astrophysics Data System (ADS)

Peng, Haijun; Wang, Wei

2016-10-01

An adaptive surrogate model-based multi-objective optimization strategy that combines the benefits of invariant manifolds and low-thrust control toward developing a low-computational-cost transfer trajectory between libration orbits around the L1 and L2 libration points in the Sun-Earth system has been proposed in this paper. A new structure for a multi-objective transfer trajectory optimization model that divides the transfer trajectory into several segments and gives the dominations for invariant manifolds and low-thrust control in different segments has been established. To reduce the computational cost of multi-objective transfer trajectory optimization, a mixed sampling strategy-based adaptive surrogate model has been proposed. Numerical simulations show that the results obtained from the adaptive surrogate-based multi-objective optimization are in agreement with the results obtained using direct multi-objective optimization methods, and the computational workload of the adaptive surrogate-based multi-objective optimization is only approximately 10% of that of direct multi-objective optimization. Furthermore, the generating efficiency of the Pareto points of the adaptive surrogate-based multi-objective optimization is approximately 8 times that of the direct multi-objective optimization. Therefore, the proposed adaptive surrogate-based multi-objective optimization provides obvious advantages over direct multi-objective optimization methods.

Multiobjective GAs, quantitative indices, and pattern classification.

PubMed

Bandyopadhyay, Sanghamitra; Pal, Sankar K; Aruna, B

2004-10-01

The concept of multiobjective optimization (MOO) has been integrated with variable length chromosomes for the development of a nonparametric genetic classifier which can overcome the problems, like overfitting/overlearning and ignoring smaller classes, as faced by single objective classifiers. The classifier can efficiently approximate any kind of linear and/or nonlinear class boundaries of a data set using an appropriate number of hyperplanes. While designing the classifier the aim is to simultaneously minimize the number of misclassified training points and the number of hyperplanes, and to maximize the product of class wise recognition scores. The concepts of validation set (in addition to training and test sets) and validation functional are introduced in the multiobjective classifier for selecting a solution from a set of nondominated solutions provided by the MOO algorithm. This genetic classifier incorporates elitism and some domain specific constraints in the search process, and is called the CEMOGA-Classifier (constrained elitist multiobjective genetic algorithm based classifier). Two new quantitative indices, namely, the purity and minimal spacing, are developed for evaluating the performance of different MOO techniques. These are used, along with classification accuracy, required number of hyperplanes and the computation time, to compare the CEMOGA-Classifier with other related ones.

Controlling n-type doping in MoO 3

DOE PAGES

Peelaers, H.; Chabinyc, M. L.; Van de Walle, C. G.

2017-02-27

Here, we study the electronic properties of native defects and intentional dopant impurities in MoO 3, a widely used transparent conductor. Using first-principles hybrid functional calculations, we show that electron polarons can be self-trapped, but they can also bind to defects; thus, they play an important role in understanding the properties of doped MoO 3. Our calculations show that oxygen vacancies can cause unintentional n-type doping in MoO 3. Mo vacancies are unlikely to form. Tc and Re impurities on the Mo site and halogens (F, Cl, and Br) on the O site all act as shallow donors but trapmore » electron polarons. Fe, Ru, and Os impurities are amphoteric and will compensate n-type MoO 3. Mn dopants are also amphoteric, and they show interesting magnetic properties. These results support the design of doping approaches that optimally exploit functionality.« less

Transfer matrix approach to electron transport in monolayer MoS2/MoO x heterostructures

NASA Astrophysics Data System (ADS)

Li, Gen

2018-05-01

Oxygen plasma treatment can introduce oxidation into monolayer MoS2 to transfer MoS2 into MoO x , causing the formation of MoS2/MoO x heterostructures. We find the MoS2/MoO x heterostructures have the similar geometry compared with GaAs/Ga1‑x Al x As semiconductor superlattice. Thus, We employ the established transfer matrix method to analyse the electron transport in the MoS2/MoO x heterostructures with double-well and step-well geometries. We also considere the coupling between transverse and longitudinal kinetic energy because the electron effective mass changes spatially in the MoS2/MoO x heterostructures. We find the resonant peaks show red shift with the increasing of transverse momentum, which is similar to the previous work studying the transverse-momentum-dependent transmission in GaAs/Ga1‑x Al x As double-barrier structure. We find electric field can enhance the magnitude of peaks and intensify the coupling between longitudinal and transverse momentums. Moreover, higher bias is applied to optimize resonant tunnelling condition to show negative differential effect can be observed in the MoS2/MoO x system.

Non-Linear Seismic Velocity Estimation from Multiple Waveform Functionals and Formal Assessment of Constraints

DTIC Science & Technology

2011-09-01

tectonically active regions such as the Middle East. For example, we previously applied the code to determine the crust and upper mantle structure...Objective Optimization (MOO) for Multiple Datasets The primary goal of our current project is to develop a tool for estimating crustal structure that...be used to obtain crustal velocity structures by modeling broadband waveform, receiver function, and surface wave dispersion data. The code has been

Numerical modeling of Czochralski growth of Li2MoO4 crystals for heat-scintillation cryogenic bolometers

NASA Astrophysics Data System (ADS)

Stelian, Carmen; Velázquez, Matias; Veber, Philippe; Ahmine, Abdelmounaim; Sand, Jean-Baptiste; Buşe, Gabriel; Cabane, Hugues; Duffar, Thierry

2018-06-01

Lithium molybdate Li2MoO4 (LMO) crystals of mass ranging between 350 and 500 g are excellent candidates to build heat-scintillation cryogenic bolometers likely to be used for the detection of rare events in astroparticle physics. In this work, numerical modeling is applied in order to investigate the Czochralski growth of Li2MoO4 crystals in an inductive furnace. The numerical model was validated by comparing the numerical predictions of the crystal-melt interface shape to experimental visualization of the growth interface. Modeling was performed for two different Czochralski furnaces that use inductive heating. The simulation of the first furnace, which was used to grow Li2MoO4 crystals of 3-4 cm in diameter, reveals non-optimal heat transfer conditions for obtaining good quality crystals. The second furnace, which will be used to grow crystals of 5 cm in diameter, was numerically optimized in order to reduce the temperature gradients in the crystal and to avoid fast crystallization of the bath at the later stages of the growth process.

Technically Speaking: Transforming Language Learning through Virtual Learning Environments (MOOs).

ERIC Educational Resources Information Center

von der Emde, Silke; Schneider, Jeffrey; Kotter, Markus

2001-01-01

Draws on experiences from a 7-week exchange between students learning German at an American college and advanced students of English at a German university. Maps out the benefits to using a MOO (multiple user domains object-oriented) for language learning: a student-centered learning environment structured by such objectives as peer teaching,…

Optimization of multi-objective micro-grid based on improved particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Zhang, Jian; Gan, Yang

2018-04-01

The paper presents a multi-objective optimal configuration model for independent micro-grid with the aim of economy and environmental protection. The Pareto solution set can be obtained by solving the multi-objective optimization configuration model of micro-grid with the improved particle swarm algorithm. The feasibility of the improved particle swarm optimization algorithm for multi-objective model is verified, which provides an important reference for multi-objective optimization of independent micro-grid.

Vertical MoSe2-MoO x p-n heterojunction and its application in optoelectronics.

PubMed

Chen, Xiaoshuang; Liu, Guangbo; Hu, Yunxia; Cao, Wenwu; Hu, PingAn; Hu, Wenping

2018-01-26

The hybrid n-type 2D transition-metal dichalcogenide (TMD)/p-type oxide van der Waals (vdW) heterojunction nanosheets consist of 2D layered MoSe 2 (the n-type 2D material) and MoO x (the p-type oxide) which are grown on SiO 2 /Si substrates for the first time via chemical vapor deposition technique, displaying the regular hexagon structures with the average length dimension of sides of ∼8 μm. Vertical MoSe 2 -MoO x p-n heterojunctions demonstrate obviously current-rectifying characteristic, and it can be tuned via gate voltage. What is more, the photodetector based on vertical MoSe 2 -MoO x heterojunctions displays optimal photoresponse behavior, generating the responsivity, detectivity, and external quantum efficiency to 3.4 A W -1 , 0.85 × 10 8 Jones, and 1665.6%, respectively, at V ds  = 5 V with the light wavelength of 254 nm under 0.29 mW cm -2 . These results furnish a building block on investigating the flexible and transparent properties of vdW and further optimizing the structure of the devices for better optoelectronic and electronic performance.

Vertical MoSe2-MoO x p-n heterojunction and its application in optoelectronics

NASA Astrophysics Data System (ADS)

Chen, Xiaoshuang; Liu, Guangbo; Hu, Yunxia; Cao, Wenwu; Hu, PingAn; Hu, Wenping

2018-01-01

The hybrid n-type 2D transition-metal dichalcogenide (TMD)/p-type oxide van der Waals (vdW) heterojunction nanosheets consist of 2D layered MoSe2 (the n-type 2D material) and MoO x (the p-type oxide) which are grown on SiO2/Si substrates for the first time via chemical vapor deposition technique, displaying the regular hexagon structures with the average length dimension of sides of ˜8 μm. Vertical MoSe2-MoO x p-n heterojunctions demonstrate obviously current-rectifying characteristic, and it can be tuned via gate voltage. What is more, the photodetector based on vertical MoSe2-MoO x heterojunctions displays optimal photoresponse behavior, generating the responsivity, detectivity, and external quantum efficiency to 3.4 A W-1, 0.85 × 108 Jones, and 1665.6%, respectively, at V ds = 5 V with the light wavelength of 254 nm under 0.29 mW cm-2. These results furnish a building block on investigating the flexible and transparent properties of vdW and further optimizing the structure of the devices for better optoelectronic and electronic performance.

Detection of hydrogen peroxide and glucose by using Tb2(MoO4)3 nanoplates as peroxidase mimics

NASA Astrophysics Data System (ADS)

Rahimi-Nasrabadi, Mehdi; Mizani, Farhang; Hosseini, Morteza; Keihan, Amir Homayoun; Ganjali, Mohammad Reza

2017-11-01

Tb2(MoO4)3 nanostructures are demonstrated for the first time to have an intrinsic peroxidase-like activity. Tb2(MoO4)3 nanoplates could efficiently catalyse the oxidation of 3,3‧,5,5‧-tetramethylbenzidine (TMB) to generate a blue dye (with an absorbance maximum at 652 nm) in the presence of H2O2. Based on the highly efficient catalytic of Tb2(MoO4)3 nanoplates, a novel system for optical determination of H2O2 and glucose was successfully established under optimized conditions. The assay had 0.0.08 μM and 0.1 μM detection limit for H2O2 and glucose, respectively. In our opinion, this enzyme mimetic has a potential to use in other oxidase based assays.

Few layered MoO3 nano sheets-SWCNT composite thin film as supercapacitor electrode

NASA Astrophysics Data System (ADS)

Dutta, Shibsankar; Akther, Jasim; De, Sukanta

2017-05-01

The increasing demands for clean and renewable energy, the advantages of high power density, long lasting and high efficiency have made Supercapacitor as one of the major emerging energy storage device.The 2D layered metal oxide nanocomposite with SWCNT is the promising candidate for energy storage and conversion. In this work we exfoliate the crystalline bulk MoO3 by simple liquid phase exfoliation to give multi-layer MoO3 dispersed in a suitable solvent. As the electrical conductivity of MoO3 is very low so, the dispersion was used to make hybrid material with SWCNT dispersion by vacuum filtration. The SWCNT-MoO3 composite showed an areal capacitance value of 1290 µF/cm2 at 10 mV/s in PVA-H2 SO4 solid gel electrolyte. This composite based electrode provides an energy density of 0.092 µWh/cm2 and a power density of 9.54 µW/cm2 at 0.01 mA/cm2

Impact of MoO3 interlayer on the energy level alignment of pentacene-C60 heterostructure.

PubMed

Zou, Ye; Mao, Hongying; Meng, Qing; Zhu, Daoben

2016-02-28

Using in situ ultraviolet photoelectron spectroscopy, the electronic structure evolutions at the interface between pentacene and fullerene (C60), a classical organic donor-acceptor heterostructure in organic electronic devices, on indium-tin oxide (ITO) and MoO3 modified ITO substrates have been investigated. The insertion of a thin layer MoO3 has a significant impact on the interfacial energy level alignment of pentacene-C60 heterostructure. For the deposition of C60 on pentacene, the energy difference between the highest occupied molecular orbital of donor and the lowest unoccupied molecular orbital of acceptor (HOMO(D)-LUMO(A)) offset of C60/pentacene heterostructure increased from 0.86 eV to 1.54 eV after the insertion of a thin layer MoO3 on ITO. In the inverted heterostructrure where pentacene was deposited on C60, the HOMO(D)-LUMO(A) offset of pentacene/C60 heterostructure increased from 1.32 to 2.20 eV after MoO3 modification on ITO. The significant difference of HOMO(D)-LUMO(A) offset shows the feasibility to optimize organic electronic device performance through interfacial engineering approaches, such as the insertion of a thin layer high work function MoO3 films.

Multi-objective optimization of a continuous bio-dissimilation process of glycerol to 1, 3-propanediol.

PubMed

Xu, Gongxian; Liu, Ying; Gao, Qunwang

2016-02-10

This paper deals with multi-objective optimization of continuous bio-dissimilation process of glycerol to 1, 3-propanediol. In order to maximize the production rate of 1, 3-propanediol, maximize the conversion rate of glycerol to 1, 3-propanediol, maximize the conversion rate of glycerol, and minimize the concentration of by-product ethanol, we first propose six new multi-objective optimization models that can simultaneously optimize any two of the four objectives above. Then these multi-objective optimization problems are solved by using the weighted-sum and normal-boundary intersection methods respectively. Both the Pareto filter algorithm and removal criteria are used to remove those non-Pareto optimal points obtained by the normal-boundary intersection method. The results show that the normal-boundary intersection method can successfully obtain the approximate Pareto optimal sets of all the proposed multi-objective optimization problems, while the weighted-sum approach cannot achieve the overall Pareto optimal solutions of some multi-objective problems. Copyright © 2015 Elsevier B.V. All rights reserved.

Wireless Sensor Network Optimization: Multi-Objective Paradigm.

PubMed

Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

2015-07-20

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

Multi-Objective Optimization for Speed and Stability of a Sony AIBO Gait

DTIC Science & Technology

2007-09-01

MULTI-OBJECTIVE OPTIMIZATION FOR SPEED AND STABILITY OF A SONY AIBO GAIT THESIS Christopher A. Patterson, Second Lieutenant, USAF AFIT/GCS...07-17 MULTI-OBJECTIVE OPTIMIZATION FOR SPEED AND STABILITY OF A SONY AIBO GAIT THESIS Presented to the Faculty Department of...MULTI-OBJECTIVE OPTIMIZATION FOR SPEED AND STABILITY OF A SONY AIBO GAIT Christopher A. Patterson, BS Second Lieutenant, USAF

Text-Based MOOing in Educational Practice: Experiences of Disinhibition

ERIC Educational Resources Information Center

Chester, Andrea

2006-01-01

Purpose: The purpose of this paper is to describe educational MOOs--MUD, object-oriented (text-based, network-accessible virtual environments) and explore how teaching and learning in such a context impacts on students' inhibitions. Design/methodology/approach: Students enrolled in a course on the psychology of cyberspace interacted for 12 weeks…

Multi-objective Optimization Design of Gear Reducer Based on Adaptive Genetic Algorithms

NASA Astrophysics Data System (ADS)

Li, Rui; Chang, Tian; Wang, Jianwei; Wei, Xiaopeng; Wang, Jinming

2008-11-01

An adaptive Genetic Algorithm (GA) is introduced to solve the multi-objective optimized design of the reducer. Firstly, according to the structure, strength, etc. in a reducer, a multi-objective optimized model of the helical gear reducer is established. And then an adaptive GA based on a fuzzy controller is introduced, aiming at the characteristics of multi-objective, multi-parameter, multi-constraint conditions. Finally, a numerical example is illustrated to show the advantages of this approach and the effectiveness of an adaptive genetic algorithm used in optimized design of a reducer.

Wireless Sensor Network Optimization: Multi-Objective Paradigm

PubMed Central

Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

2015-01-01

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks. PMID:26205271

Improved multi-objective ant colony optimization algorithm and its application in complex reasoning

NASA Astrophysics Data System (ADS)

Wang, Xinqing; Zhao, Yang; Wang, Dong; Zhu, Huijie; Zhang, Qing

2013-09-01

The problem of fault reasoning has aroused great concern in scientific and engineering fields. However, fault investigation and reasoning of complex system is not a simple reasoning decision-making problem. It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints. So far, little research has been carried out in this field. This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes. Three optimization objectives are considered simultaneously: maximum probability of average fault, maximum average importance, and minimum average complexity of test. Under the constraints of both known symptoms and the causal relationship among different components, a multi-objective optimization mathematical model is set up, taking minimizing cost of fault reasoning as the target function. Since the problem is non-deterministic polynomial-hard(NP-hard), a modified multi-objective ant colony algorithm is proposed, in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives. At last, a Pareto optimal set is acquired. Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set, through which the final fault causes can be identified according to decision-making demands, thus realize fault reasoning of the multi-constraint and multi-objective complex system. Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model, which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system.

Palladium (Pd) sensitized molybdenum trioxide (MoO3) nanobelts for nitrogen dioxide (NO2) gas detection

NASA Astrophysics Data System (ADS)

Mane, A. A.; Moholkar, A. V.

2018-01-01

The MoO3 nanobelts have been grown onto the glass substrates using chemical spray pyrolysis (CSP) deposition technique at optimized substrate temperature of 400 °C. XRD study shows that the film is polycrystalline in nature and possesses an orthorhombic crystal structure. The FE-SEM micrographs show the formation of nanobelts-like morphology of MoO3. The presence of Pd and its oxidation states in Pd-sensitized MoO3 film is confirmed using EDAX and XPS study, respectively. The percentage gas response is defined as |Rg -Ra|/Ra × 100 % where, Ra and Rg are the film resistances in presence of air and analyte gas, respectively. Before Pd sensitization, MoO3 nanobelts show NO2 gas response of 68% for 100 ppm concentration at operating temperature of 200 °C with response and recovery times of 15 s and 150 s, respectively. Selectivity coefficient study shows that the Pd-sensitized MoO3 nanobelts are more sensitive and selective towards NO2 gas among various gases such as NH3, H2S, CO, CO2 and SO2. The Pd-sensitized MoO3 nanobelts shows the enhanced response of 95.3% towards 100 ppm NO2 gas concentration with response and recovery times of 74 s and 297 s, respectively. The lower detection limit is found to be 5 ppm which is four times less than immediately dangerous to life or health (IDLH) value of 20 ppm. Finally, the proposed NO2 gas sensing mechanism based on chemisorption model is discussed.

Electrochemical Determination of Baicalin in Traditional Chinese Medicine Based on the Enhancement Effect of MoO3-Reduced Graphene Oxide Nanocomposite

NASA Astrophysics Data System (ADS)

Hu, Weibing; Zhang, Wen; Wang, Meng; Feng, Fu

2018-02-01

The nanocomposites of MoO3-reduced graphene oxide (MoO3-RGO) were synthesized by hydrothermal reduction using MoCl5 and graphene oxide as precursors. The resulting composites were characterized with scanning electron microscopy, x-ray diffraction, Fourier transform infrared spectroscopy, thermogravimetric analysis and Raman spectra, and were further used to modify the glassy carbon electrode (GCE). After optimizing the parameters, the electrochemical behavior of baicalin on different types of electrodes was investigated. The MoO3-RGO composite-modified GCE exhibited remarkably enhanced electrochemical signals of baicalin. After 90 s, under open circuit potential, oxidation and reduction peaks appeared at 0.207 V and 0.103 V, respectively. A sensitive and simple electrochemical method was proposed for the determination of baicalin in which the calibration curve ranges from 1.0 × 10-9 M to 4.3 × 10-5 M, and the detection limit is 3.81 × 10-10 M.

Structural damage detection-oriented multi-type sensor placement with multi-objective optimization

NASA Astrophysics Data System (ADS)

Lin, Jian-Fu; Xu, You-Lin; Law, Siu-Seong

2018-05-01

A structural damage detection-oriented multi-type sensor placement method with multi-objective optimization is developed in this study. The multi-type response covariance sensitivity-based damage detection method is first introduced. Two objective functions for optimal sensor placement are then introduced in terms of the response covariance sensitivity and the response independence. The multi-objective optimization problem is formed by using the two objective functions, and the non-dominated sorting genetic algorithm (NSGA)-II is adopted to find the solution for the optimal multi-type sensor placement to achieve the best structural damage detection. The proposed method is finally applied to a nine-bay three-dimensional frame structure. Numerical results show that the optimal multi-type sensor placement determined by the proposed method can avoid redundant sensors and provide satisfactory results for structural damage detection. The restriction on the number of each type of sensors in the optimization can reduce the searching space in the optimization to make the proposed method more effective. Moreover, how to select a most optimal sensor placement from the Pareto solutions via the utility function and the knee point method is demonstrated in the case study.

Copper:molybdenum sub-oxide blend as transparent conductive electrode (TCE) indium free

NASA Astrophysics Data System (ADS)

Hssein, Mehdi; Cattin, Linda; Morsli, Mustapha; Addou, Mohammed; Bernède, Jean-Christian

2016-05-01

Oxide/metal/oxide structures have been shown to be promising alternatives to ITO. In such structures, in order to decrease the high light reflection of the metal film it is embedded between two metal oxides dielectric. MoO3-x is often used as oxide due to its capacity to be a performing anode buffer layer in organic solar cells, while silver is the metal the most often used [1]. Some attempts to use cheaper metal such as copper have been done. However it was shown that Cu diffuses strongly into MoO3-x [2]. Here we used this property to grow simple new transparent conductive oxide (TCE), i.e., Cu: MoO3-x blend. After the deposition of a thin Cu layer, a film of MoO3-x is deposited by sublimation. An XPS study shows more than 50% of Cu is present at the surface of the structure. In order to limit the Cu diffusion an ultra-thin Al layer is deposited onto MoO3-x. Then, in order to obtain a good hole collecting contact with the electron donor of the organic solar cells, a second MoO3-x layer is deposited. After optimization of the thickness of the different layers, the optimum structure is as follow: Cu (12 nm) : MoO3-x (20 nm)/Al (0.5 nm)/ MoO3-x (10 nm). The sheet resistance of this structure is Rsq = 5.2 Ω/sq. and its transmittance is Tmax = 65%. The factor of merit ϕM = T10/Rsq. = 2.41 × 10-3 Ω-1, which made this new TCE promising as anode in organic solar cells. Contribution to the topical issue "Materials for Energy Harvesting, Conversion and Storage (ICOME 2015) - Elected submissions", edited by Jean-Michel Nunzi, Rachid Bennacer and Mohammed El Ganaoui

Molybdenum dioxide-based anode for solid oxide fuel cell applications

NASA Astrophysics Data System (ADS)

Kwon, Byeong Wan; Ellefson, Caleb; Breit, Joe; Kim, Jinsoo; Grant Norton, M.; Ha, Su

2013-12-01

The present paper describes the fabrication and performance of a molybdenum dioxide (MoO2)-based anode for liquid hydrocarbon/oxygenated hydrocarbon-fueled solid oxide fuel cells (SOFCs). These fuel cells first internally reform the complex liquid fuel into carbon fragments and hydrogen, which are then electrochemically oxidized to produce electrical energy without external fuel processors. The MoO2-based anode was fabricated on to an yttria-stabilized zirconia (YSZ) electrolyte via combined electrostatic spray deposition (ESD) and direct painting methods. The cell performance was measured by directly feeding liquid fuels such as n-dodecane (i.e., a model diesel/kerosene fuel) or biodiesel (i.e., a future biomass-based liquid fuel) to the MoO2-based anode at 850 °C. The maximum initial power densities obtained from our MoO2-based SOFC were 34 mW cm-2 and 45 mW cm-2 using n-dodecane and biodiesel, respectively. The initial power density of the MoO2-based SOFC was improved up to 2500 mW cm-2 by optimizing the porosity of the MoO2-based anode. To test the long-term stability of the MoO2-based anode SOFC against coking, n-dodecane was continuously fed into the cell for 24 h at the open circuit voltage (OCV). During long-term testing, voltage-current density (V-I) plots were periodically obtained and they showed no significant changes over the operation time. Microstructural examination of the tested cells indicated that the MoO2-based anode displayed negligible coke formation, which explains its stability. On the other hand, SOFCs with conventional nickel (Ni)-based anodes under the same operating conditions showed a significant amount of coke formation on the metal surface, which led to a rapid drop in cell performance. Hence, the present work demonstrates that MoO2-based anodes exhibit outstanding tolerance to coke formation. This result opens up the opportunity for more efficiently generating electrical energy from both existing transportation and next generation biomass-derived liquid fuels using liquid hydrocarbon/oxygenated hydrocarbon-fueled SOFCs.

Optimal design of groundwater remediation system using a probabilistic multi-objective fast harmony search algorithm under uncertainty

NASA Astrophysics Data System (ADS)

Luo, Qiankun; Wu, Jianfeng; Yang, Yun; Qian, Jiazhong; Wu, Jichun

2014-11-01

This study develops a new probabilistic multi-objective fast harmony search algorithm (PMOFHS) for optimal design of groundwater remediation systems under uncertainty associated with the hydraulic conductivity (K) of aquifers. The PMOFHS integrates the previously developed deterministic multi-objective optimization method, namely multi-objective fast harmony search algorithm (MOFHS) with a probabilistic sorting technique to search for Pareto-optimal solutions to multi-objective optimization problems in a noisy hydrogeological environment arising from insufficient K data. The PMOFHS is then coupled with the commonly used flow and transport codes, MODFLOW and MT3DMS, to identify the optimal design of groundwater remediation systems for a two-dimensional hypothetical test problem and a three-dimensional Indiana field application involving two objectives: (i) minimization of the total remediation cost through the engineering planning horizon, and (ii) minimization of the mass remaining in the aquifer at the end of the operational period, whereby the pump-and-treat (PAT) technology is used to clean up contaminated groundwater. Also, Monte Carlo (MC) analysis is employed to evaluate the effectiveness of the proposed methodology. Comprehensive analysis indicates that the proposed PMOFHS can find Pareto-optimal solutions with low variability and high reliability and is a potentially effective tool for optimizing multi-objective groundwater remediation problems under uncertainty.

Harvesting visible light with MoO3 nanorods modified by Fe(iii) nanoclusters for effective photocatalytic degradation of organic pollutants.

PubMed

Alam, U; Kumar, S; Bahnemann, D; Koch, J; Tegenkamp, C; Muneer, M

2018-02-07

The photocatalytic performance of MoO 3 is limited due to its weak visible light absorption ability and quick recombination of charge carriers. In the present work, we report the facile synthesis of Fe(iii)-grafted MoO 3 nanorods using a hydrothermal method followed by an impregnation technique with the aim of enhancing the light harvesting ability and photocatalytic efficiency of MoO 3 . The prepared samples were characterized through the standard analytical techniques of XRD, SEM-EDS, TEM, XPS, UV-Vis-DRS, FT-IR, TG-DTA and PL spectrophotometry. XPS and TEM analyses reveal that Fe(iii) ions are successfully grafted onto the surface of the MoO 3 nanorod with intimate interfacial contact. The photocatalytic performances of the prepared samples were investigated by studying the degradation of methylene blue (MB), rhodamine B (RhB) and 4-nitrophenol (4-NP) under visible light irradiation. The surface-modified MoO 3 with Fe(iii) ions showed excellent photocatalytic activity towards the degradation of the above-mentioned pollutants, where Fe(iii) ions act as effective cocatalytic sites to produce hydroxyl radicals through multi-electron reduction of oxygen molecules. The improved photocatalytic activity could be ascribed to the effective separation of charge carriers and efficient production of hydroxyl radicals via the rapid capture of electrons by Fe(iii) through a well-known photoinduced interfacial charge transfer mechanism. Based on scavenger analysis study, a mechanism for the enhanced photocatalytic activity has been discussed and proposed. The concept of surface grafting onto large bandgap semiconductors with ubiquitous elements opens up a new avenue for the development of visible-light-responsive photocatalysts with excellent photocatalytic activity.

EIT image regularization by a new Multi-Objective Simulated Annealing algorithm.

PubMed

Castro Martins, Thiago; Sales Guerra Tsuzuki, Marcos

2015-01-01

Multi-Objective Optimization can be used to produce regularized Electrical Impedance Tomography (EIT) images where the weight of the regularization term is not known a priori. This paper proposes a novel Multi-Objective Optimization algorithm based on Simulated Annealing tailored for EIT image reconstruction. Images are reconstructed from experimental data and compared with images from other Multi and Single Objective optimization methods. A significant performance enhancement from traditional techniques can be inferred from the results.

Sensitivity analysis of multi-objective optimization of CPG parameters for quadruped robot locomotion

NASA Astrophysics Data System (ADS)

Oliveira, Miguel; Santos, Cristina P.; Costa, Lino

2012-09-01

In this paper, a study based on sensitivity analysis is performed for a gait multi-objective optimization system that combines bio-inspired Central Patterns Generators (CPGs) and a multi-objective evolutionary algorithm based on NSGA-II. In this system, CPGs are modeled as autonomous differential equations, that generate the necessary limb movement to perform the required walking gait. In order to optimize the walking gait, a multi-objective problem with three conflicting objectives is formulated: maximization of the velocity, the wide stability margin and the behavioral diversity. The experimental results highlight the effectiveness of this multi-objective approach and the importance of the objectives to find different walking gait solutions for the quadruped robot.

Sputter-deposited WO x and MoO x for hole selective contacts

DOE PAGES

Bivour, Martin; Zähringer, Florian; Ndione, Paul F.; ...

2017-09-21

Here, reactive sputter deposited tungsten and molybdenum oxide (WO x, MoO x) thin films are tested for their ability to form a hole selective contact for Si wafer based solar cells. A characterization approach based on analyzing the band bending induced in the c-Si absorber and the external and implied open-circuit voltage of test structures was used. It is shown that the oxygen partial pressure allows to tailor the selectivity to some extent and that a direct correlation between induced band bending and hole selectivity exists. Although the selectivity of the sputtered films is inferior to the reference films depositedmore » by thermal evaporation, these results demonstrate a good starting point for further optimizations of sputtered WO x and MoO x towards higher work functions to improve the hole selectivity.« less

Sputter-deposited WO x and MoO x for hole selective contacts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bivour, Martin; Zähringer, Florian; Ndione, Paul F.

Here, reactive sputter deposited tungsten and molybdenum oxide (WO x, MoO x) thin films are tested for their ability to form a hole selective contact for Si wafer based solar cells. A characterization approach based on analyzing the band bending induced in the c-Si absorber and the external and implied open-circuit voltage of test structures was used. It is shown that the oxygen partial pressure allows to tailor the selectivity to some extent and that a direct correlation between induced band bending and hole selectivity exists. Although the selectivity of the sputtered films is inferior to the reference films depositedmore » by thermal evaporation, these results demonstrate a good starting point for further optimizations of sputtered WO x and MoO x towards higher work functions to improve the hole selectivity.« less

An adaptive evolutionary multi-objective approach based on simulated annealing.

PubMed

Li, H; Landa-Silva, D

2011-01-01

A multi-objective optimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggregation function. For example, MOEA/D is an evolutionary multi-objective optimization (EMO) algorithm that attempts to optimize multiple subproblems simultaneously by evolving a population of solutions. However, the performance of MOEA/D highly depends on the initial setting and diversity of the weight vectors. In this paper, we present an improved version of MOEA/D, called EMOSA, which incorporates an advanced local search technique (simulated annealing) and adapts the search directions (weight vectors) corresponding to various subproblems. In EMOSA, the weight vector of each subproblem is adaptively modified at the lowest temperature in order to diversify the search toward the unexplored parts of the Pareto-optimal front. Our computational results show that EMOSA outperforms six other well established multi-objective metaheuristic algorithms on both the (constrained) multi-objective knapsack problem and the (unconstrained) multi-objective traveling salesman problem. Moreover, the effects of the main algorithmic components and parameter sensitivities on the search performance of EMOSA are experimentally investigated.

Application of dragonfly algorithm for optimal performance analysis of process parameters in turn-mill operations- A case study

NASA Astrophysics Data System (ADS)

Vikram, K. Arun; Ratnam, Ch; Lakshmi, VVK; Kumar, A. Sunny; Ramakanth, RT

2018-02-01

Meta-heuristic multi-response optimization methods are widely in use to solve multi-objective problems to obtain Pareto optimal solutions during optimization. This work focuses on optimal multi-response evaluation of process parameters in generating responses like surface roughness (Ra), surface hardness (H) and tool vibration displacement amplitude (Vib) while performing operations like tangential and orthogonal turn-mill processes on A-axis Computer Numerical Control vertical milling center. Process parameters like tool speed, feed rate and depth of cut are considered as process parameters machined over brass material under dry condition with high speed steel end milling cutters using Taguchi design of experiments (DOE). Meta-heuristic like Dragonfly algorithm is used to optimize the multi-objectives like ‘Ra’, ‘H’ and ‘Vib’ to identify the optimal multi-response process parameters combination. Later, the results thus obtained from multi-objective dragonfly algorithm (MODA) are compared with another multi-response optimization technique Viz. Grey relational analysis (GRA).

Multi objective multi refinery optimization with environmental and catastrophic failure effects objectives

NASA Astrophysics Data System (ADS)

Khogeer, Ahmed Sirag

2005-11-01

Petroleum refining is a capital-intensive business. With stringent environmental regulations on the processing industry and declining refining margins, political instability, increased risk of war and terrorist attacks in which refineries and fuel transportation grids may be targeted, higher pressures are exerted on refiners to optimize performance and find the best combination of feed and processes to produce salable products that meet stricter product specifications, while at the same time meeting refinery supply commitments and of course making profit. This is done through multi objective optimization. For corporate refining companies and at the national level, Intea-Refinery and Inter-Refinery optimization is the second step in optimizing the operation of the whole refining chain as a single system. Most refinery-wide optimization methods do not cover multiple objectives such as minimizing environmental impact, avoiding catastrophic failures, or enhancing product spec upgrade effects. This work starts by carrying out a refinery-wide, single objective optimization, and then moves to multi objective-single refinery optimization. The last step is multi objective-multi refinery optimization, the objectives of which are analysis of the effects of economic, environmental, product spec, strategic, and catastrophic failure. Simulation runs were carried out using both MATLAB and ASPEN PIMS utilizing nonlinear techniques to solve the optimization problem. The results addressed the need to debottleneck some refineries or transportation media in order to meet the demand for essential products under partial or total failure scenarios. They also addressed how importing some high spec products can help recover some of the losses and what is needed in order to accomplish this. In addition, the results showed nonlinear relations among local and global objectives for some refineries. The results demonstrate that refineries can have a local multi objective optimum that does not follow the same trends as either global or local single objective optimums. Catastrophic failure effects on refinery operations and on local objectives are more significant than environmental objective effects, and changes in the capacity or the local objectives follow a discrete behavioral pattern, in contrast to environmental objective cases in which the effects are smoother. (Abstract shortened by UMI.)

Optimal Golomb Ruler Sequences Generation for Optical WDM Systems: A Novel Parallel Hybrid Multi-objective Bat Algorithm

NASA Astrophysics Data System (ADS)

Bansal, Shonak; Singh, Arun Kumar; Gupta, Neena

2017-02-01

In real-life, multi-objective engineering design problems are very tough and time consuming optimization problems due to their high degree of nonlinearities, complexities and inhomogeneity. Nature-inspired based multi-objective optimization algorithms are now becoming popular for solving multi-objective engineering design problems. This paper proposes original multi-objective Bat algorithm (MOBA) and its extended form, namely, novel parallel hybrid multi-objective Bat algorithm (PHMOBA) to generate shortest length Golomb ruler called optimal Golomb ruler (OGR) sequences at a reasonable computation time. The OGRs found their application in optical wavelength division multiplexing (WDM) systems as channel-allocation algorithm to reduce the four-wave mixing (FWM) crosstalk. The performances of both the proposed algorithms to generate OGRs as optical WDM channel-allocation is compared with other existing classical computing and nature-inspired algorithms, including extended quadratic congruence (EQC), search algorithm (SA), genetic algorithms (GAs), biogeography based optimization (BBO) and big bang-big crunch (BB-BC) optimization algorithms. Simulations conclude that the proposed parallel hybrid multi-objective Bat algorithm works efficiently as compared to original multi-objective Bat algorithm and other existing algorithms to generate OGRs for optical WDM systems. The algorithm PHMOBA to generate OGRs, has higher convergence and success rate than original MOBA. The efficiency improvement of proposed PHMOBA to generate OGRs up to 20-marks, in terms of ruler length and total optical channel bandwidth (TBW) is 100 %, whereas for original MOBA is 85 %. Finally the implications for further research are also discussed.

Coupled Low-thrust Trajectory and System Optimization via Multi-Objective Hybrid Optimal Control

NASA Technical Reports Server (NTRS)

Vavrina, Matthew A.; Englander, Jacob Aldo; Ghosh, Alexander R.

2015-01-01

The optimization of low-thrust trajectories is tightly coupled with the spacecraft hardware. Trading trajectory characteristics with system parameters ton identify viable solutions and determine mission sensitivities across discrete hardware configurations is labor intensive. Local independent optimization runs can sample the design space, but a global exploration that resolves the relationships between the system variables across multiple objectives enables a full mapping of the optimal solution space. A multi-objective, hybrid optimal control algorithm is formulated using a multi-objective genetic algorithm as an outer loop systems optimizer around a global trajectory optimizer. The coupled problem is solved simultaneously to generate Pareto-optimal solutions in a single execution. The automated approach is demonstrated on two boulder return missions.

Synthesis and characterization of electrospun molybdenum dioxide-carbon nanofibers as sulfur matrix additives for rechargeable lithium-sulfur battery applications.

PubMed

Zhuang, Ruiyuan; Yao, Shanshan; Jing, Maoxiang; Shen, Xiangqian; Xiang, Jun; Li, Tianbao; Xiao, Kesong; Qin, Shibiao

2018-01-01

One-dimensional molybdenum dioxide-carbon nanofibers (MoO 2 -CNFs) were prepared using an electrospinning technique followed by calcination, using sol-gel precursors and polyacrylonitrile (PAN) as a processing aid. The resulting samples were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, Brunauer-Emmet-Teller (BET) surface area measurements, scanning electron microscopy (SEM) and transmission electron microscopy (TEM). MoO 2 -CNFs with an average diameter of 425-575 nm obtained after heat treatment were used as a matrix to prepare sulfur/MoO 2 -CNF cathodes for lithium-sulfur (Li-S) batteries. The polysulfide adsorption and electrochemical performance tests demonstrated that MoO 2 -CNFs did not only act as polysulfide reservoirs to alleviate the shuttle effect, but also improve the electrochemical reaction kinetics during the charge-discharge processes. The effect of MoO 2 -CNF heat treatment on the cycle performance of sulfur/MoO 2 -CNFs electrodes was examined, and the data showed that MoO 2 -CNFs calcined at 850 °C delivered optimal performance with an initial capacity of 1095 mAh g -1 and 860 mAh g -1 after 50 cycles. The results demonstrated that sulfur/MoO 2 -CNF composites display a remarkably high lithium-ion diffusion coefficient, low interfacial resistance and much better electrochemical performance than pristine sulfur cathodes.

SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Z; Folkert, M; Wang, J

2016-06-15

Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidentialmore » reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.« less

Robust Optimization and Sensitivity Analysis with Multi-Objective Genetic Algorithms: Single- and Multi-Disciplinary Applications

DTIC Science & Technology

2007-01-01

multi-disciplinary optimization with uncertainty. Robust optimization and sensitivity analysis is usually used when an optimization model has...formulation is introduced in Section 2.3. We briefly discuss several definitions used in the sensitivity analysis in Section 2.4. Following in...2.5. 2.4 SENSITIVITY ANALYSIS In this section, we discuss several definitions used in Chapter 5 for Multi-Objective Sensitivity Analysis . Inner

Improving multi-objective reservoir operation optimization with sensitivity-informed dimension reduction

NASA Astrophysics Data System (ADS)

Chu, J.; Zhang, C.; Fu, G.; Li, Y.; Zhou, H.

2015-08-01

This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses global sensitivity analysis as a screening tool to reduce computational demands. Sobol's method is used to screen insensitive decision variables and guide the formulation of the optimization problems with a significantly reduced number of decision variables. This sensitivity-informed method dramatically reduces the computational demands required for attaining high-quality approximations of optimal trade-off relationships between conflicting design objectives. The search results obtained from the reduced complexity multi-objective reservoir operation problems are then used to pre-condition the full search of the original optimization problem. In two case studies, the Dahuofang reservoir and the inter-basin multi-reservoir system in Liaoning province, China, sensitivity analysis results show that reservoir performance is strongly controlled by a small proportion of decision variables. Sensitivity-informed dimension reduction and pre-conditioning are evaluated in their ability to improve the efficiency and effectiveness of multi-objective evolutionary optimization. Overall, this study illustrates the efficiency and effectiveness of the sensitivity-informed method and the use of global sensitivity analysis to inform dimension reduction of optimization problems when solving complex multi-objective reservoir operation problems.

A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser

NASA Astrophysics Data System (ADS)

Zheng, Y.; Chen, J.

2017-09-01

A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multi-objective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid's area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Pareto-optimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effectively deal with multi-objective optimizations with black-box functions.

Application of multi-objective nonlinear optimization technique for coordinated ramp-metering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haj Salem, Habib; Farhi, Nadir; Lebacque, Jean Patrick, E-mail: abib.haj-salem@ifsttar.fr, E-mail: nadir.frahi@ifsttar.fr, E-mail: jean-patrick.lebacque@ifsttar.fr

2015-03-10

This paper aims at developing a multi-objective nonlinear optimization algorithm applied to coordinated motorway ramp metering. The multi-objective function includes two components: traffic and safety. Off-line simulation studies were performed on A4 France Motorway including 4 on-ramps.

Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification.

PubMed

Taghanaki, Saeid Asgari; Kawahara, Jeremy; Miles, Brandon; Hamarneh, Ghassan

2017-07-01

Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. Optimizing traditional auto-encoders works well only if the initial weights are close to a proper solution. They are also trained to only reduce the mean squared reconstruction error (MRE) between the encoder inputs and the decoder outputs, but do not address the classification error. The goal of the current work is to test the hypothesis that extending traditional auto-encoders (which only minimize reconstruction error) to multi-objective optimization for finding Pareto-optimal solutions provides more discriminative features that will improve classification performance when compared to single-objective and other multi-objective approaches (i.e. scalarized and sequential). In this paper, we introduce a novel multi-objective optimization of deep auto-encoder networks, in which the auto-encoder optimizes two objectives: MRE and mean classification error (MCE) for Pareto-optimal solutions, rather than just MRE. These two objectives are optimized simultaneously by a non-dominated sorting genetic algorithm. We tested our method on 949 X-ray mammograms categorized into 12 classes. The results show that the features identified by the proposed algorithm allow a classification accuracy of up to 98.45%, demonstrating favourable accuracy over the results of state-of-the-art methods reported in the literature. We conclude that adding the classification objective to the traditional auto-encoder objective and optimizing for finding Pareto-optimal solutions, using evolutionary multi-objective optimization, results in producing more discriminative features. Copyright © 2017 Elsevier B.V. All rights reserved.

Leveraging Human Insights by Combining Multi-Objective Optimization with Interactive Evolution

DTIC Science & Technology

2015-03-26

application, a program that used human selections to guide the evolution of insect -like images. He was able to demonstrate that humans provide key insights...LEVERAGING HUMAN INSIGHTS BY COMBINING MULTI-OBJECTIVE OPTIMIZATION WITH INTERACTIVE EVOLUTION THESIS Joshua R. Christman, Second Lieutenant, USAF...COMBINING MULTI-OBJECTIVE OPTIMIZATION WITH INTERACTIVE EVOLUTION THESIS Presented to the Faculty Department of Electrical and Computer Engineering

Design optimization of axial flow hydraulic turbine runner: Part II - multi-objective constrained optimization method

NASA Astrophysics Data System (ADS)

Peng, Guoyi; Cao, Shuliang; Ishizuka, Masaru; Hayama, Shinji

2002-06-01

This paper is concerned with the design optimization of axial flow hydraulic turbine runner blade geometry. In order to obtain a better design plan with good performance, a new comprehensive performance optimization procedure has been presented by combining a multi-variable multi-objective constrained optimization model with a Q3D inverse computation and a performance prediction procedure. With careful analysis of the inverse design of axial hydraulic turbine runner, the total hydraulic loss and the cavitation coefficient are taken as optimization objectives and a comprehensive objective function is defined using the weight factors. Parameters of a newly proposed blade bound circulation distribution function and parameters describing positions of blade leading and training edges in the meridional flow passage are taken as optimization variables.The optimization procedure has been applied to the design optimization of a Kaplan runner with specific speed of 440 kW. Numerical results show that the performance of designed runner is successfully improved through optimization computation. The optimization model is found to be validated and it has the feature of good convergence. With the multi-objective optimization model, it is possible to control the performance of designed runner by adjusting the value of weight factors defining the comprehensive objective function. Copyright

Low-thrust orbit transfer optimization with refined Q-law and multi-objective genetic algorithm

NASA Technical Reports Server (NTRS)

Lee, Seungwon; Petropoulos, Anastassios E.; von Allmen, Paul

2005-01-01

An optimization method for low-thrust orbit transfers around a central body is developed using the Q-law and a multi-objective genetic algorithm. in the hybrid method, the Q-law generates candidate orbit transfers, and the multi-objective genetic algorithm optimizes the Q-law control parameters in order to simultaneously minimize both the consumed propellant mass and flight time of the orbit tranfer. This paper addresses the problem of finding optimal orbit transfers for low-thrust spacecraft.

Self-adaptive multi-objective harmony search for optimal design of water distribution networks

NASA Astrophysics Data System (ADS)

Choi, Young Hwan; Lee, Ho Min; Yoo, Do Guen; Kim, Joong Hoon

2017-11-01

In multi-objective optimization computing, it is important to assign suitable parameters to each optimization problem to obtain better solutions. In this study, a self-adaptive multi-objective harmony search (SaMOHS) algorithm is developed to apply the parameter-setting-free technique, which is an example of a self-adaptive methodology. The SaMOHS algorithm attempts to remove some of the inconvenience from parameter setting and selects the most adaptive parameters during the iterative solution search process. To verify the proposed algorithm, an optimal least cost water distribution network design problem is applied to three different target networks. The results are compared with other well-known algorithms such as multi-objective harmony search and the non-dominated sorting genetic algorithm-II. The efficiency of the proposed algorithm is quantified by suitable performance indices. The results indicate that SaMOHS can be efficiently applied to the search for Pareto-optimal solutions in a multi-objective solution space.

Combinatorial Optimization in Project Selection Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Dewi, Sari; Sawaluddin

2018-01-01

This paper discusses the problem of project selection in the presence of two objective functions that maximize profit and minimize cost and the existence of some limitations is limited resources availability and time available so that there is need allocation of resources in each project. These resources are human resources, machine resources, raw material resources. This is treated as a consideration to not exceed the budget that has been determined. So that can be formulated mathematics for objective function (multi-objective) with boundaries that fulfilled. To assist the project selection process, a multi-objective combinatorial optimization approach is used to obtain an optimal solution for the selection of the right project. It then described a multi-objective method of genetic algorithm as one method of multi-objective combinatorial optimization approach to simplify the project selection process in a large scope.

Improving multi-objective reservoir operation optimization with sensitivity-informed problem decomposition

NASA Astrophysics Data System (ADS)

Chu, J. G.; Zhang, C.; Fu, G. T.; Li, Y.; Zhou, H. C.

2015-04-01

This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses global sensitivity analysis as a screening tool to reduce the computational demands. Sobol's method is used to screen insensitive decision variables and guide the formulation of the optimization problems with a significantly reduced number of decision variables. This sensitivity-informed problem decomposition dramatically reduces the computational demands required for attaining high quality approximations of optimal tradeoff relationships between conflicting design objectives. The search results obtained from the reduced complexity multi-objective reservoir operation problems are then used to pre-condition the full search of the original optimization problem. In two case studies, the Dahuofang reservoir and the inter-basin multi-reservoir system in Liaoning province, China, sensitivity analysis results show that reservoir performance is strongly controlled by a small proportion of decision variables. Sensitivity-informed problem decomposition and pre-conditioning are evaluated in their ability to improve the efficiency and effectiveness of multi-objective evolutionary optimization. Overall, this study illustrates the efficiency and effectiveness of the sensitivity-informed method and the use of global sensitivity analysis to inform problem decomposition when solving the complex multi-objective reservoir operation problems.

On the usefulness of gradient information in multi-objective deformable image registration using a B-spline-based dual-dynamic transformation model: comparison of three optimization algorithms

NASA Astrophysics Data System (ADS)

Pirpinia, Kleopatra; Bosman, Peter A. N.; Sonke, Jan-Jakob; van Herk, Marcel; Alderliesten, Tanja

2015-03-01

The use of gradient information is well-known to be highly useful in single-objective optimization-based image registration methods. However, its usefulness has not yet been investigated for deformable image registration from a multi-objective optimization perspective. To this end, within a previously introduced multi-objective optimization framework, we use a smooth B-spline-based dual-dynamic transformation model that allows us to derive gradient information analytically, while still being able to account for large deformations. Within the multi-objective framework, we previously employed a powerful evolutionary algorithm (EA) that computes and advances multiple outcomes at once, resulting in a set of solutions (a so-called Pareto front) that represents efficient trade-offs between the objectives. With the addition of the B-spline-based transformation model, we studied the usefulness of gradient information in multiobjective deformable image registration using three different optimization algorithms: the (gradient-less) EA, a gradientonly algorithm, and a hybridization of these two. We evaluated the algorithms to register highly deformed images: 2D MRI slices of the breast in prone and supine positions. Results demonstrate that gradient-based multi-objective optimization significantly speeds up optimization in the initial stages of optimization. However, allowing sufficient computational resources, better results could still be obtained with the EA. Ultimately, the hybrid EA found the best overall approximation of the optimal Pareto front, further indicating that adding gradient-based optimization for multiobjective optimization-based deformable image registration can indeed be beneficial

Integrative systems modeling and multi-objective optimization

EPA Science Inventory

This presentation presents a number of algorithms, tools, and methods for utilizing multi-objective optimization within integrated systems modeling frameworks. We first present innovative methods using a genetic algorithm to optimally calibrate the VELMA and SWAT ecohydrological ...

Robust Dynamic Multi-objective Vehicle Routing Optimization Method.

PubMed

Guo, Yi-Nan; Cheng, Jian; Luo, Sha; Gong, Dun-Wei

2017-03-21

For dynamic multi-objective vehicle routing problems, the waiting time of vehicle, the number of serving vehicles, the total distance of routes were normally considered as the optimization objectives. Except for above objectives, fuel consumption that leads to the environmental pollution and energy consumption was focused on in this paper. Considering the vehicles' load and the driving distance, corresponding carbon emission model was built and set as an optimization objective. Dynamic multi-objective vehicle routing problems with hard time windows and randomly appeared dynamic customers, subsequently, were modeled. In existing planning methods, when the new service demand came up, global vehicle routing optimization method was triggered to find the optimal routes for non-served customers, which was time-consuming. Therefore, robust dynamic multi-objective vehicle routing method with two-phase is proposed. Three highlights of the novel method are: (i) After finding optimal robust virtual routes for all customers by adopting multi-objective particle swarm optimization in the first phase, static vehicle routes for static customers are formed by removing all dynamic customers from robust virtual routes in next phase. (ii)The dynamically appeared customers append to be served according to their service time and the vehicles' statues. Global vehicle routing optimization is triggered only when no suitable locations can be found for dynamic customers. (iii)A metric measuring the algorithms' robustness is given. The statistical results indicated that the routes obtained by the proposed method have better stability and robustness, but may be sub-optimum. Moreover, time-consuming global vehicle routing optimization is avoided as dynamic customers appear.

Research on connection structure of aluminumbody bus using multi-objective topology optimization

NASA Astrophysics Data System (ADS)

Peng, Q.; Ni, X.; Han, F.; Rhaman, K.; Ulianov, C.; Fang, X.

2018-01-01

For connecting Aluminum Alloy bus body aluminum components often occur the problem of failure, a new aluminum alloy connection structure is designed based on multi-objective topology optimization method. Determining the shape of the outer contour of the connection structure with topography optimization, establishing a topology optimization model of connections based on SIMP density interpolation method, going on multi-objective topology optimization, and improving the design of the connecting piece according to the optimization results. The results show that the quality of the aluminum alloy connector after topology optimization is reduced by 18%, and the first six natural frequencies are improved and the strength performance and stiffness performance are obviously improved.

Emergency strategy optimization for the environmental control system in manned spacecraft

NASA Astrophysics Data System (ADS)

Li, Guoxiang; Pang, Liping; Liu, Meng; Fang, Yufeng; Zhang, Helin

2018-02-01

It is very important for a manned environmental control system (ECS) to be able to reconfigure its operation strategy in emergency conditions. In this article, a multi-objective optimization is established to design the optimal emergency strategy for an ECS in an insufficient power supply condition. The maximum ECS lifetime and the minimum power consumption are chosen as the optimization objectives. Some adjustable key variables are chosen as the optimization variables, which finally represent the reconfigured emergency strategy. The non-dominated sorting genetic algorithm-II is adopted to solve this multi-objective optimization problem. Optimization processes are conducted at four different carbon dioxide partial pressure control levels. The study results show that the Pareto-optimal frontiers obtained from this multi-objective optimization can represent the relationship between the lifetime and the power consumption of the ECS. Hence, the preferred emergency operation strategy can be recommended for situations when there is suddenly insufficient power.

Analytic hierarchy process-based approach for selecting a Pareto-optimal solution of a multi-objective, multi-site supply-chain planning problem

NASA Astrophysics Data System (ADS)

Ayadi, Omar; Felfel, Houssem; Masmoudi, Faouzi

2017-07-01

The current manufacturing environment has changed from traditional single-plant to multi-site supply chain where multiple plants are serving customer demands. In this article, a tactical multi-objective, multi-period, multi-product, multi-site supply-chain planning problem is proposed. A corresponding optimization model aiming to simultaneously minimize the total cost, maximize product quality and maximize the customer satisfaction demand level is developed. The proposed solution approach yields to a front of Pareto-optimal solutions that represents the trade-offs among the different objectives. Subsequently, the analytic hierarchy process method is applied to select the best Pareto-optimal solution according to the preferences of the decision maker. The robustness of the solutions and the proposed approach are discussed based on a sensitivity analysis and an application to a real case from the textile and apparel industry.

Visualization Based Data Mining for Comparison Between Two Solar Cell Libraries.

PubMed

Yosipof, Abraham; Kaspi, Omer; Majhi, Koushik; Senderowitz, Hanoch

2016-12-01

Material informatics may provide meaningful insights and powerful predictions for the development of new and efficient Metal Oxide (MO) based solar cells. The main objective of this paper is to establish the usefulness of data reduction and visualization methods for analyzing data sets emerging from multiple all-MOs solar cell libraries. For this purpose, two libraries, TiO 2 |Co 3 O 4 and TiO 2 |Co 3 O 4 |MoO 3 , differing only by the presence of a MoO 3 layer in the latter were analyzed with Principal Component Analysis and Self-Organizing Maps. Both analyses suggest that the addition of the MoO 3 layer to the TiO 2 |Co 3 O 4 library has affected the overall photovoltaic (PV) activity profile of the solar cells making the two libraries clearly distinguishable from one another. Furthermore, while MoO 3 had an overall favorable effect on PV parameters, a sub-population of cells was identified which were either indifferent to its presence or even demonstrated a reduction in several parameters. © 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation.

PubMed

Warid, Warid; Hizam, Hashim; Mariun, Norman; Abdul-Wahab, Noor Izzri

2016-01-01

This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality.

An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation

PubMed Central

Warid, Warid; Hizam, Hashim; Mariun, Norman; Abdul-Wahab, Noor Izzri

2016-01-01

This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality. PMID:26954783

Global, Multi-Objective Trajectory Optimization With Parametric Spreading

NASA Technical Reports Server (NTRS)

Vavrina, Matthew A.; Englander, Jacob A.; Phillips, Sean M.; Hughes, Kyle M.

2017-01-01

Mission design problems are often characterized by multiple, competing trajectory optimization objectives. Recent multi-objective trajectory optimization formulations enable generation of globally-optimal, Pareto solutions via a multi-objective genetic algorithm. A byproduct of these formulations is that clustering in design space can occur in evolving the population towards the Pareto front. This clustering can be a drawback, however, if parametric evaluations of design variables are desired. This effort addresses clustering by incorporating operators that encourage a uniform spread over specified design variables while maintaining Pareto front representation. The algorithm is demonstrated on a Neptune orbiter mission, and enhanced multidimensional visualization strategies are presented.

A method for optimizing multi-objective reservoir operation upon human and riverine ecosystem demands

NASA Astrophysics Data System (ADS)

Ai, Xueshan; Dong, Zuo; Mo, Mingzhu

2017-04-01

The optimal reservoir operation is in generally a multi-objective problem. In real life, most of the reservoir operation optimization problems involve conflicting objectives, for which there is no single optimal solution which can simultaneously gain an optimal result of all the purposes, but rather a set of well distributed non-inferior solutions or Pareto frontier exists. On the other hand, most of the reservoirs operation rules is to gain greater social and economic benefits at the expense of ecological environment, resulting to the destruction of riverine ecology and reduction of aquatic biodiversity. To overcome these drawbacks, this study developed a multi-objective model for the reservoir operating with the conflicting functions of hydroelectric energy generation, irrigation and ecological protection. To solve the model with the objectives of maximize energy production, maximize the water demand satisfaction rate of irrigation and ecology, we proposed a multi-objective optimization method of variable penalty coefficient (VPC), which was based on integrate dynamic programming (DP) with discrete differential dynamic programming (DDDP), to generate a well distributed non-inferior along the Pareto front by changing the penalties coefficient of different objectives. This method was applied to an existing China reservoir named Donggu, through a course of a year, which is a multi-annual storage reservoir with multiple purposes. The case study results showed a good relationship between any two of the objectives and a good Pareto optimal solutions, which provide a reference for the reservoir decision makers.

Performance optimization of the power user electric energy data acquire system based on MOEA/D evolutionary algorithm

NASA Astrophysics Data System (ADS)

Ding, Zhongan; Gao, Chen; Yan, Shengteng; Yang, Canrong

2017-10-01

The power user electric energy data acquire system (PUEEDAS) is an important part of smart grid. This paper builds a multi-objective optimization model for the performance of the PUEEADS from the point of view of the combination of the comprehensive benefits and cost. Meanwhile, the Chebyshev decomposition approach is used to decompose the multi-objective optimization problem. We design a MOEA/D evolutionary algorithm to solve the problem. By analyzing the Pareto optimal solution set of multi-objective optimization problem and comparing it with the monitoring value to grasp the direction of optimizing the performance of the PUEEDAS. Finally, an example is designed for specific analysis.

Highly efficient hybrid energy generator: coupled organic photovoltaic device and randomly oriented electrospun poly(vinylidene fluoride) nanofiber.

PubMed

Park, Boongik; Lee, Kihwan; Park, Jongjin; Kim, Jongmin; Kim, Ohyun

2013-03-01

A hybrid architecture consisting of an inverted organic photovoltaic device and a randomly-oriented electrospun PVDF piezoelectric device was fabricated as a highly-efficient energy generator. It uses the inverted photovoltaic device with coupled electrospun PVDF nanofibers as tandem structure to convert solar and mechanical vibrations energy to electricity simultaneously or individually. The power conversion efficiency of the photovoltaic device was also significantly improved up to 4.72% by optimized processes such as intrinsic ZnO, MoO3 and active layer. A simple electrospinning method with the two electrode technique was adopted to achieve a high voltage of - 300 mV in PVDF piezoelectric fibers. Highly-efficient HEG using voltage adder circuit provides the conceptual possibility of realizing multi-functional energy generator whenever and wherever various energy sources are available.

Solution processed transition metal oxide anode buffer layers for efficiency and stability enhancement of polymer solar cells

NASA Astrophysics Data System (ADS)

Ameen, M. Yoosuf; Shamjid, P.; Abhijith, T.; Reddy, V. S.

2018-01-01

Polymer solar cells were fabricated with solution-processed transition metal oxides, MoO3 and V2O5 as anode buffer layers (ABLs). The optimized device with V2O5 ABL exhibited considerably higher power conversion efficiency (PCE) compared to the devices based on MoO3 and poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) ABLs. The space charge limited current measurements and impedance spectroscopy results of hole-only devices revealed that V2O5 provided a very low charge transfer resistance and high hole mobility, facilitating efficient hole transfer from the active layer to the ITO anode. More importantly, incorporation of V2O5 as ABL resulted in substantial improvement in device stability compared to MoO3 and PEDOT:PSS based devices. Unencapsulated PEDOT:PSS-based devices stored at a relative humidity of 45% have shown complete failure within 96 h. Whereas, MoO3 and V2O5 based devices stored in similar conditions retained 22% and 80% of their initial PCEs after 96 h. Significantly higher stability of the V2O5-based device is ascribed to the reduction in degradation of the anode/active layer interface, as evident from the electrical measurements.

MOOsburg: Multi-User Domain Support for a Community Network.

ERIC Educational Resources Information Center

Carroll, John M.; Rosson, Mary Beth; Isenhour, Philip L.; Van Metre, Christina; Schafer, Wendy A.; Ganoe, Craig H.

2001-01-01

Explains MOOsburg, a community-oriented MOO that models the geography of the town of Blacksburg, Virginia and is designed to be used by local residents. Highlights include the software architecture; client-server communication; spatial database; user interface; interaction; map-based navigation; application development; and future plans. (LRW)

On the Improvement of Convergence Performance for Integrated Design of Wind Turbine Blade Using a Vector Dominating Multi-objective Evolution Algorithm

NASA Astrophysics Data System (ADS)

Wang, L.; Wang, T. G.; Wu, J. H.; Cheng, G. P.

2016-09-01

A novel multi-objective optimization algorithm incorporating evolution strategies and vector mechanisms, referred as VD-MOEA, is proposed and applied in aerodynamic- structural integrated design of wind turbine blade. In the algorithm, a set of uniformly distributed vectors is constructed to guide population in moving forward to the Pareto front rapidly and maintain population diversity with high efficiency. For example, two- and three- objective designs of 1.5MW wind turbine blade are subsequently carried out for the optimization objectives of maximum annual energy production, minimum blade mass, and minimum extreme root thrust. The results show that the Pareto optimal solutions can be obtained in one single simulation run and uniformly distributed in the objective space, maximally maintaining the population diversity. In comparison to conventional evolution algorithms, VD-MOEA displays dramatic improvement of algorithm performance in both convergence and diversity preservation for handling complex problems of multi-variables, multi-objectives and multi-constraints. This provides a reliable high-performance optimization approach for the aerodynamic-structural integrated design of wind turbine blade.

Application of Multi-Objective Human Learning Optimization Method to Solve AC/DC Multi-Objective Optimal Power Flow Problem

NASA Astrophysics Data System (ADS)

Cao, Jia; Yan, Zheng; He, Guangyu

2016-06-01

This paper introduces an efficient algorithm, multi-objective human learning optimization method (MOHLO), to solve AC/DC multi-objective optimal power flow problem (MOPF). Firstly, the model of AC/DC MOPF including wind farms is constructed, where includes three objective functions, operating cost, power loss, and pollutant emission. Combining the non-dominated sorting technique and the crowding distance index, the MOHLO method can be derived, which involves individual learning operator, social learning operator, random exploration learning operator and adaptive strategies. Both the proposed MOHLO method and non-dominated sorting genetic algorithm II (NSGAII) are tested on an improved IEEE 30-bus AC/DC hybrid system. Simulation results show that MOHLO method has excellent search efficiency and the powerful ability of searching optimal. Above all, MOHLO method can obtain more complete pareto front than that by NSGAII method. However, how to choose the optimal solution from pareto front depends mainly on the decision makers who stand from the economic point of view or from the energy saving and emission reduction point of view.

Facile Solution Synthesis of Tungsten Trioxide Doped with Nanocrystalline Molybdenum Trioxide for Electrochromic Devices.

PubMed

Hasani, Amirhossein; Le, Quyet Van; Nguyen, Thang Phan; Choi, Kyoung Soon; Sohn, Woonbae; Kim, Jang-Kyo; Jang, Ho Won; Kim, Soo Young

2017-10-16

A facile, highly efficient approach to obtain molybdenum trioxide (MoO 3 )-doped tungsten trioxide (WO 3 ) is reported. An annealing process was used to transform ammonium tetrathiotungstate [(NH 4 ) 2 WS 4 ] to WO 3 in the presence of oxygen. Ammonium tetrathiomolybdate [(NH 4 ) 2 MoS 4 ] was used as a dopant to improve the film for use in an electrochromic (EC) cell. (NH 4 ) 2 MoS 4 at different concentrations (10, 20, 30, and 40 mM) was added to the (NH 4 ) 2 WS 4 precursor by sonication and the samples were annealed at 500 °C in air. Raman, X-ray diffraction, and X-ray photoelectron spectroscopy measurements confirmed that the (NH 4 ) 2 WS 4 precursor decomposed to WO 3 and the (NH 4 ) 2 MoS 4 -(NH 4 ) 2 WS 4 precursor was transformed to MoO 3 -doped WO 3 after annealing at 500 °C. It is shown that the MoO 3 -doped WO 3 film is more uniform and porous than pure WO 3 , confirming the doping quality and the privileges of the proposed method. The optimal MoO 3 -doped WO 3 used as an EC layer exhibited a high coloration efficiency of 128.1 cm 2 /C, which is larger than that of pure WO 3 (74.5 cm 2 /C). Therefore, MoO 3 -doped WO 3 synthesized by the reported method is a promising candidate for high-efficiency and low-cost smart windows.

Mechanism of corrosion of Ni base superalloys by molten Na2MoO4 at elevated temperatures

NASA Technical Reports Server (NTRS)

Misra, A. K.; Stearns, C. A.

1983-01-01

The corrosion of nickel base superalloy, U-700, by molten Na2MoO4 was studied in the temperature range of 750 deg to 950 deg C. After an induction period, the rate of corrosion is linear and catastrophic corrosion is observed. It is shown that the induction period is associated with the attainment of a minimum MoO3 activity in the melt, which corresponds to the equilibrium MoO3 activity for the reaction, 2MoO3(l) + Mo = 3MoO2(s). A mechanism is proposed to describe the catastrophic nature of corrosion, which involves transport of Ni++ through the melt resulting in formulation of NiO at the melt gas interface and basic fluxing of Cr2O3. The effect of the amount of Na2MoO4 on the corrosion kinetics was also studied. It is found that evaporation and the thermodynamic calculations for the Na2MoO4 - MoO3 system the activity of MoO3 is reduced considerably when dissolved in Na2MoO4, which causes a sharp decrease in the rate of evaporation of MoO3 from a Na2MoO4 - MoO3 melt.

Implications of Preference and Problem Formulation on the Operating Policies of Complex Multi-Reservoir Systems

NASA Astrophysics Data System (ADS)

Quinn, J.; Reed, P. M.; Giuliani, M.; Castelletti, A.

2016-12-01

Optimizing the operations of multi-reservoir systems poses several challenges: 1) the high dimension of the problem's states and controls, 2) the need to balance conflicting multi-sector objectives, and 3) understanding how uncertainties impact system performance. These difficulties motivated the development of the Evolutionary Multi-Objective Direct Policy Search (EMODPS) framework, in which multi-reservoir operating policies are parameterized in a given family of functions and then optimized for multiple objectives through simulation over a set of stochastic inputs. However, properly framing these objectives remains a severe challenge and a neglected source of uncertainty. Here, we use EMODPS to optimize operating policies for a 4-reservoir system in the Red River Basin in Vietnam, exploring the consequences of optimizing to different sets of objectives related to 1) hydropower production, 2) meeting multi-sector water demands, and 3) providing flood protection to the capital city of Hanoi. We show how coordinated operation of the reservoirs can differ markedly depending on how decision makers weigh these concerns. Moreover, we illustrate how formulation choices that emphasize the mean, tail, or variability of performance across objective combinations must be evaluated carefully. Our results show that these choices can significantly improve attainable system performance, or yield severe unintended consequences. Finally, we show that satisfactory validation of the operating policies on a set of out-of-sample stochastic inputs depends as much or more on the formulation of the objectives as on effective optimization of the policies. These observations highlight the importance of carefully considering how we abstract stakeholders' objectives and of iteratively optimizing and visualizing multiple problem formulation hypotheses to ensure that we capture the most important tradeoffs that emerge from different stakeholder preferences.

Distributed Generation Planning using Peer Enhanced Multi-objective Teaching-Learning based Optimization in Distribution Networks

NASA Astrophysics Data System (ADS)

Selvam, Kayalvizhi; Vinod Kumar, D. M.; Siripuram, Ramakanth

2017-04-01

In this paper, an optimization technique called peer enhanced teaching learning based optimization (PeTLBO) algorithm is used in multi-objective problem domain. The PeTLBO algorithm is parameter less so it reduced the computational burden. The proposed peer enhanced multi-objective based TLBO (PeMOTLBO) algorithm has been utilized to find a set of non-dominated optimal solutions [distributed generation (DG) location and sizing in distribution network]. The objectives considered are: real power loss and the voltage deviation subjected to voltage limits and maximum penetration level of DG in distribution network. Since the DG considered is capable of injecting real and reactive power to the distribution network the power factor is considered as 0.85 lead. The proposed peer enhanced multi-objective optimization technique provides different trade-off solutions in order to find the best compromise solution a fuzzy set theory approach has been used. The effectiveness of this proposed PeMOTLBO is tested on IEEE 33-bus and Indian 85-bus distribution system. The performance is validated with Pareto fronts and two performance metrics (C-metric and S-metric) by comparing with robust multi-objective technique called non-dominated sorting genetic algorithm-II and also with the basic TLBO.

Solving multi-objective optimization problems in conservation with the reference point method

PubMed Central

Dujardin, Yann; Chadès, Iadine

2018-01-01

Managing the biodiversity extinction crisis requires wise decision-making processes able to account for the limited resources available. In most decision problems in conservation biology, several conflicting objectives have to be taken into account. Most methods used in conservation either provide suboptimal solutions or use strong assumptions about the decision-maker’s preferences. Our paper reviews some of the existing approaches to solve multi-objective decision problems and presents new multi-objective linear programming formulations of two multi-objective optimization problems in conservation, allowing the use of a reference point approach. Reference point approaches solve multi-objective optimization problems by interactively representing the preferences of the decision-maker with a point in the criteria (objectives) space, called the reference point. We modelled and solved the following two problems in conservation: a dynamic multi-species management problem under uncertainty and a spatial allocation resource management problem. Results show that the reference point method outperforms classic methods while illustrating the use of an interactive methodology for solving combinatorial problems with multiple objectives. The method is general and can be adapted to a wide range of ecological combinatorial problems. PMID:29293650

Multi-Objective Programming for Lot-Sizing with Quantity Discount

NASA Astrophysics Data System (ADS)

Kang, He-Yau; Lee, Amy H. I.; Lai, Chun-Mei; Kang, Mei-Sung

2011-11-01

Multi-objective programming (MOP) is one of the popular methods for decision making in a complex environment. In a MOP, decision makers try to optimize two or more objectives simultaneously under various constraints. A complete optimal solution seldom exists, and a Pareto-optimal solution is usually used. Some methods, such as the weighting method which assigns priorities to the objectives and sets aspiration levels for the objectives, are used to derive a compromise solution. The ɛ-constraint method is a modified weight method. One of the objective functions is optimized while the other objective functions are treated as constraints and are incorporated in the constraint part of the model. This research considers a stochastic lot-sizing problem with multi-suppliers and quantity discounts. The model is transformed into a mixed integer programming (MIP) model next based on the ɛ-constraint method. An illustrative example is used to illustrate the practicality of the proposed model. The results demonstrate that the model is an effective and accurate tool for determining the replenishment of a manufacturer from multiple suppliers for multi-periods.

Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing

PubMed Central

Abubaker, Ahmad; Baharum, Adam; Alrefaei, Mahmoud

2015-01-01

This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, “MOPSOSA”. The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets to a suitable number of clusters. MOPSOSA combines the features of the multi-objective based particle swarm optimization (PSO) and the Multi-Objective Simulated Annealing (MOSA). Three cluster validity indices were optimized simultaneously to establish the suitable number of clusters and the appropriate clustering for a dataset. The first cluster validity index is centred on Euclidean distance, the second on the point symmetry distance, and the last cluster validity index is based on short distance. A number of algorithms have been compared with the MOPSOSA algorithm in resolving clustering problems by determining the actual number of clusters and optimal clustering. Computational experiments were carried out to study fourteen artificial and five real life datasets. PMID:26132309

Image Segmentation Method Using Fuzzy C Mean Clustering Based on Multi-Objective Optimization

NASA Astrophysics Data System (ADS)

Chen, Jinlin; Yang, Chunzhi; Xu, Guangkui; Ning, Li

2018-04-01

Image segmentation is not only one of the hottest topics in digital image processing, but also an important part of computer vision applications. As one kind of image segmentation algorithms, fuzzy C-means clustering is an effective and concise segmentation algorithm. However, the drawback of FCM is that it is sensitive to image noise. To solve the problem, this paper designs a novel fuzzy C-mean clustering algorithm based on multi-objective optimization. We add a parameter λ to the fuzzy distance measurement formula to improve the multi-objective optimization. The parameter λ can adjust the weights of the pixel local information. In the algorithm, the local correlation of neighboring pixels is added to the improved multi-objective mathematical model to optimize the clustering cent. Two different experimental results show that the novel fuzzy C-means approach has an efficient performance and computational time while segmenting images by different type of noises.

Uncertainty-Based Multi-Objective Optimization of Groundwater Remediation Design

NASA Astrophysics Data System (ADS)

Singh, A.; Minsker, B.

2003-12-01

Management of groundwater contamination is a cost-intensive undertaking filled with conflicting objectives and substantial uncertainty. A critical source of this uncertainty in groundwater remediation design problems comes from the hydraulic conductivity values for the aquifer, upon which the prediction of flow and transport of contaminants are dependent. For a remediation solution to be reliable in practice it is important that it is robust over the potential error in the model predictions. This work focuses on incorporating such uncertainty within a multi-objective optimization framework, to get reliable as well as Pareto optimal solutions. Previous research has shown that small amounts of sampling within a single-objective genetic algorithm can produce highly reliable solutions. However with multiple objectives the noise can interfere with the basic operations of a multi-objective solver, such as determining non-domination of individuals, diversity preservation, and elitism. This work proposes several approaches to improve the performance of noisy multi-objective solvers. These include a simple averaging approach, taking samples across the population (which we call extended averaging), and a stochastic optimization approach. All the approaches are tested on standard multi-objective benchmark problems and a hypothetical groundwater remediation case-study; the best-performing approach is then tested on a field-scale case at Umatilla Army Depot.

Multi-objective optimization to predict muscle tensions in a pinch function using genetic algorithm

NASA Astrophysics Data System (ADS)

Bensghaier, Amani; Romdhane, Lotfi; Benouezdou, Fethi

2012-03-01

This work is focused on the determination of the thumb and the index finger muscle tensions in a tip pinch task. A biomechanical model of the musculoskeletal system of the thumb and the index finger is developed. Due to the assumptions made in carrying out the biomechanical model, the formulated force analysis problem is indeterminate leading to an infinite number of solutions. Thus, constrained single and multi-objective optimization methodologies are used in order to explore the muscular redundancy and to predict optimal muscle tension distributions. Various models are investigated using the optimization process. The basic criteria to minimize are the sum of the muscle stresses, the sum of individual muscle tensions and the maximum muscle stress. The multi-objective optimization is solved using a Pareto genetic algorithm to obtain non-dominated solutions, defined as the set of optimal distributions of muscle tensions. The results show the advantage of the multi-objective formulation over the single objective one. The obtained solutions are compared to those available in the literature demonstrating the effectiveness of our approach in the analysis of the fingers musculoskeletal systems when predicting muscle tensions.

A lexicographic weighted Tchebycheff approach for multi-constrained multi-objective optimization of the surface grinding process

NASA Astrophysics Data System (ADS)

Khalilpourazari, Soheyl; Khalilpourazary, Saman

2017-05-01

In this article a multi-objective mathematical model is developed to minimize total time and cost while maximizing the production rate and surface finish quality in the grinding process. The model aims to determine optimal values of the decision variables considering process constraints. A lexicographic weighted Tchebycheff approach is developed to obtain efficient Pareto-optimal solutions of the problem in both rough and finished conditions. Utilizing a polyhedral branch-and-cut algorithm, the lexicographic weighted Tchebycheff model of the proposed multi-objective model is solved using GAMS software. The Pareto-optimal solutions provide a proper trade-off between conflicting objective functions which helps the decision maker to select the best values for the decision variables. Sensitivity analyses are performed to determine the effect of change in the grain size, grinding ratio, feed rate, labour cost per hour, length of workpiece, wheel diameter and downfeed of grinding parameters on each value of the objective function.

Multi-Objective Optimization of an In situ Bioremediation Technology to Treat Perchlorate-Contaminated Groundwater

EPA Science Inventory

The presentation shows how a multi-objective optimization method is integrated into a transport simulator (MT3D) for estimating parameters and cost of in-situ bioremediation technology to treat perchlorate-contaminated groundwater.

Direct observation of MoO 2 crystal growth from amorphous MoO 3 film

NASA Astrophysics Data System (ADS)

Nina, Kenji; Kimura, Yuki; Yokoyama, Kaori; Kido, Osamu; Binyo, Gong; Kaito, Chihiro

2008-08-01

The formation process of MoO 2 crystal from amorphous MoO 3 film has been imaged by in situ observation with a transmission electron microscope. Selective growth of flower-shaped MoO 2 crystals by heating above 673 K in vacuum was directly observed. Since the MoO 2 crystal has metallic conductivity of the order of indium oxide film containing tin (ITO film), the thin film growth of the MoO 2 phase has been discussed on the basis of a new substitute for ITO film.

Performance Optimizing Multi-Objective Adaptive Control with Time-Varying Model Reference Modification

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.; Hashemi, Kelley E.; Yucelen, Tansel; Arabi, Ehsan

2017-01-01

This paper presents a new adaptive control approach that involves a performance optimization objective. The problem is cast as a multi-objective optimal control. The control synthesis involves the design of a performance optimizing controller from a subset of control inputs. The effect of the performance optimizing controller is to introduce an uncertainty into the system that can degrade tracking of the reference model. An adaptive controller from the remaining control inputs is designed to reduce the effect of the uncertainty while maintaining a notion of performance optimization in the adaptive control system.

Parameter Estimation of Computationally Expensive Watershed Models Through Efficient Multi-objective Optimization and Interactive Decision Analytics

NASA Astrophysics Data System (ADS)

Akhtar, Taimoor; Shoemaker, Christine

2016-04-01

Watershed model calibration is inherently a multi-criteria problem. Conflicting trade-offs exist between different quantifiable calibration criterions indicating the non-existence of a single optimal parameterization. Hence, many experts prefer a manual approach to calibration where the inherent multi-objective nature of the calibration problem is addressed through an interactive, subjective, time-intensive and complex decision making process. Multi-objective optimization can be used to efficiently identify multiple plausible calibration alternatives and assist calibration experts during the parameter estimation process. However, there are key challenges to the use of multi objective optimization in the parameter estimation process which include: 1) multi-objective optimization usually requires many model simulations, which is difficult for complex simulation models that are computationally expensive; and 2) selection of one from numerous calibration alternatives provided by multi-objective optimization is non-trivial. This study proposes a "Hybrid Automatic Manual Strategy" (HAMS) for watershed model calibration to specifically address the above-mentioned challenges. HAMS employs a 3-stage framework for parameter estimation. Stage 1 incorporates the use of an efficient surrogate multi-objective algorithm, GOMORS, for identification of numerous calibration alternatives within a limited simulation evaluation budget. The novelty of HAMS is embedded in Stages 2 and 3 where an interactive visual and metric based analytics framework is available as a decision support tool to choose a single calibration from the numerous alternatives identified in Stage 1. Stage 2 of HAMS provides a goodness-of-fit measure / metric based interactive framework for identification of a small subset (typically less than 10) of meaningful and diverse set of calibration alternatives from the numerous alternatives obtained in Stage 1. Stage 3 incorporates the use of an interactive visual analytics framework for decision support in selection of one parameter combination from the alternatives identified in Stage 2. HAMS is applied for calibration of flow parameters of a SWAT model, (Soil and Water Assessment Tool) designed to simulate flow in the Cannonsville watershed in upstate New York. Results from the application of HAMS to Cannonsville indicate that efficient multi-objective optimization and interactive visual and metric based analytics can bridge the gap between the effective use of both automatic and manual strategies for parameter estimation of computationally expensive watershed models.

Preparation of MoO2/g-C3N4 composites with a high surface area and its application in deep desulfurization from model oil

NASA Astrophysics Data System (ADS)

Hou, Liang-pei; Zhao, Rong-xiang; Li, Xiu-ping; Gao, Xiao-han

2018-03-01

A series of catalysts of composition X-MoO2/g-C3N4 (X = 0, 0.5, 1, 3, 5 wt.%) were successfully synthesized by calcination of a mixture of (NH4)6Mo7O24·4H2O and g-C3N4. Oxidative desulfurization experiments were conducted using X-MoO2/g-C3N4 as a catalyst, H2O2 as an oxidant, and ionic liquids (ILs) as extraction agents. Catalysts were characterized by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Fourier-transform infrared (FT-IR), scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS), and Brunauer-Emmett-Teller analysis (BET). Characterization results suggested that MoO2 was present in the catalyst and its crystallinity improved with increased Mo-loading. The catalysts had a larger specific surface area due to the presence of MoO2 dispersed on g-C3N4. Experimental results showed that 3%-MoO2/g-C3N4 had the highest catalytic activity among all the catalysts tested. A desulfurization rate of 96.0% was achieved under optimal conditions. Through gas chromatography-mass spectrometry (GC-MS) analysis, it was shown that dibenzothoiphene sulfone was the sole product of the oxidation desulfurization reaction. An apparent activation energy of 61.1 kJ/mol was estimated based on Arrhenius equation. The activity of 3%-MoO2/g-C3N4 slightly decreased after six runs. A possible mechanism for the reaction has been proposed.

Solid State Reduction of MoO3 with Carbon via Mechanical Alloying to Synthesize Nano-Crystaline MoO2

NASA Astrophysics Data System (ADS)

Saghafi, M.; Ataie, A.; Heshmati-Manesh, S.

In this research, effect of milling time on solid state reduction of MoO3 with carbon has been investigated. It was found that mechanical activation of a mixture of MoO3 and carbon at ambient temperature by high energy ball milling was not able to reduce MoO3 to metallic molybdenum. MoO3 was converted to MoO2 at the first stage of reduction and peaks of the latter phase in X-ray diffraction patterns were detected when the milling time exceeded from 50 hours. The main effect of increased milling time at this stage was decreasing of MoO3 peak intensities and significant peak broadening due to decrease in size of crystallites. After prolonged milling, MoO3 was fully reduced to nano-crystalline MoO2 and its mean crystallite size was calculated using Williamson-Hall technique and found to be 17.5 nm. Thermodynamic investigations also confirm the possibility of reduction of MoO3 to MoO2 during the milling operation at room temperature. But, further reduction to metallic molybdenum requires thermal activation at higher temperature near 1100 K. XRD and SEM techniques were employed to evaluate the powder particles characteristics.

A multi-objective programming model for assessment the GHG emissions in MSW management

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mavrotas, George, E-mail: mavrotas@chemeng.ntua.gr; Skoulaxinou, Sotiria; Gakis, Nikos

2013-09-15

Highlights: • The multi-objective multi-period optimization model. • The solution approach for the generation of the Pareto front with mathematical programming. • The very detailed description of the model (decision variables, parameters, equations). • The use of IPCC 2006 guidelines for landfill emissions (first order decay model) in the mathematical programming formulation. - Abstract: In this study a multi-objective mathematical programming model is developed for taking into account GHG emissions for Municipal Solid Waste (MSW) management. Mathematical programming models are often used for structure, design and operational optimization of various systems (energy, supply chain, processes, etc.). The last twenty yearsmore » they are used all the more often in Municipal Solid Waste (MSW) management in order to provide optimal solutions with the cost objective being the usual driver of the optimization. In our work we consider the GHG emissions as an additional criterion, aiming at a multi-objective approach. The Pareto front (Cost vs. GHG emissions) of the system is generated using an appropriate multi-objective method. This information is essential to the decision maker because he can explore the trade-offs in the Pareto curve and select his most preferred among the Pareto optimal solutions. In the present work a detailed multi-objective, multi-period mathematical programming model is developed in order to describe the waste management problem. Apart from the bi-objective approach, the major innovations of the model are (1) the detailed modeling considering 34 materials and 42 technologies, (2) the detailed calculation of the energy content of the various streams based on the detailed material balances, and (3) the incorporation of the IPCC guidelines for the CH{sub 4} generated in the landfills (first order decay model). The equations of the model are described in full detail. Finally, the whole approach is illustrated with a case study referring to the application of the model in a Greek region.« less

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method

NASA Astrophysics Data System (ADS)

Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

2017-05-01

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability.

Network structure of Mo-oxide glasses

NASA Astrophysics Data System (ADS)

Fabian, M.; Svab, E.; Milanova, M.; Krezhov, K.

2017-01-01

The structure of molybdate glasses have been investigated by neutron and high-energy X-ray diffraction coupled with Reverse Monte Carlo (RMC) simulation technique. From the modelling the partial atomic correlation functions g ij(r), the coordination number distributions CN ij and bond angle distributions have been revealed. For binary 90MoO3-10Nd2O3 glass composition the fraction of MoO4/MoO6 was 0.55/0.25. Three type of ternary system have been studied, where the most important structural units was authenticated. For MoO3-Nd2O3-B2O3 sample mixed MoO4-BO4 and MoO4-BO3 linkages form pronounced intermediate-range order. In case of MoO3-ZnO-B2O3 series the BO3 and BO4 units are linked to MoO4 and/or ZnO4, forming mixed MoO4-BO4(BO3), MoO4-ZnO4 and ZnO4-BO4(BO3) bond-linkages.

Improved NSGA model for multi objective operation scheduling and its evaluation

NASA Astrophysics Data System (ADS)

Li, Weining; Wang, Fuyu

2017-09-01

Reasonable operation can increase the income of the hospital and improve the patient’s satisfactory level. In this paper, by using multi object operation scheduling method with improved NSGA algorithm, it shortens the operation time, reduces the operation costand lowers the operation risk, the multi-objective optimization model is established for flexible operation scheduling, through the MATLAB simulation method, the Pareto solution is obtained, the standardization of data processing. The optimal scheduling scheme is selected by using entropy weight -Topsis combination method. The results show that the algorithm is feasible to solve the multi-objective operation scheduling problem, and provide a reference for hospital operation scheduling.

Multi-objective Optimization on Helium Liquefier Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Wang, H. R.; Xiong, L. Y.; Peng, N.; Meng, Y. R.; Liu, L. Q.

2017-02-01

Research on optimization of helium liquefier is limited at home and abroad, and most of the optimization is single-objective based on Collins cycle. In this paper, a multi-objective optimization is conducted using genetic algorithm (GA) on the 40 L/h helium liquefier developed by Technical Institute of Physics and Chemistry of the Chinese Academy of Science (TIPC, CAS), steady solutions are obtained in the end. In addition, the exergy loss of the optimized system is studied in the case of with and without liquid nitrogen pre-cooling. The results have guiding significance for the future design of large helium liquefier.

Collaborative Emission Reduction Model Based on Multi-Objective Optimization for Greenhouse Gases and Air Pollutants.

PubMed

Meng, Qing-chun; Rong, Xiao-xia; Zhang, Yi-min; Wan, Xiao-le; Liu, Yuan-yuan; Wang, Yu-zhi

2016-01-01

CO2 emission influences not only global climate change but also international economic and political situations. Thus, reducing the emission of CO2, a major greenhouse gas, has become a major issue in China and around the world as regards preserving the environmental ecology. Energy consumption from coal, oil, and natural gas is primarily responsible for the production of greenhouse gases and air pollutants such as SO2 and NOX, which are the main air pollutants in China. In this study, a mathematical multi-objective optimization method was adopted to analyze the collaborative emission reduction of three kinds of gases on the basis of their common restraints in different ways of energy consumption to develop an economic, clean, and efficient scheme for energy distribution. The first part introduces the background research, the collaborative emission reduction for three kinds of gases, the multi-objective optimization, the main mathematical modeling, and the optimization method. The second part discusses the four mathematical tools utilized in this study, which include the Granger causality test to analyze the causality between air quality and pollutant emission, a function analysis to determine the quantitative relation between energy consumption and pollutant emission, a multi-objective optimization to set up the collaborative optimization model that considers energy consumption, and an optimality condition analysis for the multi-objective optimization model to design the optimal-pole algorithm and obtain an efficient collaborative reduction scheme. In the empirical analysis, the data of pollutant emission and final consumption of energies of Tianjin in 1996-2012 was employed to verify the effectiveness of the model and analyze the efficient solution and the corresponding dominant set. In the last part, several suggestions for collaborative reduction are recommended and the drawn conclusions are stated.

Collaborative Emission Reduction Model Based on Multi-Objective Optimization for Greenhouse Gases and Air Pollutants

PubMed Central

Zhang, Yi-min; Wan, Xiao-le; Liu, Yuan-yuan; Wang, Yu-zhi

2016-01-01

CO2 emission influences not only global climate change but also international economic and political situations. Thus, reducing the emission of CO2, a major greenhouse gas, has become a major issue in China and around the world as regards preserving the environmental ecology. Energy consumption from coal, oil, and natural gas is primarily responsible for the production of greenhouse gases and air pollutants such as SO2 and NOX, which are the main air pollutants in China. In this study, a mathematical multi-objective optimization method was adopted to analyze the collaborative emission reduction of three kinds of gases on the basis of their common restraints in different ways of energy consumption to develop an economic, clean, and efficient scheme for energy distribution. The first part introduces the background research, the collaborative emission reduction for three kinds of gases, the multi-objective optimization, the main mathematical modeling, and the optimization method. The second part discusses the four mathematical tools utilized in this study, which include the Granger causality test to analyze the causality between air quality and pollutant emission, a function analysis to determine the quantitative relation between energy consumption and pollutant emission, a multi-objective optimization to set up the collaborative optimization model that considers energy consumption, and an optimality condition analysis for the multi-objective optimization model to design the optimal-pole algorithm and obtain an efficient collaborative reduction scheme. In the empirical analysis, the data of pollutant emission and final consumption of energies of Tianjin in 1996–2012 was employed to verify the effectiveness of the model and analyze the efficient solution and the corresponding dominant set. In the last part, several suggestions for collaborative reduction are recommended and the drawn conclusions are stated. PMID:27010658

Computer-Mediated Peer Review of Student Papers.

ERIC Educational Resources Information Center

Sullivan, Dave; Brown, Carol E.; Nielson, Norma L.

1998-01-01

Barriers to peer review of student work can be overcome using computer-mediated systems ranging from customized software to e-mail, multiuser object-oriented systems (MOOs), and Web-based projects. (SK)

Multi-objective optimization of riparian buffer networks; valuing present and future benefits

EPA Science Inventory

Multi-objective optimization has emerged as a popular approach to support water resources planning and management. This approach provides decision-makers with a suite of management options which are generated based on metrics that represent different social, economic, and environ...

Multi-objective optimization of composite structures. A review

NASA Astrophysics Data System (ADS)

Teters, G. A.; Kregers, A. F.

1996-05-01

Studies performed on the optimization of composite structures by coworkers of the Institute of Polymers Mechanics of the Latvian Academy of Sciences in recent years are reviewed. The possibility of controlling the geometry and anisotropy of laminar composite structures will make it possible to design articles that best satisfy the requirements established for them. Conflicting requirements such as maximum bearing capacity, minimum weight and/or cost, prescribed thermal conductivity and thermal expansion, etc. usually exist for optimal design. This results in the multi-objective compromise optimization of structures. Numerical methods have been developed for solution of problems of multi-objective optimization of composite structures; parameters of the structure of the reinforcement and the geometry of the design are assigned as controlling parameters. Programs designed to run on personal computers have been compiled for multi-objective optimization of the properties of composite materials, plates, and shells. Solutions are obtained for both linear and nonlinear models. The programs make it possible to establish the Pareto compromise region and special multicriterial solutions. The problem of the multi-objective optimization of the elastic moduli of a spatially reinforced fiberglass with stochastic stiffness parameters has been solved. The region of permissible solutions and the Pareto region have been found for the elastic moduli. The dimensions of the scatter ellipse have been determined for a multidimensional Gaussian probability distribution where correlation between the composite's properties being optimized are accounted for. Two types of problems involving the optimization of a laminar rectangular composite plate are considered: the plate is considered elastic and anisotropic in the first case, and viscoelastic properties are accounted for in the second. The angle of reinforcement and the relative amount of fibers in the longitudinal direction are controlling parameters. The optimized properties are the critical stresses, thermal conductivity, and thermal expansion. The properties of a plate are determined by the properties of the components in the composite, eight of which are stochastic. The region of multi-objective compromise solutions is presented, and the parameters of the scatter ellipses of the properties are given.

Provisional-Ideal-Point-Based Multi-objective Optimization Method for Drone Delivery Problem

NASA Astrophysics Data System (ADS)

Omagari, Hiroki; Higashino, Shin-Ichiro

2018-04-01

In this paper, we proposed a new evolutionary multi-objective optimization method for solving drone delivery problems (DDP). It can be formulated as a constrained multi-objective optimization problem. In our previous research, we proposed the "aspiration-point-based method" to solve multi-objective optimization problems. However, this method needs to calculate the optimal values of each objective function value in advance. Moreover, it does not consider the constraint conditions except for the objective functions. Therefore, it cannot apply to DDP which has many constraint conditions. To solve these issues, we proposed "provisional-ideal-point-based method." The proposed method defines a "penalty value" to search for feasible solutions. It also defines a new reference solution named "provisional-ideal point" to search for the preferred solution for a decision maker. In this way, we can eliminate the preliminary calculations and its limited application scope. The results of the benchmark test problems show that the proposed method can generate the preferred solution efficiently. The usefulness of the proposed method is also demonstrated by applying it to DDP. As a result, the delivery path when combining one drone and one truck drastically reduces the traveling distance and the delivery time compared with the case of using only one truck.

Real-time adaptive ramp metering : phase I, MILOS proof of concept (multi-objective, integrated, large-scale, optimized system).

DOT National Transportation Integrated Search

2006-12-01

Over the last several years, researchers at the University of Arizonas ATLAS Center have developed an adaptive ramp : metering system referred to as MILOS (Multi-Objective, Integrated, Large-Scale, Optimized System). The goal of this project : is ...

Path synthesis of four-bar mechanisms using synergy of polynomial neural network and Stackelberg game theory

NASA Astrophysics Data System (ADS)

Ahmadi, Bahman; Nariman-zadeh, Nader; Jamali, Ali

2017-06-01

In this article, a novel approach based on game theory is presented for multi-objective optimal synthesis of four-bar mechanisms. The multi-objective optimization problem is modelled as a Stackelberg game. The more important objective function, tracking error, is considered as the leader, and the other objective function, deviation of the transmission angle from 90° (TA), is considered as the follower. In a new approach, a group method of data handling (GMDH)-type neural network is also utilized to construct an approximate model for the rational reaction set (RRS) of the follower. Using the proposed game-theoretic approach, the multi-objective optimal synthesis of a four-bar mechanism is then cast into a single-objective optimal synthesis using the leader variables and the obtained RRS of the follower. The superiority of using the synergy game-theoretic method of Stackelberg with a GMDH-type neural network is demonstrated for two case studies on the synthesis of four-bar mechanisms.

Identification of vehicle suspension parameters by design optimization

NASA Astrophysics Data System (ADS)

Tey, J. Y.; Ramli, R.; Kheng, C. W.; Chong, S. Y.; Abidin, M. A. Z.

2014-05-01

The design of a vehicle suspension system through simulation requires accurate representation of the design parameters. These parameters are usually difficult to measure or sometimes unavailable. This article proposes an efficient approach to identify the unknown parameters through optimization based on experimental results, where the covariance matrix adaptation-evolutionary strategy (CMA-es) is utilized to improve the simulation and experimental results against the kinematic and compliance tests. This speeds up the design and development cycle by recovering all the unknown data with respect to a set of kinematic measurements through a single optimization process. A case study employing a McPherson strut suspension system is modelled in a multi-body dynamic system. Three kinematic and compliance tests are examined, namely, vertical parallel wheel travel, opposite wheel travel and single wheel travel. The problem is formulated as a multi-objective optimization problem with 40 objectives and 49 design parameters. A hierarchical clustering method based on global sensitivity analysis is used to reduce the number of objectives to 30 by grouping correlated objectives together. Then, a dynamic summation of rank value is used as pseudo-objective functions to reformulate the multi-objective optimization to a single-objective optimization problem. The optimized results show a significant improvement in the correlation between the simulated model and the experimental model. Once accurate representation of the vehicle suspension model is achieved, further analysis, such as ride and handling performances, can be implemented for further optimization.

Considering Decision Variable Diversity in Multi-Objective Optimization: Application in Hydrologic Model Calibration

NASA Astrophysics Data System (ADS)

Sahraei, S.; Asadzadeh, M.

2017-12-01

Any modern multi-objective global optimization algorithm should be able to archive a well-distributed set of solutions. While the solution diversity in the objective space has been explored extensively in the literature, little attention has been given to the solution diversity in the decision space. Selection metrics such as the hypervolume contribution and crowding distance calculated in the objective space would guide the search toward solutions that are well-distributed across the objective space. In this study, the diversity of solutions in the decision-space is used as the main selection criteria beside the dominance check in multi-objective optimization. To this end, currently archived solutions are clustered in the decision space and the ones in less crowded clusters are given more chance to be selected for generating new solution. The proposed approach is first tested on benchmark mathematical test problems. Second, it is applied to a hydrologic model calibration problem with more than three objective functions. Results show that the chance of finding more sparse set of high-quality solutions increases, and therefore the analyst would receive a well-diverse set of options with maximum amount of information. Pareto Archived-Dynamically Dimensioned Search, which is an efficient and parsimonious multi-objective optimization algorithm for model calibration, is utilized in this study.

Multi-objective optimization of chromatographic rare earth element separation.

PubMed

Knutson, Hans-Kristian; Holmqvist, Anders; Nilsson, Bernt

2015-10-16

The importance of rare earth elements in modern technological industry grows, and as a result the interest for developing separation processes increases. This work is a part of developing chromatography as a rare earth element processing method. Process optimization is an important step in process development, and there are several competing objectives that need to be considered in a chromatographic separation process. Most studies are limited to evaluating the two competing objectives productivity and yield, and studies of scenarios with tri-objective optimizations are scarce. Tri-objective optimizations are much needed when evaluating the chromatographic separation of rare earth elements due to the importance of product pool concentration along with productivity and yield as process objectives. In this work, a multi-objective optimization strategy considering productivity, yield and pool concentration is proposed. This was carried out in the frame of a model based optimization study on a batch chromatography separation of the rare earth elements samarium, europium and gadolinium. The findings from the multi-objective optimization were used to provide with a general strategy for achieving desirable operation points, resulting in a productivity ranging between 0.61 and 0.75 kgEu/mcolumn(3), h(-1) and a pool concentration between 0.52 and 0.79 kgEu/m(3), while maintaining a purity above 99% and never falling below an 80% yield for the main target component europium. Copyright © 2015 Elsevier B.V. All rights reserved.

Optimization of sound absorbing performance for gradient multi-layer-assembled sintered fibrous absorbers

NASA Astrophysics Data System (ADS)

Zhang, Bo; Zhang, Weiyong; Zhu, Jian

2012-04-01

The transfer matrix method, based on plane wave theory, of multi-layer equivalent fluid is employed to evaluate the sound absorbing properties of two-layer-assembled and three-layer-assembled sintered fibrous sheets (generally regarded as a kind of compound absorber or structures). Two objective functions which are more suitable for the optimization of sound absorption properties of multi-layer absorbers within the wider frequency ranges are developed and the optimized results of using two objective functions are also compared with each other. It is found that using the two objective functions, especially the second one, may be more helpful to exert the sound absorbing properties of absorbers at lower frequencies to the best of their abilities. Then the calculation and optimization of sound absorption properties of multi-layer-assembled structures are performed by developing a simulated annealing genetic arithmetic program and using above-mentioned objective functions. Finally, based on the optimization in this work the thoughts of the gradient design over the acoustic parameters- the porosity, the tortuosity, the viscous and thermal characteristic lengths and the thickness of each samples- of porous metals are put forth and thereby some useful design criteria upon the acoustic parameters of each layer of porous fibrous metals are given while applying the multi-layer-assembled compound absorbers in noise control engineering.

Hydro-environmental management of groundwater resources: A fuzzy-based multi-objective compromise approach

NASA Astrophysics Data System (ADS)

Alizadeh, Mohammad Reza; Nikoo, Mohammad Reza; Rakhshandehroo, Gholam Reza

2017-08-01

Sustainable management of water resources necessitates close attention to social, economic and environmental aspects such as water quality and quantity concerns and potential conflicts. This study presents a new fuzzy-based multi-objective compromise methodology to determine the socio-optimal and sustainable policies for hydro-environmental management of groundwater resources, which simultaneously considers the conflicts and negotiation of involved stakeholders, uncertainties in decision makers' preferences, existing uncertainties in the groundwater parameters and groundwater quality and quantity issues. The fuzzy multi-objective simulation-optimization model is developed based on qualitative and quantitative groundwater simulation model (MODFLOW and MT3D), multi-objective optimization model (NSGA-II), Monte Carlo analysis and Fuzzy Transformation Method (FTM). Best compromise solutions (best management policies) on trade-off curves are determined using four different Fuzzy Social Choice (FSC) methods. Finally, a unanimity fallback bargaining method is utilized to suggest the most preferred FSC method. Kavar-Maharloo aquifer system in Fars, Iran, as a typical multi-stakeholder multi-objective real-world problem is considered to verify the proposed methodology. Results showed an effective performance of the framework for determining the most sustainable allocation policy in groundwater resource management.

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method.

PubMed

Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

2017-05-01

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability. Copyright © 2017 Elsevier B.V. All rights reserved.

Transient responses' optimization by means of set-based multi-objective evolution

NASA Astrophysics Data System (ADS)

Avigad, Gideon; Eisenstadt, Erella; Goldvard, Alex; Salomon, Shaul

2012-04-01

In this article, a novel solution to multi-objective problems involving the optimization of transient responses is suggested. It is claimed that the common approach of treating such problems by introducing auxiliary objectives overlooks tradeoffs that should be presented to the decision makers. This means that, if at some time during the responses, one of the responses is optimal, it should not be overlooked. An evolutionary multi-objective algorithm is suggested in order to search for these optimal solutions. For this purpose, state-wise domination is utilized with a new crowding measure for ordered sets being suggested. The approach is tested on both artificial as well as on real life problems in order to explain the methodology and demonstrate its applicability and importance. The results indicate that, from an engineering point of view, the approach possesses several advantages over existing approaches. Moreover, the applications highlight the importance of set-based evolution.

Multi-objective optimization in spatial planning: Improving the effectiveness of multi-objective evolutionary algorithms (non-dominated sorting genetic algorithm II)

NASA Astrophysics Data System (ADS)

Karakostas, Spiros

2015-05-01

The multi-objective nature of most spatial planning initiatives and the numerous constraints that are introduced in the planning process by decision makers, stakeholders, etc., synthesize a complex spatial planning context in which the concept of solid and meaningful optimization is a unique challenge. This article investigates new approaches to enhance the effectiveness of multi-objective evolutionary algorithms (MOEAs) via the adoption of a well-known metaheuristic: the non-dominated sorting genetic algorithm II (NSGA-II). In particular, the contribution of a sophisticated crossover operator coupled with an enhanced initialization heuristic is evaluated against a series of metrics measuring the effectiveness of MOEAs. Encouraging results emerge for both the convergence rate of the evolutionary optimization process and the occupation of valuable regions of the objective space by non-dominated solutions, facilitating the work of spatial planners and decision makers. Based on the promising behaviour of both heuristics, topics for further research are proposed to improve their effectiveness.

Multidisciplinary Multiobjective Optimal Design for Turbomachinery Using Evolutionary Algorithm

NASA Technical Reports Server (NTRS)

2005-01-01

This report summarizes Dr. Lian s efforts toward developing a robust and efficient tool for multidisciplinary and multi-objective optimal design for turbomachinery using evolutionary algorithms. This work consisted of two stages. The first stage (from July 2003 to June 2004) Dr. Lian focused on building essential capabilities required for the project. More specifically, Dr. Lian worked on two subjects: an enhanced genetic algorithm (GA) and an integrated optimization system with a GA and a surrogate model. The second stage (from July 2004 to February 2005) Dr. Lian formulated aerodynamic optimization and structural optimization into a multi-objective optimization problem and performed multidisciplinary and multi-objective optimizations on a transonic compressor blade based on the proposed model. Dr. Lian s numerical results showed that the proposed approach can effectively reduce the blade weight and increase the stage pressure ratio in an efficient manner. In addition, the new design was structurally safer than the original design. Five conference papers and three journal papers were published on this topic by Dr. Lian.

Dual-mode nested search method for categorical uncertain multi-objective optimization

NASA Astrophysics Data System (ADS)

Tang, Long; Wang, Hu

2016-10-01

Categorical multi-objective optimization is an important issue involved in many matching design problems. Non-numerical variables and their uncertainty are the major challenges of such optimizations. Therefore, this article proposes a dual-mode nested search (DMNS) method. In the outer layer, kriging metamodels are established using standard regular simplex mapping (SRSM) from categorical candidates to numerical values. Assisted by the metamodels, a k-cluster-based intelligent sampling strategy is developed to search Pareto frontier points. The inner layer uses an interval number method to model the uncertainty of categorical candidates. To improve the efficiency, a multi-feature convergent optimization via most-promising-area stochastic search (MFCOMPASS) is proposed to determine the bounds of objectives. Finally, typical numerical examples are employed to demonstrate the effectiveness of the proposed DMNS method.

Multi-objective optimization of piezoelectric circuitry network for mode delocalization and suppression of bladed disk

NASA Astrophysics Data System (ADS)

Yoo, David; Tang, J.

2017-04-01

Since weakly-coupled bladed disks are highly sensitive to the presence of uncertainties, they can easily undergo vibration localization. When vibration localization occurs, vibration modes of bladed disk become dramatically different from those under the perfectly periodic condition, and the dynamic response under engine-order excitation is drastically amplified. In previous studies, it is investigated that amplified vibration response can be suppressed by connecting piezoelectric circuitry into individual blades to induce the damped absorber effect, and localized vibration modes can be alleviated by integrating piezoelectric circuitry network. Delocalization of vibration modes and vibration suppression of bladed disk, however, require different optimal set of circuit parameters. In this research, multi-objective optimization approach is developed to enable finding the best circuit parameters, simultaneously achieving both objectives. In this way, the robustness and reliability in bladed disk can be ensured. Gradient-based optimizations are individually developed for mode delocalization and vibration suppression, which are then integrated into multi-objective optimization framework.

Memetic Algorithm-Based Multi-Objective Coverage Optimization for Wireless Sensor Networks

PubMed Central

Chen, Zhi; Li, Shuai; Yue, Wenjing

2014-01-01

Maintaining effective coverage and extending the network lifetime as much as possible has become one of the most critical issues in the coverage of WSNs. In this paper, we propose a multi-objective coverage optimization algorithm for WSNs, namely MOCADMA, which models the coverage control of WSNs as the multi-objective optimization problem. MOCADMA uses a memetic algorithm with a dynamic local search strategy to optimize the coverage of WSNs and achieve the objectives such as high network coverage, effective node utilization and more residual energy. In MOCADMA, the alternative solutions are represented as the chromosomes in matrix form, and the optimal solutions are selected through numerous iterations of the evolution process, including selection, crossover, mutation, local enhancement, and fitness evaluation. The experiment and evaluation results show MOCADMA can have good capabilities in maintaining the sensing coverage, achieve higher network coverage while improving the energy efficiency and effectively prolonging the network lifetime, and have a significant improvement over some existing algorithms. PMID:25360579

Memetic algorithm-based multi-objective coverage optimization for wireless sensor networks.

PubMed

Chen, Zhi; Li, Shuai; Yue, Wenjing

2014-10-30

Maintaining effective coverage and extending the network lifetime as much as possible has become one of the most critical issues in the coverage of WSNs. In this paper, we propose a multi-objective coverage optimization algorithm for WSNs, namely MOCADMA, which models the coverage control of WSNs as the multi-objective optimization problem. MOCADMA uses a memetic algorithm with a dynamic local search strategy to optimize the coverage of WSNs and achieve the objectives such as high network coverage, effective node utilization and more residual energy. In MOCADMA, the alternative solutions are represented as the chromosomes in matrix form, and the optimal solutions are selected through numerous iterations of the evolution process, including selection, crossover, mutation, local enhancement, and fitness evaluation. The experiment and evaluation results show MOCADMA can have good capabilities in maintaining the sensing coverage, achieve higher network coverage while improving the energy efficiency and effectively prolonging the network lifetime, and have a significant improvement over some existing algorithms.

Discrete particle swarm optimization to solve multi-objective limited-wait hybrid flow shop scheduling problem

NASA Astrophysics Data System (ADS)

Santosa, B.; Siswanto, N.; Fiqihesa

2018-04-01

This paper proposes a discrete Particle Swam Optimization (PSO) to solve limited-wait hybrid flowshop scheduing problem with multi objectives. Flow shop schedulimg represents the condition when several machines are arranged in series and each job must be processed at each machine with same sequence. The objective functions are minimizing completion time (makespan), total tardiness time, and total machine idle time. Flow shop scheduling model always grows to cope with the real production system accurately. Since flow shop scheduling is a NP-Hard problem then the most suitable method to solve is metaheuristics. One of metaheuristics algorithm is Particle Swarm Optimization (PSO), an algorithm which is based on the behavior of a swarm. Originally, PSO was intended to solve continuous optimization problems. Since flow shop scheduling is a discrete optimization problem, then, we need to modify PSO to fit the problem. The modification is done by using probability transition matrix mechanism. While to handle multi objectives problem, we use Pareto Optimal (MPSO). The results of MPSO is better than the PSO because the MPSO solution set produced higher probability to find the optimal solution. Besides the MPSO solution set is closer to the optimal solution

A hybrid multi-objective imperialist competitive algorithm and Monte Carlo method for robust safety design of a rail vehicle

NASA Astrophysics Data System (ADS)

Nejlaoui, Mohamed; Houidi, Ajmi; Affi, Zouhaier; Romdhane, Lotfi

2017-10-01

This paper deals with the robust safety design optimization of a rail vehicle system moving in short radius curved tracks. A combined multi-objective imperialist competitive algorithm and Monte Carlo method is developed and used for the robust multi-objective optimization of the rail vehicle system. This robust optimization of rail vehicle safety considers simultaneously the derailment angle and its standard deviation where the design parameters uncertainties are considered. The obtained results showed that the robust design reduces significantly the sensitivity of the rail vehicle safety to the design parameters uncertainties compared to the determinist one and to the literature results.

Investigation of Parametric Influence on the Properties of Al6061-SiCp Composite

NASA Astrophysics Data System (ADS)

Adebisi, A. A.; Maleque, M. A.; Bello, K. A.

2017-03-01

The influence of process parameter in stir casting play a major role on the development of aluminium reinforced silicon carbide particle (Al-SiCp) composite. This study aims to investigate the influence of process parameters on wear and density properties of Al-SiCp composite using stir casting technique. Experimental data are generated based on a four-factors-five-level central composite design of response surface methodology. Analysis of variance is utilized to confirm the adequacy and validity of developed models considering the significant model terms. Optimization of the process parameters adequately predicts the Al-SiCp composite properties with stirring speed as the most influencing factor. The aim of optimization process is to minimize wear and maximum density. The multiple objective optimization (MOO) achieved an optimal value of 14 wt% reinforcement fraction (RF), 460 rpm stirring speed (SS), 820 °C processing temperature (PTemp) and 150 secs processing time (PT). Considering the optimum parametric combination, wear mass loss achieved a minimum of 1 x 10-3 g and maximum density value of 2.780g/mm3 with a confidence and desirability level of 95.5%.

A novel method for overlapping community detection using Multi-objective optimization

NASA Astrophysics Data System (ADS)

Ebrahimi, Morteza; Shahmoradi, Mohammad Reza; Heshmati, Zainabolhoda; Salehi, Mostafa

2018-09-01

The problem of community detection as one of the most important applications of network science can be addressed effectively by multi-objective optimization. In this paper, we aim to present a novel efficient method based on this approach. Also, in this study the idea of using all Pareto fronts to detect overlapping communities is introduced. The proposed method has two main advantages compared to other multi-objective optimization based approaches. The first advantage is scalability, and the second is the ability to find overlapping communities. Despite most of the works, the proposed method is able to find overlapping communities effectively. The new algorithm works by extracting appropriate communities from all the Pareto optimal solutions, instead of choosing the one optimal solution. Empirical experiments on different features of separated and overlapping communities, on both synthetic and real networks show that the proposed method performs better in comparison with other methods.

Optimized scheme in coal-fired boiler combustion based on information entropy and modified K-prototypes algorithm

NASA Astrophysics Data System (ADS)

Gu, Hui; Zhu, Hongxia; Cui, Yanfeng; Si, Fengqi; Xue, Rui; Xi, Han; Zhang, Jiayu

2018-06-01

An integrated combustion optimization scheme is proposed for the combined considering the restriction in coal-fired boiler combustion efficiency and outlet NOx emissions. Continuous attribute discretization and reduction techniques are handled as optimization preparation by E-Cluster and C_RED methods, in which the segmentation numbers don't need to be provided in advance and can be continuously adapted with data characters. In order to obtain results of multi-objections with clustering method for mixed data, a modified K-prototypes algorithm is then proposed. This algorithm can be divided into two stages as K-prototypes algorithm for clustering number self-adaptation and clustering for multi-objective optimization, respectively. Field tests were carried out at a 660 MW coal-fired boiler to provide real data as a case study for controllable attribute discretization and reduction in boiler system and obtaining optimization parameters considering [ maxηb, minyNOx ] multi-objective rule.

A MoO2 sheet as a promising electrode material: ultrafast Li-diffusion and astonishing Li-storage capacity

NASA Astrophysics Data System (ADS)

Zhou, Yungang; Geng, Cheng

2017-03-01

The potential of MoO2 crystal as an electrode material is reported, and nanostructural MoO2 systems, including nanoparticles, nanospheres, nanobelts and nanowires, were synthesized and proved to be advanced electrode materials. A two-dimensional (2D) geometric structure represents an extreme of surface-to-volume ratio, and thus is more suitable as an electrode material in general. Stimulated by the recent fabrication of 2D MoO2, we adopted an ab initio molecular dynamics simulation and density functional theory calculation to study the stability and electrochemical properties of a MoO2 sheet. Identified by a phonon dispersion curve and potential energy curve calculations, the MoO2 sheet proved to be dynamically and thermally stable. After lithiation, similar to most promising 2D structures, we found that a Li atom can strongly adsorb on a MoO2 sheet, and the lithiated MoO2 sheet presented excellent metallic properties. Note that, compared with most promising 2D structures, we unexpectedly revealed that the diffusion barrier of the Li atom on the MoO2 sheet was much lower and the storage capacity of the MoO2 sheet was much larger. The calculated energy barrier for the diffusion of Li on the MoO2 sheet was only 75 meV, and, due to multilayer adsorption, the theoretical capacity of the MoO2 sheet can reach up to 2513 mA h g-1. Benefiting from general properties, such as strong Li-binding and excellent conductivity, and unique phenomena, such as ultrafast diffusion capacity and astonishing storage capacity, we highlight a new promising electrode material for the Li-ion battery.

MoO2-ordered mesoporous carbon hybrids as anode materials with highly improved rate capability and reversible capacity for lithium-ion battery.

PubMed

Chen, Ailian; Li, Caixia; Tang, Rui; Yin, Longwei; Qi, Yongxin

2013-08-28

A novel hybrid of MoO2-ordered mesoporous carbon (MoO2-OMC) was prepared through a two-step solvothermal chemical reaction route. The electrochemical performances of the mesoporous MoO2-OMC hybrids were examined using galvanostatical charge-discharge, cyclic voltammetry, and electrochemical impedance spectroscopy (EIS) techniques. The MoO2-OMC hybrid exhibits significantly improved electrochemical performance of high reversible capacity, high-rate capability, and excellent cycling performance as an anode electrode material for Li ion batteries. It is revealed that the MoO2-OMC hybrid could deliver the first discharge capacity of 1641.8 mA h g(-1) with an initial Coulombic efficiency of 63.6%, and a reversible capacity as high as 1049.1 mA h g(-1) even after 50 cycles at a current density of 100 mA g(-1), much higher than the theoretical capacity of MoO2 (838 mA h g(-1)) and OMC materials. The MoO2-OMC hybrid demonstrates an excellent high rate capability with capacity of ∼600 mA h g(-1) even at a charge current density of 1600 mA g(-1) after 50 cycles, which is approximately 11.1 times higher than that of the OMC (54 mA h g(-1)) materials. The improved rate capability and reversible capacity of the MoO2-OMC hybrid are attributed to a synergistic reaction between the MoO2 nanoparticles and mesoporous OMC matrices. It is noted that the electrochemical performance of the MoO2-OMC hybrid is evidently much better than the previous MoO2-based hybrids.

Fuzzy multi-objective optimization case study based on an anaerobic co-digestion process of food waste leachate and piggery wastewater.

PubMed

Choi, Angelo Earvin Sy; Park, Hung Suck

2018-06-20

This paper presents the development and evaluation of fuzzy multi-objective optimization for decision-making that includes the process optimization of anaerobic digestion (AD) process. The operating cost criteria which is a fundamental research gap in previous AD analysis was integrated for the case study in this research. In this study, the mixing ratio of food waste leachate (FWL) and piggery wastewater (PWW), calcium carbonate (CaCO 3 ) and sodium chloride (NaCl) concentrations were optimized to enhance methane production while minimizing operating cost. The results indicated a maximum of 63.3% satisfaction for both methane production and operating cost under the following optimal conditions: mixing ratio (FWL: PWW) - 1.4, CaCO 3 - 2970.5 mg/L and NaCl - 2.7 g/L. In multi-objective optimization, the specific methane yield (SMY) was 239.0 mL CH 4 /g VS added , while 41.2% volatile solids reduction (VSR) was obtained at an operating cost of 56.9 US$/ton. In comparison with the previous optimization study that utilized the response surface methodology, the SMY, VSR and operating cost of the AD process were 310 mL/g, 54% and 83.2 US$/ton, respectively. The results from multi-objective fuzzy optimization proves to show the potential application of this technique for practical decision-making in the process optimization of AD process. Copyright © 2018 Elsevier Ltd. All rights reserved.

Constrained multi-objective optimization of storage ring lattices

NASA Astrophysics Data System (ADS)

Husain, Riyasat; Ghodke, A. D.

2018-03-01

The storage ring lattice optimization is a class of constrained multi-objective optimization problem, where in addition to low beam emittance, a large dynamic aperture for good injection efficiency and improved beam lifetime are also desirable. The convergence and computation times are of great concern for the optimization algorithms, as various objectives are to be optimized and a number of accelerator parameters to be varied over a large span with several constraints. In this paper, a study of storage ring lattice optimization using differential evolution is presented. The optimization results are compared with two most widely used optimization techniques in accelerators-genetic algorithm and particle swarm optimization. It is found that the differential evolution produces a better Pareto optimal front in reasonable computation time between two conflicting objectives-beam emittance and dispersion function in the straight section. The differential evolution was used, extensively, for the optimization of linear and nonlinear lattices of Indus-2 for exploring various operational modes within the magnet power supply capabilities.

First-Principles Study of MoO3/Graphene Composite as Cathode Material for High-Performance Lithium-Ion Batteries

NASA Astrophysics Data System (ADS)

Cui, Yanhua; Zhao, Yu; Chen, Hong; Wei, Kaiyuan; Ni, Shuang; Cui, Yixiu; Shi, Siqi

2018-03-01

Using first-principles calculations, we have systematically investigated the adsorption and diffusion behavior of Li in MoO3 bulk, on MoO3 (010) surface and in MoO3/graphene composite. Our results indicate that, in case of MoO3 bulk, Li diffusion barriers in the interlayer and intralayer spaces are 0.55 eV and 0.58 eV respectively, which are too high to warrant fast Lithium-ion charge/discharge processes. While on MoO3 (010) surface, Li exhibits a diffusion barrier as low as 0.07 eV which guarantees an extremely fast Li diffusion rate during charge/discharge cycling. However, in MoO3/graphene monolayer, Li diffusion barrier is at the same level as that on MoO3 (010) surface, which also ensures a very rapid Li charge/discharge rate. The rapid Li charge/discharge rate in this system originates from the removal of the upper dangling O1 atoms which hinder the Li diffusion on the lower MoO3 layer. Besides this, due to the interaction between Li and graphene, the Li average binding energy increases to 0.14 eV compared to its value on MoO3 (010) surface which contributes to a higher voltage. Additionally, the increased ratio of surface area provides more space for Li storage and the capacity of MoO3/graphene composite increases up to 279.2 mAhg-1. The last but not the least, due to the high conductivity of graphene, the conductivity of MoO3/graphene composite enhances greatly which is beneficial for electrode materials. In the light of present results, MoO3/graphene composite exhibits higher voltage, good conductivity, large Li capacity and very rapid Li charge/discharge rate, which prove it as a promising cathode material for high-performance lithium-ion batteries (LIBs).

Optimal design and management of chlorination in drinking water networks: a multi-objective approach using Genetic Algorithms and the Pareto optimality concept

NASA Astrophysics Data System (ADS)

Nouiri, Issam

2017-11-01

This paper presents the development of multi-objective Genetic Algorithms to optimize chlorination design and management in drinking water networks (DWN). Three objectives have been considered: the improvement of the chlorination uniformity (healthy objective), the minimization of chlorine booster stations number, and the injected chlorine mass (economic objectives). The problem has been dissociated in medium and short terms ones. The proposed methodology was tested on hypothetical and real DWN. Results proved the ability of the developed optimization tool to identify relationships between the healthy and economic objectives as Pareto fronts. The proposed approach was efficient in computing solutions ensuring better chlorination uniformity while requiring the weakest injected chlorine mass when compared to other approaches. For the real DWN studied, chlorination optimization has been crowned by great improvement of free-chlorine-dosing uniformity and by a meaningful chlorine mass reduction, in comparison with the conventional chlorination.

Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural network and genetic algorithm.

PubMed

Ma, Changxi; Hao, Wei; Pan, Fuquan; Xiang, Wang

2018-01-01

Route optimization of hazardous materials transportation is one of the basic steps in ensuring the safety of hazardous materials transportation. The optimization scheme may be a security risk if road screening is not completed before the distribution route is optimized. For road screening issues of hazardous materials transportation, a road screening algorithm of hazardous materials transportation is built based on genetic algorithm and Levenberg-Marquardt neural network (GA-LM-NN) by analyzing 15 attributes data of each road network section. A multi-objective robust optimization model with adjustable robustness is constructed for the hazardous materials transportation problem of single distribution center to minimize transportation risk and time. A multi-objective genetic algorithm is designed to solve the problem according to the characteristics of the model. The algorithm uses an improved strategy to complete the selection operation, applies partial matching cross shift and single ortho swap methods to complete the crossover and mutation operation, and employs an exclusive method to construct Pareto optimal solutions. Studies show that the sets of hazardous materials transportation road can be found quickly through the proposed road screening algorithm based on GA-LM-NN, whereas the distribution route Pareto solutions with different levels of robustness can be found rapidly through the proposed multi-objective robust optimization model and algorithm.

Synthesis of α-MoO3 nano-flakes by dry oxidation of RF sputtered Mo thin films and their application in gas sensing

NASA Astrophysics Data System (ADS)

Dwivedi, Priyanka; Dhanekar, Saakshi; Das, Samaresh

2016-11-01

Synthesis of orthorhombic (α) MoO3 nano-flakes by dry oxidation of RF sputtered Mo thin film is presented. The influence of Mo thickness variation, oxidation temperature and time on the crystallographic structure, surface morphology and roughness of MoO3 thin films was studied using SEM, AFM, XRD and Raman spectroscopy. A structural study shows that MoO3 is polycrystalline in nature with an α phase. It was noticed that oxidation temperature plays an important role in the formation of nano-flakes. The synthesis technique proposed is simple and suitable for large scale productions. The synthesis parameters were optimized for the fabrication of sensors. Chrome gold-based IDE (interdigitated electrodes) structures were patterned for the electrical detection of organic vapors. Sensors were exposed to wide range 5-100 ppm of organic vapors like ethanol, acetone, IPA (isopropanol alcohol) and water vapors. α-MoO3 nano-flakes have demonstrated selective sensing to acetone in the range of 10-100 ppm at 150 °C. The morphology of such nanostructures has potential in applications such as sensor devices due to their high surface area and thermal stability.

a Heuristic Approach for Multi Objective Distribution Feeder Reconfiguration: Using Fuzzy Sets in Normalization of Objective Functions

NASA Astrophysics Data System (ADS)

Milani, Armin Ebrahimi; Haghifam, Mahmood Reza

2008-10-01

The reconfiguration is an operation process used for optimization with specific objectives by means of changing the status of switches in a distribution network. In this paper each objectives is normalized with inspiration from fuzzy sets-to cause optimization more flexible- and formulized as a unique multi-objective function. The genetic algorithm is used for solving the suggested model, in which there is no risk of non-liner objective functions and constraints. The effectiveness of the proposed method is demonstrated through the examples.

Advances in Integrating Autonomy with Acoustic Communications for Intelligent Networks of Marine Robots

DTIC Science & Technology

2013-02-01

Sonar AUV #Environmental Sampling Environmental AUV +name : string = OEX Ocean Explorer +name : string = Hammerhead Iver2 +name : string = Unicorn ...executable» Google Earth Bluefin 21 AUV ( Unicorn ) MOOS Computer GPS «serial» Bluefin 21 AUV (Macrura) MOOS Computer «acoustic» Micro-Modem «wired...Computer Bluefin 21 AUV ( Unicorn ) MOOS Computer NURC AUV (OEX) MOOS Computer Topside MOOS Computer «wifi» 5.0GHz WiLan «acoustic» Edgetech GPS

New Details about Interstellar Visitor on This Week @NASA – November 24, 2017

NASA Image and Video Library

2017-11-24

New data reveal that the interstellar asteroid that recently zipped through our solar system is rocky, cigar-shaped, and has a somewhat reddish hue. It’s the first confirmed object from another star observed in our solar system, and was discovered Oct. 19 by the University of Hawaii’s Pan-STARRS1 telescope team, funded by NASA’s Near-Earth Object Observations Program. The telescope team named it ‘Oumuamua (oh MOO-uh MOO-uh) – Hawaiian for “a messenger from afar arriving first.” The unusually-shaped asteroid, which is up to a quarter mile long and perhaps 10 times as long as it is wide, may provide new clues into how other solar systems formed. Also, Advanced Weather Satellite Launched, James Webb Space Telescope Completes Final Cryogenic Testing, Recurring Martian Streaks: Flowing Sand, Not Water? and Happy Thanksgiving, from Space!

[Location selection for Shenyang urban parks based on GIS and multi-objective location allocation model].

PubMed

Zhou, Yuan; Shi, Tie-Mao; Hu, Yuan-Man; Gao, Chang; Liu, Miao; Song, Lin-Qi

2011-12-01

Based on geographic information system (GIS) technology and multi-objective location-allocation (LA) model, and in considering of four relatively independent objective factors (population density level, air pollution level, urban heat island effect level, and urban land use pattern), an optimized location selection for the urban parks within the Third Ring of Shenyang was conducted, and the selection results were compared with the spatial distribution of existing parks, aimed to evaluate the rationality of the spatial distribution of urban green spaces. In the location selection of urban green spaces in the study area, the factor air pollution was most important, and, compared with single objective factor, the weighted analysis results of multi-objective factors could provide optimized spatial location selection of new urban green spaces. The combination of GIS technology with LA model would be a new approach for the spatial optimizing of urban green spaces.

A versatile multi-objective FLUKA optimization using Genetic Algorithms

NASA Astrophysics Data System (ADS)

Vlachoudis, Vasilis; Antoniucci, Guido Arnau; Mathot, Serge; Kozlowska, Wioletta Sandra; Vretenar, Maurizio

2017-09-01

Quite often Monte Carlo simulation studies require a multi phase-space optimization, a complicated task, heavily relying on the operator experience and judgment. Examples of such calculations are shielding calculations with stringent conditions in the cost, in residual dose, material properties and space available, or in the medical field optimizing the dose delivered to a patient under a hadron treatment. The present paper describes our implementation inside flair[1] the advanced user interface of FLUKA[2,3] of a multi-objective Genetic Algorithm[Erreur ! Source du renvoi introuvable.] to facilitate the search for the optimum solution.

An approach for aerodynamic optimization of transonic fan blades

NASA Astrophysics Data System (ADS)

Khelghatibana, Maryam

Aerodynamic design optimization of transonic fan blades is a highly challenging problem due to the complexity of flow field inside the fan, the conflicting design requirements and the high-dimensional design space. In order to address all these challenges, an aerodynamic design optimization method is developed in this study. This method automates the design process by integrating a geometrical parameterization method, a CFD solver and numerical optimization methods that can be applied to both single and multi-point optimization design problems. A multi-level blade parameterization is employed to modify the blade geometry. Numerical analyses are performed by solving 3D RANS equations combined with SST turbulence model. Genetic algorithms and hybrid optimization methods are applied to solve the optimization problem. In order to verify the effectiveness and feasibility of the optimization method, a singlepoint optimization problem aiming to maximize design efficiency is formulated and applied to redesign a test case. However, transonic fan blade design is inherently a multi-faceted problem that deals with several objectives such as efficiency, stall margin, and choke margin. The proposed multi-point optimization method in the current study is formulated as a bi-objective problem to maximize design and near-stall efficiencies while maintaining the required design pressure ratio. Enhancing these objectives significantly deteriorate the choke margin, specifically at high rotational speeds. Therefore, another constraint is embedded in the optimization problem in order to prevent the reduction of choke margin at high speeds. Since capturing stall inception is numerically very expensive, stall margin has not been considered as an objective in the problem statement. However, improving near-stall efficiency results in a better performance at stall condition, which could enhance the stall margin. An investigation is therefore performed on the Pareto-optimal solutions to demonstrate the relation between near-stall efficiency and stall margin. The proposed method is applied to redesign NASA rotor 67 for single and multiple operating conditions. The single-point design optimization showed +0.28 points improvement of isentropic efficiency at design point, while the design pressure ratio and mass flow are, respectively, within 0.12% and 0.11% of the reference blade. Two cases of multi-point optimization are performed: First, the proposed multi-point optimization problem is relaxed by removing the choke margin constraint in order to demonstrate the relation between near-stall efficiency and stall margin. An investigation on the Pareto-optimal solutions of this optimization shows that the stall margin has been increased with improving near-stall efficiency. The second multi-point optimization case is performed with considering all the objectives and constraints. One selected optimized design on the Pareto front presents +0.41, +0.56 and +0.9 points improvement in near-peak efficiency, near-stall efficiency and stall margin, respectively. The design pressure ratio and mass flow are, respectively, within 0.3% and 0.26% of the reference blade. Moreover the optimized design maintains the required choking margin. Detailed aerodynamic analyses are performed to investigate the effect of shape optimization on shock occurrence, secondary flows, tip leakage and shock/tip-leakage interactions in both single and multi-point optimizations.

NARMAX model identification of a palm oil biodiesel engine using multi-objective optimization differential evolution

NASA Astrophysics Data System (ADS)

Mansor, Zakwan; Zakaria, Mohd Zakimi; Nor, Azuwir Mohd; Saad, Mohd Sazli; Ahmad, Robiah; Jamaluddin, Hishamuddin

2017-09-01

This paper presents the black-box modelling of palm oil biodiesel engine (POB) using multi-objective optimization differential evolution (MOODE) algorithm. Two objective functions are considered in the algorithm for optimization; minimizing the number of term of a model structure and minimizing the mean square error between actual and predicted outputs. The mathematical model used in this study to represent the POB system is nonlinear auto-regressive moving average with exogenous input (NARMAX) model. Finally, model validity tests are applied in order to validate the possible models that was obtained from MOODE algorithm and lead to select an optimal model.

[Multi-mathematical modelings for compatibility optimization of Jiangzhi granules].

PubMed

Yang, Ming; Zhang, Li; Ge, Yingli; Lu, Yanliu; Ji, Guang

2011-12-01

To investigate into the method of "multi activity index evaluation and combination optimized of mult-component" for Chinese herbal formulas. According to the scheme of uniform experimental design, efficacy experiment, multi index evaluation, least absolute shrinkage, selection operator (LASSO) modeling, evolutionary optimization algorithm, validation experiment, we optimized the combination of Jiangzhi granules based on the activity indexes of blood serum ALT, ALT, AST, TG, TC, HDL, LDL and TG level of liver tissues, ratio of liver tissue to body. Analytic hierarchy process (AHP) combining with criteria importance through intercriteria correlation (CRITIC) for multi activity index evaluation was more reasonable and objective, it reflected the information of activity index's order and objective sample data. LASSO algorithm modeling could accurately reflect the relationship between different combination of Jiangzhi granule and the activity comprehensive indexes. The optimized combination of Jiangzhi granule showed better values of the activity comprehensive indexed than the original formula after the validation experiment. AHP combining with CRITIC can be used for multi activity index evaluation and LASSO algorithm, it is suitable for combination optimized of Chinese herbal formulas.

Co-optimization of Energy and Demand-Side Reserves in Day-Ahead Electricity Markets

NASA Astrophysics Data System (ADS)

Surender Reddy, S.; Abhyankar, A. R.; Bijwe, P. R.

2015-04-01

This paper presents a new multi-objective day-ahead market clearing (DAMC) mechanism with demand-side reserves/demand response (DR) offers, considering realistic voltage-dependent load modeling. The paper proposes objectives such as social welfare maximization (SWM) including demand-side reserves, and load served error (LSE) minimization. In this paper, energy and demand-side reserves are cleared simultaneously through co-optimization process. The paper clearly brings out the unsuitability of conventional SWM for DAMC in the presence of voltage-dependent loads, due to reduction of load served (LS). Under such circumstances multi-objective DAMC with DR offers is essential. Multi-objective Strength Pareto Evolutionary Algorithm 2+ (SPEA 2+) has been used to solve the optimization problem. The effectiveness of the proposed scheme is confirmed with results obtained from IEEE 30 bus system.

Profile shape optimization in multi-jet impingement cooling of dimpled topologies for local heat transfer enhancement

NASA Astrophysics Data System (ADS)

Negi, Deepchand Singh; Pattamatta, Arvind

2015-04-01

The present study deals with shape optimization of dimples on the target surface in multi-jet impingement heat transfer. Bezier polynomial formulation is incorporated to generate profile shapes for the dimple profile generation and a multi-objective optimization is performed. The optimized dimple shape exhibits higher local Nusselt number values compared to the reference hemispherical dimpled plate optimized shape which can be used to alleviate local temperature hot spots on target surface.

A Generalized Decision Framework Using Multi-objective Optimization for Water Resources Planning

NASA Astrophysics Data System (ADS)

Basdekas, L.; Stewart, N.; Triana, E.

2013-12-01

Colorado Springs Utilities (CSU) is currently engaged in an Integrated Water Resource Plan (IWRP) to address the complex planning scenarios, across multiple time scales, currently faced by CSU. The modeling framework developed for the IWRP uses a flexible data-centered Decision Support System (DSS) with a MODSIM-based modeling system to represent the operation of the current CSU raw water system coupled with a state-of-the-art multi-objective optimization algorithm. Three basic components are required for the framework, which can be implemented for planning horizons ranging from seasonal to interdecadal. First, a water resources system model is required that is capable of reasonable system simulation to resolve performance metrics at the appropriate temporal and spatial scales of interest. The system model should be an existing simulation model, or one developed during the planning process with stakeholders, so that 'buy-in' has already been achieved. Second, a hydrologic scenario tool(s) capable of generating a range of plausible inflows for the planning period of interest is required. This may include paleo informed or climate change informed sequences. Third, a multi-objective optimization model that can be wrapped around the system simulation model is required. The new generation of multi-objective optimization models do not require parameterization which greatly reduces problem complexity. Bridging the gap between research and practice will be evident as we use a case study from CSU's planning process to demonstrate this framework with specific competing water management objectives. Careful formulation of objective functions, choice of decision variables, and system constraints will be discussed. Rather than treating results as theoretically Pareto optimal in a planning process, we use the powerful multi-objective optimization models as tools to more efficiently and effectively move out of the inferior decision space. The use of this framework will help CSU evaluate tradeoffs in a continually changing world.

A multi-objective optimization model for hub network design under uncertainty: An inexact rough-interval fuzzy approach

NASA Astrophysics Data System (ADS)

Niakan, F.; Vahdani, B.; Mohammadi, M.

2015-12-01

This article proposes a multi-objective mixed-integer model to optimize the location of hubs within a hub network design problem under uncertainty. The considered objectives include minimizing the maximum accumulated travel time, minimizing the total costs including transportation, fuel consumption and greenhouse emissions costs, and finally maximizing the minimum service reliability. In the proposed model, it is assumed that for connecting two nodes, there are several types of arc in which their capacity, transportation mode, travel time, and transportation and construction costs are different. Moreover, in this model, determining the capacity of the hubs is part of the decision-making procedure and balancing requirements are imposed on the network. To solve the model, a hybrid solution approach is utilized based on inexact programming, interval-valued fuzzy programming and rough interval programming. Furthermore, a hybrid multi-objective metaheuristic algorithm, namely multi-objective invasive weed optimization (MOIWO), is developed for the given problem. Finally, various computational experiments are carried out to assess the proposed model and solution approaches.

Multi-objective based spectral unmixing for hyperspectral images

NASA Astrophysics Data System (ADS)

Xu, Xia; Shi, Zhenwei

2017-02-01

Sparse hyperspectral unmixing assumes that each observed pixel can be expressed by a linear combination of several pure spectra in a priori library. Sparse unmixing is challenging, since it is usually transformed to a NP-hard l0 norm based optimization problem. Existing methods usually utilize a relaxation to the original l0 norm. However, the relaxation may bring in sensitive weighted parameters and additional calculation error. In this paper, we propose a novel multi-objective based algorithm to solve the sparse unmixing problem without any relaxation. We transform sparse unmixing to a multi-objective optimization problem, which contains two correlative objectives: minimizing the reconstruction error and controlling the endmember sparsity. To improve the efficiency of multi-objective optimization, a population-based randomly flipping strategy is designed. Moreover, we theoretically prove that the proposed method is able to recover a guaranteed approximate solution from the spectral library within limited iterations. The proposed method can directly deal with l0 norm via binary coding for the spectral signatures in the library. Experiments on both synthetic and real hyperspectral datasets demonstrate the effectiveness of the proposed method.

Multi Objective Optimization of Yarn Quality and Fibre Quality Using Evolutionary Algorithm

NASA Astrophysics Data System (ADS)

Ghosh, Anindya; Das, Subhasis; Banerjee, Debamalya

2013-03-01

The quality and cost of resulting yarn play a significant role to determine its end application. The challenging task of any spinner lies in producing a good quality yarn with added cost benefit. The present work does a multi-objective optimization on two objectives, viz. maximization of cotton yarn strength and minimization of raw material quality. The first objective function has been formulated based on the artificial neural network input-output relation between cotton fibre properties and yarn strength. The second objective function is formulated with the well known regression equation of spinning consistency index. It is obvious that these two objectives are conflicting in nature i.e. not a single combination of cotton fibre parameters does exist which produce maximum yarn strength and minimum cotton fibre quality simultaneously. Therefore, it has several optimal solutions from which a trade-off is needed depending upon the requirement of user. In this work, the optimal solutions are obtained with an elitist multi-objective evolutionary algorithm based on Non-dominated Sorting Genetic Algorithm II (NSGA-II). These optimum solutions may lead to the efficient exploitation of raw materials to produce better quality yarns at low costs.

Processing Technology Selection for Municipal Sewage Treatment Based on a Multi-Objective Decision Model under Uncertainty.

PubMed

Chen, Xudong; Xu, Zhongwen; Yao, Liming; Ma, Ning

2018-03-05

This study considers the two factors of environmental protection and economic benefits to address municipal sewage treatment. Based on considerations regarding the sewage treatment plant construction site, processing technology, capital investment, operation costs, water pollutant emissions, water quality and other indicators, we establish a general multi-objective decision model for optimizing municipal sewage treatment plant construction. Using the construction of a sewage treatment plant in a suburb of Chengdu as an example, this paper tests the general model of multi-objective decision-making for the sewage treatment plant construction by implementing a genetic algorithm. The results show the applicability and effectiveness of the multi-objective decision model for the sewage treatment plant. This paper provides decision and technical support for the optimization of municipal sewage treatment.

A risk-based multi-objective model for optimal placement of sensors in water distribution system

NASA Astrophysics Data System (ADS)

Naserizade, Sareh S.; Nikoo, Mohammad Reza; Montaseri, Hossein

2018-02-01

In this study, a new stochastic model based on Conditional Value at Risk (CVaR) and multi-objective optimization methods is developed for optimal placement of sensors in water distribution system (WDS). This model determines minimization of risk which is caused by simultaneous multi-point contamination injection in WDS using CVaR approach. The CVaR considers uncertainties of contamination injection in the form of probability distribution function and calculates low-probability extreme events. In this approach, extreme losses occur at tail of the losses distribution function. Four-objective optimization model based on NSGA-II algorithm is developed to minimize losses of contamination injection (through CVaR of affected population and detection time) and also minimize the two other main criteria of optimal placement of sensors including probability of undetected events and cost. Finally, to determine the best solution, Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), as a subgroup of Multi Criteria Decision Making (MCDM) approach, is utilized to rank the alternatives on the trade-off curve among objective functions. Also, sensitivity analysis is done to investigate the importance of each criterion on PROMETHEE results considering three relative weighting scenarios. The effectiveness of the proposed methodology is examined through applying it to Lamerd WDS in the southwestern part of Iran. The PROMETHEE suggests 6 sensors with suitable distribution that approximately cover all regions of WDS. Optimal values related to CVaR of affected population and detection time as well as probability of undetected events for the best optimal solution are equal to 17,055 persons, 31 mins and 0.045%, respectively. The obtained results of the proposed methodology in Lamerd WDS show applicability of CVaR-based multi-objective simulation-optimization model for incorporating the main uncertainties of contamination injection in order to evaluate extreme value of losses in WDS.

Numerical simulation and growth of Li2Zn2(MoO4)3 single crystals by the top seeded solution growth technique

NASA Astrophysics Data System (ADS)

Sukharev, V.; Sukhanova, E.; Mozhevitina, E.; Sadovsky, A.; Avetissov, I.

2017-06-01

Li2O - ZnO - MoO3 pseudo ternary system was used for the growth of Li2Zn2(MoO4)3 crystals by the top seeded solution growth technique in which MoO3 was used as a solvent. Properties of the melts (density, viscosity) have been experimentally measured at different temperatures and compositions of Li2O - ZnO - MoO3 pseudo ternary system. Heat mass transfer in the crystal growth setup was numerically simulated. Using the simulation results a real growth setup was made, Li2Zn2(MoO4)3 crystals were grown and their properties were studied.

The feasibility of manual parameter tuning for deformable breast MR image registration from a multi-objective optimization perspective.

PubMed

Pirpinia, Kleopatra; Bosman, Peter A N; Loo, Claudette E; Winter-Warnars, Gonneke; Janssen, Natasja N Y; Scholten, Astrid N; Sonke, Jan-Jakob; van Herk, Marcel; Alderliesten, Tanja

2017-06-23

Deformable image registration is typically formulated as an optimization problem involving a linearly weighted combination of terms that correspond to objectives of interest (e.g. similarity, deformation magnitude). The weights, along with multiple other parameters, need to be manually tuned for each application, a task currently addressed mainly via trial-and-error approaches. Such approaches can only be successful if there is a sensible interplay between parameters, objectives, and desired registration outcome. This, however, is not well established. To study this interplay, we use multi-objective optimization, where multiple solutions exist that represent the optimal trade-offs between the objectives, forming a so-called Pareto front. Here, we focus on weight tuning. To study the space a user has to navigate during manual weight tuning, we randomly sample multiple linear combinations. To understand how these combinations relate to desirability of registration outcome, we associate with each outcome a mean target registration error (TRE) based on expert-defined anatomical landmarks. Further, we employ a multi-objective evolutionary algorithm that optimizes the weight combinations, yielding a Pareto front of solutions, which can be directly navigated by the user. To study how the complexity of manual weight tuning changes depending on the registration problem, we consider an easy problem, prone-to-prone breast MR image registration, and a hard problem, prone-to-supine breast MR image registration. Lastly, we investigate how guidance information as an additional objective influences the prone-to-supine registration outcome. Results show that the interplay between weights, objectives, and registration outcome makes manual weight tuning feasible for the prone-to-prone problem, but very challenging for the harder prone-to-supine problem. Here, patient-specific, multi-objective weight optimization is needed, obtaining a mean TRE of 13.6 mm without guidance information reduced to 7.3 mm with guidance information, but also providing a Pareto front that exhibits an intuitively sensible interplay between weights, objectives, and registration outcome, allowing outcome selection.

The feasibility of manual parameter tuning for deformable breast MR image registration from a multi-objective optimization perspective

NASA Astrophysics Data System (ADS)

Pirpinia, Kleopatra; Bosman, Peter A. N.; E Loo, Claudette; Winter-Warnars, Gonneke; Y Janssen, Natasja N.; Scholten, Astrid N.; Sonke, Jan-Jakob; van Herk, Marcel; Alderliesten, Tanja

2017-07-01

Deformable image registration is typically formulated as an optimization problem involving a linearly weighted combination of terms that correspond to objectives of interest (e.g. similarity, deformation magnitude). The weights, along with multiple other parameters, need to be manually tuned for each application, a task currently addressed mainly via trial-and-error approaches. Such approaches can only be successful if there is a sensible interplay between parameters, objectives, and desired registration outcome. This, however, is not well established. To study this interplay, we use multi-objective optimization, where multiple solutions exist that represent the optimal trade-offs between the objectives, forming a so-called Pareto front. Here, we focus on weight tuning. To study the space a user has to navigate during manual weight tuning, we randomly sample multiple linear combinations. To understand how these combinations relate to desirability of registration outcome, we associate with each outcome a mean target registration error (TRE) based on expert-defined anatomical landmarks. Further, we employ a multi-objective evolutionary algorithm that optimizes the weight combinations, yielding a Pareto front of solutions, which can be directly navigated by the user. To study how the complexity of manual weight tuning changes depending on the registration problem, we consider an easy problem, prone-to-prone breast MR image registration, and a hard problem, prone-to-supine breast MR image registration. Lastly, we investigate how guidance information as an additional objective influences the prone-to-supine registration outcome. Results show that the interplay between weights, objectives, and registration outcome makes manual weight tuning feasible for the prone-to-prone problem, but very challenging for the harder prone-to-supine problem. Here, patient-specific, multi-objective weight optimization is needed, obtaining a mean TRE of 13.6 mm without guidance information reduced to 7.3 mm with guidance information, but also providing a Pareto front that exhibits an intuitively sensible interplay between weights, objectives, and registration outcome, allowing outcome selection.

Combined Economic and Hydrologic Modeling to Support Collaborative Decision Making Processes

NASA Astrophysics Data System (ADS)

Sheer, D. P.

2008-12-01

For more than a decade, the core concept of the author's efforts in support of collaborative decision making has been a combination of hydrologic simulation and multi-objective optimization. The modeling has generally been used to support collaborative decision making processes. The OASIS model developed by HydroLogics Inc. solves a multi-objective optimization at each time step using a mixed integer linear program (MILP). The MILP can be configured to include any user defined objective, including but not limited too economic objectives. For example, an estimated marginal value for water for crops and M&I use were included in the objective function to drive trades in a model of the lower Rio Grande. The formulation of the MILP, constraints and objectives, in any time step is conditional: it changes based on the value of state variables and dynamic external forcing functions, such as rainfall, hydrology, market prices, arrival of migratory fish, water temperature, etc. It therefore acts as a dynamic short term multi-objective economic optimization for each time step. MILP is capable of solving a general problem that includes a very realistic representation of the physical system characteristics in addition to the normal multi-objective optimization objectives and constraints included in economic models. In all of these models, the short term objective function is a surrogate for achieving long term multi-objective results. The long term performance for any alternative (especially including operating strategies) is evaluated by simulation. An operating rule is the combination of conditions, parameters, constraints and objectives used to determine the formulation of the short term optimization in each time step. Heuristic wrappers for the simulation program have been developed improve the parameters of an operating rule, and are initiating research on a wrapper that will allow us to employ a genetic algorithm to improve the form of the rule (conditions, constraints, and short term objectives) as well. In the models operating rules represent different models of human behavior, and the objective of the modeling is to find rules for human behavior that perform well in terms of long term human objectives. The conceptual model used to represent human behavior incorporates economic multi-objective optimization for surrogate objectives, and rules that set those objectives based on current conditions and accounting for uncertainty, at least implicitly. The author asserts that real world operating rules follow this form and have evolved because they have been perceived as successful in the past. Thus, the modeling efforts focus on human behavior in much the same way that economic models focus on human behavior. This paper illustrates the above concepts with real world examples.

A master-slave parallel hybrid multi-objective evolutionary algorithm for groundwater remediation design under general hydrogeological conditions

NASA Astrophysics Data System (ADS)

Wu, J.; Yang, Y.; Luo, Q.; Wu, J.

2012-12-01

This study presents a new hybrid multi-objective evolutionary algorithm, the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), whereby the global search ability of niched Pareto tabu search (NPTS) is improved by the diversification of candidate solutions arose from the evolving nondominated sorting genetic algorithm II (NSGA-II) population. Also, the NPTSGA coupled with the commonly used groundwater flow and transport codes, MODFLOW and MT3DMS, is developed for multi-objective optimal design of groundwater remediation systems. The proposed methodology is then applied to a large-scale field groundwater remediation system for cleanup of large trichloroethylene (TCE) plume at the Massachusetts Military Reservation (MMR) in Cape Cod, Massachusetts. Furthermore, a master-slave (MS) parallelization scheme based on the Message Passing Interface (MPI) is incorporated into the NPTSGA to implement objective function evaluations in distributed processor environment, which can greatly improve the efficiency of the NPTSGA in finding Pareto-optimal solutions to the real-world application. This study shows that the MS parallel NPTSGA in comparison with the original NPTS and NSGA-II can balance the tradeoff between diversity and optimality of solutions during the search process and is an efficient and effective tool for optimizing the multi-objective design of groundwater remediation systems under complicated hydrogeologic conditions.

Modeling and optimization of the multiobjective stochastic joint replenishment and delivery problem under supply chain environment.

PubMed

Wang, Lin; Qu, Hui; Liu, Shan; Dun, Cai-xia

2013-01-01

As a practical inventory and transportation problem, it is important to synthesize several objectives for the joint replenishment and delivery (JRD) decision. In this paper, a new multiobjective stochastic JRD (MSJRD) of the one-warehouse and n-retailer systems considering the balance of service level and total cost simultaneously is proposed. The goal of this problem is to decide the reasonable replenishment interval, safety stock factor, and traveling routing. Secondly, two approaches are designed to handle this complex multi-objective optimization problem. Linear programming (LP) approach converts the multi-objective to single objective, while a multi-objective evolution algorithm (MOEA) solves a multi-objective problem directly. Thirdly, three intelligent optimization algorithms, differential evolution algorithm (DE), hybrid DE (HDE), and genetic algorithm (GA), are utilized in LP-based and MOEA-based approaches. Results of the MSJRD with LP-based and MOEA-based approaches are compared by a contrastive numerical example. To analyses the nondominated solution of MOEA, a metric is also used to measure the distribution of the last generation solution. Results show that HDE outperforms DE and GA whenever LP or MOEA is adopted.

Modeling and Optimization of the Multiobjective Stochastic Joint Replenishment and Delivery Problem under Supply Chain Environment

PubMed Central

Dun, Cai-xia

2013-01-01

As a practical inventory and transportation problem, it is important to synthesize several objectives for the joint replenishment and delivery (JRD) decision. In this paper, a new multiobjective stochastic JRD (MSJRD) of the one-warehouse and n-retailer systems considering the balance of service level and total cost simultaneously is proposed. The goal of this problem is to decide the reasonable replenishment interval, safety stock factor, and traveling routing. Secondly, two approaches are designed to handle this complex multi-objective optimization problem. Linear programming (LP) approach converts the multi-objective to single objective, while a multi-objective evolution algorithm (MOEA) solves a multi-objective problem directly. Thirdly, three intelligent optimization algorithms, differential evolution algorithm (DE), hybrid DE (HDE), and genetic algorithm (GA), are utilized in LP-based and MOEA-based approaches. Results of the MSJRD with LP-based and MOEA-based approaches are compared by a contrastive numerical example. To analyses the nondominated solution of MOEA, a metric is also used to measure the distribution of the last generation solution. Results show that HDE outperforms DE and GA whenever LP or MOEA is adopted. PMID:24302880

Probing optimal measurement configuration for optical scatterometry by the multi-objective genetic algorithm

NASA Astrophysics Data System (ADS)

Chen, Xiuguo; Gu, Honggang; Jiang, Hao; Zhang, Chuanwei; Liu, Shiyuan

2018-04-01

Measurement configuration optimization (MCO) is a ubiquitous and important issue in optical scatterometry, whose aim is to probe the optimal combination of measurement conditions, such as wavelength, incidence angle, azimuthal angle, and/or polarization directions, to achieve a higher measurement precision for a given measuring instrument. In this paper, the MCO problem is investigated and formulated as a multi-objective optimization problem, which is then solved by the multi-objective genetic algorithm (MOGA). The case study on the Mueller matrix scatterometry for the measurement of a Si grating verifies the feasibility of the MOGA in handling the MCO problem in optical scatterometry by making a comparison with the Monte Carlo simulations. Experiments performed at the achieved optimal measurement configuration also show good agreement between the measured and calculated best-fit Mueller matrix spectra. The proposed MCO method based on MOGA is expected to provide a more general and practical means to solve the MCO problem in the state-of-the-art optical scatterometry.

Global dynamic optimization approach to predict activation in metabolic pathways.

PubMed

de Hijas-Liste, Gundián M; Klipp, Edda; Balsa-Canto, Eva; Banga, Julio R

2014-01-06

During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been successfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework. In this work we consider a more general multi-objective formulation and we present solutions based on recent developments in global dynamic optimization techniques. We illustrate the performance and capabilities of these techniques considering two sets of problems. First, we consider a set of single-objective examples of increasing complexity taken from the recent literature. We analyze the multimodal character of the associated non linear optimization problems, and we also evaluate different global optimization approaches in terms of numerical robustness, efficiency and scalability. Second, we consider generalized multi-objective formulations for several examples, and we show how this framework results in more biologically meaningful results. The proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to metabolic networks with arbitrary topologies, non-linear dynamics and constraints.

Multi-physics optimization of three-dimensional microvascular polymeric components

NASA Astrophysics Data System (ADS)

Aragón, Alejandro M.; Saksena, Rajat; Kozola, Brian D.; Geubelle, Philippe H.; Christensen, Kenneth T.; White, Scott R.

2013-01-01

This work discusses the computational design of microvascular polymeric materials, which aim at mimicking the behavior found in some living organisms that contain a vascular system. The optimization of the topology of the embedded three-dimensional microvascular network is carried out by coupling a multi-objective constrained genetic algorithm with a finite-element based physics solver, the latter validated through experiments. The optimization is carried out on multiple conflicting objective functions, namely the void volume fraction left by the network, the energy required to drive the fluid through the network and the maximum temperature when the material is subjected to thermal loads. The methodology presented in this work results in a viable alternative for the multi-physics optimization of these materials for active-cooling applications.

Syntheses and crystal structures of two topologically related modifications of Cs(2)[(UO(2))(2)(MoO(4))(3)].

PubMed

Krivovichev, S V; Cahill, C L; Burns, P C

2002-01-14

Two polymorphs of Cs(2)(UO(2))(2)(MoO(4))(3) have been synthesized by hydrothermal (alpha-phase) and high-temperature (beta-phase) routes. Both were characterized by single-crystal X-ray diffraction: alpha-Cs(2)(UO(2))(2)(MoO(4))(3), orthorhombic, Pna2(1), a = 20.4302(15) A, b = 8.5552(7) A, c = 9.8549(7) A, Z = 4; beta-Cs(2)(UO(2))(2)(MoO(4))(3), tetragonal, P4(2)/n, a = 10.1367(8) A, c = 16.2831(17) A, Z = 4. The structures of both phases consist of linked UO(7) pentagonal bipyramids and MoO(4) tetrahedra: alpha-Cs(2)(UO(2))(2)(MoO(4))(3) is a framework compound with large channels parallel to the c axis. Two cesium sites are located in these channels and are coordinated by 8 and 10 oxygen atoms. The structure of beta-Cs(2)(UO(2))(2)(MoO(4))(3) contains corrugated [(UO(2))(2)(MoO(4))(3)] sheets that are parallel to (001). The cesium cations are located between the sheets and are coordinated by eight oxygen atoms. The structures are topologically related; both can be described in terms of chains of 5-connected UO(7) pentagonal bipyramids and 3- and 4-connected MoO(4) tetrahedra.

Integration of multi-objective structural optimization into cementless hip prosthesis design: Improved Austin-Moore model.

PubMed

Kharmanda, G

2016-11-01

A new strategy of multi-objective structural optimization is integrated into Austin-Moore prosthesis in order to improve its performance. The new resulting model is so-called Improved Austin-Moore. The topology optimization is considered as a conceptual design stage to sketch several kinds of hollow stems according to the daily loading cases. The shape optimization presents the detailed design stage considering several objectives. Here, A new multiplicative formulation is proposed as a performance scale in order to define the best compromise between several requirements. Numerical applications on 2D and 3D problems are carried out to show the advantages of the proposed model.

Using MOEA with Redistribution and Consensus Branches to Infer Phylogenies.

PubMed

Min, Xiaoping; Zhang, Mouzhao; Yuan, Sisi; Ge, Shengxiang; Liu, Xiangrong; Zeng, Xiangxiang; Xia, Ningshao

2017-12-26

In recent years, to infer phylogenies, which are NP-hard problems, more and more research has focused on using metaheuristics. Maximum Parsimony and Maximum Likelihood are two effective ways to conduct inference. Based on these methods, which can also be considered as the optimal criteria for phylogenies, various kinds of multi-objective metaheuristics have been used to reconstruct phylogenies. However, combining these two time-consuming methods results in those multi-objective metaheuristics being slower than a single objective. Therefore, we propose a novel, multi-objective optimization algorithm, MOEA-RC, to accelerate the processes of rebuilding phylogenies using structural information of elites in current populations. We compare MOEA-RC with two representative multi-objective algorithms, MOEA/D and NAGA-II, and a non-consensus version of MOEA-RC on three real-world datasets. The result is, within a given number of iterations, MOEA-RC achieves better solutions than the other algorithms.

Fuzzy multi objective transportation problem – evolutionary algorithm approach

NASA Astrophysics Data System (ADS)

Karthy, T.; Ganesan, K.

2018-04-01

This paper deals with fuzzy multi objective transportation problem. An fuzzy optimal compromise solution is obtained by using Fuzzy Genetic Algorithm. A numerical example is provided to illustrate the methodology.

Pricing and location decisions in multi-objective facility location problem with M/M/m/k queuing systems

NASA Astrophysics Data System (ADS)

Tavakkoli-Moghaddam, Reza; Vazifeh-Noshafagh, Samira; Taleizadeh, Ata Allah; Hajipour, Vahid; Mahmoudi, Amin

2017-01-01

This article presents a new multi-objective model for a facility location problem with congestion and pricing policies. This model considers situations in which immobile service facilities are congested by a stochastic demand following M/M/m/k queues. The presented model belongs to the class of mixed-integer nonlinear programming models and NP-hard problems. To solve such a hard model, a new multi-objective optimization algorithm based on a vibration theory, namely multi-objective vibration damping optimization (MOVDO), is developed. In order to tune the algorithms parameters, the Taguchi approach using a response metric is implemented. The computational results are compared with those of the non-dominated ranking genetic algorithm and non-dominated sorting genetic algorithm. The outputs demonstrate the robustness of the proposed MOVDO in large-sized problems.

A new multi-objective optimization model for preventive maintenance and replacement scheduling of multi-component systems

NASA Astrophysics Data System (ADS)

Moghaddam, Kamran S.; Usher, John S.

2011-07-01

In this article, a new multi-objective optimization model is developed to determine the optimal preventive maintenance and replacement schedules in a repairable and maintainable multi-component system. In this model, the planning horizon is divided into discrete and equally-sized periods in which three possible actions must be planned for each component, namely maintenance, replacement, or do nothing. The objective is to determine a plan of actions for each component in the system while minimizing the total cost and maximizing overall system reliability simultaneously over the planning horizon. Because of the complexity, combinatorial and highly nonlinear structure of the mathematical model, two metaheuristic solution methods, generational genetic algorithm, and a simulated annealing are applied to tackle the problem. The Pareto optimal solutions that provide good tradeoffs between the total cost and the overall reliability of the system can be obtained by the solution approach. Such a modeling approach should be useful for maintenance planners and engineers tasked with the problem of developing recommended maintenance plans for complex systems of components.

Evolutionary algorithms for multi-objective optimization: fuzzy preference aggregation and multisexual EAs

NASA Astrophysics Data System (ADS)

Bonissone, Stefano R.

2001-11-01

There are many approaches to solving multi-objective optimization problems using evolutionary algorithms. We need to select methods for representing and aggregating preferences, as well as choosing strategies for searching in multi-dimensional objective spaces. First we suggest the use of linguistic variables to represent preferences and the use of fuzzy rule systems to implement tradeoff aggregations. After a review of alternatives EA methods for multi-objective optimizations, we explore the use of multi-sexual genetic algorithms (MSGA). In using a MSGA, we need to modify certain parts of the GAs, namely the selection and crossover operations. The selection operator groups solutions according to their gender tag to prepare them for crossover. The crossover is modified by appending a gender tag at the end of the chromosome. We use single and double point crossovers. We determine the gender of the offspring by the amount of genetic material provided by each parent. The parent that contributed the most to the creation of a specific offspring determines the gender that the offspring will inherit. This is still a work in progress, and in the conclusion we examine many future extensions and experiments.

Multi-Objective Bidding Strategy for Genco Using Non-Dominated Sorting Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Saksinchai, Apinat; Boonchuay, Chanwit; Ongsakul, Weerakorn

2010-06-01

This paper proposes a multi-objective bidding strategy for a generation company (GenCo) in uniform price spot market using non-dominated sorting particle swarm optimization (NSPSO). Instead of using a tradeoff technique, NSPSO is introduced to solve the multi-objective strategic bidding problem considering expected profit maximization and risk (profit variation) minimization. Monte Carlo simulation is employed to simulate rivals' bidding behavior. Test results indicate that the proposed approach can provide the efficient non-dominated solution front effectively. In addition, it can be used as a decision making tool for a GenCo compromising between expected profit and price risk in spot market.

SU-F-T-342: Dosimetric Constraint Prediction Guided Automatic Mulit-Objective Optimization for Intensity Modulated Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, T; Zhou, L; Li, Y

Purpose: For intensity modulated radiotherapy, the plan optimization is time consuming with difficulties of selecting objectives and constraints, and their relative weights. A fast and automatic multi-objective optimization algorithm with abilities to predict optimal constraints and manager their trade-offs can help to solve this problem. Our purpose is to develop such a framework and algorithm for a general inverse planning. Methods: There are three main components contained in this proposed multi-objective optimization framework: prediction of initial dosimetric constraints, further adjustment of constraints and plan optimization. We firstly use our previously developed in-house geometry-dosimetry correlation model to predict the optimal patient-specificmore » dosimetric endpoints, and treat them as initial dosimetric constraints. Secondly, we build an endpoint(organ) priority list and a constraint adjustment rule to repeatedly tune these constraints from their initial values, until every single endpoint has no room for further improvement. Lastly, we implement a voxel-independent based FMO algorithm for optimization. During the optimization, a model for tuning these voxel weighting factors respecting to constraints is created. For framework and algorithm evaluation, we randomly selected 20 IMRT prostate cases from the clinic and compared them with our automatic generated plans, in both the efficiency and plan quality. Results: For each evaluated plan, the proposed multi-objective framework could run fluently and automatically. The voxel weighting factor iteration time varied from 10 to 30 under an updated constraint, and the constraint tuning time varied from 20 to 30 for every case until no more stricter constraint is allowed. The average total costing time for the whole optimization procedure is ∼30mins. By comparing the DVHs, better OAR dose sparing could be observed in automatic generated plan, for 13 out of the 20 cases, while others are with competitive results. Conclusion: We have successfully developed a fast and automatic multi-objective optimization for intensity modulated radiotherapy. This work is supported by the National Natural Science Foundation of China (No: 81571771)« less

Multi-criteria multi-stakeholder decision analysis using a fuzzy-stochastic approach for hydrosystem management

NASA Astrophysics Data System (ADS)

Subagadis, Y. H.; Schütze, N.; Grundmann, J.

2014-09-01

The conventional methods used to solve multi-criteria multi-stakeholder problems are less strongly formulated, as they normally incorporate only homogeneous information at a time and suggest aggregating objectives of different decision-makers avoiding water-society interactions. In this contribution, Multi-Criteria Group Decision Analysis (MCGDA) using a fuzzy-stochastic approach has been proposed to rank a set of alternatives in water management decisions incorporating heterogeneous information under uncertainty. The decision making framework takes hydrologically, environmentally, and socio-economically motivated conflicting objectives into consideration. The criteria related to the performance of the physical system are optimized using multi-criteria simulation-based optimization, and fuzzy linguistic quantifiers have been used to evaluate subjective criteria and to assess stakeholders' degree of optimism. The proposed methodology is applied to find effective and robust intervention strategies for the management of a coastal hydrosystem affected by saltwater intrusion due to excessive groundwater extraction for irrigated agriculture and municipal use. Preliminary results show that the MCGDA based on a fuzzy-stochastic approach gives useful support for robust decision-making and is sensitive to the decision makers' degree of optimism.

Optimization of a Turboprop UAV for Maximum Loiter and Specific Power Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Dinc, Ali

2016-09-01

In this study, a genuine code was developed for optimization of selected parameters of a turboprop engine for an unmanned aerial vehicle (UAV) by employing elitist genetic algorithm. First, preliminary sizing of a UAV and its turboprop engine was done, by the code in a given mission profile. Secondly, single and multi-objective optimization were done for selected engine parameters to maximize loiter duration of UAV or specific power of engine or both. In single objective optimization, as first case, UAV loiter time was improved with an increase of 17.5% from baseline in given boundaries or constraints of compressor pressure ratio and burner exit temperature. In second case, specific power was enhanced by 12.3% from baseline. In multi-objective optimization case, where previous two objectives are considered together, loiter time and specific power were increased by 14.2% and 9.7% from baseline respectively, for the same constraints.

Multi-Objective Ant Colony Optimization Based on the Physarum-Inspired Mathematical Model for Bi-Objective Traveling Salesman Problems

PubMed Central

Zhang, Zili; Gao, Chao; Lu, Yuxiao; Liu, Yuxin; Liang, Mingxin

2016-01-01

Bi-objective Traveling Salesman Problem (bTSP) is an important field in the operations research, its solutions can be widely applied in the real world. Many researches of Multi-objective Ant Colony Optimization (MOACOs) have been proposed to solve bTSPs. However, most of MOACOs suffer premature convergence. This paper proposes an optimization strategy for MOACOs by optimizing the initialization of pheromone matrix with the prior knowledge of Physarum-inspired Mathematical Model (PMM). PMM can find the shortest route between two nodes based on the positive feedback mechanism. The optimized algorithms, named as iPM-MOACOs, can enhance the pheromone in the short paths and promote the search ability of ants. A series of experiments are conducted and experimental results show that the proposed strategy can achieve a better compromise solution than the original MOACOs for solving bTSPs. PMID:26751562

Multi-Objective Ant Colony Optimization Based on the Physarum-Inspired Mathematical Model for Bi-Objective Traveling Salesman Problems.

PubMed

Zhang, Zili; Gao, Chao; Lu, Yuxiao; Liu, Yuxin; Liang, Mingxin

2016-01-01

Bi-objective Traveling Salesman Problem (bTSP) is an important field in the operations research, its solutions can be widely applied in the real world. Many researches of Multi-objective Ant Colony Optimization (MOACOs) have been proposed to solve bTSPs. However, most of MOACOs suffer premature convergence. This paper proposes an optimization strategy for MOACOs by optimizing the initialization of pheromone matrix with the prior knowledge of Physarum-inspired Mathematical Model (PMM). PMM can find the shortest route between two nodes based on the positive feedback mechanism. The optimized algorithms, named as iPM-MOACOs, can enhance the pheromone in the short paths and promote the search ability of ants. A series of experiments are conducted and experimental results show that the proposed strategy can achieve a better compromise solution than the original MOACOs for solving bTSPs.

Potholes on the Infobahn: Hazardous Conditions Ahead?

ERIC Educational Resources Information Center

Laughon, Sally; Hanson, William R.

1996-01-01

Alerts educators to potentially objectionable Internet materials. Electronic mail, newsgroups, file transfer protocol sites, Internet Relay Chat (IRC), Multiuser Dungeons (MUDs), and Multiuser Object Oriented (MOOs) are services whose user anonymity can embolden discussions regarding sex, prejudice, religious dogma, and gambling. Teachers may wish…

Improved current extraction from ZnO/PbS quantum dot heterojunction photovoltaics using a MoO3 interfacial layer.

PubMed

Brown, Patrick R; Lunt, Richard R; Zhao, Ni; Osedach, Timothy P; Wanger, Darcy D; Chang, Liang-Yi; Bawendi, Moungi G; Bulović, Vladimir

2011-07-13

The ability to engineer interfacial energy offsets in photovoltaic devices is one of the keys to their optimization. Here, we demonstrate that improvements in power conversion efficiency may be attained for ZnO/PbS heterojunction quantum dot photovoltaics through the incorporation of a MoO(3) interlayer between the PbS colloidal quantum dot film and the top-contact anode. Through a combination of current-voltage characterization, circuit modeling, Mott-Schottky analysis, and external quantum efficiency measurements performed with bottom- and top-illumination, these enhancements are shown to stem from the elimination of a reverse-bias Schottky diode present at the PbS/anode interface. The incorporation of the high-work-function MoO(3) layer pins the Fermi level of the top contact, effectively decoupling the device performance from the work function of the anode and resulting in a high open-circuit voltage (0.59 ± 0.01 V) for a range of different anode materials. Corresponding increases in short-circuit current and fill factor enable 1.5-fold, 2.3-fold, and 4.5-fold enhancements in photovoltaic device efficiency for gold, silver, and ITO anodes, respectively, and result in a power conversion efficiency of 3.5 ± 0.4% for a device employing a gold anode.

Enhanced photoelectrocatalytic decomposition of copper cyanide complexes and simultaneous recovery of copper with a Bi2MoO6 electrode under visible light by EDTA/K4P2O7.

PubMed

Zhao, Xu; Zhang, Juanjuan; Qiao, Meng; Liu, Huijuan; Qu, Jiuhui

2015-04-07

Simultaneous photoelectrocatalytic (PEC) oxidation of cyanides and recovery of copper in a PEC reactor with a Bi(2)MoO(6) photoanode was investigated at alkaline conditions under visible light irradiation. The surface variation of the Bi(2)MoO(6) photoanode and titanium cathode was characterized. The Cu mass distribution onto the anode, in the solution, and onto the cathode was fully investigated. In the individual PEC oxidation of copper cyanides, the formation of a black copper oxide on the anode occurred. By keeping the initial cyanide concentration at 0.01 mM, the effect of EDTA/K(4)P(2)O(7) was examined at different molar ratios of EDTA/K(4)P(2)O(7) to cyanide. It was indicated that the oxidation of cyanides increased and simultaneous copper electrodeposition with zero value onto the cathode was feasible at pH 11. Under the optimal conditions, the total cyanide concentration was lowered from 250 to 5.0 mg/L, and the Cu recovery efficiency deposited onto the cathode was higher than 90%. Cyanate was the only product. The role of the photogenerated hole in the oxidation of cyanide ions was confirmed.

Synthesis and Characterization of AlCl3 Impregnated Molybdenum Oxide as Heterogeneous Nano-Catalyst for the Friedel-Crafts Acylation Reaction in Ambient Condition.

PubMed

Jadhav, Arvind H; Chinnappan, Amutha; Hiremath, Vishwanath; Seo, Jeong Gil

2015-10-01

Aluminum trichloride (AlCl3) impregnated molybdenum oxide heterogeneous nano-catalyst was prepared by using simple impregnation method. The prepared heterogeneous catalyst was characterized by powder X-ray diffraction, FT-IR spectroscopy, solid-state NMR spectroscopy, SEM imaging, and EDX mapping. The catalytic activity of this protocol was evaluated as heterogeneous catalyst for the Friedel-Crafts acylation reaction at room temperature. The impregnated MoO4(AlCl2)2 catalyst showed tremendous catalytic activity in Friedel-Crafts acylation reaction under solvent-free and mild reaction condition. As a result, 84.0% yield of acyl product with 100% consumption of reactants in 18 h reaction time at room temperature was achieved. The effects of different solvents system with MoO4(AlCl2)2 catalyst in acylation reaction was also investigated. By using optimized reaction condition various acylated derivatives were prepared. In addition, the catalyst was separated by simple filtration process after the reaction and reused several times. Therefore, heterogeneous MoO4(AlCl2)2 catalyst was found environmentally benign catalyst, very convenient, high yielding, and clean method for the Friedel-Crafts acylation reaction under solvent-free and ambient reaction condition.

Multi-objective optimization of radiotherapy: distributed Q-learning and agent-based simulation

NASA Astrophysics Data System (ADS)

Jalalimanesh, Ammar; Haghighi, Hamidreza Shahabi; Ahmadi, Abbas; Hejazian, Hossein; Soltani, Madjid

2017-09-01

Radiotherapy (RT) is among the regular techniques for the treatment of cancerous tumours. Many of cancer patients are treated by this manner. Treatment planning is the most important phase in RT and it plays a key role in therapy quality achievement. As the goal of RT is to irradiate the tumour with adequately high levels of radiation while sparing neighbouring healthy tissues as much as possible, it is a multi-objective problem naturally. In this study, we propose an agent-based model of vascular tumour growth and also effects of RT. Next, we use multi-objective distributed Q-learning algorithm to find Pareto-optimal solutions for calculating RT dynamic dose. We consider multiple objectives and each group of optimizer agents attempt to optimise one of them, iteratively. At the end of each iteration, agents compromise the solutions to shape the Pareto-front of multi-objective problem. We propose a new approach by defining three schemes of treatment planning created based on different combinations of our objectives namely invasive, conservative and moderate. In invasive scheme, we enforce killing cancer cells and pay less attention about irradiation effects on normal cells. In conservative scheme, we take more care of normal cells and try to destroy cancer cells in a less stressed manner. The moderate scheme stands in between. For implementation, each of these schemes is handled by one agent in MDQ-learning algorithm and the Pareto optimal solutions are discovered by the collaboration of agents. By applying this methodology, we could reach Pareto treatment plans through building different scenarios of tumour growth and RT. The proposed multi-objective optimisation algorithm generates robust solutions and finds the best treatment plan for different conditions.

Pareto Tracer: a predictor-corrector method for multi-objective optimization problems

NASA Astrophysics Data System (ADS)

Martín, Adanay; Schütze, Oliver

2018-03-01

This article proposes a novel predictor-corrector (PC) method for the numerical treatment of multi-objective optimization problems (MOPs). The algorithm, Pareto Tracer (PT), is capable of performing a continuation along the set of (local) solutions of a given MOP with k objectives, and can cope with equality and box constraints. Additionally, the first steps towards a method that manages general inequality constraints are also introduced. The properties of PT are first discussed theoretically and later numerically on several examples.

Informed multi-objective decision-making in environmental management using Pareto optimality

Treesearch

Maureen C. Kennedy; E. David Ford; Peter Singleton; Mark Finney; James K. Agee

2008-01-01

Effective decisionmaking in environmental management requires the consideration of multiple objectives that may conflict. Common optimization methods use weights on the multiple objectives to aggregate them into a single value, neglecting valuable insight into the relationships among the objectives in the management problem.

Oxidation process of MoO xC y to MoO 3: kinetics and mechanism

NASA Astrophysics Data System (ADS)

Aleman-Vázquez, L. O.; Torres-García, E.; Rodríguez-Gattorno, G.; Ocotlán-Flores, J.; Camacho-López, M. A.; Cano, J. L.

2004-10-01

A non-isothermal kinetic study of the oxidation of "carbon-modified MoO3" in the temperature range of 150-550°C by simultaneous TGA-DTA was investigated. During the oxidation process, two thermal events were detected, which are associated with the oxidation of carbon in MoOxCy and MoO2 to MoO3. The model-free and model-fitting kinetic approaches have been applied to TGA experimental data. The solid state-kinetics of the oxidation of MoOxCy to MoO3 is governed by F1 (unimolecular decay), which suggests that the reaction is of the first order with respect to oxygen concentration. The constant (Ea)α value (about 115±5 kJ/mol) for this first stage can be related to the nature of the reaction site in the MoO3 matrix. This indicates that oxidation occurs in well-defined lattice position sites (energetically equivalent). On the other hand, for the second stage of oxidation, MoO2 to MoO3, the isoconversional analysis shows a complex (Ea)α dependence on (α) and reveals a typical behavior for competitive reaction. A D2 (two-dimensional diffusion) mechanism with a variable activation energy value in the range 110-200 kJ/mol was obtained. This can be interpreted as an inter-layer oxygen diffusion in the solid bulk, which does not exclude other simultaneous mechanism reactions.

An optimal design of wind turbine and ship structure based on neuro-response surface method

NASA Astrophysics Data System (ADS)

Lee, Jae-Chul; Shin, Sung-Chul; Kim, Soo-Young

2015-07-01

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

Getting the most out of additional guidance information in deformable image registration by leveraging multi-objective optimization

NASA Astrophysics Data System (ADS)

Alderliesten, Tanja; Bosman, Peter A. N.; Bel, Arjan

2015-03-01

Incorporating additional guidance information, e.g., landmark/contour correspondence, in deformable image registration is often desirable and is typically done by adding constraints or cost terms to the optimization function. Commonly, deciding between a "hard" constraint and a "soft" additional cost term as well as the weighting of cost terms in the optimization function is done on a trial-and-error basis. The aim of this study is to investigate the advantages of exploiting guidance information by taking a multi-objective optimization perspective. Hereto, next to objectives related to match quality and amount of deformation, we define a third objective related to guidance information. Multi-objective optimization eliminates the need to a-priori tune a weighting of objectives in a single optimization function or the strict requirement of fulfilling hard guidance constraints. Instead, Pareto-efficient trade-offs between all objectives are found, effectively making the introduction of guidance information straightforward, independent of its type or scale. Further, since complete Pareto fronts also contain less interesting parts (i.e., solutions with near-zero deformation effort), we study how adaptive steering mechanisms can be incorporated to automatically focus more on solutions of interest. We performed experiments on artificial and real clinical data with large differences, including disappearing structures. Results show the substantial benefit of using additional guidance information. Moreover, compared to the 2-objective case, additional computational cost is negligible. Finally, with the same computational budget, use of the adaptive steering mechanism provides superior solutions in the area of interest.

Solution processable mixed-solvent exfoliated MoS2 nanosheets for efficient and robust organic light-emitting diodes

NASA Astrophysics Data System (ADS)

Liu, Chia-Wei; Wang, Chia; Liao, Chia-Wei; Golder, Jan; Tsai, Ming-Chih; Young, Hong-Tsu; Chen, Chin-Ti; Wu, Chih-I.

2018-04-01

We demonstrate the use of solution-processed molybdenum trioxide (MoO3) nanoparticle-decorated molybdenum disulfide (MoS2) nanosheets (MoS2/MoO3) as hole injection layer (HIL) in organic lighting diodes (OLEDs). The device performance is shown to be significantly improved by the introduction of such MoS2/MoO3 HIL without any post-ultraviolet-ozone treatment, and is shown to better the performance of devices fabricated using conventional poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) and MoO3 nanoparticle HILs. The MoS2/MoO3 nanosheets form a compact film, as smooth as PEDOT:PSS films and smoother than MoO3 nanoparticle films, when simply spin-coated on indium tin oxide substrates. The improvement in device efficiency can be attributed to the smooth surface of the nanostructured MoS2/MoO3 HIL and the excellent conductivity characteristics of the two-dimensional (2D) layered material (MoS2), which facilitate carrier transport in the device and reduce the sheet resistance. Moreover, the long-term stability of OLED devices that use such MoS2/MoO3 layers is shown to be improved dramatically compared with hygroscopic and acidic PEDOT:PSS-based devices.

Promising features of Moringa oleifera oil: recent updates and perspectives.

PubMed

Nadeem, Muhammad; Imran, Muhammad

2016-12-08

Lipids are the concentrated source of energy, fat soluble vitamins, essential fatty acids, carriers of flavours and many bio-active compounds with important role in maintaining physiological functions of biological body. Moringa oleifera is native to Himalaya and widely grown in many Asian and African countries with seed oil content range from 35-40%. Moringa oleifera oil (MOO) has light yellow colour with mild nutty flavour and fatty acids composition suggests that MOO is highly suitable for both edible and non-edible applications. MOO is extremely resistant to autoxidation which can be used as an antioxidant for the long term stabilization of commercial edible oils. Thermal stability of MOO is greater than soybean, sunflower, canola and cottonseed oils. High oleic contents of MOO are believed to have the capability of increasing beneficial HDL cholesterol and decreased the serum cholesterol and triglycerides. MOO applications have also been explored in cosmetics, folk medicines and skin care formulations. Overall, this review focuses on commercial production status, food applications, antioxidant characteristics, health benefits, thermal stability, fractionation, cholesterol contents, medicinal, nutraceutical action, toxicological evaluation, biodiesel production, personal care formulations and future perspectives of the MOO for the stake holders to process and utilize MOO as a new source of edible oil for industrial purpose.

Quantitative determination of oxygen defects, surface lewis acidity, and catalytic properties of mesoporous MoO3/SBA-15 catalysts

NASA Astrophysics Data System (ADS)

González, Julio; Wang, Jin An; Chen, Lifang; Manríquez, Maria; Salmones, José; Limas, Roberto; Arellano, Ulises

2018-07-01

A set of MoO3/SBA-15 mesoporous catalysts were characterized with a variety of spectroscopic techniques and their crystalline structures were refined with Rietveld method. Oxygen defect concentration, crystallite size, phase composition, surface acidity, mesoporous regularity, and textural properties were reported. Both α-MoO3 and β-MoO3 phases coexisted but α-MoO3 was predominated. Oxygen defects were created in the orthorhombic structure and its concentration decreased from 3.08% for the 20 wt%MoO3/SBA-15 to 0.55% for the 25 wt%MoO3/SBA-15. All the MoO3/SBA-15 catalysts chiefly contained a big number of Lewis acid sites originating from oxygen defects in MoO3 crystals. In the absence of formic acid, the oxidation of 4,6-dibenzothiophene (4,6-DMDBT) in a model diesel was almost proportional to the number of Lewis acid sites. In the presence of formic acid, 4,6-DMDBT oxidation was significantly affected by the formation of surface peroxometallic complex and Lewis acidity. Formic acid addition could improve the ODS efficiency by promoting peroxometallic complex formation and enhancing oxidant stability. Under the optimal reaction condition using the best 15 and 20 wt%MoO3/SBA-15 catalysts, more than 99% 4,6-DMDBT could be removed at 70 °C within 30 min. This work confirmed that 4,6-DMDBT oxidation is a texture and particle size sensitive and Lewis acidity dependent reaction. This work also shows that crystalline structure refinement combination with experiments can gain new insights in the design of heterogeneous nanocatalysts and help to better understand the catalytic behavior in the oxidative desulfurization reactions.

An efficient and accurate solution methodology for bilevel multi-objective programming problems using a hybrid evolutionary-local-search algorithm.

PubMed

Deb, Kalyanmoy; Sinha, Ankur

2010-01-01

Bilevel optimization problems involve two optimization tasks (upper and lower level), in which every feasible upper level solution must correspond to an optimal solution to a lower level optimization problem. These problems commonly appear in many practical problem solving tasks including optimal control, process optimization, game-playing strategy developments, transportation problems, and others. However, they are commonly converted into a single level optimization problem by using an approximate solution procedure to replace the lower level optimization task. Although there exist a number of theoretical, numerical, and evolutionary optimization studies involving single-objective bilevel programming problems, not many studies look at the context of multiple conflicting objectives in each level of a bilevel programming problem. In this paper, we address certain intricate issues related to solving multi-objective bilevel programming problems, present challenging test problems, and propose a viable and hybrid evolutionary-cum-local-search based algorithm as a solution methodology. The hybrid approach performs better than a number of existing methodologies and scales well up to 40-variable difficult test problems used in this study. The population sizing and termination criteria are made self-adaptive, so that no additional parameters need to be supplied by the user. The study indicates a clear niche of evolutionary algorithms in solving such difficult problems of practical importance compared to their usual solution by a computationally expensive nested procedure. The study opens up many issues related to multi-objective bilevel programming and hopefully this study will motivate EMO and other researchers to pay more attention to this important and difficult problem solving activity.

Robust design of multiple trailing edge flaps for helicopter vibration reduction: A multi-objective bat algorithm approach

NASA Astrophysics Data System (ADS)

Mallick, Rajnish; Ganguli, Ranjan; Seetharama Bhat, M.

2015-09-01

The objective of this study is to determine an optimal trailing edge flap configuration and flap location to achieve minimum hub vibration levels and flap actuation power simultaneously. An aeroelastic analysis of a soft in-plane four-bladed rotor is performed in conjunction with optimal control. A second-order polynomial response surface based on an orthogonal array (OA) with 3-level design describes both the objectives adequately. Two new orthogonal arrays called MGB2P-OA and MGB4P-OA are proposed to generate nonlinear response surfaces with all interaction terms for two and four parameters, respectively. A multi-objective bat algorithm (MOBA) approach is used to obtain the optimal design point for the mutually conflicting objectives. MOBA is a recently developed nature-inspired metaheuristic optimization algorithm that is based on the echolocation behaviour of bats. It is found that MOBA inspired Pareto optimal trailing edge flap design reduces vibration levels by 73% and flap actuation power by 27% in comparison with the baseline design.

Multi-objective Optimization of a Solar Humidification Dehumidification Desalination Unit

NASA Astrophysics Data System (ADS)

Rafigh, M.; Mirzaeian, M.; Najafi, B.; Rinaldi, F.; Marchesi, R.

2017-11-01

In the present paper, a humidification-dehumidification desalination unit integrated with solar system is considered. In the first step mathematical model of the whole plant is represented. Next, taking into account the logical constraints, the performance of the system is optimized. On one hand it is desired to have higher energetic efficiency, while on the other hand, higher efficiency results in an increment in the required area for each subsystem which consequently leads to an increase in the total cost of the plant. In the present work, the optimum solution is achieved when the specific energy of the solar heater and also the areas of humidifier and dehumidifier are minimized. Due to the fact that considered objective functions are in conflict, conventional optimization methods are not applicable. Hence, multi objective optimization using genetic algorithm which is an efficient tool for dealing with problems with conflicting objectives has been utilized and a set of optimal solutions called Pareto front each of which is a tradeoff between the mentioned objectives is generated.

Multi-objective optimization in systematic conservation planning and the representation of genetic variability among populations.

PubMed

Schlottfeldt, S; Walter, M E M T; Carvalho, A C P L F; Soares, T N; Telles, M P C; Loyola, R D; Diniz-Filho, J A F

2015-06-18

Biodiversity crises have led scientists to develop strategies for achieving conservation goals. The underlying principle of these strategies lies in systematic conservation planning (SCP), in which there are at least 2 conflicting objectives, making it a good candidate for multi-objective optimization. Although SCP is typically applied at the species level (or hierarchically higher), it can be used at lower hierarchical levels, such as using alleles as basic units for analysis, for conservation genetics. Here, we propose a method of SCP using a multi-objective approach. We used non-dominated sorting genetic algorithm II in order to identify the smallest set of local populations of Dipteryx alata (baru) (a Brazilian Cerrado species) for conservation, representing the known genetic diversity and using allele frequency information associated with heterozygosity and Hardy-Weinberg equilibrium. We worked in 3 variations for the problem. First, we reproduced a previous experiment, but using a multi-objective approach. We found that the smallest set of populations needed to represent all alleles under study was 7, corroborating the results of the previous study, but with more distinct solutions. In the 2nd and 3rd variations, we performed simultaneous optimization of 4 and 5 objectives, respectively. We found similar but refined results for 7 populations, and a larger portfolio considering intra-specific diversity and persistence with populations ranging from 8-22. This is the first study to apply multi-objective algorithms to an SCP problem using alleles at the population level as basic units for analysis.

Multi-Objective Random Search Algorithm for Simultaneously Optimizing Wind Farm Layout and Number of Turbines

NASA Astrophysics Data System (ADS)

Feng, Ju; Shen, Wen Zhong; Xu, Chang

2016-09-01

A new algorithm for multi-objective wind farm layout optimization is presented. It formulates the wind turbine locations as continuous variables and is capable of optimizing the number of turbines and their locations in the wind farm simultaneously. Two objectives are considered. One is to maximize the total power production, which is calculated by considering the wake effects using the Jensen wake model combined with the local wind distribution. The other is to minimize the total electrical cable length. This length is assumed to be the total length of the minimal spanning tree that connects all turbines and is calculated by using Prim's algorithm. Constraints on wind farm boundary and wind turbine proximity are also considered. An ideal test case shows the proposed algorithm largely outperforms a famous multi-objective genetic algorithm (NSGA-II). In the real test case based on the Horn Rev 1 wind farm, the algorithm also obtains useful Pareto frontiers and provides a wide range of Pareto optimal layouts with different numbers of turbines for a real-life wind farm developer.

Scalability of surrogate-assisted multi-objective optimization of antenna structures exploiting variable-fidelity electromagnetic simulation models

NASA Astrophysics Data System (ADS)

Koziel, Slawomir; Bekasiewicz, Adrian

2016-10-01

Multi-objective optimization of antenna structures is a challenging task owing to the high computational cost of evaluating the design objectives as well as the large number of adjustable parameters. Design speed-up can be achieved by means of surrogate-based optimization techniques. In particular, a combination of variable-fidelity electromagnetic (EM) simulations, design space reduction techniques, response surface approximation models and design refinement methods permits identification of the Pareto-optimal set of designs within a reasonable timeframe. Here, a study concerning the scalability of surrogate-assisted multi-objective antenna design is carried out based on a set of benchmark problems, with the dimensionality of the design space ranging from six to 24 and a CPU cost of the EM antenna model from 10 to 20 min per simulation. Numerical results indicate that the computational overhead of the design process increases more or less quadratically with the number of adjustable geometric parameters of the antenna structure at hand, which is a promising result from the point of view of handling even more complex problems.

Multi-objective optimization of an industrial penicillin V bioreactor train using non-dominated sorting genetic algorithm.

PubMed

Lee, Fook Choon; Rangaiah, Gade Pandu; Ray, Ajay Kumar

2007-10-15

Bulk of the penicillin produced is used as raw material for semi-synthetic penicillin (such as amoxicillin and ampicillin) and semi-synthetic cephalosporins (such as cephalexin and cefadroxil). In the present paper, an industrial penicillin V bioreactor train is optimized for multiple objectives simultaneously. An industrial train, comprising a bank of identical bioreactors, is run semi-continuously in a synchronous fashion. The fermentation taking place in a bioreactor is modeled using a morphologically structured mechanism. For multi-objective optimization for two and three objectives, the elitist non-dominated sorting genetic algorithm (NSGA-II) is chosen. Instead of a single optimum as in the traditional optimization, a wide range of optimal design and operating conditions depicting trade-offs of key performance indicators such as batch cycle time, yield, profit and penicillin concentration, is successfully obtained. The effects of design and operating variables on the optimal solutions are discussed in detail. Copyright 2007 Wiley Periodicals, Inc.

Multi-Objective Aerodynamic Optimization of the Streamlined Shape of High-Speed Trains Based on the Kriging Model.

PubMed

Xu, Gang; Liang, Xifeng; Yao, Shuanbao; Chen, Dawei; Li, Zhiwei

2017-01-01

Minimizing the aerodynamic drag and the lift of the train coach remains a key issue for high-speed trains. With the development of computing technology and computational fluid dynamics (CFD) in the engineering field, CFD has been successfully applied to the design process of high-speed trains. However, developing a new streamlined shape for high-speed trains with excellent aerodynamic performance requires huge computational costs. Furthermore, relationships between multiple design variables and the aerodynamic loads are seldom obtained. In the present study, the Kriging surrogate model is used to perform a multi-objective optimization of the streamlined shape of high-speed trains, where the drag and the lift of the train coach are the optimization objectives. To improve the prediction accuracy of the Kriging model, the cross-validation method is used to construct the optimal Kriging model. The optimization results show that the two objectives are efficiently optimized, indicating that the optimization strategy used in the present study can greatly improve the optimization efficiency and meet the engineering requirements.

Molecular-scale properties of MoO3 -doped pentacene

NASA Astrophysics Data System (ADS)

Ha, Sieu D.; Meyer, Jens; Kahn, Antoine

2010-10-01

The mechanisms of molecular doping in organic electronic materials are explored through investigation of pentacene p -doped with molybdenum trioxide (MoO3) . Doping is confirmed with ultraviolet photoelectron spectroscopy. Isolated dopants are imaged at the molecular scale using scanning tunneling microscopy (STM) and effects due to localized holes are observed. The results demonstrate that donated charges are localized by the counterpotential of ionized dopants in MoO3 -doped pentacene, generalizing similar effects previously observed for pentacene doped with tetrafluoro-tetracyanoquinodimethane. Such localized hole effects are only observed for low molecular weight MoO3 species. It is shown that for larger MoO3 polymers and clusters, the ionized dopant potential is sufficiently large as to mask the effect of the localized hole in STM images. Current-voltage measurements recorded using scanning tunneling spectroscopy reveal that electron conductivity decreases in MoO3 -doped films, as expected for electron capture and p -doping.

Nonlinear bioheat transfer models and multi-objective numerical optimization of the cryosurgery operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kudryashov, Nikolay A.; Shilnikov, Kirill E.

Numerical computation of the three dimensional problem of the freezing interface propagation during the cryosurgery coupled with the multi-objective optimization methods is used in order to improve the efficiency and safety of the cryosurgery operations performing. Prostate cancer treatment and cutaneous cryosurgery are considered. The heat transfer in soft tissue during the thermal exposure to low temperature is described by the Pennes bioheat model and is coupled with an enthalpy method for blurred phase change computations. The finite volume method combined with the control volume approximation of the heat fluxes is applied for the cryosurgery numerical modeling on the tumormore » tissue of a quite arbitrary shape. The flux relaxation approach is used for the stability improvement of the explicit finite difference schemes. The method of the additional heating elements mounting is studied as an approach to control the cellular necrosis front propagation. Whereas the undestucted tumor tissue and destucted healthy tissue volumes are considered as objective functions, the locations of additional heating elements in cutaneous cryosurgery and cryotips in prostate cancer cryotreatment are considered as objective variables in multi-objective problem. The quasi-gradient method is proposed for the searching of the Pareto front segments as the multi-objective optimization problem solutions.« less

Tuning rules for robust FOPID controllers based on multi-objective optimization with FOPDT models.

PubMed

Sánchez, Helem Sabina; Padula, Fabrizio; Visioli, Antonio; Vilanova, Ramon

2017-01-01

In this paper a set of optimally balanced tuning rules for fractional-order proportional-integral-derivative controllers is proposed. The control problem of minimizing at once the integrated absolute error for both the set-point and the load disturbance responses is addressed. The control problem is stated as a multi-objective optimization problem where a first-order-plus-dead-time process model subject to a robustness, maximum sensitivity based, constraint has been considered. A set of Pareto optimal solutions is obtained for different normalized dead times and then the optimal balance between the competing objectives is obtained by choosing the Nash solution among the Pareto-optimal ones. A curve fitting procedure has then been applied in order to generate suitable tuning rules. Several simulation results show the effectiveness of the proposed approach. Copyright © 2016. Published by Elsevier Ltd.

A hybrid multi-objective evolutionary algorithm for wind-turbine blade optimization

NASA Astrophysics Data System (ADS)

Sessarego, M.; Dixon, K. R.; Rival, D. E.; Wood, D. H.

2015-08-01

A concurrent-hybrid non-dominated sorting genetic algorithm (hybrid NSGA-II) has been developed and applied to the simultaneous optimization of the annual energy production, flapwise root-bending moment and mass of the NREL 5 MW wind-turbine blade. By hybridizing a multi-objective evolutionary algorithm (MOEA) with gradient-based local search, it is believed that the optimal set of blade designs could be achieved in lower computational cost than for a conventional MOEA. To measure the convergence between the hybrid and non-hybrid NSGA-II on a wind-turbine blade optimization problem, a computationally intensive case was performed using the non-hybrid NSGA-II. From this particular case, a three-dimensional surface representing the optimal trade-off between the annual energy production, flapwise root-bending moment and blade mass was achieved. The inclusion of local gradients in the blade optimization, however, shows no improvement in the convergence for this three-objective problem.

Fatigue design of a cellular phone folder using regression model-based multi-objective optimization

NASA Astrophysics Data System (ADS)

Kim, Young Gyun; Lee, Jongsoo

2016-08-01

In a folding cellular phone, the folding device is repeatedly opened and closed by the user, which eventually results in fatigue damage, particularly to the front of the folder. Hence, it is important to improve the safety and endurance of the folder while also reducing its weight. This article presents an optimal design for the folder front that maximizes its fatigue endurance while minimizing its thickness. Design data for analysis and optimization were obtained experimentally using a test jig. Multi-objective optimization was carried out using a nonlinear regression model. Three regression methods were employed: back-propagation neural networks, logistic regression and support vector machines. The AdaBoost ensemble technique was also used to improve the approximation. Two-objective Pareto-optimal solutions were identified using the non-dominated sorting genetic algorithm (NSGA-II). Finally, a numerically optimized solution was validated against experimental product data, in terms of both fatigue endurance and thickness index.

Investigating multi-objective fluence and beam orientation IMRT optimization

NASA Astrophysics Data System (ADS)

Potrebko, Peter S.; Fiege, Jason; Biagioli, Matthew; Poleszczuk, Jan

2017-07-01

Radiation Oncology treatment planning requires compromises to be made between clinical objectives that are invariably in conflict. It would be beneficial to have a ‘bird’s-eye-view’ perspective of the full spectrum of treatment plans that represent the possible trade-offs between delivering the intended dose to the planning target volume (PTV) while optimally sparing the organs-at-risk (OARs). In this work, the authors demonstrate Pareto-aware radiotherapy evolutionary treatment optimization (PARETO), a multi-objective tool featuring such bird’s-eye-view functionality, which optimizes fluence patterns and beam angles for intensity-modulated radiation therapy (IMRT) treatment planning. The problem of IMRT treatment plan optimization is managed as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. To achieve this, PARETO is built around a powerful multi-objective evolutionary algorithm, called Ferret, which simultaneously optimizes multiple fitness functions that encode the attributes of the desired dose distribution for the PTV and OARs. The graphical interfaces within PARETO provide useful information such as: the convergence behavior during optimization, trade-off plots between the competing objectives, and a graphical representation of the optimal solution database allowing for the rapid exploration of treatment plan quality through the evaluation of dose-volume histograms and isodose distributions. PARETO was evaluated for two relatively complex clinical cases, a paranasal sinus and a pancreas case. The end result of each PARETO run was a database of optimal (non-dominated) treatment plans that demonstrated trade-offs between the OAR and PTV fitness functions, which were all equally good in the Pareto-optimal sense (where no one objective can be improved without worsening at least one other). Ferret was able to produce high quality solutions even though a large number of parameters, such as beam fluence and beam angles, were included in the optimization.

Optimization of Land Use Suitability for Agriculture Using Integrated Geospatial Model and Genetic Algorithms

NASA Astrophysics Data System (ADS)

Mansor, S. B.; Pormanafi, S.; Mahmud, A. R. B.; Pirasteh, S.

2012-08-01

In this study, a geospatial model for land use allocation was developed from the view of simulating the biological autonomous adaptability to environment and the infrastructural preference. The model was developed based on multi-agent genetic algorithm. The model was customized to accommodate the constraint set for the study area, namely the resource saving and environmental-friendly. The model was then applied to solve the practical multi-objective spatial optimization allocation problems of land use in the core region of Menderjan Basin in Iran. The first task was to study the dominant crops and economic suitability evaluation of land. Second task was to determine the fitness function for the genetic algorithms. The third objective was to optimize the land use map using economical benefits. The results has indicated that the proposed model has much better performance for solving complex multi-objective spatial optimization allocation problems and it is a promising method for generating land use alternatives for further consideration in spatial decision-making.

Negotiating designs of multi-purpose reservoir systems in international basins

NASA Astrophysics Data System (ADS)

Geressu, Robel; Harou, Julien

2016-04-01

Given increasing agricultural and energy demands, coordinated management of multi-reservoir systems could help increase production without further stressing available water resources. However, regional or international disputes about water-use rights pose a challenge to efficient expansion and management of many large reservoir systems. Even when projects are likely to benefit all stakeholders, agreeing on the design, operation, financing, and benefit sharing can be challenging. This is due to the difficulty of considering multiple stakeholder interests in the design of projects and understanding the benefit trade-offs that designs imply. Incommensurate performance metrics, incomplete knowledge on system requirements, lack of objectivity in managing conflict and difficulty to communicate complex issue exacerbate the problem. This work proposes a multi-step hybrid multi-objective optimization and multi-criteria ranking approach for supporting negotiation in water resource systems. The approach uses many-objective optimization to generate alternative efficient designs and reveal the trade-offs between conflicting objectives. This enables informed elicitation of criteria weights for further multi-criteria ranking of alternatives. An ideal design would be ranked as best by all stakeholders. Resource-sharing mechanisms such as power-trade and/or cost sharing may help competing stakeholders arrive at designs acceptable to all. Many-objective optimization helps suggests efficient designs (reservoir site, its storage size and operating rule) and coordination levels considering the perspectives of multiple stakeholders simultaneously. We apply the proposed approach to a proof-of-concept study of the expansion of the Blue Nile transboundary reservoir system.

A game theory-reinforcement learning (GT-RL) method to develop optimal operation policies for multi-operator reservoir systems

NASA Astrophysics Data System (ADS)

Madani, Kaveh; Hooshyar, Milad

2014-11-01

Reservoir systems with multiple operators can benefit from coordination of operation policies. To maximize the total benefit of these systems the literature has normally used the social planner's approach. Based on this approach operation decisions are optimized using a multi-objective optimization model with a compound system's objective. While the utility of the system can be increased this way, fair allocation of benefits among the operators remains challenging for the social planner who has to assign controversial weights to the system's beneficiaries and their objectives. Cooperative game theory provides an alternative framework for fair and efficient allocation of the incremental benefits of cooperation. To determine the fair and efficient utility shares of the beneficiaries, cooperative game theory solution methods consider the gains of each party in the status quo (non-cooperation) as well as what can be gained through the grand coalition (social planner's solution or full cooperation) and partial coalitions. Nevertheless, estimation of the benefits of different coalitions can be challenging in complex multi-beneficiary systems. Reinforcement learning can be used to address this challenge and determine the gains of the beneficiaries for different levels of cooperation, i.e., non-cooperation, partial cooperation, and full cooperation, providing the essential input for allocation based on cooperative game theory. This paper develops a game theory-reinforcement learning (GT-RL) method for determining the optimal operation policies in multi-operator multi-reservoir systems with respect to fairness and efficiency criteria. As the first step to underline the utility of the GT-RL method in solving complex multi-agent multi-reservoir problems without a need for developing compound objectives and weight assignment, the proposed method is applied to a hypothetical three-agent three-reservoir system.

Research on vehicle routing optimization for the terminal distribution of B2C E-commerce firms

NASA Astrophysics Data System (ADS)

Zhang, Shiyun; Lu, Yapei; Li, Shasha

2018-05-01

In this paper, we established a half open multi-objective optimization model for the vehicle routing problem of B2C (business-to-customer) E-Commerce firms. To minimize the current transport distance as well as the disparity between the excepted shipments and the transport capacity in the next distribution, we applied the concept of dominated solution and Pareto solutions to the standard particle swarm optimization and proposed a MOPSO (multi-objective particle swarm optimization) algorithm to support the model. Besides, we also obtained the optimization solution of MOPSO algorithm based on data randomly generated through the system, which verified the validity of the model.

Anti-buckling design of variable stiffness composite cylinder under combined loading based on the multi-objective optimization method

NASA Astrophysics Data System (ADS)

Zheng, Y.; Chen, J.

2018-06-01

Variable stiffness composite structures take full advantages of composite’s design ability. An enlarged design space will make the structure’s performance more excellent. Through an optimal design of a variable stiffness cylinder, the buckling capacity of the cylinder will be increased as compared with its constant stiffness counterpart. In this paper, variable stiffness composite cylinders sustaining combined loadings are considered, and the optimization is conducted based on the multi-objective optimization method. The results indicate that variable stiffness cylinder’s loading capacity is increased significantly as compared with the constant stiffness, especially when an inhomogeneous loading is considered.

Biokinetic model-based multi-objective optimization of Dunaliella tertiolecta cultivation using elitist non-dominated sorting genetic algorithm with inheritance.

PubMed

Sinha, Snehal K; Kumar, Mithilesh; Guria, Chandan; Kumar, Anup; Banerjee, Chiranjib

2017-10-01

Algal model based multi-objective optimization using elitist non-dominated sorting genetic algorithm with inheritance was carried out for batch cultivation of Dunaliella tertiolecta using NPK-fertilizer. Optimization problems involving two- and three-objective functions were solved simultaneously. The objective functions are: maximization of algae-biomass and lipid productivity with minimization of cultivation time and cost. Time variant light intensity and temperature including NPK-fertilizer, NaCl and NaHCO 3 loadings are the important decision variables. Algal model involving Monod/Andrews adsorption kinetics and Droop model with internal nutrient cell quota was used for optimization studies. Sets of non-dominated (equally good) Pareto optimal solutions were obtained for the problems studied. It was observed that time variant optimal light intensity and temperature trajectories, including optimum NPK fertilizer, NaCl and NaHCO 3 concentration has significant influence to improve biomass and lipid productivity under minimum cultivation time and cost. Proposed optimization studies may be helpful to implement the control strategy in scale-up operation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multiple utility constrained multi-objective programs using Bayesian theory

NASA Astrophysics Data System (ADS)

Abbasian, Pooneh; Mahdavi-Amiri, Nezam; Fazlollahtabar, Hamed

2018-03-01

A utility function is an important tool for representing a DM's preference. We adjoin utility functions to multi-objective optimization problems. In current studies, usually one utility function is used for each objective function. Situations may arise for a goal to have multiple utility functions. Here, we consider a constrained multi-objective problem with each objective having multiple utility functions. We induce the probability of the utilities for each objective function using Bayesian theory. Illustrative examples considering dependence and independence of variables are worked through to demonstrate the usefulness of the proposed model.

Non-enzymatic glucose sensing properties of MoO3 nanorods: experimental and density functional theory investigations

NASA Astrophysics Data System (ADS)

Sharma, Maneesha; Gangan, Abhijeet; Chakraborty, Brahmananda; Sekhar Rout, Chandra

2017-11-01

We report the growth of monoclinic MoO3 nanorods by a simple and highly reproducible hydrothermal method. Structural and morphological studies provide significant insights about the phase and crystalline structure of the synthesized samples. Further, the non-enzymatic glucose sensing properties were investigated and the MoO3 nanorods exhibited a sensitivity of 15.4 µA µM-1 cm-2 in the 5-175 µM linear range. Also, a quick response time of 8 s towards glucose molecules was observed, exhibiting an excellent electrochemical activity. We have also performed density functional theory (DFT) simulations to qualitatively support our experimental observations by investigating the interactions and charge-transfer mechanism of glucose on MoO3. There is a strong interaction between glucose and the MoO3 surface due to charge transfer from a bonded O atom of glucose to a Mo atom of MoO3 resulting in a strong hybridization between the p orbital of O and d orbital of Mo. Thus, the MoO3 nanorod-based electrodes are found to be good glucose sensing materials for practical industrial applications.

Dehydration, Dehydrogenation, and Condensation of Alcohols on Supported Oxide Catalysts Based on Cyclic (WO3)3 and (MoO3)3 Clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rousseau, Roger J.; Dixon, David A.; Kay, Bruce D.

2014-01-01

Supported early transition metal oxides have important applications in numerous catalytic reactions. In this article we review preparation and activity of well-defined model WO3 and MoO3 catalysts prepared via deposition of cyclic gas-phase (WO3)3 and (MoO3)3 clusters generated by sublimation of WO3 and MoO3 powders. Conversion of small aliphatic alcohols to alkenes, aldehydes/ketons, and ethers is employed to probe the structure-activity relationships on model WO3 and MoO3 catalysts ranging from unsupported (WO3)3 and (MoO3)3 clusters embedded in alcohol matrices, to (WO3)3 clusters supported on surfaces of other oxides, and epitaxial and nanoporous WO3 films. Detailed theoretical calculations reveal the underlyingmore » reaction mechanisms and provide insight into the origin of the differences in the WO3 and MoO3 reactivity. For the range of interrogated (WO3)3 they further shed light into the role structure and binding of (WO3)3 clusters with the support play in determining their catalytic activity.« less

Multi-Objective Optimization of Spacecraft Trajectories for Small-Body Coverage Missions

NASA Technical Reports Server (NTRS)

Hinckley, David, Jr.; Englander, Jacob; Hitt, Darren

2017-01-01

Visual coverage of surface elements of a small-body object requires multiple images to be taken that meet many requirements on their viewing angles, illumination angles, times of day, and combinations thereof. Designing trajectories capable of maximizing total possible coverage may not be useful since the image target sequence and the feasibility of said sequence given the rotation-rate limitations of the spacecraft are not taken into account. This work presents a means of optimizing, in a multi-objective manner, surface target sequences that account for such limitations.

Multi-objective decision-making model based on CBM for an aircraft fleet

NASA Astrophysics Data System (ADS)

Luo, Bin; Lin, Lin

2018-04-01

Modern production management patterns, in which multi-unit (e.g., a fleet of aircrafts) are managed in a holistic manner, have brought new challenges for multi-unit maintenance decision making. To schedule a good maintenance plan, not only does the individual machine maintenance have to be considered, but also the maintenance of the other individuals have to be taken into account. Since most condition-based maintenance researches for aircraft focused on solely reducing maintenance cost or maximizing the availability of single aircraft, as well as considering that seldom researches concentrated on both the two objectives: minimizing cost and maximizing the availability of a fleet (total number of available aircraft in fleet), a multi-objective decision-making model based on condition-based maintenance concentrated both on the above two objectives is established. Furthermore, in consideration of the decision maker may prefer providing the final optimal result in the form of discrete intervals instead of a set of points (non-dominated solutions) in real decision-making problem, a novel multi-objective optimization method based on support vector regression is proposed to solve the above multi-objective decision-making model. Finally, a case study regarding a fleet is conducted, with the results proving that the approach efficiently generates outcomes that meet the schedule requirements.

Evaluation of Genetic Algorithm Concepts Using Model Problems. Part 2; Multi-Objective Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.

2003-01-01

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of simple model problems. Several new features including a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all optimization problems attempted. The binning algorithm generally provides pareto front quality enhancements and moderate convergence efficiency improvements for most of the model problems. The gene-space transformation procedure provides a large convergence efficiency enhancement for problems with non-convoluted pareto fronts and a degradation in efficiency for problems with convoluted pareto fronts. The most difficult problems --multi-mode search spaces with a large number of genes and convoluted pareto fronts-- require a large number of function evaluations for GA convergence, but always converge.

Design Optimization of a Centrifugal Fan with Splitter Blades

NASA Astrophysics Data System (ADS)

Heo, Man-Woong; Kim, Jin-Hyuk; Kim, Kwang-Yong

2015-05-01

Multi-objective optimization of a centrifugal fan with additionally installed splitter blades was performed to simultaneously maximize the efficiency and pressure rise using three-dimensional Reynolds-averaged Navier-Stokes equations and hybrid multi-objective evolutionary algorithm. Two design variables defining the location of splitter, and the height ratio between inlet and outlet of impeller were selected for the optimization. In addition, the aerodynamic characteristics of the centrifugal fan were investigated with the variation of design variables in the design space. Latin hypercube sampling was used to select the training points, and response surface approximation models were constructed as surrogate models of the objective functions. With the optimization, both the efficiency and pressure rise of the centrifugal fan with splitter blades were improved considerably compared to the reference model.

[Optimal solution and analysis of muscular force during standing balance].

PubMed

Wang, Hongrui; Zheng, Hui; Liu, Kun

2015-02-01

The present study was aimed at the optimal solution of the main muscular force distribution in the lower extremity during standing balance of human. The movement musculoskeletal system of lower extremity was simplified to a physical model with 3 joints and 9 muscles. Then on the basis of this model, an optimum mathematical model was built up to solve the problem of redundant muscle forces. Particle swarm optimization (PSO) algorithm is used to calculate the single objective and multi-objective problem respectively. The numerical results indicated that the multi-objective optimization could be more reasonable to obtain the distribution and variation of the 9 muscular forces. Finally, the coordination of each muscle group during maintaining standing balance under the passive movement was qualitatively analyzed using the simulation results obtained.

MoO2 nanosheets embedded in amorphous carbon matrix for sodium-ion batteries

NASA Astrophysics Data System (ADS)

He, Hong; Man, Yuhong; Yang, Jingang; Xie, Jiale; Xu, Maowen

2017-10-01

MoO2 nanosheets embedded in the amorphous carbon matrix (MoO2/C) are successfully synthesized via a facile hydrothermal method and investigated as an anode for sodium-ion batteries. Because of the efficient ion transport channels and good volume change accommodation, MoO2/C delivers a discharge/charge capacity of 367.8/367.0 mAh g-1 with high coulombic efficiency (99.4%) after 100 cycles at a current density of 50 mA g-1.

Grid Transmission Expansion Planning Model Based on Grid Vulnerability

NASA Astrophysics Data System (ADS)

Tang, Quan; Wang, Xi; Li, Ting; Zhang, Quanming; Zhang, Hongli; Li, Huaqiang

2018-03-01

Based on grid vulnerability and uniformity theory, proposed global network structure and state vulnerability factor model used to measure different grid models. established a multi-objective power grid planning model which considering the global power network vulnerability, economy and grid security constraint. Using improved chaos crossover and mutation genetic algorithm to optimize the optimal plan. For the problem of multi-objective optimization, dimension is not uniform, the weight is not easy given. Using principal component analysis (PCA) method to comprehensive assessment of the population every generation, make the results more objective and credible assessment. the feasibility and effectiveness of the proposed model are validated by simulation results of Garver-6 bus system and Garver-18 bus.

A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation.

PubMed

Wang, Rui; Zhou, Yongquan; Zhao, Chengyan; Wu, Haizhou

2015-01-01

Multi-threshold image segmentation is a powerful image processing technique that is used for the preprocessing of pattern recognition and computer vision. However, traditional multilevel thresholding methods are computationally expensive because they involve exhaustively searching the optimal thresholds to optimize the objective functions. To overcome this drawback, this paper proposes a flower pollination algorithm with a randomized location modification. The proposed algorithm is used to find optimal threshold values for maximizing Otsu's objective functions with regard to eight medical grayscale images. When benchmarked against other state-of-the-art evolutionary algorithms, the new algorithm proves itself to be robust and effective through numerical experimental results including Otsu's objective values and standard deviations.

Simultaneous segmentation of the bone and cartilage surfaces of a knee joint in 3D

NASA Astrophysics Data System (ADS)

Yin, Y.; Zhang, X.; Anderson, D. D.; Brown, T. D.; Hofwegen, C. Van; Sonka, M.

2009-02-01

We present a novel framework for the simultaneous segmentation of multiple interacting surfaces belonging to multiple mutually interacting objects. The method is a non-trivial extension of our previously reported optimal multi-surface segmentation. Considering an example application of knee-cartilage segmentation, the framework consists of the following main steps: 1) Shape model construction: Building a mean shape for each bone of the joint (femur, tibia, patella) from interactively segmented volumetric datasets. Using the resulting mean-shape model - identification of cartilage, non-cartilage, and transition areas on the mean-shape bone model surfaces. 2) Presegmentation: Employment of iterative optimal surface detection method to achieve approximate segmentation of individual bone surfaces. 3) Cross-object surface mapping: Detection of inter-bone equidistant separating sheets to help identify corresponding vertex pairs for all interacting surfaces. 4) Multi-object, multi-surface graph construction and final segmentation: Construction of a single multi-bone, multi-surface graph so that two surfaces (bone and cartilage) with zero and non-zero intervening distances can be detected for each bone of the joint, according to whether or not cartilage can be locally absent or present on the bone. To define inter-object relationships, corresponding vertex pairs identified using the separating sheets were interlinked in the graph. The graph optimization algorithm acted on the entire multiobject, multi-surface graph to yield a globally optimal solution. The segmentation framework was tested on 16 MR-DESS knee-joint datasets from the Osteoarthritis Initiative database. The average signed surface positioning error for the 6 detected surfaces ranged from 0.00 to 0.12 mm. When independently initialized, the signed reproducibility error of bone and cartilage segmentation ranged from 0.00 to 0.26 mm. The results showed that this framework provides robust, accurate, and reproducible segmentation of the knee joint bone and cartilage surfaces of the femur, tibia, and patella. As a general segmentation tool, the developed framework can be applied to a broad range of multi-object segmentation problems.

Two-phase framework for near-optimal multi-target Lambert rendezvous

NASA Astrophysics Data System (ADS)

Bang, Jun; Ahn, Jaemyung

2018-03-01

This paper proposes a two-phase framework to obtain a near-optimal solution of multi-target Lambert rendezvous problem. The objective of the problem is to determine the minimum-cost rendezvous sequence and trajectories to visit a given set of targets within a maximum mission duration. The first phase solves a series of single-target rendezvous problems for all departure-arrival object pairs to generate the elementary solutions, which provides candidate rendezvous trajectories. The second phase formulates a variant of traveling salesman problem (TSP) using the elementary solutions prepared in the first phase and determines the final rendezvous sequence and trajectories of the multi-target rendezvous problem. The validity of the proposed optimization framework is demonstrated through an asteroid exploration case study.

Modeling the effects of variable groundwater chemistry on adsorption of molybdate

USGS Publications Warehouse

Stollenwerk, Kenneth G.

1995-01-01

Laboratory experiments were used to identify and quantify processes having a significant effect on molybdate (MoO42−) adsorption in a shallow alluvial aquifer on Cape Cod, assachusetts. Aqueous chemistry in the aquifer changes as a result of treated sewage effluent mixing with groundwater. Molybdate adsorption decreased as pH, ionic strength, and the concentration of competing anions increased. A diffuse-layer surface complexation model was used to simulate adsorption of MoO42−, phosphate (PO43−), and sulfate (SO42−) on aquifer sediment. Equilibrium constants for the model were calculated by calibration to data from batch experiments. The model was then used in a one-dimensional solute transport program to successfully simulate initial breakthrough of MoO42− from column experiments. A shortcoming of the solute transport program was the inability to account for kinetics of physical and chemical processes. This resulted in a failure of the model to predict the slow rate of desorption of MoO42− from the columns. The mobility of MoO42− ncreased with ionic strength and with the formation of aqueous complexes with calcium, magnesium, and sodium. Failure to account for MoO42− speciation and ionic strength in the model resulted in overpredicting MoO42− adsorption. Qualitatively, the laboratory data predicted the observed behavior of MoO42− in the aquifer, where retardation of MoO42− was greatest in uncontaminated roundwater having low pH, low ionic strength, and low concentrations of PO43− and SO42−.

Probing microstructure and phase evolution of α-MoO 3 nanobelts for sodium-ion batteries by in situ transmission electron microscopy

DOE PAGES

Xia, Weiwei; Xu, Feng; Zhu, Chongyang; ...

2016-07-15

The fundamental electrochemical reaction mechanisms and the phase transformation pathways of layer-structured α-MoO 3 nanobelt during the sodiation/desodiation process to date remain largely unknown. In this study, to observe the real-time sodiation/desodiaton behaviors of α-MoO 3 during electrochemical cycling, we construct a MoO 3 anode sodium-ion battery inside a transmission electron microscope (TEM). Utilizing in situ TEM and electron diffraction pattern (EDP) observation, α-MoO 3 nanobelts are found to undergo a unique multi-step phase transformation. Upon the first sodiation, α-MoO 3 nanobelts initially form amorphous Na xMoO3 phase and are subsequently sodiated into intermediate phase of crystalline NaMoO 2, finallymore » resulting in the crystallized Mo nanograins embedded within the Na 2O matrix. During the first desodiation process, Mo nanograins are firstly re-oxidized into intermediate phase NaMoO 2 that is further transformed into amorphous Na 2MoO 3, resulting in an irreversible phase transformation. Upon subsequent sodiation/desodiation cycles, however, a stable and reversible phase transformation between crystalline Mo and amorphous Na2MoO 3 phases has been revealed. In conclusion, our work provides an in-deepth understanding of the phase transformation pathways of α-MoO 3 nanobelts upon electrochemical sodiation/desodiation processes, with the hope of assistance in designing sodium-ion batteries with enhanced performance.« less

Optimization of autoregressive, exogenous inputs-based typhoon inundation forecasting models using a multi-objective genetic algorithm

NASA Astrophysics Data System (ADS)

Ouyang, Huei-Tau

2017-07-01

Three types of model for forecasting inundation levels during typhoons were optimized: the linear autoregressive model with exogenous inputs (LARX), the nonlinear autoregressive model with exogenous inputs with wavelet function (NLARX-W) and the nonlinear autoregressive model with exogenous inputs with sigmoid function (NLARX-S). The forecast performance was evaluated by three indices: coefficient of efficiency, error in peak water level and relative time shift. Historical typhoon data were used to establish water-level forecasting models that satisfy all three objectives. A multi-objective genetic algorithm was employed to search for the Pareto-optimal model set that satisfies all three objectives and select the ideal models for the three indices. Findings showed that the optimized nonlinear models (NLARX-W and NLARX-S) outperformed the linear model (LARX). Among the nonlinear models, the optimized NLARX-W model achieved a more balanced performance on the three indices than the NLARX-S models and is recommended for inundation forecasting during typhoons.

A Multi-Objective Decision Making Approach for Solving the Image Segmentation Fusion Problem.

PubMed

Khelifi, Lazhar; Mignotte, Max

2017-08-01

Image segmentation fusion is defined as the set of methods which aim at merging several image segmentations, in a manner that takes full advantage of the complementarity of each one. Previous relevant researches in this field have been impeded by the difficulty in identifying an appropriate single segmentation fusion criterion, providing the best possible, i.e., the more informative, result of fusion. In this paper, we propose a new model of image segmentation fusion based on multi-objective optimization which can mitigate this problem, to obtain a final improved result of segmentation. Our fusion framework incorporates the dominance concept in order to efficiently combine and optimize two complementary segmentation criteria, namely, the global consistency error and the F-measure (precision-recall) criterion. To this end, we present a hierarchical and efficient way to optimize the multi-objective consensus energy function related to this fusion model, which exploits a simple and deterministic iterative relaxation strategy combining the different image segments. This step is followed by a decision making task based on the so-called "technique for order performance by similarity to ideal solution". Results obtained on two publicly available databases with manual ground truth segmentations clearly show that our multi-objective energy-based model gives better results than the classical mono-objective one.

Multi-objective possibilistic model for portfolio selection with transaction cost

NASA Astrophysics Data System (ADS)

Jana, P.; Roy, T. K.; Mazumder, S. K.

2009-06-01

In this paper, we introduce the possibilistic mean value and variance of continuous distribution, rather than probability distributions. We propose a multi-objective Portfolio based model and added another entropy objective function to generate a well diversified asset portfolio within optimal asset allocation. For quantifying any potential return and risk, portfolio liquidity is taken into account and a multi-objective non-linear programming model for portfolio rebalancing with transaction cost is proposed. The models are illustrated with numerical examples.

Synthesis of MoS2 and MoO2 for their applications in H2 generation and lithium ion batteries: a review.

PubMed

Zhao, Yufei; Zhang, Yuxia; Yang, Zhiyu; Yan, Yiming; Sun, Kening

2013-08-01

Scientists increasingly witness the applications of MoS 2 and MoO 2 in the field of energy conversion and energy storage. On the one hand, MoS 2 and MoO 2 have been widely utilized as promising catalysts for electrocatalytic or photocatalytic hydrogen evolution in aqueous solution. On the other hand, MoS 2 and MoO 2 have also been verified as efficient electrode material for lithium ion batteries. In this review, the synthesis, structure and properties of MoS 2 and MoO 2 are briefly summarized according to their applications for H 2 generation and lithium ion batteries. Firstly, we overview the recent advancements in the morphology control of MoS 2 and MoO 2 and their applications as electrocatalysts for hydrogen evolution reactions. Secondly, we focus on the photo-induced water splitting for H 2 generation, in which MoS 2 acts as an important co-catalyst when combined with other semiconductor catalysts. The newly reported research results of the significant functions of MoS 2 nanocomposites in photo-induced water splitting are presented. Thirdly, we introduce the advantages of MoS 2 and MoO 2 for their enhanced cyclic performance and high capacity as electrode materials of lithium ion batteries. Recent key achievements in MoS 2 - and MoO 2 -based lithium ion batteries are highlighted. Finally, we discuss the future scope and the important challenges emerging from these fascinating materials.

Building Online Communities. Take Your Site beyond Content: Construct a Society on the Web.

ERIC Educational Resources Information Center

Glaser, Mark

1997-01-01

Discusses the establishment of online, or virtual, communities on the World Wide Web. Topics include corporate sites; community planning; virtual reality; games; America Online projects; MUDs (multiuser dungeons) and MOOs (multiuser object oriented); and a list of contacts for online community resources. (LRW)

Roles for Technology in Collaborative Teaching.

ERIC Educational Resources Information Center

Bonvallet, Susan; De Luce, Judith

2001-01-01

Describes a collaborative upper level Latin literature course taught at a secondary school and a university that used a variety of technologies, including a MOO and e-mail. The design of this course on Plautus'"Aulularia" is discussed, including objectives, learning goals, and collaborative assignments. Argues that informed use of technology can…

A novel model-based evolutionary algorithm for multi-objective deformable image registration with content mismatch and large deformations: benchmarking efficiency and quality

NASA Astrophysics Data System (ADS)

Bouter, Anton; Alderliesten, Tanja; Bosman, Peter A. N.

2017-02-01

Taking a multi-objective optimization approach to deformable image registration has recently gained attention, because such an approach removes the requirement of manually tuning the weights of all the involved objectives. Especially for problems that require large complex deformations, this is a non-trivial task. From the resulting Pareto set of solutions one can then much more insightfully select a registration outcome that is most suitable for the problem at hand. To serve as an internal optimization engine, currently used multi-objective algorithms are competent, but rather inefficient. In this paper we largely improve upon this by introducing a multi-objective real-valued adaptation of the recently introduced Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) for discrete optimization. In this work, GOMEA is tailored specifically to the problem of deformable image registration to obtain substantially improved efficiency. This improvement is achieved by exploiting a key strength of GOMEA: iteratively improving small parts of solutions, allowing to faster exploit the impact of such updates on the objectives at hand through partial evaluations. We performed experiments on three registration problems. In particular, an artificial problem containing a disappearing structure, a pair of pre- and post-operative breast CT scans, and a pair of breast MRI scans acquired in prone and supine position were considered. Results show that compared to the previously used evolutionary algorithm, GOMEA obtains a speed-up of up to a factor of 1600 on the tested registration problems while achieving registration outcomes of similar quality.

The optimal design of UAV wing structure

NASA Astrophysics Data System (ADS)

Długosz, Adam; Klimek, Wiktor

2018-01-01

The paper presents an optimal design of UAV wing, made of composite materials. The aim of the optimization is to improve strength and stiffness together with reduction of the weight of the structure. Three different types of functionals, which depend on stress, stiffness and the total mass are defined. The paper presents an application of the in-house implementation of the evolutionary multi-objective algorithm in optimization of the UAV wing structure. Values of the functionals are calculated on the basis of results obtained from numerical simulations. Numerical FEM model, consisting of different composite materials is created. Adequacy of the numerical model is verified by results obtained from the experiment, performed on a tensile testing machine. Examples of multi-objective optimization by means of Pareto-optimal set of solutions are presented.

Multi-objective optimization of GENIE Earth system models.

PubMed

Price, Andrew R; Myerscough, Richard J; Voutchkov, Ivan I; Marsh, Robert; Cox, Simon J

2009-07-13

The tuning of parameters in climate models is essential to provide reliable long-term forecasts of Earth system behaviour. We apply a multi-objective optimization algorithm to the problem of parameter estimation in climate models. This optimization process involves the iterative evaluation of response surface models (RSMs), followed by the execution of multiple Earth system simulations. These computations require an infrastructure that provides high-performance computing for building and searching the RSMs and high-throughput computing for the concurrent evaluation of a large number of models. Grid computing technology is therefore essential to make this algorithm practical for members of the GENIE project.

Stochastic HKMDHE: A multi-objective contrast enhancement algorithm

NASA Astrophysics Data System (ADS)

Pratiher, Sawon; Mukhopadhyay, Sabyasachi; Maity, Srideep; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2018-02-01

This contribution proposes a novel extension of the existing `Hyper Kurtosis based Modified Duo-Histogram Equalization' (HKMDHE) algorithm, for multi-objective contrast enhancement of biomedical images. A novel modified objective function has been formulated by joint optimization of the individual histogram equalization objectives. The optimal adequacy of the proposed methodology with respect to image quality metrics such as brightness preserving abilities, peak signal-to-noise ratio (PSNR), Structural Similarity Index (SSIM) and universal image quality metric has been experimentally validated. The performance analysis of the proposed Stochastic HKMDHE with existing histogram equalization methodologies like Global Histogram Equalization (GHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) has been given for comparative evaluation.

Atomic layer deposition of TiO2 shells on MoO3 nanobelts allowing enhanced lithium storage performance.

PubMed

Xie, Sanmu; Cao, Daxian; She, Yiyi; Wang, Hongkang; Shi, Jian-Wen; Leung, Micheal K H; Niu, Chunming

2018-06-26

Atomic layer deposition (ALD) of TiO2 shells on MoO3 nanobelts (denote as TiO2@MoO3) is realized using a home-made ALD system, which allows a controllable hydrolysis reaction of TiCl4-H2O on an atomic scale. When used as an anode material for lithium ion batteries, the TiO2@MoO3 electrode demonstrates much enhanced lithium storage performance including higher specific capacity, better cycling stability and rate capability.

Enhanced photocatalytic activity of Te-doped Bi2MoO6 under visible light irradiation: Effective separation of photogenerated carriers resulted from inhomogeneous lattice distortion and improved electron capturing ability

NASA Astrophysics Data System (ADS)

Chen, Shuguang; Li, Yuhan; Wu, Zixu; Wu, Baoxin; Li, Haibin; Li, Fujin

2017-05-01

Te-doped Bi2MoO6 photocatalyst was hydrothermally synthesized, and nonmetal atoms Te were homogeneously incorporated into Bi2MoO6 lattice with the substitution of Te4+ to Mo6+. With increasing Te-doping concentration in Bi2MoO6, no detectable band-gap narrowing but more and more severe inhomogeneous lattice distortions were determined. The activity of Bi2MoO6 photocatalyst was evaluated through methylene blue degradation under visible light irradiation (λ>410 nm) and was greatly enhanced by Te-doping. When Te-doped Bi2MoO6 was synthesized at Te/Mo molar ratio of 7.5%, a maximum first-order rate constant of methylene blue degradation was obtained. The inhomogeneous lattice distortion generated an internal dipole moment, and the holes generated with the substitution of Te4+ to Mo6+ acted as the capturing centers of photogenerated electrons, thus the effective separation of photogenerated carriers was facilitated to result in a relatively high concentration of holes on the surface of Te-doped Bi2MoO6 to be favorable for the efficient methylene blue degradation.

Improved photoelectrochemical performance of BiVO4/MoO3 heterostructure thin films

NASA Astrophysics Data System (ADS)

Kodan, Nisha; Mehta, B. R.

2018-05-01

Bismuth vanadate (BiVO4) and Molybdenum trioxide (MoO3) thin films have been prepared by RF sputtering technique. BiVO4 thin films were deposited on indium doped tin oxide (In: SnO2; ITO) substrates at room temperature and 80W applied rf power. The prepared BiVO4 thin films were further annealed at 450°C for 2 hours in air to obtain crystalline monoclinic phase and successively coated with MoO3 thin films deposited at 150W rf power and 400°C for 30 minutes. The effect of coupling BiVO4 and MoO3 on the structural, optical and photoelectrochemical (PEC) properties have been studied. Optical studies reveal that coupling of BiVO4 and MoO3 results in improvement of optical absorption in visible region of solar spectrum. PEC study shows approximate 3-fold and 38-fold increment in photocurrent values of BiVO4/MoO3 (0.38 mA/cm2) heterostructure thin film as compared to MoO3 (0.15 mA/cm2) and BiVO4 (10 µA/cm2) thin films at applied bias of 1 V vs Ag/AgCl in 0.5 M Na2SO4 (pH=7) electrolyte.

Single MoO3 nanoribbon waveguides: good building blocks as elements and interconnects for nanophotonic applications

NASA Astrophysics Data System (ADS)

Zhang, Li; Wu, Guoqing; Gu, Fuxing; Zeng, Heping

2015-11-01

Exploring new nanowaveguide materials and structures is of great scientific interest and technological significance for optical and photonic applications. In this work, high-quality single-crystal MoO3 nanoribbons (NRs) are synthesized and used for optical guiding. External light sources are efficiently launched into the single MoO3 NRs using silica fiber tapers. It is found that single MoO3 NRs are as good nanowaveguides with loss optical losses (typically less than 0.1 dB/μm) and broadband optical guiding in the visible/near-infrared region. Single MoO3 NRs have good Raman gains that are comparable to those of semiconductor nanowaveguides, but the second harmonic generation efficiencies are about 4 orders less than those of semiconductor nanowaveguides. And also no any third-order nonlinear optical effects are observed at high pump power. A hybrid Fabry-Pérot cavity containing an active CdSe nanowire and a passive MoO3 NR is also demonstrated, and the ability of coupling light from other active nanostructures and fluorescent liquid solutions has been further demonstrated. These optical properties make single MoO3 NRs attractive building blocks as elements and interconnects in miniaturized photonic circuitries and devices.

Deterministic methods for multi-control fuel loading optimization

NASA Astrophysics Data System (ADS)

Rahman, Fariz B. Abdul

We have developed a multi-control fuel loading optimization code for pressurized water reactors based on deterministic methods. The objective is to flatten the fuel burnup profile, which maximizes overall energy production. The optimal control problem is formulated using the method of Lagrange multipliers and the direct adjoining approach for treatment of the inequality power peaking constraint. The optimality conditions are derived for a multi-dimensional multi-group optimal control problem via calculus of variations. Due to the Hamiltonian having a linear control, our optimal control problem is solved using the gradient method to minimize the Hamiltonian and a Newton step formulation to obtain the optimal control. We are able to satisfy the power peaking constraint during depletion with the control at beginning of cycle (BOC) by building the proper burnup path forward in time and utilizing the adjoint burnup to propagate the information back to the BOC. Our test results show that we are able to achieve our objective and satisfy the power peaking constraint during depletion using either the fissile enrichment or burnable poison as the control. Our fuel loading designs show an increase of 7.8 equivalent full power days (EFPDs) in cycle length compared with 517.4 EFPDs for the AP600 first cycle.

Optimal harvesting for a predator-prey agent-based model using difference equations.

PubMed

Oremland, Matthew; Laubenbacher, Reinhard

2015-03-01

In this paper, a method known as Pareto optimization is applied in the solution of a multi-objective optimization problem. The system in question is an agent-based model (ABM) wherein global dynamics emerge from local interactions. A system of discrete mathematical equations is formulated in order to capture the dynamics of the ABM; while the original model is built up analytically from the rules of the model, the paper shows how minor changes to the ABM rule set can have a substantial effect on model dynamics. To address this issue, we introduce parameters into the equation model that track such changes. The equation model is amenable to mathematical theory—we show how stability analysis can be performed and validated using ABM data. We then reduce the equation model to a simpler version and implement changes to allow controls from the ABM to be tested using the equations. Cohen's weighted κ is proposed as a measure of similarity between the equation model and the ABM, particularly with respect to the optimization problem. The reduced equation model is used to solve a multi-objective optimization problem via a technique known as Pareto optimization, a heuristic evolutionary algorithm. Results show that the equation model is a good fit for ABM data; Pareto optimization provides a suite of solutions to the multi-objective optimization problem that can be implemented directly in the ABM.

Derivation of optimal joint operating rules for multi-purpose multi-reservoir water-supply system

NASA Astrophysics Data System (ADS)

Tan, Qiao-feng; Wang, Xu; Wang, Hao; Wang, Chao; Lei, Xiao-hui; Xiong, Yi-song; Zhang, Wei

2017-08-01

The derivation of joint operating policy is a challenging task for a multi-purpose multi-reservoir system. This study proposed an aggregation-decomposition model to guide the joint operation of multi-purpose multi-reservoir system, including: (1) an aggregated model based on the improved hedging rule to ensure the long-term water-supply operating benefit; (2) a decomposed model to allocate the limited release to individual reservoirs for the purpose of maximizing the total profit of the facing period; and (3) a double-layer simulation-based optimization model to obtain the optimal time-varying hedging rules using the non-dominated sorting genetic algorithm II, whose objectives were to minimize maximum water deficit and maximize water supply reliability. The water-supply system of Li River in Guangxi Province, China, was selected for the case study. The results show that the operating policy proposed in this study is better than conventional operating rules and aggregated standard operating policy for both water supply and hydropower generation due to the use of hedging mechanism and effective coordination among multiple objectives.

Seasonal-Scale Optimization of Conventional Hydropower Operations in the Upper Colorado System

NASA Astrophysics Data System (ADS)

Bier, A.; Villa, D.; Sun, A.; Lowry, T. S.; Barco, J.

2011-12-01

Sandia National Laboratories is developing the Hydropower Seasonal Concurrent Optimization for Power and the Environment (Hydro-SCOPE) tool to examine basin-wide conventional hydropower operations at seasonal time scales. This tool is part of an integrated, multi-laboratory project designed to explore different aspects of optimizing conventional hydropower operations. The Hydro-SCOPE tool couples a one-dimensional reservoir model with a river routing model to simulate hydrology and water quality. An optimization engine wraps around this model framework to solve for long-term operational strategies that best meet the specific objectives of the hydrologic system while honoring operational and environmental constraints. The optimization routines are provided by Sandia's open source DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) software. Hydro-SCOPE allows for multi-objective optimization, which can be used to gain insight into the trade-offs that must be made between objectives. The Hydro-SCOPE tool is being applied to the Upper Colorado Basin hydrologic system. This system contains six reservoirs, each with its own set of objectives (such as maximizing revenue, optimizing environmental indicators, meeting water use needs, or other objectives) and constraints. This leads to a large optimization problem with strong connectedness between objectives. The systems-level approach used by the Hydro-SCOPE tool allows simultaneous analysis of these objectives, as well as understanding of potential trade-offs related to different objectives and operating strategies. The seasonal-scale tool will be tightly integrated with the other components of this project, which examine day-ahead and real-time planning, environmental performance, hydrologic forecasting, and plant efficiency.

A stepwise, multi-objective, multi-variable parameter optimization method for the APEX model

USDA-ARS?s Scientific Manuscript database

Proper parameterization enables hydrological models to make reliable estimates of non-point source pollution for effective control measures. The automatic calibration of hydrologic models requires significant computational power limiting its application. The study objective was to develop and eval...

Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.

PubMed

Jiménez, Fernando; Sánchez, Gracia; Juárez, José M

2014-03-01

This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation is given. Therefore, any fuzzy classifier model must be both accurate and interpretable. The proposed methodology is a three-step process: (1) multi-objective constrained optimization of a patient's data set, using Pareto-based elitist multi-objective evolutionary algorithms to maximize accuracy and minimize the complexity (number of rules) of classifiers, subject to interpretability constraints; this step produces a set of alternative (Pareto) classifiers; (2) linguistic labeling, which assigns a linguistic label to each fuzzy set of the classifiers; this step is essential to the interpretability of the classifiers; (3) decision making, whereby a classifier is chosen, if it is satisfactory, according to the preferences of the decision maker. If no classifier is satisfactory for the decision maker, the process starts again in step (1) with a different input parameter set. The performance of three multi-objective evolutionary algorithms, niched pre-selection multi-objective algorithm, elitist Pareto-based multi-objective evolutionary algorithm for diversity reinforcement (ENORA) and the non-dominated sorting genetic algorithm (NSGA-II), was tested using a patient's data set from an intensive care burn unit and a standard machine learning data set from an standard machine learning repository. The results are compared using the hypervolume multi-objective metric. Besides, the results have been compared with other non-evolutionary techniques and validated with a multi-objective cross-validation technique. Our proposal improves the classification rate obtained by other non-evolutionary techniques (decision trees, artificial neural networks, Naive Bayes, and case-based reasoning) obtaining with ENORA a classification rate of 0.9298, specificity of 0.9385, and sensitivity of 0.9364, with 14.2 interpretable fuzzy rules on average. Our proposal improves the accuracy and interpretability of the classifiers, compared with other non-evolutionary techniques. We also conclude that ENORA outperforms niched pre-selection and NSGA-II algorithms. Moreover, given that our multi-objective evolutionary methodology is non-combinational based on real parameter optimization, the time cost is significantly reduced compared with other evolutionary approaches existing in literature based on combinational optimization. Copyright © 2014 Elsevier B.V. All rights reserved.

Prediction of protein-protein interaction network using a multi-objective optimization approach.

PubMed

Chowdhury, Archana; Rakshit, Pratyusha; Konar, Amit

2016-06-01

Protein-Protein Interactions (PPIs) are very important as they coordinate almost all cellular processes. This paper attempts to formulate PPI prediction problem in a multi-objective optimization framework. The scoring functions for the trial solution deal with simultaneous maximization of functional similarity, strength of the domain interaction profiles, and the number of common neighbors of the proteins predicted to be interacting. The above optimization problem is solved using the proposed Firefly Algorithm with Nondominated Sorting. Experiments undertaken reveal that the proposed PPI prediction technique outperforms existing methods, including gene ontology-based Relative Specific Similarity, multi-domain-based Domain Cohesion Coupling method, domain-based Random Decision Forest method, Bagging with REP Tree, and evolutionary/swarm algorithm-based approaches, with respect to sensitivity, specificity, and F1 score.

Comparative study on cubic and tetragonal CexZr1-xO2 supported MoO3-catalysts for sulfur-resistant methanation

NASA Astrophysics Data System (ADS)

Liu, Zhaopeng; Xu, Yan; Cheng, Jiaming; Wang, Weihan; Wang, Baowei; Li, Zhenhua; Ma, Xinbin

2018-03-01

In this paper, two kinds of CexZr1-xO2 solid solution carriers with different Ce/Zr ratio were prepared by one-step co-precipitation method: the cubic Ce0.8Zr0.2O2 and the tetragonal Ce0.2Zr0.8O2 support. The MoO3/Ce0.8Zr0.2O2 and MoO3/Ce0.2Zr0.8O2 catalysts were prepared by incipient wetness impregnation method for comparative study on sulfur-resistant methanation reaction. The N2 adsorption/desorption, X-ray diffraction (XRD), Raman spectroscopy (RS), X-ray photoelectron (XPS), transmission electron microscopy (TEM), temperature-programmed reduction by hydrogen (H2-TPR) were undertaken to characterize the physico-chemical properties of the samples. The results indicated that the prepared MoO3/CexZr1-xO2 catalysts have a mesoporous structure with high surface area and uniform pore size distribution, achieving good MoO3 dispersion on CexZr1-xO2 supports. As for the catalytic performance of sulfur-resistant methanation, the cubic MoO3/Ce0.8Zr0.2O2 exhibited better than the tetragonal MoO3/Ce0.2Zr0.8O2 catalyst at reaction temperature 400 °C and 450 °C. CO conversion on the cubic MoO3/Ce0.8Zr0.2O2 catalyst was 50.1% at 400 °C and 75.5% at 450 °C, which is respectively 7% and 20% higher than that on the tetragonal MoO3/Ce0.2Zr0.8O2 catalyst. These were mainly attributed to higher content of active MoS2 on the surface of catalyst, the enhanced oxygen mobility, increased Mo-species dispersion as well as the excellent reducibility resulted from the increased amount of the reducible Ce3+ on the cubic MoO3/Ce0.8Zr0.2O2 catalyst.

Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm.

PubMed

Feng, Yen-Yi; Wu, I-Chin; Chen, Tzu-Li

2017-03-01

The number of emergency cases or emergency room visits rapidly increases annually, thus leading to an imbalance in supply and demand and to the long-term overcrowding of hospital emergency departments (EDs). However, current solutions to increase medical resources and improve the handling of patient needs are either impractical or infeasible in the Taiwanese environment. Therefore, EDs must optimize resource allocation given limited medical resources to minimize the average length of stay of patients and medical resource waste costs. This study constructs a multi-objective mathematical model for medical resource allocation in EDs in accordance with emergency flow or procedure. The proposed mathematical model is complex and difficult to solve because its performance value is stochastic; furthermore, the model considers both objectives simultaneously. Thus, this study develops a multi-objective simulation optimization algorithm by integrating a non-dominated sorting genetic algorithm II (NSGA II) with multi-objective computing budget allocation (MOCBA) to address the challenges of multi-objective medical resource allocation. NSGA II is used to investigate plausible solutions for medical resource allocation, and MOCBA identifies effective sets of feasible Pareto (non-dominated) medical resource allocation solutions in addition to effectively allocating simulation or computation budgets. The discrete event simulation model of ED flow is inspired by a Taiwan hospital case and is constructed to estimate the expected performance values of each medical allocation solution as obtained through NSGA II. Finally, computational experiments are performed to verify the effectiveness and performance of the integrated NSGA II and MOCBA method, as well as to derive non-dominated medical resource allocation solutions from the algorithms.

A General Approach to Access Morphologies of Polyoxometalates in Solution by Using SAXS: An Ab Initio Modeling Protocol.

PubMed

Li, Mu; Wang, Weiyu; Yin, Panchao

2018-05-02

Herein, we reported a general protocol for an ab initio modeling approach to deduce structure information of polyoxometalates (POMs) in solutions from scattering data collected by the small-angle X-ray scattering (SAXS) technique. To validate the protocol, the morphologies of a serious of known POMs in either aqueous or organic solvents were analyzed. The obtained particle morphologies were compared and confirmed with previous reported crystal structures. To extend the feasibility of the protocol to an unknown system of aqueous solutions of Na 2 MoO 4 with the pH ranging from -1 to 8.35, the formation of {Mo 36 } clusters was probed, identified, and confirmed by SAXS. The approach was further optimized with a multi-processing capability to achieve fast analysis of experimental data, thereby, facilitating in situ studies of formations of POMs in solutions. The advantage of this approach is to generate intuitive 3D models of POMs in solutions without confining information such as symmetries and possible sizes. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Hierarchical MoO3/SnS2 core-shell nanowires with enhanced electrochemical performance for lithium-ion batteries.

PubMed

Hu, Chenli; Shu, Haibo; Shen, Zihong; Zhao, Tianfeng; Liang, Pei; Chen, Xiaoshuang

2018-06-27

Two-dimensional (2D) tin disulfide (SnS2) is a promising anode material for lithium-ion batteries (LIBs) because of its high theoretical capacity. The main challenges associated with the SnS2 electrodes are the poor cycling stability and low rate capability due to structural degradation in the discharge/charge process. Here, a facile two-step synthesis method is developed to fabricate hierarchical MoO3/SnS2 core-shell nanowires, where ultrathin SnS2 nanosheets are vertically anchored on MoO3 nanobelts to induce a heterointerface. Benefiting from the unique structural and compositional characteristics, the hierarchical MoO3/SnS2 core-shell nanowires exhibit excellent electrochemical performance and deliver a high reversible capacity of 504 mA h g-1 after 100 stable cycles at a current density of 100 mA g-1, which is far superior to the MoO3 and SnS2 electrodes. An analysis of lithiation dynamics based on ab initio molecular dynamics simulations demonstrates that the formation of a hierarchical MoO3/SnS2 core-shell heterostructure can effectively suppress the rapid dissociation of shell-layer SnS2 nanosheets via the interfacial coupling effect and the central MoO3 backbone can trap and support the polysulfide in the discharge/charge process. The results are responsible for the high storage capacity and rate capability of MoO3/SnS2 electrode materials. This work provides a novel design strategy for constructing high-performance electrodes for LIBs.

Toxicity of the molybdate anion in soil is partially explained by effects of the accompanying cation or by soil pH.

PubMed

Buekers, Jurgen; Mertens, Jelle; Smolders, Erik

2010-06-01

Previous studies have shown that toxicity of cationic trace metals in soil is partially confounded by effects of the accompanying anions. A similar assessment is reported here for toxicity of an oxyanion, i.e., molybdate (MoO(4) (2-)), the soil toxicity of which is relatively unexplored. Solubility and toxicity were compared between the soluble sodium molybdate (Na(2)MoO(4)) and the sparingly soluble molybdenum trioxide (MoO(3)). Confounding effects of salinity were excluded by referencing the Na(2)MoO(4) effect to that of sodium chloride (NaCl). The pH decrease from the acid MoO(3) amendment was equally referenced to a hydrochloric (HCl) treatment or a lime-controlled MoO(3) treatment. The concentrations of molybdenum (Mo) in soil solution or calcium chloride (CaCl(2)) 0.01 M extracts were only marginally affected by either MoO(3) or Na(2)MoO(4) as an Mo source after 10 to 13 days of equilibration. Effects of Mo on soil nitrification were fully confounded by associated changes in salinity or pH. Effects of Mo on growth of wheat seedlings (Triticum aestivum L) were more pronounced than those on nitrification, and toxicity thresholds were unaffected by the form of added Mo. The Mo thresholds for wheat growth were not confounded by pH or salinity at incipient toxicity. It is concluded that oxyanion toxicity might be confounded in relatively insensitive tests for which reference treatments should be included. Copyright 2010 SETAC.

Supported molybdenum oxides as effective catalysts for the catalytic fast pyrolysis of lignocellulosic biomass

DOE PAGES

Murugappan, Karthick; Mukarakate, Calvin; Budhi, Sridhar; ...

2016-07-12

The catalytic fast pyrolysis (CFP) of pine was investigated over 10 wt% MoO 3/TiO 2 and MoO 3/ZrO 2 at 500 °C and H 2 pressures ≤ 0.75 bar. The product distributions were monitored in real time using a molecular beam mass spectrometer (MBMS). Both supported MoO 3 catalysts show different levels of deoxygenation based on the cumulative biomass to MoO 3 mass ratio exposed to the catalytic bed. For biomass to MoO 3 mass ratios <1.5, predominantly olefinic and aromatic hydrocarbons are produced with no detectable oxygen-containing species. For ratios ≥ 1.5, partially deoxygenated species comprised of furans andmore » phenols are observed, with a concomitant decrease of olefinic and aromatic hydrocarbons. For ratios ≥ 5, primary pyrolysis vapours break through the bed, indicating the onset of catalyst deactivation. Product quantification with a tandem micropyrolyzer-GCMS setup shows that fresh supported MoO 3 catalysts convert ca. 27 mol% of the original carbon into hydrocarbons comprised predominantly of aromatics (7 C%), olefins (18 C%) and paraffins (2 C%), comparable to the total hydrocarbon yield obtained with HZSM-5 operated under similar reaction conditions. In conclusion, post-reaction XPS analysis on supported MoO 3/ZrO 2 and MoO 3/TiO 2 catalysts reveal that ca. 50% of Mo surface species exist in their partially reduced forms (i.e., Mo 5+ and Mo 3+), and that catalyst deactivation is likely associated to coking.« less

Studies on the hot corrosion of a nickel-base superalloy, Udimet 700

NASA Technical Reports Server (NTRS)

Misra, A. K.

1984-01-01

The hot corrosion of a nickel-base superalloy, Udimet 700, was studied in the temperature range of 884 to 965 C and with different amounts of Na2SO4. Two different modes of degradation were identified: (1) formation of Na2MoO4 - MoO3 melt and fluxing by this melt, and (2) formation of large interconnected sulfides. The dissolution of Cr2O3, TiO2 in the Na2SO4 melt does not play a significant role in the overall corrosion process. The conditions for the formation of massive interconnected sulfides were identified and a mechanism of degradation due to sulfide formation is described. The formation of Ns2MoO4 - MoO3 melt requires an induction period and various physiochemical processes during the induction period were identified. The factors affecting the length of the induction period were also examined. The melt penetration through the oxide appears to be the prime mode of degradation whether the degradation is due to the formation of sulfides or the formation of the Na2MoO4 - MoO3 melt.

Multi-criteria objective based climate change impact assessment for multi-purpose multi-reservoir systems

NASA Astrophysics Data System (ADS)

Müller, Ruben; Schütze, Niels

2014-05-01

Water resources systems with reservoirs are expected to be sensitive to climate change. Assessment studies that analyze the impact of climate change on the performance of reservoirs can be divided in two groups: (1) Studies that simulate the operation under projected inflows with the current set of operational rules. Due to non adapted operational rules the future performance of these reservoirs can be underestimated and the impact overestimated. (2) Studies that optimize the operational rules for best adaption of the system to the projected conditions before the assessment of the impact. The latter allows for estimating more realistically future performance and adaption strategies based on new operation rules are available if required. Multi-purpose reservoirs serve various, often conflicting functions. If all functions cannot be served simultaneously at a maximum level, an effective compromise between multiple objectives of the reservoir operation has to be provided. Yet under climate change the historically preferenced compromise may no longer be the most suitable compromise in the future. Therefore a multi-objective based climate change impact assessment approach for multi-purpose multi-reservoir systems is proposed in the study. Projected inflows are provided in a first step using a physically based rainfall-runoff model. In a second step, a time series model is applied to generate long-term inflow time series. Finally, the long-term inflow series are used as driving variables for a simulation-based multi-objective optimization of the reservoir system in order to derive optimal operation rules. As a result, the adapted Pareto-optimal set of diverse best compromise solutions can be presented to the decision maker in order to assist him in assessing climate change adaption measures with respect to the future performance of the multi-purpose reservoir system. The approach is tested on a multi-purpose multi-reservoir system in a mountainous catchment in Germany. A climate change assessment is performed for climate change scenarios based on the SRES emission scenarios A1B, B1 and A2 for a set of statistically downscaled meteorological data. The future performance of the multi-purpose multi-reservoir system is quantified and possible intensifications of trade-offs between management goals or reservoir utilizations are shown.

Multi-objective Optimization of Pulsed Gas Metal Arc Welding Process Using Neuro NSGA-II

NASA Astrophysics Data System (ADS)

Pal, Kamal; Pal, Surjya K.

2018-05-01

Weld quality is a critical issue in fabrication industries where products are custom-designed. Multi-objective optimization results number of solutions in the pareto-optimal front. Mathematical regression model based optimization methods are often found to be inadequate for highly non-linear arc welding processes. Thus, various global evolutionary approaches like artificial neural network, genetic algorithm (GA) have been developed. The present work attempts with elitist non-dominated sorting GA (NSGA-II) for optimization of pulsed gas metal arc welding process using back propagation neural network (BPNN) based weld quality feature models. The primary objective to maintain butt joint weld quality is the maximization of tensile strength with minimum plate distortion. BPNN has been used to compute the fitness of each solution after adequate training, whereas NSGA-II algorithm generates the optimum solutions for two conflicting objectives. Welding experiments have been conducted on low carbon steel using response surface methodology. The pareto-optimal front with three ranked solutions after 20th generations was considered as the best without further improvement. The joint strength as well as transverse shrinkage was found to be drastically improved over the design of experimental results as per validated pareto-optimal solutions obtained.

Analysis and optimization of hybrid electric vehicle thermal management systems

NASA Astrophysics Data System (ADS)

Hamut, H. S.; Dincer, I.; Naterer, G. F.

2014-02-01

In this study, the thermal management system of a hybrid electric vehicle is optimized using single and multi-objective evolutionary algorithms in order to maximize the exergy efficiency and minimize the cost and environmental impact of the system. The objective functions are defined and decision variables, along with their respective system constraints, are selected for the analysis. In the multi-objective optimization, a Pareto frontier is obtained and a single desirable optimal solution is selected based on LINMAP decision-making process. The corresponding solutions are compared against the exergetic, exergoeconomic and exergoenvironmental single objective optimization results. The results show that the exergy efficiency, total cost rate and environmental impact rate for the baseline system are determined to be 0.29, ¢28 h-1 and 77.3 mPts h-1 respectively. Moreover, based on the exergoeconomic optimization, 14% higher exergy efficiency and 5% lower cost can be achieved, compared to baseline parameters at an expense of a 14% increase in the environmental impact. Based on the exergoenvironmental optimization, a 13% higher exergy efficiency and 5% lower environmental impact can be achieved at the expense of a 27% increase in the total cost.

Modeling and Optimization of Coordinative Operation of Hydro-wind-photovoltaic Considering Power Generation and Output Fluctuation

NASA Astrophysics Data System (ADS)

Wang, Xianxun; Mei, Yadong

2017-04-01

Coordinative operation of hydro-wind-photovoltaic is the solution of mitigating the conflict of power generation and output fluctuation of new energy and conquering the bottleneck of new energy development. Due to the deficiencies of characterizing output fluctuation, depicting grid construction and disposal of power abandon, the research of coordinative mechanism is influenced. In this paper, the multi-object and multi-hierarchy model of coordinative operation of hydro-wind-photovoltaic is built with the aim of maximizing power generation and minimizing output fluctuation and the constraints of topotaxy of power grid and balanced disposal of power abandon. In the case study, the comparison of uncoordinative and coordinative operation is carried out with the perspectives of power generation, power abandon and output fluctuation. By comparison from power generation, power abandon and output fluctuation between separate operation and coordinative operation of multi-power, the coordinative mechanism is studied. Compared with running solely, coordinative operation of hydro-wind-photovoltaic can gain the compensation benefits. Peak-alternation operation reduces the power abandon significantly and maximizes resource utilization effectively by compensating regulation of hydropower. The Pareto frontier of power generation and output fluctuation is obtained through multiple-objective optimization. It clarifies the relationship of mutual influence between these two objects. When coordinative operation is taken, output fluctuation can be markedly reduced at the cost of a slight decline of power generation. The power abandon also drops sharply compared with operating separately. Applying multi-objective optimization method to optimize the coordinate operation, Pareto optimal solution set of power generation and output fluctuation is achieved.

Evaluation of a stepwise, multi-objective, multi-variable parameter optimization method for the APEX model

USDA-ARS?s Scientific Manuscript database

Hydrologic models are essential tools for environmental assessment of agricultural non-point source pollution. The automatic calibration of hydrologic models, though efficient, demands significant computational power, which can limit its application. The study objective was to investigate a cost e...

The photoelectronic behaviors of MoO3-loaded ZrO2/carbon cluster nanocomposite materials

NASA Astrophysics Data System (ADS)

Matsui, H.; Ishiko, A.; Karuppuchamy, S.; Hassan, M. A.; Yoshihara, M.

2012-03-01

A novel nano-sized ZrO2/carbon cluster composite materials (Ic's) were successfully obtained by the calcination of ZrCl4/starch complexes I's under an argon atmosphere. Pt- and/or MoO3-loaded ZrO2/carbon clusters composite materials were also prepared by doping Pt and/or MoO3 particles on the surface of Ic's. The surface characterization of the composite materials was carried out using transmission electron microscopy (TEM). The TEM observation of the materials showed the presence of particles with the diameters of a few nanometers, possibly Pt particles, and of 50-100 nm, possibly MoO3 particles, in the matrix. Pt- and/or MoO3-loaded ZrO2/carbon cluster composite materials show the efficient photocatalytic activity under visible light irradiation.

Effects of Na2MoO4 and Na2WO4 on molybdenum and tungsten electrodes for the alkali metal thermoelectric converter (AMTEC)

NASA Technical Reports Server (NTRS)

Williams, R. M.; Wheeler, B. L.; Jeffries-Nakamura, B.; Loveland, M. E.; Bankston, C. P.

1988-01-01

The effects of adding Na2MoO4 and Na2WO4 to porous Mo and W electrodes, respectively, on the performance and impedance characteristics of the electrodes in an alkali metal thermoelectric converter (AMTEC) were investigated. It was found that corrosion of the porous electrode by Na2MoO4 or Na2WO4 to form Na2MO3O6 and WO2, respectively, and recrystallization of the Mo or W as the salt evaporates, result in major morphological changes including a loss of columnar structure and a significant increase in porosity. This effect is more pronounced in Na2MoO4/Mo electrodes, due to the lower stability of Na2MoO4.

Application of Advanced Multi-Core Processor Technologies to Oceanographic Research

DTIC Science & Technology

2014-09-30

Jordan Stanway are taking on the work of analyzing their code, and we are working on the Robot Operating System (ROS) and MOOS-DB systems to evaluate...Linux/GNU operating system that should reduce the time required to build the kernel and userspace significantly. This part of the work is vital to...the platform to be used not only as a service, but also as a private deployable package. As much as possible, this system was built using operating

MONSS: A multi-objective nonlinear simplex search approach

NASA Astrophysics Data System (ADS)

Zapotecas-Martínez, Saúl; Coello Coello, Carlos A.

2016-01-01

This article presents a novel methodology for dealing with continuous box-constrained multi-objective optimization problems (MOPs). The proposed algorithm adopts a nonlinear simplex search scheme in order to obtain multiple elements of the Pareto optimal set. The search is directed by a well-distributed set of weight vectors, each of which defines a scalarization problem that is solved by deforming a simplex according to the movements described by Nelder and Mead's method. Considering an MOP with n decision variables, the simplex is constructed using n+1 solutions which minimize different scalarization problems defined by n+1 neighbor weight vectors. All solutions found in the search are used to update a set of solutions considered to be the minima for each separate problem. In this way, the proposed algorithm collectively obtains multiple trade-offs among the different conflicting objectives, while maintaining a proper representation of the Pareto optimal front. In this article, it is shown that a well-designed strategy using just mathematical programming techniques can be competitive with respect to the state-of-the-art multi-objective evolutionary algorithms against which it was compared.

An optimal autonomous microgrid cluster based on distributed generation droop parameter optimization and renewable energy sources using an improved grey wolf optimizer

NASA Astrophysics Data System (ADS)

Moazami Goodarzi, Hamed; Kazemi, Mohammad Hosein

2018-05-01

Microgrid (MG) clustering is regarded as an important driver in improving the robustness of MGs. However, little research has been conducted on providing appropriate MG clustering. This article addresses this shortfall. It proposes a novel multi-objective optimization approach for finding optimal clustering of autonomous MGs by focusing on variables such as distributed generation (DG) droop parameters, the location and capacity of DG units, renewable energy sources, capacitors and powerline transmission. Power losses are minimized and voltage stability is improved while virtual cut-set lines with minimum power transmission for clustering MGs are obtained. A novel chaotic grey wolf optimizer (CGWO) algorithm is applied to solve the proposed multi-objective problem. The performance of the approach is evaluated by utilizing a 69-bus MG in several scenarios.

The Composition of Dramatic Experience: The Play Element in Student Electronic Projects.

ERIC Educational Resources Information Center

Rouzie, Albert

2000-01-01

Analyzes two student group Hypertext projects and a MOO (multiuser domain, object-oriented) project. Finds the play element can enrich readerly experience through the creation of a dramatic experience of information. Suggests that composition instructors need to recognize the play element in computer-based composition and encourage the development…

Multi-Objective Hybrid Optimal Control for Multiple-Flyby Interplanetary Mission Design Using Chemical Propulsion

NASA Technical Reports Server (NTRS)

Englander, Jacob; Vavrina, Matthew

2015-01-01

The customer (scientist or project manager) most often does not want just one point solution to the mission design problem Instead, an exploration of a multi-objective trade space is required. For a typical main-belt asteroid mission the customer might wish to see the trade-space of: Launch date vs. Flight time vs. Deliverable mass, while varying the destination asteroid, planetary flybys, launch year, etcetera. To address this question we use a multi-objective discrete outer-loop which defines many single objective real-valued inner-loop problems.

Controller design for wind turbine load reduction via multiobjective parameter synthesis

NASA Astrophysics Data System (ADS)

Hoffmann, A. F.; Weiβ, F. A.

2016-09-01

During the design process for a wind turbine load reduction controller many different, sometimes conflicting requirements must be fulfilled simultaneously. If the requirements can be expressed as mathematical criteria, such a design problem can be solved by a criterion-vector and multi-objective design optimization. The software environment MOPS (Multi-Objective Parameter Synthesis) supports the engineer for such a design optimization. In this paper MOPS is applied to design a multi-objective load reduction controller for the well-known DTU 10 MW reference wind turbine. A significant reduction in the fatigue criteria especially the blade damage can be reached by the use of an additional Individual Pitch Controller (IPC) and an additional tower damper. This reduction is reached as a trade-off with an increase of actuator load.

Production Task Queue Optimization Based on Multi-Attribute Evaluation for Complex Product Assembly Workshop.

PubMed

Li, Lian-Hui; Mo, Rong

2015-01-01

The production task queue has a great significance for manufacturing resource allocation and scheduling decision. Man-made qualitative queue optimization method has a poor effect and makes the application difficult. A production task queue optimization method is proposed based on multi-attribute evaluation. According to the task attributes, the hierarchical multi-attribute model is established and the indicator quantization methods are given. To calculate the objective indicator weight, criteria importance through intercriteria correlation (CRITIC) is selected from three usual methods. To calculate the subjective indicator weight, BP neural network is used to determine the judge importance degree, and then the trapezoid fuzzy scale-rough AHP considering the judge importance degree is put forward. The balanced weight, which integrates the objective weight and the subjective weight, is calculated base on multi-weight contribution balance model. The technique for order preference by similarity to an ideal solution (TOPSIS) improved by replacing Euclidean distance with relative entropy distance is used to sequence the tasks and optimize the queue by the weighted indicator value. A case study is given to illustrate its correctness and feasibility.

Production Task Queue Optimization Based on Multi-Attribute Evaluation for Complex Product Assembly Workshop

PubMed Central

Li, Lian-hui; Mo, Rong

2015-01-01

The production task queue has a great significance for manufacturing resource allocation and scheduling decision. Man-made qualitative queue optimization method has a poor effect and makes the application difficult. A production task queue optimization method is proposed based on multi-attribute evaluation. According to the task attributes, the hierarchical multi-attribute model is established and the indicator quantization methods are given. To calculate the objective indicator weight, criteria importance through intercriteria correlation (CRITIC) is selected from three usual methods. To calculate the subjective indicator weight, BP neural network is used to determine the judge importance degree, and then the trapezoid fuzzy scale-rough AHP considering the judge importance degree is put forward. The balanced weight, which integrates the objective weight and the subjective weight, is calculated base on multi-weight contribution balance model. The technique for order preference by similarity to an ideal solution (TOPSIS) improved by replacing Euclidean distance with relative entropy distance is used to sequence the tasks and optimize the queue by the weighted indicator value. A case study is given to illustrate its correctness and feasibility. PMID:26414758

X-ray absorption and Raman spectroscopy studies of molybdenum environments in borosilicate waste glasses

NASA Astrophysics Data System (ADS)

McKeown, David A.; Gan, Hao; Pegg, Ian L.

2017-05-01

Mo-containing high-level nuclear waste borosilicate glasses were investigated as part of an effort to improve Mo loading while avoiding yellow phase crystallization. Previous work showed that additions of vanadium decrease yellow phase formation and increases Mo solubility. X-ray absorption spectroscopy (XAS) and Raman spectroscopy were used to characterize Mo environments in HLW borosilicate glasses and to investigate possible structural relationships between Mo and V. Mo XAS spectra for the glasses indicate isolated tetrahedral Mo6+O4 with Mo-O distances near 1.75 Å. V XANES indicate tetrahedral V5+O4 as the dominant species. Raman spectra show composition dependent trends, where Mo-O symmetrical stretch mode frequencies (ν1) are sensitive to the mix of alkali and alkaline earth cations, decreasing by up to 10 cm-1 for glasses that change from Li+ to Na+ as the dominant network-modifying species. This indicates that MoO4 tetrahedra are isolated from the borosilicate network and are surrounded, at least partly, by Na+ and Li+. Secondary ν1 frequency effects, with changes up to 7 cm-1, were also observed with increasing V2O5 and MoO3 content. These secondary trends may indicate MoO4-MoO4 and MoO4-VO4 clustering, suggesting that V additions may stabilize Mo in the matrix with respect to yellow phase formation.

Atomic layer deposition and post-growth thermal annealing of ultrathin MoO3 layers on silicon substrates: Formation of surface nanostructures

NASA Astrophysics Data System (ADS)

Liu, Hongfei; Yang, Ren Bin; Yang, Weifeng; Jin, Yunjiang; Lee, Coryl J. J.

2018-05-01

Ultrathin MoO3 layers have been grown on Si substrates at 120 °C by atomic layer deposition (ALD) using molybdenum hexacarbonyl [Mo(CO)6] and ozone (O3) as the Mo- and O-source precursors, respectively. The ultrathin films were further annealed in air at Tann = 550-750 °C for 15 min. Scanning-electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray photoelectron spectroscopy have been employed to evaluate the morphological and elemental properties as well as their evolutions upon annealing of the thin films. They revealed an interfacial SiOx layer in between the MoO3 layer and the Si substrate; this SiOx layer converted into SiO2 during the annealing; and the equivalent thickness of the MoO3 (SiO2) layer decreased (increased) with the increase in Tann. Particles with diameters smaller than 50 nm emerged at Tann = 550 °C and their sizes (density) were reduced (increased) by increasing Tann to 650 °C. A further increase of Tann to 750 °C resulted in telephone-cord-like MoO3 structures, initiated from isolated particles on the surface. These observations have been discussed and interpreted based on temperature-dependent atomic interdiffusions, surface evaporations, and/or melting of MoO3, which shed new light on ALD MoO3 towards its electronic applications.

A plate-on-plate sandwiched Z-scheme heterojunction photocatalyst: BiOBr-Bi2MoO6 with enhanced photocatalytic performance

NASA Astrophysics Data System (ADS)

Wang, Shengyao; Yang, Xianglong; Zhang, Xuehao; Ding, Xing; Yang, Zixin; Dai, Ke; Chen, Hao

2017-01-01

In this study, a direct Z-scheme heterojunction BiOBr-Bi2MoO6 with greatly enhanced visible light photocatalytic performance was fabricated via a two-step coprecipitation method. It was indicated that a plate-on-plate heterojunctions be present between BiOBr and Bi2MoO6 through different characterization techniques including X-ray powder diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), UV-vis diffuse reflectance spectroscopy (DRS) and photoelectrochemical measurements. The crystal structure and morphology analysis revealed that the heterointerface in BiOBr-Bi2MoO6 occurred mainly on the (001) facets of BiOBr and (001) facets of Bi2MoO6. The photocatalytic activity of the BiOBr-Bi2MoO6 was investigated by degradation of RhB and about 66.7% total organic carbon (TOC) could be removed. Ciprofloxacin (CIP) was employed to rule out the photosensitization. It was implied that the higher activity of BiOBr-Bi2MoO6 could be attribute to the strong redox ability in the Z-scheme system, which was subsequently confirmed by photoluminescence spectroscopy (PL) and active spices trapping experiments. This study provides a promising platform for Z-scheme heterojunction constructing and also sheds light on highly efficient visible-light-driven photocatalysts designing.

Carbon nanotubes-bridged molybdenum trioxide nanosheets as high performance anode for lithium ion batteries

NASA Astrophysics Data System (ADS)

Sun, Haiyan; Hanlon, Damien; Dinh, Duc Anh; Boland, John B.; Esau Del Rio Castillo, Antonio; Di Giovanni, Carlo; Ansaldo, Alberto; Pellegrini, Vittorio; Coleman, Jonathan N.; Bonaccorso, Francesco

2018-01-01

The search for novel nanomaterials driving the development of high-performance electrodes in lithium ion batteries (LIBs) is at the cutting edge of research in the field of energy storage. Here, we report on the synthesis of single wall carbon nanotube (SWNT)-bridged molybdenum trioxide (MoO3) nanosheets as anode material for LIBs. We exploit liquid phase exfoliation of layered MoO3 crystallites to produce multilayer MoO3 nanosheets dispersed in isopropanol, which are then mixed with solution processed SWNTs in the same solvent. The addition of SWNTs to the MoO3 nanosheets provides the conductive framework for electron transport, as well as a bridge structure, which buffers the volume expansion upon lithiation/de-lithiation. We demonstrate that the hybrid SWNT-bridged MoO3 structure is beneficial for both the mechanical stability and the electrochemical characteristics of the anodes leading to a specific capacity of 865 mAh g-1 at 100 mA g-1 after 100 cycles, with a columbic efficiency approaching 100% and a capacity fading of 0.02% per cycle. The low-cost, non-toxic, binder-free hybrid MoO3/SWNT here developed represents a step forward for the applicability of exfoliated MoO3 in LIB anodes, delivering high energy and power densities as well as long lifetime.

Covalent heterogenization of discrete bis(8-quinolinolato)dioxomolybdenum(VI) and dioxotungsten(VI) complexes by a metal-template/metal-exchange method: Cyclooctene epoxidation catalysts with enhanced performances

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Ying; Chattopadhyay, Soma; Shibata, Tomohiro

A metal-template/metal-exchange method was used to imprint covalently attached bis(8- quinolinolato)dioxomolybdenum(VI) and dioxotungsten(VI) complexes onto large surface-area, mesoporous SBA-15 silica to obtain discrete MoO2 VIT and WO2 VIT catalysts bearing different metal loadings, respectively. Homogeneous counterparts, MoO2 VIN and WO2 VIN, as well as randomly ligandgrafted heterogeneous analogues, MoO2 VIG and WO2 VIG, were also prepared for comparison. X-ray absorption fine structure (XAFS), pair distribution function (PDF) and UV–vis data demonstrate that MoO2 VIT and WO2 VIT adopt a more solution-like bis(8-quinolinol) coordination environment than MoO2 VIG and WO2 VIG, respectively. Correspondingly, the templated MoVI and WVI catalysts show superiormore » performances to their randomly grafted counterparts and neat analogues in the epoxidation of cyclooctene. It is found that the representative MoO2 VIT-10% catalyst can be recycled up to five times without significant loss of reactivity, and heterogeneity test confirms the high stability of MoO2 VIT-10% catalyst against leaching of active species into solution. The homogeneity of the discrete bis(8-quinolinol) metal spheres templated on SBA-15 should be responsible for the superior performances.« less

An Improved Multi-Objective Programming with Augmented ε-Constraint Method for Hazardous Waste Location-Routing Problems

PubMed Central

Yu, Hao; Solvang, Wei Deng

2016-01-01

Hazardous waste location-routing problems are of importance due to the potential risk for nearby residents and the environment. In this paper, an improved mathematical formulation is developed based upon a multi-objective mixed integer programming approach. The model aims at assisting decision makers in selecting locations for different facilities including treatment plants, recycling plants and disposal sites, providing appropriate technologies for hazardous waste treatment, and routing transportation. In the model, two critical factors are taken into account: system operating costs and risk imposed on local residents, and a compensation factor is introduced to the risk objective function in order to account for the fact that the risk level imposed by one type of hazardous waste or treatment technology may significantly vary from that of other types. Besides, the policy instruments for promoting waste recycling are considered, and their influence on the costs and risk of hazardous waste management is also discussed. The model is coded and calculated in Lingo optimization solver, and the augmented ε-constraint method is employed to generate the Pareto optimal curve of the multi-objective optimization problem. The trade-off between different objectives is illustrated in the numerical experiment. PMID:27258293

An Improved Multi-Objective Programming with Augmented ε-Constraint Method for Hazardous Waste Location-Routing Problems.

PubMed

Yu, Hao; Solvang, Wei Deng

2016-05-31

Hazardous waste location-routing problems are of importance due to the potential risk for nearby residents and the environment. In this paper, an improved mathematical formulation is developed based upon a multi-objective mixed integer programming approach. The model aims at assisting decision makers in selecting locations for different facilities including treatment plants, recycling plants and disposal sites, providing appropriate technologies for hazardous waste treatment, and routing transportation. In the model, two critical factors are taken into account: system operating costs and risk imposed on local residents, and a compensation factor is introduced to the risk objective function in order to account for the fact that the risk level imposed by one type of hazardous waste or treatment technology may significantly vary from that of other types. Besides, the policy instruments for promoting waste recycling are considered, and their influence on the costs and risk of hazardous waste management is also discussed. The model is coded and calculated in Lingo optimization solver, and the augmented ε-constraint method is employed to generate the Pareto optimal curve of the multi-objective optimization problem. The trade-off between different objectives is illustrated in the numerical experiment.

A Coral Reef Algorithm Based on Learning Automata for the Coverage Control Problem of Heterogeneous Directional Sensor Networks

PubMed Central

Li, Ming; Miao, Chunyan; Leung, Cyril

2015-01-01

Coverage control is one of the most fundamental issues in directional sensor networks. In this paper, the coverage optimization problem in a directional sensor network is formulated as a multi-objective optimization problem. It takes into account the coverage rate of the network, the number of working sensor nodes and the connectivity of the network. The coverage problem considered in this paper is characterized by the geographical irregularity of the sensed events and heterogeneity of the sensor nodes in terms of sensing radius, field of angle and communication radius. To solve this multi-objective problem, we introduce a learning automata-based coral reef algorithm for adaptive parameter selection and use a novel Tchebycheff decomposition method to decompose the multi-objective problem into a single-objective problem. Simulation results show the consistent superiority of the proposed algorithm over alternative approaches. PMID:26690162

A Coral Reef Algorithm Based on Learning Automata for the Coverage Control Problem of Heterogeneous Directional Sensor Networks.

PubMed

Li, Ming; Miao, Chunyan; Leung, Cyril

2015-12-04

Coverage control is one of the most fundamental issues in directional sensor networks. In this paper, the coverage optimization problem in a directional sensor network is formulated as a multi-objective optimization problem. It takes into account the coverage rate of the network, the number of working sensor nodes and the connectivity of the network. The coverage problem considered in this paper is characterized by the geographical irregularity of the sensed events and heterogeneity of the sensor nodes in terms of sensing radius, field of angle and communication radius. To solve this multi-objective problem, we introduce a learning automata-based coral reef algorithm for adaptive parameter selection and use a novel Tchebycheff decomposition method to decompose the multi-objective problem into a single-objective problem. Simulation results show the consistent superiority of the proposed algorithm over alternative approaches.

77 FR 9279 - Self-Regulatory Organizations; BATS Exchange, Inc.; Notice of Filing and Immediate Effectiveness...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-02-16

...-032). Specifically, the Exchange proposes to amend several portions of Rule 11.23 to allow MOO, LOO, and LLOO orders to participate in IPO Auctions. Under the proposal, MOO orders would behave like... modifications to the definitions of MOO, LOO, and LLOO orders and to make clear that these Opening Auction...

Multi-objective optimization to evaluate tradeoffs among forest ecosystem services following fire hazard reduction in the Deschutes National Forest, USA

Treesearch

Svetlana A. (Kushch) Schroder; Sandor F. Toth; Robert L. Deal; Gregory J. Ettl

2016-01-01

Forest owners worldwide are increasingly interested in managing forests to provide a broad suite of Ecosystem services, balancing multiple objectives and evaluating management activities in terms of Potential tradeoffs. We describe a multi-objective mathematical programming model to quantify tradeoffs in expected sediment delivery and the preservation of Northern...

Surrogate Based Uni/Multi-Objective Optimization and Distribution Estimation Methods

NASA Astrophysics Data System (ADS)

Gong, W.; Duan, Q.; Huo, X.

2017-12-01

Parameter calibration has been demonstrated as an effective way to improve the performance of dynamic models, such as hydrological models, land surface models, weather and climate models etc. Traditional optimization algorithms usually cost a huge number of model evaluations, making dynamic model calibration very difficult, or even computationally prohibitive. With the help of a serious of recently developed adaptive surrogate-modelling based optimization methods: uni-objective optimization method ASMO, multi-objective optimization method MO-ASMO, and probability distribution estimation method ASMO-PODE, the number of model evaluations can be significantly reduced to several hundreds, making it possible to calibrate very expensive dynamic models, such as regional high resolution land surface models, weather forecast models such as WRF, and intermediate complexity earth system models such as LOVECLIM. This presentation provides a brief introduction to the common framework of adaptive surrogate-based optimization algorithms of ASMO, MO-ASMO and ASMO-PODE, a case study of Common Land Model (CoLM) calibration in Heihe river basin in Northwest China, and an outlook of the potential applications of the surrogate-based optimization methods.

A dynamic multi-level optimal design method with embedded finite-element modeling for power transformers

NASA Astrophysics Data System (ADS)

Zhang, Yunpeng; Ho, Siu-lau; Fu, Weinong

2018-05-01

This paper proposes a dynamic multi-level optimal design method for power transformer design optimization (TDO) problems. A response surface generated by second-order polynomial regression analysis is updated dynamically by adding more design points, which are selected by Shifted Hammersley Method (SHM) and calculated by finite-element method (FEM). The updating stops when the accuracy requirement is satisfied, and optimized solutions of the preliminary design are derived simultaneously. The optimal design level is modulated through changing the level of error tolerance. Based on the response surface of the preliminary design, a refined optimal design is added using multi-objective genetic algorithm (MOGA). The effectiveness of the proposed optimal design method is validated through a classic three-phase power TDO problem.

Multi-objective aerodynamic shape optimization of small livestock trailers

NASA Astrophysics Data System (ADS)

Gilkeson, C. A.; Toropov, V. V.; Thompson, H. M.; Wilson, M. C. T.; Foxley, N. A.; Gaskell, P. H.

2013-11-01

This article presents a formal optimization study of the design of small livestock trailers, within which the majority of animals are transported to market in the UK. The benefits of employing a headboard fairing to reduce aerodynamic drag without compromising the ventilation of the animals' microclimate are investigated using a multi-stage process involving computational fluid dynamics (CFD), optimal Latin hypercube (OLH) design of experiments (DoE) and moving least squares (MLS) metamodels. Fairings are parameterized in terms of three design variables and CFD solutions are obtained at 50 permutations of design variables. Both global and local search methods are employed to locate the global minimum from metamodels of the objective functions and a Pareto front is generated. The importance of carefully selecting an objective function is demonstrated and optimal fairing designs, offering drag reductions in excess of 5% without compromising animal ventilation, are presented.

MoOx thin films deposited by magnetron sputtering as an anode for aqueous micro-supercapacitors

PubMed Central

Liu, Can; Li, Zhengcao; Zhang, Zhengjun

2013-01-01

In order to examine the potential application of non-stoichiometric molybdenum oxide as anode materials for aqueous micro-supercapacitors, conductive MoOx films (2 ⩽ x ⩽ 2.3) deposited via RF magnetron sputtering at different temperatures were systematically studied for composition, structure and electrochemical properties in an aqueous solution of Li2SO4. The MoOx (x ≈ 2.3) film deposited at 150 °C exhibited a higher areal capacitance (31 mF cm−2 measured at 5 mV s−1), best rate capability and excellent stability at potentials below −0.1 V versus saturated calomel electrode, compared to the films deposited at room temperature and at higher temperatures. These superior properties were attributed to the multi-valence composition and mixed-phase microstructure, i.e., the coexistence of MoO2 nanocrystals and amorphous MoOx (2.3 < x ⩽ 3). A mechanism combining Mo(IV) oxidation/reduction on the hydrated MoO2 grain surfaces and cation intercalation/extrusion is proposed to illustrate the pseudo-capacitive process. PMID:27877625

MoOx thin films deposited by magnetron sputtering as an anode for aqueous micro-supercapacitors

NASA Astrophysics Data System (ADS)

Liu, Can; Li, Zhengcao; Zhang, Zhengjun

2013-12-01

In order to examine the potential application of non-stoichiometric molybdenum oxide as anode materials for aqueous micro-supercapacitors, conductive MoOx films (2 ⩽ x ⩽ 2.3) deposited via RF magnetron sputtering at different temperatures were systematically studied for composition, structure and electrochemical properties in an aqueous solution of Li2SO4. The MoOx (x ≈ 2.3) film deposited at 150 °C exhibited a higher areal capacitance (31 mF cm-2 measured at 5 mV s-1), best rate capability and excellent stability at potentials below -0.1 V versus saturated calomel electrode, compared to the films deposited at room temperature and at higher temperatures. These superior properties were attributed to the multi-valence composition and mixed-phase microstructure, i.e., the coexistence of MoO2 nanocrystals and amorphous MoOx (2.3 < x ⩽ 3). A mechanism combining Mo(IV) oxidation/reduction on the hydrated MoO2 grain surfaces and cation intercalation/extrusion is proposed to illustrate the pseudo-capacitive process.

Carbon-based coating containing ultrafine MoO2 nanoparticles as an integrated anode for high-performance lithium-ion batteries

NASA Astrophysics Data System (ADS)

Li, Quanyi; Yang, Qi; Zhao, Yanhong; Wan, Bin

2017-10-01

Copper-supported MoO2-C composite as an integrated anode with excellent battery performance was synthesized by a facile knife coating technique followed by heat treatment in a vacuum. The obtained samples were characterized by X-ray diffraction (XRD), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), thermal analysis, nitrogen adsorption and desorption analysis, field emission scanning microscopy (FESEM), and transmission electron microscopy (TEM). The results show the MoO2-C composite coating is comprised of a porous carbon matrix with a pore size of 1-3 nm and ultrafine MoO2 nanoparticles with a size of 5-10 nm encapsulated inside, the coating is tightly attached on the surface of copper foil, and the interface between them is free of cracks. Stable PAN-DMF-H2O system containing ammonium molybdate suitable for knife coating technique and the MoO2-C composite with ultrafine MoO2 nanoparticles encapsulated in the carbon matrix can be prepared through controlling amount of added ammonium molybdate solution. The copper-supported MoO2-C composite coating can be directly utilized as the integrated anode for lithium-ion batteries (LIBs). It delivers a capacity of 814 mA h g-1 at a current density of 100 mA g-1 after 100 cycles without apparent capacity fading. Furthermore, with increase of current densities to 200, 500, 1000, 2000, and 5000 mA g-1, it exhibits average capacities of 809, 697, 568, 383, and 188 mA h g-1. Its outstanding electrochemical performance is attributed to combined merits of integrated anode and structure with ultrafine MoO2 nanoparticles embedded in the porous carbon matrix.

Studies on up/down-conversion emission of Yb3+ sensitized Er3+ doped MLa2(MoO4)4 (M = Ba, Sr and Ca) phosphors for thermometry and optical heating

NASA Astrophysics Data System (ADS)

Sinha, Shriya; Kumar, Kaushal

2018-01-01

The photoluminescence properties of Yb3+ sensitized Er3+ doped BaLa2(MoO4)4, SrLa2(MoO4)4 and CaLa2(MoO4)4 phosphors synthesized via hydrothermal method are investigated upon 980 nm and 380 nm light excitations. The phase, purity, and morphology of the samples are characterized by X-ray diffraction, Fourier transform infrared spectroscopy and Field emission scanning electron microscope. Among these three phosphors, the strongest emission intensity is seen in BaLa2(MoO4)4: Er3+/Yb3+ through both the 980 nm and 380 nm light excitations and is explained by the lifetime measurement of 4S3/2 level of Er3+ ion. Temperature sensing measurements were performed by using the fluorescence intensity ratio (FIR) of green emission bands originated from the two thermally coupled 2H11/2 → 4I15/2 and 4S3//2 → 4I15/2 transitions of Er3+ and maximum temperature sensitivity of 1.05% K-1 at 305 K is found for BLa2(MoO4)4: Er3+/Yb3+ sample. Moreover, the laser induced heating is measured in the samples and the maximum temperature of the sample particles is calculated as 422 K at 76 W/cm2 in BaLa2(MoO4)4: Er3+/Yb3+, pointing out large amount of heat generation in such phosphors. The BaLa2(MoO4)4: Er3+/Yb3+ also exhibits higher photothermal conversion efficiency of 46.7%.

A comparative study of CeO2-Al2O3 support prepared with different methods and its application on MoO3/CeO2-Al2O3 catalyst for sulfur-resistant methanation

NASA Astrophysics Data System (ADS)

Jiang, Minhong; Wang, Baowei; Yao, Yuqin; Li, Zhenhua; Ma, Xinbin; Qin, Shaodong; Sun, Qi

2013-11-01

The CeO2-Al2O3 supports prepared with impregnation (IM), deposition precipitation (DP), and solution combustion (SC) methods for MoO3/CeO2-Al2O3 catalyst were investigated in the sulfur-resistant methanation. The supports and catalysts were characterized by N2-physisorption, transmission electron microscopy (TEM), X-ray diffraction (XRD), Raman spectroscopy (RS), and temperature-programmed reduction (TPR). The N2-physisorption results indicated that the DP method was favorable for obtaining better textural properties. The TEM and RS results suggested that there is a CeO2 layer on the surface of the support prepared with DP method. This CeO2 layer not only prevented the interaction between MoO3 and γ-Al2O3 to form Al2(MoO4)3 species, but also improved the dispersion of MoO3 in the catalyst. Accordingly, the catalysts whose supports were prepared with DP method exhibited the best catalytic activity. The catalysts whose supports were prepared with SC method had the worst catalytic activity. This was caused by the formation of Al2(MoO4)3 and crystalline MoO3. Additionally, the CeO2 layer resulted in the instability of catalysts in reaction process. The increasing of calcination temperature of supports reduced the catalytic activity of all catalysts. The decrease extent of the catalysts whose supports were prepared with DP method was the lowest as the CeO2 layer prevented the interaction between MoO3 and γ-Al2O3.

Remote sensing imagery classification using multi-objective gravitational search algorithm

NASA Astrophysics Data System (ADS)

Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie

2016-10-01

Simultaneous optimization of different validity measures can capture different data characteristics of remote sensing imagery (RSI) and thereby achieving high quality classification results. In this paper, two conflicting cluster validity indices, the Xie-Beni (XB) index and the fuzzy C-means (FCM) (Jm) measure, are integrated with a diversity-enhanced and memory-based multi-objective gravitational search algorithm (DMMOGSA) to present a novel multi-objective optimization based RSI classification method. In this method, the Gabor filter method is firstly implemented to extract texture features of RSI. Then, the texture features are syncretized with the spectral features to construct the spatial-spectral feature space/set of the RSI. Afterwards, cluster of the spectral-spatial feature set is carried out on the basis of the proposed method. To be specific, cluster centers are randomly generated initially. After that, the cluster centers are updated and optimized adaptively by employing the DMMOGSA. Accordingly, a set of non-dominated cluster centers are obtained. Therefore, numbers of image classification results of RSI are produced and users can pick up the most promising one according to their problem requirements. To quantitatively and qualitatively validate the effectiveness of the proposed method, the proposed classification method was applied to classifier two aerial high-resolution remote sensing imageries. The obtained classification results are compared with that produced by two single cluster validity index based and two state-of-the-art multi-objective optimization algorithms based classification results. Comparison results show that the proposed method can achieve more accurate RSI classification.

Interface Structure of MoO3 on Organic Semiconductors

PubMed Central

White, Robin T.; Thibau, Emmanuel S.; Lu, Zheng-Hong

2016-01-01

We have systematically studied interface structure formed by vapor-phase deposition of typical transition metal oxide MoO3 on organic semiconductors. Eight organic hole transport materials have been used in this study. Ultraviolet photoelectron spectroscopy and X-ray photoelectron spectroscopy are used to measure the evolution of the physical, chemical and electronic structure of the interfaces at various stages of MoO3 deposition on these organic semiconductor surfaces. For the interface physical structure, it is found that MoO3 diffuses into the underlying organic layer, exhibiting a trend of increasing diffusion with decreasing molecular molar mass. For the interface chemical structure, new carbon and molybdenum core-level states are observed, as a result of interfacial electron transfer from organic semiconductor to MoO3. For the interface electronic structure, energy level alignment is observed in agreement with the universal energy level alignment rule of molecules on metal oxides, despite deposition order inversion. PMID:26880185

Environment-Aware Production Scheduling for Paint Shops in Automobile Manufacturing: A Multi-Objective Optimization Approach

PubMed Central

Zhang, Rui

2017-01-01

The traditional way of scheduling production processes often focuses on profit-driven goals (such as cycle time or material cost) while tending to overlook the negative impacts of manufacturing activities on the environment in the form of carbon emissions and other undesirable by-products. To bridge the gap, this paper investigates an environment-aware production scheduling problem that arises from a typical paint shop in the automobile manufacturing industry. In the studied problem, an objective function is defined to minimize the emission of chemical pollutants caused by the cleaning of painting devices which must be performed each time before a color change occurs. Meanwhile, minimization of due date violations in the downstream assembly shop is also considered because the two shops are interrelated and connected by a limited-capacity buffer. First, we have developed a mixed-integer programming formulation to describe this bi-objective optimization problem. Then, to solve problems of practical size, we have proposed a novel multi-objective particle swarm optimization (MOPSO) algorithm characterized by problem-specific improvement strategies. A branch-and-bound algorithm is designed for accurately assessing the most promising solutions. Finally, extensive computational experiments have shown that the proposed MOPSO is able to match the solution quality of an exact solver on small instances and outperform two state-of-the-art multi-objective optimizers in literature on large instances with up to 200 cars. PMID:29295603

Environment-Aware Production Schedulingfor Paint Shops in Automobile Manufacturing: A Multi-Objective Optimization Approach.

PubMed

Zhang, Rui

2017-12-25

The traditional way of scheduling production processes often focuses on profit-driven goals (such as cycle time or material cost) while tending to overlook the negative impacts of manufacturing activities on the environment in the form of carbon emissions and other undesirable by-products. To bridge the gap, this paper investigates an environment-aware production scheduling problem that arises from a typical paint shop in the automobile manufacturing industry. In the studied problem, an objective function is defined to minimize the emission of chemical pollutants caused by the cleaning of painting devices which must be performed each time before a color change occurs. Meanwhile, minimization of due date violations in the downstream assembly shop is also considered because the two shops are interrelated and connected by a limited-capacity buffer. First, we have developed a mixed-integer programming formulation to describe this bi-objective optimization problem. Then, to solve problems of practical size, we have proposed a novel multi-objective particle swarm optimization (MOPSO) algorithm characterized by problem-specific improvement strategies. A branch-and-bound algorithm is designed for accurately assessing the most promising solutions. Finally, extensive computational experiments have shown that the proposed MOPSO is able to match the solution quality of an exact solver on small instances and outperform two state-of-the-art multi-objective optimizers in literature on large instances with up to 200 cars.

Ecologically and economically conscious design of the injected pultrusion process via multi-objective optimization

NASA Astrophysics Data System (ADS)

Srinivasagupta, Deepak; Kardos, John L.

2004-05-01

Injected pultrusion (IP) is an environmentally benign continuous process for low-cost manufacture of prismatic polymer composites. IP has been of recent regulatory interest as an option to achieve significant vapour emissions reduction. This work describes the design of the IP process with multiple design objectives. In our previous work (Srinivasagupta D et al 2003 J. Compos. Mater. at press), an algorithm for economic design using a validated three-dimensional physical model of the IP process was developed, subject to controllability considerations. In this work, this algorithm was used in a multi-objective optimization approach to simultaneously meet economic, quality related, and environmental objectives. The retrofit design of a bench-scale set-up was considered, and the concept of exergy loss in the process, as well as in vapour emission, was introduced. The multi-objective approach was able to determine the optimal values of the processing parameters such as heating zone temperatures and resin injection pressure, as well as the equipment specifications (die dimensions, heater, puller and pump ratings) that satisfy the various objectives in a weighted sense, and result in enhanced throughput rates. The economic objective did not coincide with the environmental objective, and a compromise became necessary. It was seen that most of the exergy loss is in the conversion of electric power into process heating. Vapour exergy loss was observed to be negligible for the most part.

Fuzzy physical programming for Space Manoeuvre Vehicles trajectory optimization based on hp-adaptive pseudospectral method

NASA Astrophysics Data System (ADS)

Chai, Runqi; Savvaris, Al; Tsourdos, Antonios

2016-06-01

In this paper, a fuzzy physical programming (FPP) method has been introduced for solving multi-objective Space Manoeuvre Vehicles (SMV) skip trajectory optimization problem based on hp-adaptive pseudospectral methods. The dynamic model of SMV is elaborated and then, by employing hp-adaptive pseudospectral methods, the problem has been transformed to nonlinear programming (NLP) problem. According to the mission requirements, the solutions were calculated for each single-objective scenario. To get a compromised solution for each target, the fuzzy physical programming (FPP) model is proposed. The preference function is established with considering the fuzzy factor of the system such that a proper compromised trajectory can be acquired. In addition, the NSGA-II is tested to obtain the Pareto-optimal solution set and verify the Pareto optimality of the FPP solution. Simulation results indicate that the proposed method is effective and feasible in terms of dealing with the multi-objective skip trajectory optimization for the SMV.

Sensitivity Analysis Based Approaches for Mitigating the Effects of Reducible Interval Input Uncertainty on Single- and Multi-Disciplinary Systems Using Multi-Objective Optimization

DTIC Science & Technology

2010-01-01

Multi-Disciplinary, Multi-Output Sensitivity Analysis ( MIMOSA ) .........29 3.1 Introduction to Research Thrust 1...39 3.3 MIMOSA Approach ..........................................................................................41 3.3.1...Collaborative Consistency of MIMOSA .......................................................41 3.3.2 Formulation of MIMOSA

Porous MoO2 nanowires as stable and high-rate negative electrodes for electrochemical capacitors.

PubMed

Zheng, Dezhou; Feng, Haobin; Zhang, Xiyue; He, Xinjun; Yu, Minghao; Lu, Xihong; Tong, Yexiang

2017-04-04

Free-standing porous MoO 2 nanowires with extraordinary capacitive performance are developed as high-performance electrodes for electrochemical capacitors. The as-obtained MoO 2 electrode exhibits a remarkable capacitance of 424.4 mF cm -2 with excellent electrochemical durability (no capacitance decay after 10 000 cycles at various scan rates).

Welcome GoMOOS users! | NERACOOS

Science.gov Websites

previous version of NERACOOS is available for a limited time: http://www2.neracoos.org Home Welcome GoMOOS . GoMOOS Real Time Data Real-time buoy conditions can be found through the NERACOOS Real Time Data Portal accessing real-time data in the Gulf of Maine. Federal funding shortfalls in 2008 and 2009 necessitated the

Redox Chemistry of Molybdenum Trioxide for Ultrafast Hydrogen-Ion Storage.

PubMed

Wang, Xianfu; Xie, Yiming; Tang, Kai; Wang, Chao; Yan, Chenglin

2018-05-11

Hydrogen ions are ideal charge carriers for rechargeable batteries due to their small ionic radius and wide availability. However, little attention has been paid to hydrogen-ion storage devices because they generally deliver relatively low Coulombic efficiency as a result of the hydrogen evolution reaction that occurs in an aqueous electrolyte. Herein, we successfully demonstrate that hydrogen ions can be electrochemically stored in an inorganic molybdenum trioxide (MoO 3 ) electrode with high Coulombic efficiency and stability. The as-obtained electrode exhibits ultrafast hydrogen-ion storage properties with a specific capacity of 88 mA hg -1 at an ultrahigh rate of 100 C. The redox reaction mechanism of the MoO 3 electrode in the hydrogen-ion cell was investigated in detail. The results reveal a conversion reaction of the MoO 3 electrode into H 0.88 MoO 3 during the first hydrogen-ion insertion process and reversible intercalation/deintercalation of hydrogen ions between H 0.88 MoO 3 and H 0.12 MoO 3 during the following cycles. This study reveals new opportunities for the development of high-power energy storage devices with lightweight elements. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Catalytic performance of V2O5-MoO3/γ-Al2O3 catalysts for partial oxidation of n-hexane1

NASA Astrophysics Data System (ADS)

Mahmoudian, R.; Khodadadi, Z.; Mahdavi, Vahid; Salehi, Mohammed

2016-01-01

In the current study, a series of V2O5-MoO3 catalyst supported on γ-Al2O3 with various V2O5 and MoO3 loadings was prepared by wet impregnation technique. The characterization of prepared catalysts includes BET surface area, powder X-ray diffraction (XRD), and oxygen chemisorptions. The partial oxidation of n-hexane by air over V2O5-MoO3/γ-Al2O3 catalysts was carried out under flow condition in a fixed bed glass reactor. The effect of V2O5 loading, temperature, MoO3 loading, and n-hexane LHSV on the n-hexane conversion and the product selectivity were investigated. The partial oxygenated products of n-hexane oxidation were ethanol, acetic anhydride, acetic acid, and acetaldehyde. The 10% V2O5-1%MoO3/γ-Al2O3 was found in most active and selective catalyst during partial oxidation of n-hexane. The results indicated that by increasing the temperature, the n-hexane conversion increases as well, although the selectivity of the products passes through a maximum by increasing the temperature.

Revealing the transport properties of the spin-polarized β‧-Tb2(MoO4)3: DFT+U

NASA Astrophysics Data System (ADS)

Reshak, A. H.

2017-11-01

The thermoelectric properties of the spin-polarized β‧-Tb2(MoO4)3 phase are calculated using first-principles and second-principles methods to solve the semi-classical Bloch-Boltzmann transport equations. It is interesting to highlight that the calculated electronic band structure reveals that the β‧-Tb2(MoO4)3 has parabolic bands in the vicinity of the Fermi level (EF); therefore, the carriers exhibit low effective mass and hence high mobility. The existence of strong covalent bonds between Mo and O in the MoO4 tetrahedrons is more favorable for the transport of the carriers than the ionic bond. It has been found that the carrier concentration of spin-up (↑) and spin-down (↓) increases linearly with increasing the temperature and exhibits a maximum carrier concentration at EF. The calculations reveal that the β‧-Tb2(MoO4)3 exhibits maximum electrical conductivity, minimum electronic thermal conductivity, a large Seebeck coefficient and a high power factor at EF for (↑) and (↓). Therefore, the vicinity of EF is the area where the β‧-Tb2(MoO4)3 is expected to show maximum efficiency.

Pressure-induced phase transition and fracture in α-MoO3 nanoribbons

NASA Astrophysics Data System (ADS)

Silveira, Jose V.; Vieira, Luciana L.; Aguiar, Acrisio L.; Freire, Paulo T. C.; Mendes Filho, Josue; Alves, Oswaldo L.; Souza Filho, Antonio G.

2018-03-01

MoO3 nanoribbons were studied under different pressure conditions ranging from 0 to 21 GPa at room temperature. The effect of the applied pressure on the spectroscopic and morphologic properties of the MoO3 nanoribbons was investigated by means of Raman spectroscopy and scanning electron microscopy techniques. The pressure dependent Raman spectra of the MoO3 nanoribbons indicate that a structural phase transition occurs at 5 GPa from the orthorhombic α-MoO3 phase (Pbnm) to the monoclinic MoO3-II phase (P21/m), which remains stable up to 21 GPa. Such phase transformation occurs at considerably lower pressure than the critical pressure for α-MoO3 microcrystals (12 GPa). We suggested that the applanate morphology combined with the presence of crystalline defects in the sample play an important role in the phase transition of the MoO3 nanoribbons. Frequencies and linewidths of the Raman bands as a function of pressure also suggest a pressure-induced morphological change and the decreasing of the nanocrystal size. The observed spectroscopic changes are supported by electron microscopy images, which clearly show a pressure-induced morphologic change in MoO3 nanoribbons.

Multi-objective optimization in quantum parameter estimation

NASA Astrophysics Data System (ADS)

Gong, BeiLi; Cui, Wei

2018-04-01

We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved, it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives: (1) maximizing the Fisher information, improving the parameter estimation precision, and (2) minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ɛ-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.

Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization.

PubMed

Chen, Zhihuan; Yuan, Yanbin; Yuan, Xiaohui; Huang, Yuehua; Li, Xianshan; Li, Wenwu

2015-05-01

A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Digital fabrication of multi-material biomedical objects.

PubMed

Cheung, H H; Choi, S H

2009-12-01

This paper describes a multi-material virtual prototyping (MMVP) system for modelling and digital fabrication of discrete and functionally graded multi-material objects for biomedical applications. The MMVP system consists of a DMMVP module, an FGMVP module and a virtual reality (VR) simulation module. The DMMVP module is used to model discrete multi-material (DMM) objects, while the FGMVP module is for functionally graded multi-material (FGM) objects. The VR simulation module integrates these two modules to perform digital fabrication of multi-material objects, which can be subsequently visualized and analysed in a virtual environment to optimize MMLM processes for fabrication of product prototypes. Using the MMVP system, two biomedical objects, including a DMM human spine and an FGM intervertebral disc spacer are modelled and digitally fabricated for visualization and analysis in a VR environment. These studies show that the MMVP system is a practical tool for modelling, visualization, and subsequent fabrication of biomedical objects of discrete and functionally graded multi-materials for biomedical applications. The system may be adapted to control MMLM machines with appropriate hardware for physical fabrication of biomedical objects.

Particle Swarm Optimization Toolbox

NASA Technical Reports Server (NTRS)

Grant, Michael J.

2010-01-01

The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry trajectory and guidance design for the Mars Science Laboratory mission but may be applied to any optimization problem.

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.

2004-01-01

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Multi-objective optimization model of CNC machining to minimize processing time and environmental impact

NASA Astrophysics Data System (ADS)

Hamada, Aulia; Rosyidi, Cucuk Nur; Jauhari, Wakhid Ahmad

2017-11-01

Minimizing processing time in a production system can increase the efficiency of a manufacturing company. Processing time are influenced by application of modern technology and machining parameter. Application of modern technology can be apply by use of CNC machining, one of the machining process can be done with a CNC machining is turning. However, the machining parameters not only affect the processing time but also affect the environmental impact. Hence, optimization model is needed to optimize the machining parameters to minimize the processing time and environmental impact. This research developed a multi-objective optimization to minimize the processing time and environmental impact in CNC turning process which will result in optimal decision variables of cutting speed and feed rate. Environmental impact is converted from environmental burden through the use of eco-indicator 99. The model were solved by using OptQuest optimization software from Oracle Crystal Ball.

Optimization of response surface and neural network models in conjugation with desirability function for estimation of nutritional needs of methionine, lysine, and threonine in broiler chickens.

PubMed

Mehri, Mehran

2014-07-01

The optimization algorithm of a model may have significant effects on the final optimal values of nutrient requirements in poultry enterprises. In poultry nutrition, the optimal values of dietary essential nutrients are very important for feed formulation to optimize profit through minimizing feed cost and maximizing bird performance. This study was conducted to introduce a novel multi-objective algorithm, desirability function, for optimization the bird response models based on response surface methodology (RSM) and artificial neural network (ANN). The growth databases on the central composite design (CCD) were used to construct the RSM and ANN models and optimal values for 3 essential amino acids including lysine, methionine, and threonine in broiler chicks have been reevaluated using the desirable function in both analytical approaches from 3 to 16 d of age. Multi-objective optimization results showed that the most desirable function was obtained for ANN-based model (D = 0.99) where the optimal levels of digestible lysine (dLys), digestible methionine (dMet), and digestible threonine (dThr) for maximum desirability were 13.2, 5.0, and 8.3 g/kg of diet, respectively. However, the optimal levels of dLys, dMet, and dThr in the RSM-based model were estimated at 11.2, 5.4, and 7.6 g/kg of diet, respectively. This research documented that the application of ANN in the broiler chicken model along with a multi-objective optimization algorithm such as desirability function could be a useful tool for optimization of dietary amino acids in fractional factorial experiments, in which the use of the global desirability function may be able to overcome the underestimations of dietary amino acids resulting from the RSM model. © 2014 Poultry Science Association Inc.

Multi-Objective Optimization of Moving-magnet Linear Oscillatory Motor Using Response Surface Methodology with Quantum-Behaved PSO Operator

NASA Astrophysics Data System (ADS)

Lei, Meizhen; Wang, Liqiang

2018-01-01

To reduce the difficulty of manufacturing and increase the magnetic thrust density, a moving-magnet linear oscillatory motor (MMLOM) without inner-stators was Proposed. To get the optimal design of maximum electromagnetic thrust with minimal permanent magnetic material, firstly, the 3D finite element analysis (FEA) model of the MMLOM was built and verified by comparison with prototype experiment result. Then the influence of design parameters of permanent magnet (PM) on the electromagnetic thrust was systematically analyzed by the 3D FEA to get the design parameters. Secondly, response surface methodology (RSM) was employed to build the response surface model of the new MMLOM, which can obtain an analytical model of the PM volume and thrust. Then a multi-objective optimization methods for design parameters of PM, using response surface methodology (RSM) with a quantum-behaved PSO (QPSO) operator, was proposed. Then the way to choose the best design parameters of PM among the multi-objective optimization solution sets was proposed. Then the 3D FEA of the optimal design candidates was compared. The comparison results showed that the proposed method can obtain the best combination of the geometric parameters of reducing the PM volume and increasing the thrust.

Seismic velocity structure of the crust and upper mantle beneath the Texas-Gulf of Mexico margin from joint inversion of Ps and Sp receiver functions and surface wave dispersion

NASA Astrophysics Data System (ADS)

Agrawal, M.; Pulliam, J.; Sen, M. K.

2013-12-01

The seismic structure beneath Texas Gulf Coast Plain (GCP) is determined via velocity analysis of stacked common conversion point (CCP) Ps and Sp receiver functions and surface wave dispersion. The GCP is a portion of a ocean-continental transition zone, or 'passive margin', where seismic imaging of lithospheric Earth structure via passive seismic techniques has been rare. Seismic data from a temporary array of 22 broadband stations, spaced 16-20 km apart, on a ~380-km-long profile from Matagorda Island, a barrier island in the Gulf of Mexico, to Johnson City, Texas were employed to construct a coherent image of the crust and uppermost mantle. CCP stacking was applied to data from teleseismic earthquakes to enhance the signal-to-noise ratios of converted phases, such as Ps phases. An inaccurate velocity model, used for time-to-depth conversion in CCP stacking, may produce higher errors, especially in a region of substantial lateral velocity variations. An accurate velocity model is therefore essential to constructing high quality depth-domain images. To find accurate velocity P- and S-wave models, we applied a joint modeling approach that searches for best-fitting models via simulated annealing. This joint inversion approach, which we call 'multi objective optimization in seismology' (MOOS), simultaneously models Ps receiver functions, Sp receiver functions and group velocity surface wave dispersion curves after assigning relative weights for each objective function. Weights are computed from the standard deviations of the data. Statistical tools such as the posterior parameter correlation matrix and posterior probability density (PPD) function are used to evaluate the constraints that each data type places on model parameters. They allow us to identify portions of the model that are well or poorly constrained.

X-ray absorption and Raman spectroscopy studies of molybdenum environments in borosilicate waste glasses

DOE Office of Scientific and Technical Information (OSTI.GOV)

McKeown, David A.; Gan, Hao; Pegg, Ian L.

2017-05-01

Mo-containing high-level nuclear waste borosilicate glasses were investigated as part of an effort to improve Mo loading while avoiding yellow phase crystallization. Previous work showed that additions of vanadium decrease yellow phase formation and increases Mo solubility. X-ray absorption spectroscopy (XAS) and Raman spectroscopy were used to characterize Mo environments in HLW borosilicate glasses and to investigate possible structural relationships between Mo and V. Mo XAS spectra for the glasses indicate isolated tetrahedral Mo6+O4 with Mo-O distances near 1.75 Å. V XANES indicate tetrahedral V5+O4 as the dominant species. Raman spectra show composition dependent trends, where Mo-O symmetrical stretch modemore » frequencies (ν1) are sensitive to the mix of alkali and alkaline earth cations, decreasing by up to 10 cm-1 for glasses that change from Li+ to Na+ as the dominant network-modifying species. This indicates that MoO4 tetrahedra are isolated from the borosilicate network and are surrounded, at least partly, by Na+ and Li+. Secondary ν1 frequency effects, with changes up to 7 cm-1, were also observed with increasing V2O5 and MoO3 content. These secondary trends may indicate MoO4-MoO4 and MoO4-VO4 clustering, suggesting that V additions may stabilize Mo in the matrix with respect to yellow phase formation.« less

Multi-objective optimization for generating a weighted multi-model ensemble

NASA Astrophysics Data System (ADS)

Lee, H.

2017-12-01

Many studies have demonstrated that multi-model ensembles generally show better skill than each ensemble member. When generating weighted multi-model ensembles, the first step is measuring the performance of individual model simulations using observations. There is a consensus on the assignment of weighting factors based on a single evaluation metric. When considering only one evaluation metric, the weighting factor for each model is proportional to a performance score or inversely proportional to an error for the model. While this conventional approach can provide appropriate combinations of multiple models, the approach confronts a big challenge when there are multiple metrics under consideration. When considering multiple evaluation metrics, it is obvious that a simple averaging of multiple performance scores or model ranks does not address the trade-off problem between conflicting metrics. So far, there seems to be no best method to generate weighted multi-model ensembles based on multiple performance metrics. The current study applies the multi-objective optimization, a mathematical process that provides a set of optimal trade-off solutions based on a range of evaluation metrics, to combining multiple performance metrics for the global climate models and their dynamically downscaled regional climate simulations over North America and generating a weighted multi-model ensemble. NASA satellite data and the Regional Climate Model Evaluation System (RCMES) software toolkit are used for assessment of the climate simulations. Overall, the performance of each model differs markedly with strong seasonal dependence. Because of the considerable variability across the climate simulations, it is important to evaluate models systematically and make future projections by assigning optimized weighting factors to the models with relatively good performance. Our results indicate that the optimally weighted multi-model ensemble always shows better performance than an arithmetic ensemble mean and may provide reliable future projections.

Multi-sensor image fusion algorithm based on multi-objective particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Xie, Xia-zhu; Xu, Ya-wei

2017-11-01

On the basis of DT-CWT (Dual-Tree Complex Wavelet Transform - DT-CWT) theory, an approach based on MOPSO (Multi-objective Particle Swarm Optimization Algorithm) was proposed to objectively choose the fused weights of low frequency sub-bands. High and low frequency sub-bands were produced by DT-CWT. Absolute value of coefficients was adopted as fusion rule to fuse high frequency sub-bands. Fusion weights in low frequency sub-bands were used as particles in MOPSO. Spatial Frequency and Average Gradient were adopted as two kinds of fitness functions in MOPSO. The experimental result shows that the proposed approach performances better than Average Fusion and fusion methods based on local variance and local energy respectively in brightness, clarity and quantitative evaluation which includes Entropy, Spatial Frequency, Average Gradient and QAB/F.

A Multi-Objective Optimization Technique to Model the Pareto Front of Organic Dielectric Polymers

NASA Astrophysics Data System (ADS)

Gubernatis, J. E.; Mannodi-Kanakkithodi, A.; Ramprasad, R.; Pilania, G.; Lookman, T.

Multi-objective optimization is an area of decision making that is concerned with mathematical optimization problems involving more than one objective simultaneously. Here we describe two new Monte Carlo methods for this type of optimization in the context of their application to the problem of designing polymers with more desirable dielectric and optical properties. We present results of applying these Monte Carlo methods to a two-objective problem (maximizing the total static band dielectric constant and energy gap) and a three objective problem (maximizing the ionic and electronic contributions to the static band dielectric constant and energy gap) of a 6-block organic polymer. Our objective functions were constructed from high throughput DFT calculations of 4-block polymers, following the method of Sharma et al., Nature Communications 5, 4845 (2014) and Mannodi-Kanakkithodi et al., Scientific Reports, submitted. Our high throughput and Monte Carlo methods of analysis extend to general N-block organic polymers. This work was supported in part by the LDRD DR program of the Los Alamos National Laboratory and in part by a Multidisciplinary University Research Initiative (MURI) Grant from the Office of Naval Research.

Universal approximators for multi-objective direct policy search in water reservoir management problems: a comparative analysis

NASA Astrophysics Data System (ADS)

Giuliani, Matteo; Mason, Emanuele; Castelletti, Andrea; Pianosi, Francesca

2014-05-01

The optimal operation of water resources systems is a wide and challenging problem due to non-linearities in the model and the objectives, high dimensional state-control space, and strong uncertainties in the hydroclimatic regimes. The application of classical optimization techniques (e.g., SDP, Q-learning, gradient descent-based algorithms) is strongly limited by the dimensionality of the system and by the presence of multiple, conflicting objectives. This study presents a novel approach which combines Direct Policy Search (DPS) and Multi-Objective Evolutionary Algorithms (MOEAs) to solve high-dimensional state and control space problems involving multiple objectives. DPS, also known as parameterization-simulation-optimization in the water resources literature, is a simulation-based approach where the reservoir operating policy is first parameterized within a given family of functions and, then, the parameters optimized with respect to the objectives of the management problem. The selection of a suitable class of functions to which the operating policy belong to is a key step, as it might restrict the search for the optimal policy to a subspace of the decision space that does not include the optimal solution. In the water reservoir literature, a number of classes have been proposed. However, many of these rules are based largely on empirical or experimental successes and they were designed mostly via simulation and for single-purpose reservoirs. In a multi-objective context similar rules can not easily inferred from the experience and the use of universal function approximators is generally preferred. In this work, we comparatively analyze two among the most common universal approximators: artificial neural networks (ANN) and radial basis functions (RBF) under different problem settings to estimate their scalability and flexibility in dealing with more and more complex problems. The multi-purpose HoaBinh water reservoir in Vietnam, accounting for hydropower production and flood control, is used as a case study. Preliminary results show that the RBF policy parametrization is more effective than the ANN one. In particular, the approximated Pareto front obtained with RBF control policies successfully explores the full tradeoff space between the two conflicting objectives, while most of the ANN solutions results to be Pareto-dominated by the RBF ones.

77 FR 23536 - Self-Regulatory Organizations; NYSE Arca, Inc.; Notice of Filing and Immediate Effectiveness of...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-04-19

... Orders (``MOO Orders''). The text of the proposed rule change is available at the Exchange, www.nyse.com.... Purpose The Exchange proposes to amend NYSE Arca Equities Rule 7.31(t) to provide for LOO and MOO Orders... orders that are only executed within an auction.\\4\\ As proposed, LOO and MOO Orders would be defined...

Application of fuzzy theories to formulation of multi-objective design problems. [for helicopters

NASA Technical Reports Server (NTRS)

Dhingra, A. K.; Rao, S. S.; Miura, H.

1988-01-01

Much of the decision making in real world takes place in an environment in which the goals, the constraints, and the consequences of possible actions are not known precisely. In order to deal with imprecision quantitatively, the tools of fuzzy set theory can by used. This paper demonstrates the effectiveness of fuzzy theories in the formulation and solution of two types of helicopter design problems involving multiple objectives. The first problem deals with the determination of optimal flight parameters to accomplish a specified mission in the presence of three competing objectives. The second problem addresses the optimal design of the main rotor of a helicopter involving eight objective functions. A method of solving these multi-objective problems using nonlinear programming techniques is presented. Results obtained using fuzzy formulation are compared with those obtained using crisp optimization techniques. The outlined procedures are expected to be useful in situations where doubt arises about the exactness of permissible values, degree of credibility, and correctness of statements and judgements.

Irrigation water allocation optimization using multi-objective evolutionary algorithm (MOEA) - a review

NASA Astrophysics Data System (ADS)

Fanuel, Ibrahim Mwita; Mushi, Allen; Kajunguri, Damian

2018-03-01

This paper analyzes more than 40 papers with a restricted area of application of Multi-Objective Genetic Algorithm, Non-Dominated Sorting Genetic Algorithm-II and Multi-Objective Differential Evolution (MODE) to solve the multi-objective problem in agricultural water management. The paper focused on different application aspects which include water allocation, irrigation planning, crop pattern and allocation of available land. The performance and results of these techniques are discussed. The review finds that there is a potential to use MODE to analyzed the multi-objective problem, the application is more significance due to its advantage of being simple and powerful technique than any Evolutionary Algorithm. The paper concludes with the hopeful new trend of research that demand effective use of MODE; inclusion of benefits derived from farm byproducts and production costs into the model.

A multi-resolution strategy for a multi-objective deformable image registration framework that accommodates large anatomical differences

NASA Astrophysics Data System (ADS)

Alderliesten, Tanja; Bosman, Peter A. N.; Sonke, Jan-Jakob; Bel, Arjan

2014-03-01

Currently, two major challenges dominate the field of deformable image registration. The first challenge is related to the tuning of the developed methods to specific problems (i.e. how to best combine different objectives such as similarity measure and transformation effort). This is one of the reasons why, despite significant progress, clinical implementation of such techniques has proven to be difficult. The second challenge is to account for large anatomical differences (e.g. large deformations, (dis)appearing structures) that occurred between image acquisitions. In this paper, we study a framework based on multi-objective optimization to improve registration robustness and to simplify tuning for specific applications. Within this framework we specifically consider the use of an advanced model-based evolutionary algorithm for optimization and a dual-dynamic transformation model (i.e. two "non-fixed" grids: one for the source- and one for the target image) to accommodate for large anatomical differences. The framework computes and presents multiple outcomes that represent efficient trade-offs between the different objectives (a so-called Pareto front). In image processing it is common practice, for reasons of robustness and accuracy, to use a multi-resolution strategy. This is, however, only well-established for single-objective registration methods. Here we describe how such a strategy can be realized for our multi-objective approach and compare its results with a single-resolution strategy. For this study we selected the case of prone-supine breast MRI registration. Results show that the well-known advantages of a multi-resolution strategy are successfully transferred to our multi-objective approach, resulting in superior (i.e. Pareto-dominating) outcomes.

A hybrid solution approach for a multi-objective closed-loop logistics network under uncertainty

NASA Astrophysics Data System (ADS)

Mehrbod, Mehrdad; Tu, Nan; Miao, Lixin

2015-06-01

The design of closed-loop logistics (forward and reverse logistics) has attracted growing attention with the stringent pressures of customer expectations, environmental concerns and economic factors. This paper considers a multi-product, multi-period and multi-objective closed-loop logistics network model with regard to facility expansion as a facility location-allocation problem, which more closely approximates real-world conditions. A multi-objective mixed integer nonlinear programming formulation is linearized by defining new variables and adding new constraints to the model. By considering the aforementioned model under uncertainty, this paper develops a hybrid solution approach by combining an interactive fuzzy goal programming approach and robust counterpart optimization based on three well-known robust counterpart optimization formulations. Finally, this paper compares the results of the three formulations using different test scenarios and parameter-sensitive analysis in terms of the quality of the final solution, CPU time, the level of conservatism, the degree of closeness to the ideal solution, the degree of balance involved in developing a compromise solution, and satisfaction degree.

A Mixed Integer Linear Programming Approach to Electrical Stimulation Optimization Problems.

PubMed

Abouelseoud, Gehan; Abouelseoud, Yasmine; Shoukry, Amin; Ismail, Nour; Mekky, Jaidaa

2018-02-01

Electrical stimulation optimization is a challenging problem. Even when a single region is targeted for excitation, the problem remains a constrained multi-objective optimization problem. The constrained nature of the problem results from safety concerns while its multi-objectives originate from the requirement that non-targeted regions should remain unaffected. In this paper, we propose a mixed integer linear programming formulation that can successfully address the challenges facing this problem. Moreover, the proposed framework can conclusively check the feasibility of the stimulation goals. This helps researchers to avoid wasting time trying to achieve goals that are impossible under a chosen stimulation setup. The superiority of the proposed framework over alternative methods is demonstrated through simulation examples.

A multi-objective simulation-optimization model for in situ bioremediation of groundwater contamination: Application of bargaining theory

NASA Astrophysics Data System (ADS)

Raei, Ehsan; Nikoo, Mohammad Reza; Pourshahabi, Shokoufeh

2017-08-01

In the present study, a BIOPLUME III simulation model is coupled with a non-dominating sorting genetic algorithm (NSGA-II)-based model for optimal design of in situ groundwater bioremediation system, considering preferences of stakeholders. Ministry of Energy (MOE), Department of Environment (DOE), and National Disaster Management Organization (NDMO) are three stakeholders in the groundwater bioremediation problem in Iran. Based on the preferences of these stakeholders, the multi-objective optimization model tries to minimize: (1) cost; (2) sum of contaminant concentrations that violate standard; (3) contaminant plume fragmentation. The NSGA-II multi-objective optimization method gives Pareto-optimal solutions. A compromised solution is determined using fallback bargaining with impasse to achieve a consensus among the stakeholders. In this study, two different approaches are investigated and compared based on two different domains for locations of injection and extraction wells. At the first approach, a limited number of predefined locations is considered according to previous similar studies. At the second approach, all possible points in study area are investigated to find optimal locations, arrangement, and flow rate of injection and extraction wells. Involvement of the stakeholders, investigating all possible points instead of a limited number of locations for wells, and minimizing the contaminant plume fragmentation during bioremediation are new innovations in this research. Besides, the simulation period is divided into smaller time intervals for more efficient optimization. Image processing toolbox in MATLAB® software is utilized for calculation of the third objective function. In comparison with previous studies, cost is reduced using the proposed methodology. Dispersion of the contaminant plume is reduced in both presented approaches using the third objective function. Considering all possible points in the study area for determining the optimal locations of the wells in the second approach leads to more desirable results, i.e. decreasing the contaminant concentrations to a standard level and 20% to 40% cost reduction.

Nucleation and growth mechanism for flame synthesis of MoO2 hollow microchannels with nanometer wall thickness.

PubMed

Merchan-Merchan, Wilson; Saveliev, Alexei V; Taylor, Aaron M

2009-12-01

The growth and morphological evolution of molybdenum-oxide microstructures formed in the high temperature environment of a counter-flow oxy-fuel flame using molybdenum probes is studied. Experiments conducted using various probe retention times show the sequence of the morphological changes. The morphological row begins with micron size objects exhibiting polygonal cubic shape, develops into elongated channels, changes to large structures with leaf-like shape, and ends in dendritic structures. Time of probe-flame interaction is found to be a governing parameter controlling the wide variety of morphological patterns; a molecular level growth mechanism is attributed to their development. This study reveals that the structures are grown in several consecutive stages: material "evaporation and transportation", "transformation", "nucleation", "initial growth", "intermediate growth", and "final growth". XRD analysis shows that the chemical compositions of all structures correspond to MoO(2).

Genetic algorithm approaches for conceptual design of spacecraft systems including multi-objective optimization and design under uncertainty

NASA Astrophysics Data System (ADS)

Hassan, Rania A.

In the design of complex large-scale spacecraft systems that involve a large number of components and subsystems, many specialized state-of-the-art design tools are employed to optimize the performance of various subsystems. However, there is no structured system-level concept-architecting process. Currently, spacecraft design is heavily based on the heritage of the industry. Old spacecraft designs are modified to adapt to new mission requirements, and feasible solutions---rather than optimal ones---are often all that is achieved. During the conceptual phase of the design, the choices available to designers are predominantly discrete variables describing major subsystems' technology options and redundancy levels. The complexity of spacecraft configurations makes the number of the system design variables that need to be traded off in an optimization process prohibitive when manual techniques are used. Such a discrete problem is well suited for solution with a Genetic Algorithm, which is a global search technique that performs optimization-like tasks. This research presents a systems engineering framework that places design requirements at the core of the design activities and transforms the design paradigm for spacecraft systems to a top-down approach rather than the current bottom-up approach. To facilitate decision-making in the early phases of the design process, the population-based search nature of the Genetic Algorithm is exploited to provide computationally inexpensive---compared to the state-of-the-practice---tools for both multi-objective design optimization and design optimization under uncertainty. In terms of computational cost, those tools are nearly on the same order of magnitude as that of standard single-objective deterministic Genetic Algorithm. The use of a multi-objective design approach provides system designers with a clear tradeoff optimization surface that allows them to understand the effect of their decisions on all the design objectives under consideration simultaneously. Incorporating uncertainties avoids large safety margins and unnecessary high redundancy levels. The focus on low computational cost for the optimization tools stems from the objective that improving the design of complex systems should not be achieved at the expense of a costly design methodology.

Improved performance of organic light-emitting diodes with MoO3 interlayer by oblique angle deposition.

PubMed

Liu, S W; Divayana, Y; Sun, X W; Wang, Y; Leck, K S; Demir, H V

2011-02-28

We fabricated and demonstrated improved organic light emitting diodes (OLEDs) in a thin film architecture of indium tin oxide (ITO)/ molybdenum trioxide (MoO3) (20 nm)/N,N'-Di(naphth-2-yl)-N,N'-diphenyl-benzidine (NPB) (50 nm)/ tris-(8-hydroxyquinoline) (Alq3) (70 nm)/Mg:Ag (200 nm) using an oblique angle deposition technique by which MoO3 was deposited at oblique angles (θ) with respect to the surface normal. It was found that, without sacrificing the power efficiency of the device, the device current efficiency and external quantum efficiency were significantly enhanced at an oblique deposition angle of θ=60° for MoO3.

Knowledge Discovery for Transonic Regional-Jet Wing through Multidisciplinary Design Exploration

NASA Astrophysics Data System (ADS)

Chiba, Kazuhisa; Obayashi, Shigeru; Morino, Hiroyuki

Data mining is an important facet of solving multi-objective optimization problem. Because it is one of the effective manner to discover the design knowledge in the multi-objective optimization problem which obtains large data. In the present study, data mining has been performed for a large-scale and real-world multidisciplinary design optimization (MDO) to provide knowledge regarding the design space. The MDO among aerodynamics, structures, and aeroelasticity of the regional-jet wing was carried out using high-fidelity evaluation models on the adaptive range multi-objective genetic algorithm. As a result, nine non-dominated solutions were generated and used for tradeoff analysis among three objectives. All solutions evaluated during the evolution were analyzed for the tradeoffs and influence of design variables using a self-organizing map to extract key features of the design space. Although the MDO results showed the inverted gull-wings as non-dominated solutions, one of the key features found by data mining was the non-gull wing geometry. When this knowledge was applied to one optimum solution, the resulting design was found to have better performance compared with the original geometry designed in the conventional manner.

A multi-objective approach to solid waste management.

PubMed

Galante, Giacomo; Aiello, Giuseppe; Enea, Mario; Panascia, Enrico

2010-01-01

The issue addressed in this paper consists in the localization and dimensioning of transfer stations, which constitute a necessary intermediate level in the logistic chain of the solid waste stream, from municipalities to the incinerator. Contextually, the determination of the number and type of vehicles involved is carried out in an integrated optimization approach. The model considers both initial investment and operative costs related to transportation and transfer stations. Two conflicting objectives are evaluated, the minimization of total cost and the minimization of environmental impact, measured by pollution. The design of the integrated waste management system is hence approached in a multi-objective optimization framework. To determine the best means of compromise, goal programming, weighted sum and fuzzy multi-objective techniques have been employed. The proposed analysis highlights how different attitudes of the decision maker towards the logic and structure of the problem result in the employment of different methodologies and the obtaining of different results. The novel aspect of the paper lies in the proposal of an effective decision support system for operative waste management, rather than a further contribution to the transportation problem. The model was applied to the waste management of optimal territorial ambit (OTA) of Palermo (Italy). 2010 Elsevier Ltd. All rights reserved.

A multi-objective approach to solid waste management

DOE Office of Scientific and Technical Information (OSTI.GOV)

Galante, Giacomo, E-mail: galante@dtpm.unipa.i; Aiello, Giuseppe; Enea, Mario

2010-08-15

The issue addressed in this paper consists in the localization and dimensioning of transfer stations, which constitute a necessary intermediate level in the logistic chain of the solid waste stream, from municipalities to the incinerator. Contextually, the determination of the number and type of vehicles involved is carried out in an integrated optimization approach. The model considers both initial investment and operative costs related to transportation and transfer stations. Two conflicting objectives are evaluated, the minimization of total cost and the minimization of environmental impact, measured by pollution. The design of the integrated waste management system is hence approached inmore » a multi-objective optimization framework. To determine the best means of compromise, goal programming, weighted sum and fuzzy multi-objective techniques have been employed. The proposed analysis highlights how different attitudes of the decision maker towards the logic and structure of the problem result in the employment of different methodologies and the obtaining of different results. The novel aspect of the paper lies in the proposal of an effective decision support system for operative waste management, rather than a further contribution to the transportation problem. The model was applied to the waste management of optimal territorial ambit (OTA) of Palermo (Italy).« less

Optimal Energy Management for a Smart Grid using Resource-Aware Utility Maximization

NASA Astrophysics Data System (ADS)

Abegaz, Brook W.; Mahajan, Satish M.; Negeri, Ebisa O.

2016-06-01

Heterogeneous energy prosumers are aggregated to form a smart grid based energy community managed by a central controller which could maximize their collective energy resource utilization. Using the central controller and distributed energy management systems, various mechanisms that harness the power profile of the energy community are developed for optimal, multi-objective energy management. The proposed mechanisms include resource-aware, multi-variable energy utility maximization objectives, namely: (1) maximizing the net green energy utilization, (2) maximizing the prosumers' level of comfortable, high quality power usage, and (3) maximizing the economic dispatch of energy storage units that minimize the net energy cost of the energy community. Moreover, an optimal energy management solution that combines the three objectives has been implemented by developing novel techniques of optimally flexible (un)certainty projection and appliance based pricing decomposition in an IBM ILOG CPLEX studio. A real-world, per-minute data from an energy community consisting of forty prosumers in Amsterdam, Netherlands is used. Results show that each of the proposed mechanisms yields significant increases in the aggregate energy resource utilization and welfare of prosumers as compared to traditional peak-power reduction methods. Furthermore, the multi-objective, resource-aware utility maximization approach leads to an optimal energy equilibrium and provides a sustainable energy management solution as verified by the Lagrangian method. The proposed resource-aware mechanisms could directly benefit emerging energy communities in the world to attain their energy resource utilization targets.

Structural Optimization of a Force Balance Using a Computational Experiment Design

NASA Technical Reports Server (NTRS)

Parker, P. A.; DeLoach, R.

2002-01-01

This paper proposes a new approach to force balance structural optimization featuring a computational experiment design. Currently, this multi-dimensional design process requires the designer to perform a simplification by executing parameter studies on a small subset of design variables. This one-factor-at-a-time approach varies a single variable while holding all others at a constant level. Consequently, subtle interactions among the design variables, which can be exploited to achieve the design objectives, are undetected. The proposed method combines Modern Design of Experiments techniques to direct the exploration of the multi-dimensional design space, and a finite element analysis code to generate the experimental data. To efficiently search for an optimum combination of design variables and minimize the computational resources, a sequential design strategy was employed. Experimental results from the optimization of a non-traditional force balance measurement section are presented. An approach to overcome the unique problems associated with the simultaneous optimization of multiple response criteria is described. A quantitative single-point design procedure that reflects the designer's subjective impression of the relative importance of various design objectives, and a graphical multi-response optimization procedure that provides further insights into available tradeoffs among competing design objectives are illustrated. The proposed method enhances the intuition and experience of the designer by providing new perspectives on the relationships between the design variables and the competing design objectives providing a systematic foundation for advancements in structural design.

Efficient Transfer Doping of Carbon Nanotube Forests by MoO3.

PubMed

Esconjauregui, Santiago; D'Arsié, Lorenzo; Guo, Yuzheng; Yang, Junwei; Sugime, Hisashi; Caneva, Sabina; Cepek, Cinzia; Robertson, John

2015-10-27

We dope nanotube forests using evaporated MoO3 and observe the forest resistivity to decrease by 2 orders of magnitude, reaching values as low as ∼5 × 10(-5) Ωcm, thus approaching that of copper. Using in situ photoemission spectroscopy, we determine the minimum necessary MoO3 thickness to dope a forest and study the underlying doping mechanism. Homogenous coating and tube compaction emerge as key factors for decreasing the forest resistivity. When all nanotubes are fully coated with MoO3 and packed, conduction channels are created both inside the nanotubes and on the outside oxide layer. This is supported by density functional theory calculations, which show a shift of the Fermi energy of the nanotubes and the conversion of the oxide into a layer of metallic character. MoO3 doping removes the need for chirality control during nanotube growth and represents a step forward toward the use of forests in next-generation electronics and in power cables or conductive polymers.

Sol-gel spin coated well adhered MoO3 thin films as an alternative counter electrode for dye sensitized solar cells

NASA Astrophysics Data System (ADS)

Mutta, Geeta R.; Popuri, Srinivasa R.; Wilson, John I. B.; Bennett, Nick S.

2016-11-01

In this work, we aim to develop a viable, inexpensive and non-toxic material for counter electrodes in dye sensitized solar cells (DSSCs). We employed an ultra-simple synthesis process to deposit MoO3 thin films at low temperature by sol-gel spin coating technique. These MoO3 films showed good transparency. It is predicted that there will be 150 times reduction of precursors cost by realizing MoO3 thin films as a counter electrode in DSSCs compared to commercial Pt. We achieved a device efficiency of about 20 times higher than that of the previous reported values. In summary we develop a simple low cost preparation of MoO3 films with an easily scaled up process along with good device efficiency. This work encourages the development of novel and relatively new materials and paves the way for massive reduction of industrial costs which is a prime step for commercialization of DSSCs.

Amorphous/crystalline hybrid MoO2 nanosheets for high-energy lithium-ion capacitors.

PubMed

Zhao, Xu; Wang, Hong-En; Cao, Jian; Cai, Wei; Sui, Jiehe

2017-09-26

A carbon-free MoO 2 nanosheet with amorphous/crystalline hybrid domain was synthesized, and demonstrated to be an efficient host material for lithium-ion capacitors. Discrepant crystallinity in MoO 2 shows unique boundaries, which can improve Li-ion diffusion through the electrode. Improved rate capacities and cycling stability open the door to design of high-performance lithium ion capacitor bridging batteries and supercapacitors.

Facile Preparation of Nano-Bi2MoO6/Diatomite Composite for Enhancing Photocatalytic Performance under Visible Light Irradiation

PubMed Central

Gong, Jiuyan; Liu, Jianshe; Song, Wendong; Ji, Lili

2018-01-01

In this work, a new nano-Bi2MoO6/diatomite composite photocatalyst was successfully synthesized by a facile solvothermal method. Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and UV-vis diffuse reflection spectroscopy (DRS) were employed to investigate the morphology, crystal structure, and optical properties. It was shown that nanometer-scaled Bi2MoO6 crystals were well-deposited on the surface of Bi2MoO6/diatomite. The photocatalytic activity of the obtained samples was evaluated by the degradation of rhodamine B (RhB) under the visible light (λ > 420 nm) irradiation. Moreover, trapping experiments were performed to investigate the possible photocatalytic reaction mechanism. The results showed that the nano-Bi2MoO6/diatomite composite with the mass ratio of Bi2MoO6 to diatomaceous earth of 70% exhibited the highest activity, and the RhB degradation efficiency reached 97.6% within 60 min. The main active species were revealed to be h+ and•O2−. As a photocatalytic reactor, its recycling performance showed a good stability and reusability. This new composite photocatalyst material holds great promise in the engineering field for the environmental remediation. PMID:29425138

New Approach for Gas Phase Synthesis and Growth Mechanism of MoS2 Fullerene-like Nanoparticles

NASA Astrophysics Data System (ADS)

Zak, Alla; Feldman, Yishay; Alperovich, Vladimir; Rosentsveig, Rita; Tenne, Reshef

2002-10-01

Inorganic fullerene-like (hollow onion-like) nanoparticles (IF) and nanotubes are of significant interest over the past few years due to their unusual crystallographic morphology and their interesting physical properties. The synthesis of inorganic fullerene-like spherical MoS2 nanoparticles (IF-MoS2) of 5-300nm in diameter was studied in the present work. This process is based on the previous formation of suboxide (MoO3-x) 5-300nm nanoparticles and their subsequent sulfidization. During the sulfidization process the overall geometrical parameters of the suboxide nanoparticles are preserved. The oxide nanoparticles were obtained in-situ by the condensation of the evaporated MoO3 powder precursor. The condensation was provoked not by cooling (conventional method for nano-size particle formation), but by a chemical reaction (partial reduction of the MoO3 vapor by hydrogen). In this case the vapor pressure of the product (MoO2) was much lower than that of the precursor (MoO3). Based on the comprehensive understanding of the IF-MoS2 growth mechanism from MoO3 powder, a gas phase reactor, which allowed reproducible preparation of a pure IF-MoS2 phase (up to 100mg/batch) with controllable sizes, is demonstrated. Further scale-up of this production is underway.

Phase transitions in (NH4)2MoO2F4 crystal

NASA Astrophysics Data System (ADS)

Krylov, Alexander; Laptash, Natalia; Vtyurin, Alexander; Krylova, Svetlana

2016-11-01

The mechanisms of temperature and high pressure phase transitions have been studied by Raman spectroscopy. Room temperature (295 K) experiments under high hydrostatic pressure up to 3.6 GPa for (NH4)2 MoO2 F4 have been carried out. Experimental data indicates a phase transition into a new high-pressure phase for (NH4)2 MoO2 F4 at 1.2 GPa. This phase transition is related to the ordering anion octahedron groups [MoO2 F4]2- and is not associated with ammonium group. Raman spectra of small non-oriented crystals ranging from 10 to 350 K have been observed. The experiment shows anion groups [MoO2 F4]2- and ammonium in high temperature phase are disordered. The phase transition at T1 = 269.8 K is of the first-order, close to the tricritical point. The first temperature phase transition is related to the ordering anion octahedron groups [MoO2 F4]2-. Second phase transitions T2 = 180 K are associated with the ordering of ammonium. The data presented within this study demonstrate that 2D correlation analysis combined with traditional Raman spectroscopy are powerful tool to study phase transitions in the crystals.

Working Group 3: Operations Analysis for Systems of System within a Networked C2 Context: Introduction, Purpose, and Approach

DTIC Science & Technology

2012-01-01

1200 Session 3 – C2 Framework, OR Methods MOOs, MOEs, MOPs Development Case Study – 1300-1630 Session 4 – Findings...Objective 1: Understand the impact of the application of traditional operational research techniques to networked C2 systems. • Objective 2: Develop ...for the network. 3. Cost measures including cost and time to implement the solution (for example, a basic rule-of-thumb I use for development

Fabrication of potato-like silver molybdate microstructures for photocatalytic degradation of chronic toxicity ciprofloxacin and highly selective electrochemical detection of H2O2

NASA Astrophysics Data System (ADS)

Kumar, J. Vinoth; Karthik, R.; Chen, Shen-Ming; Muthuraj, V.; Karuppiah, Chelladurai

2016-09-01

In the present work, potato-like silver molybdate (Ag2MoO4) microstructures were synthesized through a simple hydrothermal method. The microstructures of Ag2MoO4 were characterized by various analytical and spectroscopic techniques such as XRD, FTIR, Raman, SEM, EDX and XPS. Interestingly, the as-prepared Ag2MoO4 showed excellent photocatalytic and electrocatalytic activity for the degradation of ciprofloxacin (CIP) and electrochemical detection of hydrogen peroxide (H2O2), respectively. The ultraviolet-visible (UV-Vis) spectroscopy results revealed that the potato-like Ag2MoO4 microstructures could offer a high photocatalytic activity towards the degradation of CIP under UV-light illumination, leads to rapid degradation within 40 min with a degradation rate of above 98%. In addition, the cyclic voltammetry (CV) and amperometry studies were realized that the electrochemical performance of Ag2MoO4 modified electrode toward H2O2 detection. Our H2O2 sensor shows a wide linear range and lower detection limit of 0.04-240 μM and 0.03 μM, respectively. The Ag2MoO4 modified electrode exhibits a high selectivity towards the detection of H2O2 in the presence of different biological interferences. These results suggested that the development of potato-like Ag2MoO4 microstructure could be an efficient photocatalyst as well as electrocatalyst in the potential application of environmental, biomedical and pharmaceutical samples.

Multi-Objective Optimization of Friction Stir Welding Process Parameters of AA6061-T6 and AA7075-T6 Using a Biogeography Based Optimization Algorithm

PubMed Central

Tamjidy, Mehran; Baharudin, B. T. Hang Tuah; Paslar, Shahla; Matori, Khamirul Amin; Sulaiman, Shamsuddin; Fadaeifard, Firouz

2017-01-01

The development of Friction Stir Welding (FSW) has provided an alternative approach for producing high-quality welds, in a fast and reliable manner. This study focuses on the mechanical properties of the dissimilar friction stir welding of AA6061-T6 and AA7075-T6 aluminum alloys. The FSW process parameters such as tool rotational speed, tool traverse speed, tilt angle, and tool offset influence the mechanical properties of the friction stir welded joints significantly. A mathematical regression model is developed to determine the empirical relationship between the FSW process parameters and mechanical properties, and the results are validated. In order to obtain the optimal values of process parameters that simultaneously optimize the ultimate tensile strength, elongation, and minimum hardness in the heat affected zone (HAZ), a metaheuristic, multi objective algorithm based on biogeography based optimization is proposed. The Pareto optimal frontiers for triple and dual objective functions are obtained and the best optimal solution is selected through using two different decision making techniques, technique for order of preference by similarity to ideal solution (TOPSIS) and Shannon’s entropy. PMID:28772893

Multi-Objective Optimization of Friction Stir Welding Process Parameters of AA6061-T6 and AA7075-T6 Using a Biogeography Based Optimization Algorithm.

PubMed

Tamjidy, Mehran; Baharudin, B T Hang Tuah; Paslar, Shahla; Matori, Khamirul Amin; Sulaiman, Shamsuddin; Fadaeifard, Firouz

2017-05-15

The development of Friction Stir Welding (FSW) has provided an alternative approach for producing high-quality welds, in a fast and reliable manner. This study focuses on the mechanical properties of the dissimilar friction stir welding of AA6061-T6 and AA7075-T6 aluminum alloys. The FSW process parameters such as tool rotational speed, tool traverse speed, tilt angle, and tool offset influence the mechanical properties of the friction stir welded joints significantly. A mathematical regression model is developed to determine the empirical relationship between the FSW process parameters and mechanical properties, and the results are validated. In order to obtain the optimal values of process parameters that simultaneously optimize the ultimate tensile strength, elongation, and minimum hardness in the heat affected zone (HAZ), a metaheuristic, multi objective algorithm based on biogeography based optimization is proposed. The Pareto optimal frontiers for triple and dual objective functions are obtained and the best optimal solution is selected through using two different decision making techniques, technique for order of preference by similarity to ideal solution (TOPSIS) and Shannon's entropy.

Ag nanoparticle decorated molybdenum oxide structures: growth, characterization, DFT studies and their application to enhanced field emission

NASA Astrophysics Data System (ADS)

Guha, Puspendu; Ghosh, Arnab; Thapa, Ranjit; Mathan Kumar, E.; Kirishwaran, Sabari; Singh, Ranveer; Satyam, Parlapalli V.

2017-10-01

We report a simple single step growth of α-MoO3 structures and energetically suitable site specific Ag nanoparticle (NP) decorated α-MoO3 structures on varied substrates, having almost similar morphologies and oxygen vacancies. We elucidate possible growth mechanisms in light of experimental findings and density functional theory (DFT) calculations. We experimentally establish and verified by DFT calculations that the MoO3(010) surface is a weakly interacting and stable surface compared to other orientations. From DFT study, the binding energy is found to be higher for (100) and (001) surfaces (˜-0.98 eV), compared to the (010) surface (˜-0.15 eV) and thus it is likely that Ag NP formation is not favorable on the MoO3(010) surface. The Ag decorated MoO3 (Ag-MoO3) nanostructured sample shows enhanced field emission properties with an approimately 2.1 times lower turn-on voltage of 1.67 V μm-1 and one order higher field enhancement factor (β) of 8.6 × 104 compared to the MoO3 sample without Ag incorporation. From Kelvin probe force microscopy measurements, the average local work function (Φ) is found to be approximately 0.47 eV smaller for the Ag-MoO3 sample (˜5.70 ± 0.05 eV) compared to the MoO3 sample (˜6.17 ± 0.05 eV) and the reduction in Φ can be attributed to the shifting Fermi level of MoO3 toward vacuum via electron injection from Ag NPs to MoO3. The presence of oxygen vacancies together with Ag NPs lead to the highest β and lowest turn-on field among the reported values under the MoO3 emitter category.

Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm.

PubMed

Rani, R Ranjani; Ramyachitra, D

2016-12-01

Multiple sequence alignment (MSA) is a widespread approach in computational biology and bioinformatics. MSA deals with how the sequences of nucleotides and amino acids are sequenced with possible alignment and minimum number of gaps between them, which directs to the functional, evolutionary and structural relationships among the sequences. Still the computation of MSA is a challenging task to provide an efficient accuracy and statistically significant results of alignments. In this work, the Bacterial Foraging Optimization Algorithm was employed to align the biological sequences which resulted in a non-dominated optimal solution. It employs Multi-objective, such as: Maximization of Similarity, Non-gap percentage, Conserved blocks and Minimization of gap penalty. BAliBASE 3.0 benchmark database was utilized to examine the proposed algorithm against other methods In this paper, two algorithms have been proposed: Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC) and Bacterial Foraging Optimization Algorithm. It was found that Hybrid Genetic Algorithm with Artificial Bee Colony performed better than the existing optimization algorithms. But still the conserved blocks were not obtained using GA-ABC. Then BFO was used for the alignment and the conserved blocks were obtained. The proposed Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO) was compared with widely used MSA methods Clustal Omega, Kalign, MUSCLE, MAFFT, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC). The final results show that the proposed MO-BFO algorithm yields better alignment than most widely used methods. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

System design and improvement of an emergency department using Simulation-Based Multi-Objective Optimization

NASA Astrophysics Data System (ADS)

Goienetxea Uriarte, A.; Ruiz Zúñiga, E.; Urenda Moris, M.; Ng, A. H. C.

2015-05-01

Discrete Event Simulation (DES) is nowadays widely used to support decision makers in system analysis and improvement. However, the use of simulation for improving stochastic logistic processes is not common among healthcare providers. The process of improving healthcare systems involves the necessity to deal with trade-off optimal solutions that take into consideration a multiple number of variables and objectives. Complementing DES with Multi-Objective Optimization (SMO) creates a superior base for finding these solutions and in consequence, facilitates the decision-making process. This paper presents how SMO has been applied for system improvement analysis in a Swedish Emergency Department (ED). A significant number of input variables, constraints and objectives were considered when defining the optimization problem. As a result of the project, the decision makers were provided with a range of optimal solutions which reduces considerably the length of stay and waiting times for the ED patients. SMO has proved to be an appropriate technique to support healthcare system design and improvement processes. A key factor for the success of this project has been the involvement and engagement of the stakeholders during the whole process.

Multi-Objective Optimization of a Turbofan for an Advanced, Single-Aisle Transport

NASA Technical Reports Server (NTRS)

Berton, Jeffrey J.; Guynn, Mark D.

2012-01-01

Considerable interest surrounds the design of the next generation of single-aisle commercial transports in the Boeing 737 and Airbus A320 class. Aircraft designers will depend on advanced, next-generation turbofan engines to power these airplanes. The focus of this study is to apply single- and multi-objective optimization algorithms to the conceptual design of ultrahigh bypass turbofan engines for this class of aircraft, using NASA s Subsonic Fixed Wing Project metrics as multidisciplinary objectives for optimization. The independent design variables investigated include three continuous variables: sea level static thrust, wing reference area, and aerodynamic design point fan pressure ratio, and four discrete variables: overall pressure ratio, fan drive system architecture (i.e., direct- or gear-driven), bypass nozzle architecture (i.e., fixed- or variable geometry), and the high- and low-pressure compressor work split. Ramp weight, fuel burn, noise, and emissions are the parameters treated as dependent objective functions. These optimized solutions provide insight to the ultrahigh bypass engine design process and provide information to NASA program management to help guide its technology development efforts.

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.

2005-01-01

A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Design for sustainability of industrial symbiosis based on emergy and multi-objective particle swarm optimization.

PubMed

Ren, Jingzheng; Liang, Hanwei; Dong, Liang; Sun, Lu; Gao, Zhiqiu

2016-08-15

Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied by the proposed method, a few of compromises between high profitability and high sustainability can be obtained for the decision-makers/stakeholders to make decision. Copyright © 2016 Elsevier B.V. All rights reserved.

An optimal generic model for multi-parameters and big data optimizing: a laboratory experimental study

NASA Astrophysics Data System (ADS)

Utama, D. N.; Ani, N.; Iqbal, M. M.

2018-03-01

Optimization is a process for finding parameter (parameters) that is (are) able to deliver an optimal value for an objective function. Seeking an optimal generic model for optimizing is a computer science study that has been being practically conducted by numerous researchers. Generic model is a model that can be technically operated to solve any varieties of optimization problem. By using an object-oriented method, the generic model for optimizing was constructed. Moreover, two types of optimization method, simulated-annealing and hill-climbing, were functioned in constructing the model and compared to find the most optimal one then. The result said that both methods gave the same result for a value of objective function and the hill-climbing based model consumed the shortest running time.

Multi-objective optimization for model predictive control.

PubMed

Wojsznis, Willy; Mehta, Ashish; Wojsznis, Peter; Thiele, Dirk; Blevins, Terry

2007-06-01

This paper presents a technique of multi-objective optimization for Model Predictive Control (MPC) where the optimization has three levels of the objective function, in order of priority: handling constraints, maximizing economics, and maintaining control. The greatest weights are assigned dynamically to control or constraint variables that are predicted to be out of their limits. The weights assigned for economics have to out-weigh those assigned for control objectives. Control variables (CV) can be controlled at fixed targets or within one- or two-sided ranges around the targets. Manipulated Variables (MV) can have assigned targets too, which may be predefined values or current actual values. This MV functionality is extremely useful when economic objectives are not defined for some or all the MVs. To achieve this complex operation, handle process outputs predicted to go out of limits, and have a guaranteed solution for any condition, the technique makes use of the priority structure, penalties on slack variables, and redefinition of the constraint and control model. An engineering implementation of this approach is shown in the MPC embedded in an industrial control system. The optimization and control of a distillation column, the standard Shell heavy oil fractionator (HOF) problem, is adequately achieved with this MPC.

Swarm intelligence for multi-objective optimization of synthesis gas production

NASA Astrophysics Data System (ADS)

Ganesan, T.; Vasant, P.; Elamvazuthi, I.; Ku Shaari, Ku Zilati

2012-11-01

In the chemical industry, the production of methanol, ammonia, hydrogen and higher hydrocarbons require synthesis gas (or syn gas). The main three syn gas production methods are carbon dioxide reforming (CRM), steam reforming (SRM) and partial-oxidation of methane (POM). In this work, multi-objective (MO) optimization of the combined CRM and POM was carried out. The empirical model and the MO problem formulation for this combined process were obtained from previous works. The central objectives considered in this problem are methane conversion, carbon monoxide selectivity and the hydrogen to carbon monoxide ratio. The MO nature of the problem was tackled using the Normal Boundary Intersection (NBI) method. Two techniques (Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO)) were then applied in conjunction with the NBI method. The performance of the two algorithms and the quality of the solutions were gauged by using two performance metrics. Comparative studies and results analysis were then carried out on the optimization results.

Multiobjective optimization for Groundwater Nitrate Pollution Control. Application to El Salobral-Los Llanos aquifer (Spain).

NASA Astrophysics Data System (ADS)

Llopis-Albert, C.; Peña-Haro, S.; Pulido-Velazquez, M.; Molina, J.

2012-04-01

Water quality management is complex due to the inter-relations between socio-political, environmental and economic constraints and objectives. In order to choose an appropriate policy to reduce nitrate pollution in groundwater it is necessary to consider different objectives, often in conflict. In this paper, a hydro-economic modeling framework, based on a non-linear optimization(CONOPT) technique, which embeds simulation of groundwater mass transport through concentration response matrices, is used to study optimal policies for groundwater nitrate pollution control under different objectives and constraints. Three objectives were considered: recovery time (for meeting the environmental standards, as required by the EU Water Framework Directive and Groundwater Directive), maximum nitrate concentration in groundwater, and net benefits in agriculture. Another criterion was added: the reliability of meeting the nitrate concentration standards. The approach allows deriving the trade-offs between the reliability of meeting the standard, the net benefits from agricultural production and the recovery time. Two different policies were considered: spatially distributed fertilizer standards or quotas (obtained through multi-objective optimization) and fertilizer prices. The multi-objective analysis allows to compare the achievement of the different policies, Pareto fronts (or efficiency frontiers) and tradeoffs for the set of mutually conflicting objectives. The constraint method is applied to generate the set of non-dominated solutions. The multi-objective framework can be used to design groundwater management policies taking into consideration different stakeholders' interests (e.g., policy makers, agricultures or environmental groups). The methodology was applied to the El Salobral-Los Llanos aquifer in Spain. Over the past 30 years the area has undertaken a significant socioeconomic development, mainly due to the intensive groundwater use for irrigated crops, which has provoked a steady decline of groundwater levels as well as high nitrate concentrations at certain locations (above 50 mg/l.). The results showed the usefulness of this multi-objective hydro-economic approach for designing sustainable nitrate pollution control policies (as fertilizer quotas or efficient fertilizer pricing policies) with insight into the economic cost of satisfying the environmental constraints and the tradeoffs with different time horizons.

Non-Native Speaker Interaction Management Strategies in a Network-Based Virtual Environment

ERIC Educational Resources Information Center

Peterson, Mark

2008-01-01

This article investigates the dyad-based communication of two groups of non-native speakers (NNSs) of English involved in real time interaction in a type of text-based computer-mediated communication (CMC) tool known as a MOO. The object of this semester long study was to examine the ways in which the subjects managed their L2 interaction during…

Optimization of multi-objective integrated process planning and scheduling problem using a priority based optimization algorithm

NASA Astrophysics Data System (ADS)

Ausaf, Muhammad Farhan; Gao, Liang; Li, Xinyu

2015-12-01

For increasing the overall performance of modern manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the complexity of handling process planning and scheduling simultaneously, most of the research work has been limited to solving the integrated process planning and scheduling (IPPS) problem for a single objective function. As there are many conflicting objectives when dealing with process planning and scheduling, real world problems cannot be fully captured considering only a single objective for optimization. Therefore considering multi-objective IPPS (MOIPPS) problem is inevitable. Unfortunately, only a handful of research papers are available on solving MOIPPS problem. In this paper, an optimization algorithm for solving MOIPPS problem is presented. The proposed algorithm uses a set of dispatching rules coupled with priority assignment to optimize the IPPS problem for various objectives like makespan, total machine load, total tardiness, etc. A fixed sized external archive coupled with a crowding distance mechanism is used to store and maintain the non-dominated solutions. To compare the results with other algorithms, a C-matric based method has been used. Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed method is an efficient approach for solving the MOIPPS problem.

Structural Optimization for Reliability Using Nonlinear Goal Programming

NASA Technical Reports Server (NTRS)

El-Sayed, Mohamed E.

1999-01-01

This report details the development of a reliability based multi-objective design tool for solving structural optimization problems. Based on two different optimization techniques, namely sequential unconstrained minimization and nonlinear goal programming, the developed design method has the capability to take into account the effects of variability on the proposed design through a user specified reliability design criterion. In its sequential unconstrained minimization mode, the developed design tool uses a composite objective function, in conjunction with weight ordered design objectives, in order to take into account conflicting and multiple design criteria. Multiple design criteria of interest including structural weight, load induced stress and deflection, and mechanical reliability. The nonlinear goal programming mode, on the other hand, provides for a design method that eliminates the difficulty of having to define an objective function and constraints, while at the same time has the capability of handling rank ordered design objectives or goals. For simulation purposes the design of a pressure vessel cover plate was undertaken as a test bed for the newly developed design tool. The formulation of this structural optimization problem into sequential unconstrained minimization and goal programming form is presented. The resulting optimization problem was solved using: (i) the linear extended interior penalty function method algorithm; and (ii) Powell's conjugate directions method. Both single and multi-objective numerical test cases are included demonstrating the design tool's capabilities as it applies to this design problem.

The development of multi-objective optimization model for excess bagasse utilization: A case study for Thailand

DOE Office of Scientific and Technical Information (OSTI.GOV)

Buddadee, Bancha; Wirojanagud, Wanpen; Watts, Daniel J.

In this paper, a multi-objective optimization model is proposed as a tool to assist in deciding for the proper utilization scheme of excess bagasse produced in sugarcane industry. Two major scenarios for excess bagasse utilization are considered in the optimization. The first scenario is the typical situation when excess bagasse is used for the onsite electricity production. In case of the second scenario, excess bagasse is processed for the offsite ethanol production. Then the ethanol is blended with an octane rating of 91 gasoline by a portion of 10% and 90% by volume respectively and the mixture is used asmore » alternative fuel for gasoline vehicles in Thailand. The model proposed in this paper called 'Environmental System Optimization' comprises the life cycle impact assessment of global warming potential (GWP) and the associated cost followed by the multi-objective optimization which facilitates in finding out the optimal proportion of the excess bagasse processed in each scenario. Basic mathematical expressions for indicating the GWP and cost of the entire process of excess bagasse utilization are taken into account in the model formulation and optimization. The outcome of this study is the methodology developed for decision-making concerning the excess bagasse utilization available in Thailand in view of the GWP and economic effects. A demonstration example is presented to illustrate the advantage of the methodology which may be used by the policy maker. The methodology developed is successfully performed to satisfy both environmental and economic objectives over the whole life cycle of the system. It is shown in the demonstration example that the first scenario results in positive GWP while the second scenario results in negative GWP. The combination of these two scenario results in positive or negative GWP depending on the preference of the weighting given to each objective. The results on economics of all scenarios show the satisfied outcomes.« less

Transient control for cascaded EDFAs by using a multi-objective optimization approach

NASA Astrophysics Data System (ADS)

Freitas, Marcio; Givigi, Sidney N., Jr.; Klein, Jackson; Calmon, Luiz C.; de Almeida, Ailson R.

2004-11-01

Erbium-doped fiber amplifiers (EDFA) have been used for some years now in building effective optical systems for the most diverse applications. For some applications, it is necessary to introduce some feedback control laws in order to avoid the generation of transients that could create impairments in the system. In this paper, we use a multi-objective optimization approach based on genetic algorithms, to study the introduction of proportional-derivative (PD) controllers into systems of cascaded EDFAs. We compare the use of individual controllers for each amplifier to the use of controllers to sets of amplifiers.

Optimal allocation of industrial PV-storage micro-grid considering important load

NASA Astrophysics Data System (ADS)

He, Shaohua; Ju, Rong; Yang, Yang; Xu, Shuai; Liang, Lei

2018-03-01

At present, the industrial PV-storage micro-grid has been widely used. This paper presents an optimal allocation model of PV-storage micro-grid capacity considering the important load of industrial users. A multi-objective optimization model is established to promote the local extinction of PV power generation and the maximum investment income of the enterprise as the objective function. Particle swarm optimization (PSO) is used to solve the case of a city in Jiangsu Province, the results are analyzed economically.

Differential evolution-based multi-objective optimization for the definition of a health indicator for fault diagnostics and prognostics

NASA Astrophysics Data System (ADS)

Baraldi, P.; Bonfanti, G.; Zio, E.

2018-03-01

The identification of the current degradation state of an industrial component and the prediction of its future evolution is a fundamental step for the development of condition-based and predictive maintenance approaches. The objective of the present work is to propose a general method for extracting a health indicator to measure the amount of component degradation from a set of signals measured during operation. The proposed method is based on the combined use of feature extraction techniques, such as Empirical Mode Decomposition and Auto-Associative Kernel Regression, and a multi-objective Binary Differential Evolution (BDE) algorithm for selecting the subset of features optimal for the definition of the health indicator. The objectives of the optimization are desired characteristics of the health indicator, such as monotonicity, trendability and prognosability. A case study is considered, concerning the prediction of the remaining useful life of turbofan engines. The obtained results confirm that the method is capable of extracting health indicators suitable for accurate prognostics.

Multi-objective evolutionary optimization for constructing neural networks for virtual reality visual data mining: application to geophysical prospecting.

PubMed

Valdés, Julio J; Barton, Alan J

2007-05-01

A method for the construction of virtual reality spaces for visual data mining using multi-objective optimization with genetic algorithms on nonlinear discriminant (NDA) neural networks is presented. Two neural network layers (the output and the last hidden) are used for the construction of simultaneous solutions for: (i) a supervised classification of data patterns and (ii) an unsupervised similarity structure preservation between the original data matrix and its image in the new space. A set of spaces are constructed from selected solutions along the Pareto front. This strategy represents a conceptual improvement over spaces computed by single-objective optimization. In addition, genetic programming (in particular gene expression programming) is used for finding analytic representations of the complex mappings generating the spaces (a composition of NDA and orthogonal principal components). The presented approach is domain independent and is illustrated via application to the geophysical prospecting of caves.

Light illumination intensity dependence of photovoltaic parameter in polymer solar cells with ammonium heptamolybdate as hole extraction layer.

PubMed

Liu, Zhiyong; Niu, Shengli; Wang, Ning

2018-01-01

A low-temperature, solution-processed molybdenum oxide (MoO X ) layer and a facile method for polymer solar cells (PSCs) is developed. The PSCs based on a MoO X layer as the hole extraction layer (HEL) is a significant advance for achieving higher photovoltaic performance, especially under weaker light illumination intensity. Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS) measurements show that the (NH 4 ) 6 Mo 7 O 24 molecule decomposes and forms the molybdenum oxide (MoO X ) molecule when undergoing thermal annealing treatment. In this study, PSCs with the MoO X layer as the HEL exhibited better photovoltaic performance, especially under weak light illumination intensity (from 100 to 10mWcm -2 ) compared to poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS)-based PSCs. Analysis of the current density-voltage (J-V) characteristics at various light intensities provides information on the different recombination mechanisms in the PSCs with a MoO X and PEDOT:PSS layer as the HEL. That the slopes of the open-circuit voltage (V OC ) versus light illumination intensity plots are close to 1 unity (kT/q) reveals that bimolecular recombination is the dominant and weaker monomolecular recombination mechanism in open-circuit conditions. That the slopes of the short-circuit current density (J SC ) versus light illumination intensity plots are close to 1 reveals that the effective charge carrier transport and collection mechanism of the MoO X /indium tin oxide (ITO) anode is the weaker bimolecular recombination in short-circuit conditions. Our results indicate that MoO X is an alternative candidate for high-performance PSCs, especially under weak light illumination intensity. Copyright © 2017 Elsevier Inc. All rights reserved.

Stable 1T-phase MoS2 as an effective electron mediator promoting photocatalytic hydrogen production.

PubMed

Shi, Jian-Wen; Zou, Yajun; Ma, Dandan; Fan, Zhaoyang; Cheng, Linhao; Sun, Diankun; Wang, Zeyan; Niu, Chunming; Wang, Lianzhou

2018-05-17

Coupling two semiconductors together to construct a Z-scheme type photocatalytic system is an efficient strategy to solve the serious recombination challenge of photogenerated electrons and holes. In this work, we develop a novel composite photocatalyst by sandwiching metallic 1T-phase MoS2 nanosheets between MoO3 and g-C3N4 (MoO3/1T-MoS2/g-C3N4) for the first time. The metallic 1T-phase MoS2 acts as an efficient electron mediator between MoO3 and g-C3N4 to construct an all-solid-state Z-scheme photocatalytic system, resulting in a highly-efficient spatial charge separation and transfer process. Benefiting from this, the newly developed MoO3/1T-MoS2/g-C3N4 exhibits a drastically enhanced photocatalytic H2 evolution rate of 513.0 μmol h-1 g-1 under visible light irradiation (>420 nm), which is nearly 12 times higher than that of the pure g-C3N4 (39.5 μmol h-1 g-1), and 3.5 times higher than that of MoO3/g-C3N4 (145.7 μmol h-1 g-1). More importantly, the originally unstable 1T-phase MoS2 becomes very stable in MoO3/1T-MoS2/g-C3N4 because of the sandwich structure where 1T-phase MoS2 is protected by MoO3 and g-C3N4, which endows the photocatalyst with excellent photostability. It is believed that this study will provide new insights into the design of efficient and stable Z-scheme heterostructures for photocatalytic applications.

Design and Optimization Method of a Two-Disk Rotor System

NASA Astrophysics Data System (ADS)

Huang, Jingjing; Zheng, Longxi; Mei, Qing

2016-04-01

An integrated analytical method based on multidisciplinary optimization software Isight and general finite element software ANSYS was proposed in this paper. Firstly, a two-disk rotor system was established and the mode, humorous response and transient response at acceleration condition were analyzed with ANSYS. The dynamic characteristics of the two-disk rotor system were achieved. On this basis, the two-disk rotor model was integrated to the multidisciplinary design optimization software Isight. According to the design of experiment (DOE) and the dynamic characteristics, the optimization variables, optimization objectives and constraints were confirmed. After that, the multi-objective design optimization of the transient process was carried out with three different global optimization algorithms including Evolutionary Optimization Algorithm, Multi-Island Genetic Algorithm and Pointer Automatic Optimizer. The optimum position of the two-disk rotor system was obtained at the specified constraints. Meanwhile, the accuracy and calculation numbers of different optimization algorithms were compared. The optimization results indicated that the rotor vibration reached the minimum value and the design efficiency and quality were improved by the multidisciplinary design optimization in the case of meeting the design requirements, which provided the reference to improve the design efficiency and reliability of the aero-engine rotor.

Rapid Flame Synthesis of Atomically Thin MoO3 down to Monolayer Thickness for Effective Hole Doping of WSe2.

PubMed

Cai, Lili; McClellan, Connor J; Koh, Ai Leen; Li, Hong; Yalon, Eilam; Pop, Eric; Zheng, Xiaolin

2017-06-14

Two-dimensional (2D) molybdenum trioxide (MoO 3 ) with mono- or few-layer thickness can potentially advance many applications, ranging from optoelectronics, catalysis, sensors, and batteries to electrochromic devices. Such ultrathin MoO 3 sheets can also be integrated with other 2D materials (e.g., as dopants) to realize new or improved electronic devices. However, there is lack of a rapid and scalable method to controllably grow mono- or few-layer MoO 3 . Here, we report the first demonstration of using a rapid (<2 min) flame synthesis method to deposit mono- and few-layer MoO 3 sheets (several microns in lateral dimension) on a wide variety of layered materials, including mica, MoS 2 , graphene, and WSe 2 , based on van der Waals epitaxy. The flame-grown ultrathin MoO 3 sheet functions as an efficient hole doping layer for WSe 2 , enabling WSe 2 to reach the lowest sheet and contact resistance reported to date among all the p-type 2D materials (∼6.5 kΩ/□ and ∼0.8 kΩ·μm, respectively). These results demonstrate that flame synthesis is a rapid and scalable pathway to growing atomically thin 2D metal oxides, opening up new opportunities for advancing 2D electronics.

The Ways of the Hand: A Study of Hand Function among Blind, Visually Impaired and Visually Impaired Multi-Handicapped Children and Adolescents.

ERIC Educational Resources Information Center

Rogow, Sally M.

1987-01-01

The manual development of 148 blind, visually impaired, and visually impaired multi-handicapped students, aged 3-19, was studied. Results indicated a significant relationship between object manipulation and speech, and an inverse relationship between object manipulation and stereotypic hand mannerisms. Optimal development of manual functions and…

The benefits of adaptive parametrization in multi-objective Tabu Search optimization

NASA Astrophysics Data System (ADS)

Ghisu, Tiziano; Parks, Geoffrey T.; Jaeggi, Daniel M.; Jarrett, Jerome P.; Clarkson, P. John

2010-10-01

In real-world optimization problems, large design spaces and conflicting objectives are often combined with a large number of constraints, resulting in a highly multi-modal, challenging, fragmented landscape. The local search at the heart of Tabu Search, while being one of its strengths in highly constrained optimization problems, requires a large number of evaluations per optimization step. In this work, a modification of the pattern search algorithm is proposed: this modification, based on a Principal Components' Analysis of the approximation set, allows both a re-alignment of the search directions, thereby creating a more effective parametrization, and also an informed reduction of the size of the design space itself. These changes make the optimization process more computationally efficient and more effective - higher quality solutions are identified in fewer iterations. These advantages are demonstrated on a number of standard analytical test functions (from the ZDT and DTLZ families) and on a real-world problem (the optimization of an axial compressor preliminary design).

Complexity of line-seru conversion for different scheduling rules and two improved exact algorithms for the multi-objective optimization.

PubMed

Yu, Yang; Wang, Sihan; Tang, Jiafu; Kaku, Ikou; Sun, Wei

2016-01-01

Productivity can be greatly improved by converting the traditional assembly line to a seru system, especially in the business environment with short product life cycles, uncertain product types and fluctuating production volumes. Line-seru conversion includes two decision processes, i.e., seru formation and seru load. For simplicity, however, previous studies focus on the seru formation with a given scheduling rule in seru load. We select ten scheduling rules usually used in seru load to investigate the influence of different scheduling rules on the performance of line-seru conversion. Moreover, we clarify the complexities of line-seru conversion for ten different scheduling rules from the theoretical perspective. In addition, multi-objective decisions are often used in line-seru conversion. To obtain Pareto-optimal solutions of multi-objective line-seru conversion, we develop two improved exact algorithms based on reducing time complexity and space complexity respectively. Compared with the enumeration based on non-dominated sorting to solve multi-objective problem, the two improved exact algorithms saves computation time greatly. Several numerical simulation experiments are performed to show the performance improvement brought by the two proposed exact algorithms.

Iso-oriented monolayer α-MoO 3 (010) films epitaxially grown on SrTiO 3 (001)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Du, Yingge; Li, Guoqiang; Peterson, Erik W.

The ability to synthesis well-ordered two-dimensional materials under ultra-high vacuum and directly characterize them by other techniques in-situ can greatly advance our current understanding on their physical and chemical properties. In this paper, we demonstrate that iso-oriented α-MoO3 films with as low as single monolayer thickness can be reproducibly grown on SrTiO3(001) substrates by molecular beam epitaxy ( (010)MoO3 || (001)STO, [100]MoO3 || [100]STO or [010]STO) through a self-limiting process. While one in-plane lattice parameter of the MoO3 is very close to that of the SrTiO3 (aMoO3 = 3.96 Å, aSTO = 3.905 Å), the lattice mismatch along other directionmore » is large (~5%, cMoO3 = 3.70 Å), which leads to relaxation as clearly observed from the splitting of streaks in reflection high-energy electron diffraction (RHEED) patterns. A narrow range in the growth temperature is found to be optimal for the growth of monolayer α-MoO3 films. Increasing deposition time will not lead to further increase in thickness, which is explained by a balance between deposition and thermal desorption due to the weak van der Waals force between α-MoO3 layers. Lowering growth temperature after the initial iso-oriented α-MoO3 monolayer leads to thicker α-MoO3(010) films with excellent crystallinity.« less

HYDROTHERMAL SYNTHESIS OF α-MoO3 NANORODS FOR NO2 DETECTION

NASA Astrophysics Data System (ADS)

Bai, Shouli; Chen, Song; Tian, Yuan; Luo, Ruixian; Li, Dianqing; Chen, Aifan

2012-12-01

Thermodynamically stable molybdenum trioxide nanorods have been successfully synthesized by a simple hydrothermal process. The product exhibits high-quality, single-crystalline layered orthorhombic structure (α-MoO3), and aspect ratio over 20 by characterizations of X-ray diffraction (XRD), scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HR-TEM) and Fourier transform infrared (FT-IR). The growth mechanism of α-MoO3 nanorods can be understood by electroneutral and dehydration reaction, which is highly dependent on solution acidity and hydrothermal temperature. The sensing tests show that the sensor based on MoO3 nanorods exhibits high sensitivity to NO2 and is not interferred by CO and CH4, which makes this kind sensor a competitive candidate for NO2 detection. The intrinsic sensing performance of MoO3 maybe arise from its nonstoichiometry of MoO3 owing to the presence of Mo5+ and oxygen vacancy in MoO3 lattice, which has been confirmed by X-ray photoelectron spectroscopy (XPS) analysis. The sensing mechanism of MoO3 for NO2 is also discussed.

A Scalable and Robust Multi-Agent Approach to Distributed Optimization

NASA Technical Reports Server (NTRS)

Tumer, Kagan

2005-01-01

Modularizing a large optimization problem so that the solutions to the subproblems provide a good overall solution is a challenging problem. In this paper we present a multi-agent approach to this problem based on aligning the agent objectives with the system objectives, obviating the need to impose external mechanisms to achieve collaboration among the agents. This approach naturally addresses scaling and robustness issues by ensuring that the agents do not rely on the reliable operation of other agents We test this approach in the difficult distributed optimization problem of imperfect device subset selection [Challet and Johnson, 2002]. In this problem, there are n devices, each of which has a "distortion", and the task is to find the subset of those n devices that minimizes the average distortion. Our results show that in large systems (1000 agents) the proposed approach provides improvements of over an order of magnitude over both traditional optimization methods and traditional multi-agent methods. Furthermore, the results show that even in extreme cases of agent failures (i.e., half the agents fail midway through the simulation) the system remains coordinated and still outperforms a failure-free and centralized optimization algorithm.

On the Optimization of Aerospace Plane Ascent Trajectory

NASA Astrophysics Data System (ADS)

Al-Garni, Ahmed; Kassem, Ayman Hamdy

A hybrid heuristic optimization technique based on genetic algorithms and particle swarm optimization has been developed and tested for trajectory optimization problems with multi-constraints and a multi-objective cost function. The technique is used to calculate control settings for two types for ascending trajectories (constant dynamic pressure and minimum-fuel-minimum-heat) for a two-dimensional model of an aerospace plane. A thorough statistical analysis is done on the hybrid technique to make comparisons with both basic genetic algorithms and particle swarm optimization techniques with respect to convergence and execution time. Genetic algorithm optimization showed better execution time performance while particle swarm optimization showed better convergence performance. The hybrid optimization technique, benefiting from both techniques, showed superior robust performance compromising convergence trends and execution time.

Assessing the weighted multi-objective adaptive surrogate model optimization to derive large-scale reservoir operating rules with sensitivity analysis

NASA Astrophysics Data System (ADS)

Zhang, Jingwen; Wang, Xu; Liu, Pan; Lei, Xiaohui; Li, Zejun; Gong, Wei; Duan, Qingyun; Wang, Hao

2017-01-01

The optimization of large-scale reservoir system is time-consuming due to its intrinsic characteristics of non-commensurable objectives and high dimensionality. One way to solve the problem is to employ an efficient multi-objective optimization algorithm in the derivation of large-scale reservoir operating rules. In this study, the Weighted Multi-Objective Adaptive Surrogate Model Optimization (WMO-ASMO) algorithm is used. It consists of three steps: (1) simplifying the large-scale reservoir operating rules by the aggregation-decomposition model, (2) identifying the most sensitive parameters through multivariate adaptive regression splines (MARS) for dimensional reduction, and (3) reducing computational cost and speeding the searching process by WMO-ASMO, embedded with weighted non-dominated sorting genetic algorithm II (WNSGAII). The intercomparison of non-dominated sorting genetic algorithm (NSGAII), WNSGAII and WMO-ASMO are conducted in the large-scale reservoir system of Xijiang river basin in China. Results indicate that: (1) WNSGAII surpasses NSGAII in the median of annual power generation, increased by 1.03% (from 523.29 to 528.67 billion kW h), and the median of ecological index, optimized by 3.87% (from 1.879 to 1.809) with 500 simulations, because of the weighted crowding distance and (2) WMO-ASMO outperforms NSGAII and WNSGAII in terms of better solutions (annual power generation (530.032 billion kW h) and ecological index (1.675)) with 1000 simulations and computational time reduced by 25% (from 10 h to 8 h) with 500 simulations. Therefore, the proposed method is proved to be more efficient and could provide better Pareto frontier.

A multi-material topology optimization approach for wrinkle-free design of cable-suspended membrane structures

NASA Astrophysics Data System (ADS)

Luo, Yangjun; Niu, Yanzhuang; Li, Ming; Kang, Zhan

2017-06-01

In order to eliminate stress-related wrinkles in cable-suspended membrane structures and to provide simple and reliable deployment, this study presents a multi-material topology optimization model and an effective solution procedure for generating optimal connected layouts for membranes and cables. On the basis of the principal stress criterion of membrane wrinkling behavior and the density-based interpolation of multi-phase materials, the optimization objective is to maximize the total structural stiffness while satisfying principal stress constraints and specified material volume requirements. By adopting the cosine-type relaxation scheme to avoid the stress singularity phenomenon, the optimization model is successfully solved through a standard gradient-based algorithm. Four-corner tensioned membrane structures with different loading cases were investigated to demonstrate the effectiveness of the proposed method in automatically finding the optimal design composed of curved boundary cables and wrinkle-free membranes.

Single and Multiple Object Tracking Using a Multi-Feature Joint Sparse Representation.

PubMed

Hu, Weiming; Li, Wei; Zhang, Xiaoqin; Maybank, Stephen

2015-04-01

In this paper, we propose a tracking algorithm based on a multi-feature joint sparse representation. The templates for the sparse representation can include pixel values, textures, and edges. In the multi-feature joint optimization, noise or occlusion is dealt with using a set of trivial templates. A sparse weight constraint is introduced to dynamically select the relevant templates from the full set of templates. A variance ratio measure is adopted to adaptively adjust the weights of different features. The multi-feature template set is updated adaptively. We further propose an algorithm for tracking multi-objects with occlusion handling based on the multi-feature joint sparse reconstruction. The observation model based on sparse reconstruction automatically focuses on the visible parts of an occluded object by using the information in the trivial templates. The multi-object tracking is simplified into a joint Bayesian inference. The experimental results show the superiority of our algorithm over several state-of-the-art tracking algorithms.

A Hybrid Optimization Framework with POD-based Order Reduction and Design-Space Evolution Scheme

NASA Astrophysics Data System (ADS)

Ghoman, Satyajit S.

The main objective of this research is to develop an innovative multi-fidelity multi-disciplinary design, analysis and optimization suite that integrates certain solution generation codes and newly developed innovative tools to improve the overall optimization process. The research performed herein is divided into two parts: (1) the development of an MDAO framework by integration of variable fidelity physics-based computational codes, and (2) enhancements to such a framework by incorporating innovative features extending its robustness. The first part of this dissertation describes the development of a conceptual Multi-Fidelity Multi-Strategy and Multi-Disciplinary Design Optimization Environment (M3 DOE), in context of aircraft wing optimization. M 3 DOE provides the user a capability to optimize configurations with a choice of (i) the level of fidelity desired, (ii) the use of a single-step or multi-step optimization strategy, and (iii) combination of a series of structural and aerodynamic analyses. The modularity of M3 DOE allows it to be a part of other inclusive optimization frameworks. The M 3 DOE is demonstrated within the context of shape and sizing optimization of the wing of a Generic Business Jet aircraft. Two different optimization objectives, viz. dry weight minimization, and cruise range maximization are studied by conducting one low-fidelity and two high-fidelity optimization runs to demonstrate the application scope of M3 DOE. The second part of this dissertation describes the development of an innovative hybrid optimization framework that extends the robustness of M 3 DOE by employing a proper orthogonal decomposition-based design-space order reduction scheme combined with the evolutionary algorithm technique. The POD method of extracting dominant modes from an ensemble of candidate configurations is used for the design-space order reduction. The snapshot of candidate population is updated iteratively using evolutionary algorithm technique of fitness-driven retention. This strategy capitalizes on the advantages of evolutionary algorithm as well as POD-based reduced order modeling, while overcoming the shortcomings inherent with these techniques. When linked with M3 DOE, this strategy offers a computationally efficient methodology for problems with high level of complexity and a challenging design-space. This newly developed framework is demonstrated for its robustness on a nonconventional supersonic tailless air vehicle wing shape optimization problem.

Comparison of Evolutionary (Genetic) Algorithm and Adjoint Methods for Multi-Objective Viscous Airfoil Optimizations

NASA Technical Reports Server (NTRS)

Pulliam, T. H.; Nemec, M.; Holst, T.; Zingg, D. W.; Kwak, Dochan (Technical Monitor)

2002-01-01

A comparison between an Evolutionary Algorithm (EA) and an Adjoint-Gradient (AG) Method applied to a two-dimensional Navier-Stokes code for airfoil design is presented. Both approaches use a common function evaluation code, the steady-state explicit part of the code,ARC2D. The parameterization of the design space is a common B-spline approach for an airfoil surface, which together with a common griding approach, restricts the AG and EA to the same design space. Results are presented for a class of viscous transonic airfoils in which the optimization tradeoff between drag minimization as one objective and lift maximization as another, produces the multi-objective design space. Comparisons are made for efficiency, accuracy and design consistency.

Application of ESI-HRMS for molybdenum speciation in natural waters: An investigation of molybdate-halide reactions.

PubMed

Dang, Duc Huy; Evans, R Douglas

2018-03-01

High resolution electrospray ionization mass spectrometry (ESI-HRMS) was used to study the speciation of molybdate in interaction with halides (Cl, F, Br). Desolvation during electrospray ionization induced alteration of aqueous species but method optimization successfully suppressed artefact compounds. At low Mo concentrations, chloro(oxo)molybdate and fluoro(oxo)molybdate species were found and in natural samples, MoO 3 Cl was detected for the first time, to the best of our knowledge. Apparent equilibrium constants for Cl substitution on molybdate were calculated for a range of pH values from 4.5 to 8.5. A minor alteration in speciation during the gas phase (conversion of doubly charged MoO 4 2- to HMoO 4 - ) did not allow investigation of the molybdate acid-base properties; however this could be determined by speciation modeling. This study provides further evidence that ESI-HRMS is a fast and suitable tool to Deceasedassess the speciation of inorganic compounds such as Mo. Copyright © 2017 Elsevier B.V. All rights reserved.

Raman band intensities of tellurite glasses.

PubMed

Plotnichenko, V G; Sokolov, V O; Koltashev, V V; Dianov, E M; Grishin, I A; Churbanov, M F

2005-05-15

Raman spectra of TeO2-based glasses doped with WO3, ZnO, GeO2, TiO2, MoO3, and Sb2O3 are measured. The intensity of bands in the Raman spectra of MoO3-TeO2 and MoO3-WO3-TeO2 glasses is shown to be 80-95 times higher than that for silica glass. It is shown that these glasses can be considered as one of the most promising materials for Raman fiber amplifiers.

Coastal aquifer management under parameter uncertainty: Ensemble surrogate modeling based simulation-optimization

NASA Astrophysics Data System (ADS)

Janardhanan, S.; Datta, B.

2011-12-01

Surrogate models are widely used to develop computationally efficient simulation-optimization models to solve complex groundwater management problems. Artificial intelligence based models are most often used for this purpose where they are trained using predictor-predictand data obtained from a numerical simulation model. Most often this is implemented with the assumption that the parameters and boundary conditions used in the numerical simulation model are perfectly known. However, in most practical situations these values are uncertain. Under these circumstances the application of such approximation surrogates becomes limited. In our study we develop a surrogate model based coupled simulation optimization methodology for determining optimal pumping strategies for coastal aquifers considering parameter uncertainty. An ensemble surrogate modeling approach is used along with multiple realization optimization. The methodology is used to solve a multi-objective coastal aquifer management problem considering two conflicting objectives. Hydraulic conductivity and the aquifer recharge are considered as uncertain values. Three dimensional coupled flow and transport simulation model FEMWATER is used to simulate the aquifer responses for a number of scenarios corresponding to Latin hypercube samples of pumping and uncertain parameters to generate input-output patterns for training the surrogate models. Non-parametric bootstrap sampling of this original data set is used to generate multiple data sets which belong to different regions in the multi-dimensional decision and parameter space. These data sets are used to train and test multiple surrogate models based on genetic programming. The ensemble of surrogate models is then linked to a multi-objective genetic algorithm to solve the pumping optimization problem. Two conflicting objectives, viz, maximizing total pumping from beneficial wells and minimizing the total pumping from barrier wells for hydraulic control of saltwater intrusion are considered. The salinity levels resulting at strategic locations due to these pumping are predicted using the ensemble surrogates and are constrained to be within pre-specified levels. Different realizations of the concentration values are obtained from the ensemble predictions corresponding to each candidate solution of pumping. Reliability concept is incorporated as the percent of the total number of surrogate models which satisfy the imposed constraints. The methodology was applied to a realistic coastal aquifer system in Burdekin delta area in Australia. It was found that all optimal solutions corresponding to a reliability level of 0.99 satisfy all the constraints and as reducing reliability level decreases the constraint violation increases. Thus ensemble surrogate model based simulation-optimization was found to be useful in deriving multi-objective optimal pumping strategies for coastal aquifers under parameter uncertainty.

Improving the Photo-Oxidative Performance of Bi2MoO6 by Harnessing the Synergy between Spatial Charge Separation and Rational Co-Catalyst Deposition.

PubMed

Wu, Xuelian; Hart, Judy N; Wen, Xiaoming; Wang, Liang; Du, Yi; Dou, Shi Xue; Ng, Yun Hau; Amal, Rose; Scott, Jason

2018-03-21

It has been reported that photogenerated electrons and holes can be directed toward specific crystal facets of a semiconductor particle, which is believed to arise from the differences in their surface electronic structures, suggesting that different facets can act as either photoreduction or photo-oxidation sites. This study examines the propensity for this effect to occur in faceted, plate-like bismuth molybdate (Bi 2 MoO 6 ), which is a useful photocatalyst for water oxidation. Photoexcited electrons and holes are shown to be spatially separated toward the {100} and {001}/{010} facets of Bi 2 MoO 6 , respectively, by facet-dependent photodeposition of noble metals (Pt, Au, and Ag) and metal oxides (PbO 2 , MnO x , and CoO x ). Theoretical calculations revealed that differences in energy levels between the conduction bands and valence bands of the {100} and {001}/{010} facets can contribute to electrons and holes being drawn to different surfaces of the plate-like Bi 2 MoO 6 . Utilizing this knowledge, the photo-oxidative capability of Bi 2 MoO 6 was improved by adding an efficient water oxidation co-catalyst, CoO x , to the system, whereby the extent of enhancement was shown to be governed by the co-catalyst location. A greater oxygen evolution occurred when CoO x was selectively deposited on the hole-rich {001}/{010} facets of Bi 2 MoO 6 compared to when CoO x was randomly located across all of the facets. The elevated performance exhibited for the selectively loaded CoO x /Bi 2 MoO 6 was ascribed to the greater opportunity for hole trapping by the co-catalyst being accentuated over other potentially detrimental effects, such as the co-catalyst acting as a recombination medium and/or covering reactive sites. The results indicate that harnessing the synergy between the spatial charge separation and the co-catalyst location on the appropriate facets of plate-like Bi 2 MoO 6 can promote its photocatalytic activity.

The optimal location of piezoelectric actuators and sensors for vibration control of plates

NASA Astrophysics Data System (ADS)

Kumar, K. Ramesh; Narayanan, S.

2007-12-01

This paper considers the optimal placement of collocated piezoelectric actuator-sensor pairs on a thin plate using a model-based linear quadratic regulator (LQR) controller. LQR performance is taken as objective for finding the optimal location of sensor-actuator pairs. The problem is formulated using the finite element method (FEM) as multi-input-multi-output (MIMO) model control. The discrete optimal sensor and actuator location problem is formulated in the framework of a zero-one optimization problem. A genetic algorithm (GA) is used to solve the zero-one optimization problem. Different classical control strategies like direct proportional feedback, constant-gain negative velocity feedback and the LQR optimal control scheme are applied to study the control effectiveness.

Wing Configuration Impact on Design Optimums for a Subsonic Passenger Transport

NASA Technical Reports Server (NTRS)

Wells, Douglas P.

2014-01-01

This study sought to compare four aircraft wing configurations at a conceptual level using a multi-disciplinary optimization (MDO) process. The MDO framework used was created by Georgia Institute of Technology and Virginia Polytechnic Institute and State University. They created a multi-disciplinary design and optimization environment that could capture the unique features of the truss-braced wing (TBW) configuration. The four wing configurations selected for the study were a low wing cantilever installation, a high wing cantilever, a strut-braced wing, and a single jury TBW. The mission that was used for this study was a 160 passenger transport aircraft with a design range of 2,875 nautical miles at the design payload, flown at a cruise Mach number of 0.78. This paper includes discussion and optimization results for multiple design objectives. Five design objectives were chosen to illustrate the impact of selected objective on the optimization result: minimum takeoff gross weight (TOGW), minimum operating empty weight, minimum block fuel weight, maximum start of cruise lift-to-drag ratio, and minimum start of cruise drag coefficient. The results show that the design objective selected will impact the characteristics of the optimized aircraft. Although minimum life cycle cost was not one of the objectives, TOGW is often used as a proxy for life cycle cost. The low wing cantilever had the lowest TOGW followed by the strut-braced wing.

Multi-Objective Mission Route Planning Using Particle Swarm Optimization

DTIC Science & Technology

2002-03-01

solutions to complex problems using particles that interact with each other. Both Particle Swarm Optimization (PSO) and the Ant System (AS) have been...EXPERIMENTAL DESING PROCESS..............................................................55 5.1. Introduction...46 18. Phenotype level particle interaction

Application of multi-objective optimization to pooled experiments of next generation sequencing for detection of rare mutations.

PubMed

Zilinskas, Julius; Lančinskas, Algirdas; Guarracino, Mario Rosario

2014-01-01

In this paper we propose some mathematical models to plan a Next Generation Sequencing experiment to detect rare mutations in pools of patients. A mathematical optimization problem is formulated for optimal pooling, with respect to minimization of the experiment cost. Then, two different strategies to replicate patients in pools are proposed, which have the advantage to decrease the overall costs. Finally, a multi-objective optimization formulation is proposed, where the trade-off between the probability to detect a mutation and overall costs is taken into account. The proposed solutions are devised in pursuance of the following advantages: (i) the solution guarantees mutations are detectable in the experimental setting, and (ii) the cost of the NGS experiment and its biological validation using Sanger sequencing is minimized. Simulations show replicating pools can decrease overall experimental cost, thus making pooling an interesting option.

A new method to optimize natural convection heat sinks

NASA Astrophysics Data System (ADS)

Lampio, K.; Karvinen, R.

2017-08-01

The performance of a heat sink cooled by natural convection is strongly affected by its geometry, because buoyancy creates flow. Our model utilizes analytical results of forced flow and convection, and only conduction in a solid, i.e., the base plate and fins, is solved numerically. Sufficient accuracy for calculating maximum temperatures in practical applications is proved by comparing the results of our model with some simple analytical and computational fluid dynamics (CFD) solutions. An essential advantage of our model is that it cuts down on calculation CPU time by many orders of magnitude compared with CFD. The shorter calculation time makes our model well suited for multi-objective optimization, which is the best choice for improving heat sink geometry, because many geometrical parameters with opposite effects influence the thermal behavior. In multi-objective optimization, optimal locations of components and optimal dimensions of the fin array can be found by simultaneously minimizing the heat sink maximum temperature, size, and mass. This paper presents the principles of the particle swarm optimization (PSO) algorithm and applies it as a basis for optimizing existing heat sinks.

MULTI-OBJECTIVE OPTIMAL DESIGN OF GROUNDWATER REMEDIATION SYSTEMS: APPLICATION OF THE NICHED PARETO GENETIC ALGORITHM (NPGA). (R826614)

EPA Science Inventory

A multiobjective optimization algorithm is applied to a groundwater quality management problem involving remediation by pump-and-treat (PAT). The multiobjective optimization framework uses the niched Pareto genetic algorithm (NPGA) and is applied to simultaneously minimize the...

The System of Simulation and Multi-objective Optimization for the Roller Kiln

NASA Astrophysics Data System (ADS)

Huang, He; Chen, Xishen; Li, Wugang; Li, Zhuoqiu

It is somewhat a difficult researching problem, to get the building parameters of the ceramic roller kiln simulation model. A system integrated of evolutionary algorithms (PSO, DE and DEPSO) and computational fluid dynamics (CFD), is proposed to solve the problem. And the temperature field uniformity and the environment disruption are studied in this paper. With the help of the efficient parallel calculation, the ceramic roller kiln temperature field uniformity and the NOx emissions field have been researched in the system at the same time. A multi-objective optimization example of the industrial roller kiln proves that the system is of excellent parameter exploration capability.

Multi-objective group scheduling optimization integrated with preventive maintenance

NASA Astrophysics Data System (ADS)

Liao, Wenzhu; Zhang, Xiufang; Jiang, Min

2017-11-01

This article proposes a single-machine-based integration model to meet the requirements of production scheduling and preventive maintenance in group production. To describe the production for identical/similar and different jobs, this integrated model considers the learning and forgetting effects. Based on machine degradation, the deterioration effect is also considered. Moreover, perfect maintenance and minimal repair are adopted in this integrated model. The multi-objective of minimizing total completion time and maintenance cost is taken to meet the dual requirements of delivery date and cost. Finally, a genetic algorithm is developed to solve this optimization model, and the computation results demonstrate that this integrated model is effective and reliable.

A performance-oriented power transformer design methodology using multi-objective evolutionary optimization

PubMed Central

Adly, Amr A.; Abd-El-Hafiz, Salwa K.

2014-01-01

Transformers are regarded as crucial components in power systems. Due to market globalization, power transformer manufacturers are facing an increasingly competitive environment that mandates the adoption of design strategies yielding better performance at lower costs. In this paper, a power transformer design methodology using multi-objective evolutionary optimization is proposed. Using this methodology, which is tailored to be target performance design-oriented, quick rough estimation of transformer design specifics may be inferred. Testing of the suggested approach revealed significant qualitative and quantitative match with measured design and performance values. Details of the proposed methodology as well as sample design results are reported in the paper. PMID:26257939

A performance-oriented power transformer design methodology using multi-objective evolutionary optimization.

PubMed

Adly, Amr A; Abd-El-Hafiz, Salwa K

2015-05-01

Transformers are regarded as crucial components in power systems. Due to market globalization, power transformer manufacturers are facing an increasingly competitive environment that mandates the adoption of design strategies yielding better performance at lower costs. In this paper, a power transformer design methodology using multi-objective evolutionary optimization is proposed. Using this methodology, which is tailored to be target performance design-oriented, quick rough estimation of transformer design specifics may be inferred. Testing of the suggested approach revealed significant qualitative and quantitative match with measured design and performance values. Details of the proposed methodology as well as sample design results are reported in the paper.

Identification of mutated driver pathways in cancer using a multi-objective optimization model.

PubMed

Zheng, Chun-Hou; Yang, Wu; Chong, Yan-Wen; Xia, Jun-Feng

2016-05-01

New-generation high-throughput technologies, including next-generation sequencing technology, have been extensively applied to solve biological problems. As a result, large cancer genomics projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium are producing large amount of rich and diverse data in multiple cancer types. The identification of mutated driver genes and driver pathways from these data is a significant challenge. Genome aberrations in cancer cells can be divided into two types: random 'passenger mutation' and functional 'driver mutation'. In this paper, we introduced a Multi-objective Optimization model based on a Genetic Algorithm (MOGA) to solve the maximum weight submatrix problem, which can be employed to identify driver genes and driver pathways promoting cancer proliferation. The maximum weight submatrix problem defined to find mutated driver pathways is based on two specific properties, i.e., high coverage and high exclusivity. The multi-objective optimization model can adjust the trade-off between high coverage and high exclusivity. We proposed an integrative model by combining gene expression data and mutation data to improve the performance of the MOGA algorithm in a biological context. Copyright © 2016 Elsevier Ltd. All rights reserved.

Electronic properties of MoS2 / MoOx interfaces: Implications in Tunnel Field Effect Transistors and Hole Contacts

DOE PAGES

Santosh, K. C.; Longo, Roberto; Addou, Rafik; ...

2016-09-26

In an electronic device based on two dimensional (2D) transitional metal dichalcogenides (TMDs), finding a low resistance metal contact is critical in order to achieve the desired performance. However, due to the unusual Fermi level pinning in metal/2D TMD interface, the performance is limited. Here, we investigate the electronic properties of TMDs and transition metal oxide (TMO) interfaces (MoS 2/MoO 3) using density functional theory (DFT). Our results demonstrate that, due to the large work function of MoO 3 and the relative band alignment with MoS 2, together with small energy gap, the MoS 2/MoO 3 interface is a goodmore » candidate for a tunnel field effect (TFET)-type device. Moreover, if the interface is not stoichiometric because of the presence of oxygen vacancies in MoO 3, the heterostructure is more suitable for p-type (hole) contacts, exhibiting an Ohmic electrical behavior as experimentally demonstrated for different TMO/TMD interfaces. Our results reveal that the defect state induced by an oxygen vacancy in the MoO3 aligns with the valance band of MoS 2, showing an insignificant impact on the band gap of the TMD. This result highlights the role of oxygen vacancies in oxides on facilitating appropriate contacts at the MoS 2 and MoO x (x < 3) interface, which consistently explains the available experimental observations.« less

Nanoscale Mo- MoO3 Entrapped in Engineering Thermoplastic: Inorganic Pathway to Bactericidal and Fungicidal Action.

PubMed

Qureshi, Nilam; Chaudhari, Ravindra; Mane, Pramod; Shinde, Manish; Jadakar, Sandesh; Rane, Sunit; Kale, Bharat; Bhalerao, Anand; Amalnerkar, Dinesh

2016-04-01

In our contemporary endeavor, metallic molybdenum (Mo) and semiconducting molybdenum trioxide (MoO3) nanostructures have been simultaneously generated via solid state reaction between molybdenum (III) chloride (MoCl3) and polyphenylene sulfide (PPS) at 285 (°)C in unimolar ratio for different time durations, namely, 6 h, 24 h, and 48 h. The resultant nanocomposites (NCs) revealed formation of predominantly metallic Mo for all the samples. However, MoO3 gradually gained prominent position as secondary phase with rise in reaction time. The present study was intended to investigate the antibacterial potential of metal-metal oxide-polymer NCs, i.e., Mo- MoO3-PPS against microorganisms, viz., Pseudomonas aeruginosa, Staphylococcus aureus, Klebsiella pneumoniae, and Aspergillus fumigatus. The antibacterial activity of the NCs was evaluated by agar well diffusion investigation. Maximum sensitivity concentrations of NCs were determined by finding out minimum inhibitory concentration (MIC) and minimum bactericidal/fungicidal concentration (MBC/MFC). Moreover, the NCs prepared at reaction time of 48 h exhibited best MBC values and were tested with time kill assay which revealed that the growth of S. aureus was substantially inhibited by Mo- MoO3-PPS NCs. This synchronized formation of Mo- MoO3 nanostructures in an engineering thermoplastic may have potential antimicrobial applications in biomedical devices and components. Prima facie results on antifungal activity are indicative of the fact that these materials can show anti-cancer behavior.

First scintillating bolometer tests of a CLYMENE R&D on Li2MoO4 scintillators towards a large-scale double-beta decay experiment

NASA Astrophysics Data System (ADS)

Buşe, G.; Giuliani, A.; de Marcillac, P.; Marnieros, S.; Nones, C.; Novati, V.; Olivieri, E.; Poda, D. V.; Redon, T.; Sand, J.-B.; Veber, P.; Velázquez, M.; Zolotarova, A. S.

2018-05-01

A new R&D on lithium molybdate scintillators has begun within a project CLYMENE (Czochralski growth of Li2MoO4 crYstals for the scintillating boloMeters used in the rare EveNts sEarches). One of the main goals of the CLYMENE is a realization of a Li2MoO4 crystal growth line to be complementary to the one recently developed by LUMINEU in view of a mass production capacity for CUPID, a next-generation tonne-scale bolometric experiment to search for neutrinoless double-beta decay. In the present paper we report the investigation of performance and radiopurity of 158-g and 13.5-g scintillating bolometers based on a first large-mass (230 g) Li2MoO4 crystal scintillator developed within the CLYMENE project. In particular, a good energy resolution (2-7 keV FWHM in the energy range of 0.2-5 MeV), one of the highest light yield (0.97 keV/MeV) amongst Li2MoO4 scintillating bolometers, an efficient alpha particles discrimination (10 σ) and potentially low internal radioactive contamination (below 0.2-0.3 mBq/kg of U/Th, but 1.4 mBq/kg of 210Po) demonstrate prospects of the CLYMENE in the development of high quality and radiopure Li2MoO4 scintillators for CUPID.

Multi-objective experimental design for (13)C-based metabolic flux analysis.

PubMed

Bouvin, Jeroen; Cajot, Simon; D'Huys, Pieter-Jan; Ampofo-Asiama, Jerry; Anné, Jozef; Van Impe, Jan; Geeraerd, Annemie; Bernaerts, Kristel

2015-10-01

(13)C-based metabolic flux analysis is an excellent technique to resolve fluxes in the central carbon metabolism but costs can be significant when using specialized tracers. This work presents a framework for cost-effective design of (13)C-tracer experiments, illustrated on two different networks. Linear and non-linear optimal input mixtures are computed for networks for Streptomyces lividans and a carcinoma cell line. If only glucose tracers are considered as labeled substrate for a carcinoma cell line or S. lividans, the best parameter estimation accuracy is obtained by mixtures containing high amounts of 1,2-(13)C2 glucose combined with uniformly labeled glucose. Experimental designs are evaluated based on a linear (D-criterion) and non-linear approach (S-criterion). Both approaches generate almost the same input mixture, however, the linear approach is favored due to its low computational effort. The high amount of 1,2-(13)C2 glucose in the optimal designs coincides with a high experimental cost, which is further enhanced when labeling is introduced in glutamine and aspartate tracers. Multi-objective optimization gives the possibility to assess experimental quality and cost at the same time and can reveal excellent compromise experiments. For example, the combination of 100% 1,2-(13)C2 glucose with 100% position one labeled glutamine and the combination of 100% 1,2-(13)C2 glucose with 100% uniformly labeled glutamine perform equally well for the carcinoma cell line, but the first mixture offers a decrease in cost of $ 120 per ml-scale cell culture experiment. We demonstrated the validity of a multi-objective linear approach to perform optimal experimental designs for the non-linear problem of (13)C-metabolic flux analysis. Tools and a workflow are provided to perform multi-objective design. The effortless calculation of the D-criterion can be exploited to perform high-throughput screening of possible (13)C-tracers, while the illustrated benefit of multi-objective design should stimulate its application within the field of (13)C-based metabolic flux analysis. Copyright © 2015 Elsevier Inc. All rights reserved.

A fuzzy multi-objective linear programming approach for integrated sheep farming and wildlife in land management decisions: a case study in the Patagonian rangelands

NASA Astrophysics Data System (ADS)

Metternicht, Graciela; Blanco, Paula; del Valle, Hector; Laterra, Pedro; Hardtke, Leonardo; Bouza, Pablo

2015-04-01

Wildlife is part of the Patagonian rangelands sheep farming environment, with the potential of providing extra revenue to livestock owners. As sheep farming became less profitable, farmers and ranchers could focus on sustainable wildlife harvesting. It has been argued that sustainable wildlife harvesting is ecologically one of the most rational forms of land use because of its potential to provide multiple products of high value, while reducing pressure on ecosystems. The guanaco (Lama guanicoe) is the most conspicuous wild ungulate of Patagonia. Guanaco ?bre, meat, pelts and hides are economically valuable and have the potential to be used within the present Patagonian context of production systems. Guanaco populations in South America, including Patagonia, have experienced a sustained decline. Causes for this decline are related to habitat alteration, competition for forage with sheep, and lack of reasonable management plans to develop livelihoods for ranchers. In this study we propose an approach to explicitly determinate optimal stocking rates based on trade-offs between guanaco density and livestock grazing intensity on rangelands. The focus of our research is on finding optimal sheep stocking rates at paddock level, to ensure the highest production outputs while: a) meeting requirements of sustainable conservation of guanacos over their minimum viable population; b) maximizing soil carbon sequestration, and c) minimizing soil erosion. In this way, determination of optimal stocking rate in rangelands becomes a multi-objective optimization problem that can be addressed using a Fuzzy Multi-Objective Linear Programming (MOLP) approach. Basically, this approach converts multi-objective problems into single-objective optimizations, by introducing a set of objective weights. Objectives are represented using fuzzy set theory and fuzzy memberships, enabling each objective function to adopt a value between 0 and 1. Each objective function indicates the satisfaction of the decision maker towards the respective objective. Fuzzy logic is closer to intuitive thinking used by decision makers, making it a user-friendly approach for them to select alternatives. The proposed approach was applied in a study area of approximately 40,000 hectares in semiarid Patagonian rangelands where extensive, continuous sheep grazing for wool production is the main land use. Multi- and hyper-spectral data were combined with ancillary data within a GIS environment, and used to derive maps of forage production, guanacos density, soil organic carbon and soil erosion. Different scenarios, with different objectives weights were evaluated. Results showed that under scenario 1, where livestock production is predicted to have the highest values, guanaco numbers decrease substantially as well as soil carbon sequestration, and soil erosion exhibit the highest values. On the other hand, when guanaco population is prioritized, livestock production has the lowest value. A compromise alternative resulted from a scenario where variables are assigned same weight; under this condition, high livestock production is predicted, while conservation of guanaco population is sustainable, carbon sequestration is maximized and soil erosion minimized.

Mission planning optimization of video satellite for ground multi-object staring imaging

NASA Astrophysics Data System (ADS)

Cui, Kaikai; Xiang, Junhua; Zhang, Yulin

2018-03-01

This study investigates the emergency scheduling problem of ground multi-object staring imaging for a single video satellite. In the proposed mission scenario, the ground objects require a specified duration of staring imaging by the video satellite. The planning horizon is not long, i.e., it is usually shorter than one orbit period. A binary decision variable and the imaging order are used as the design variables, and the total observation revenue combined with the influence of the total attitude maneuvering time is regarded as the optimization objective. Based on the constraints of the observation time windows, satellite attitude adjustment time, and satellite maneuverability, a constraint satisfaction mission planning model is established for ground object staring imaging by a single video satellite. Further, a modified ant colony optimization algorithm with tabu lists (Tabu-ACO) is designed to solve this problem. The proposed algorithm can fully exploit the intelligence and local search ability of ACO. Based on full consideration of the mission characteristics, the design of the tabu lists can reduce the search range of ACO and improve the algorithm efficiency significantly. The simulation results show that the proposed algorithm outperforms the conventional algorithm in terms of optimization performance, and it can obtain satisfactory scheduling results for the mission planning problem.

Optimization of Landscape Services under Uncoordinated Management by Multiple Landowners

PubMed Central

Porto, Miguel; Correia, Otília; Beja, Pedro

2014-01-01

Landscapes are often patchworks of private properties, where composition and configuration patterns result from cumulative effects of the actions of multiple landowners. Securing the delivery of services in such multi-ownership landscapes is challenging, because it is difficult to assure tight compliance to spatially explicit management rules at the level of individual properties, which may hinder the conservation of critical landscape features. To deal with these constraints, a multi-objective simulation-optimization procedure was developed to select non-spatial management regimes that best meet landscape-level objectives, while accounting for uncoordinated and uncertain response of individual landowners to management rules. Optimization approximates the non-dominated Pareto frontier, combining a multi-objective genetic algorithm and a simulator that forecasts trends in landscape pattern as a function of management rules implemented annually by individual landowners. The procedure was demonstrated with a case study for the optimum scheduling of fuel treatments in cork oak forest landscapes, involving six objectives related to reducing management costs (1), reducing fire risk (3), and protecting biodiversity associated with mid- and late-successional understories (2). There was a trade-off between cost, fire risk and biodiversity objectives, that could be minimized by selecting management regimes involving ca. 60% of landowners clearing the understory at short intervals (around 5 years), and the remaining managing at long intervals (ca. 75 years) or not managing. The optimal management regimes produces a mosaic landscape dominated by stands with herbaceous and low shrub understories, but also with a satisfactory representation of old understories, that was favorable in terms of both fire risk and biodiversity. The simulation-optimization procedure presented can be extended to incorporate a wide range of landscape dynamic processes, management rules and quantifiable objectives. It may thus be adapted to other socio-ecological systems, particularly where specific patterns of landscape heterogeneity are to be maintained despite imperfect management by multiple landowners. PMID:24465833

Multi-metric calibration of hydrological model to capture overall flow regimes

NASA Astrophysics Data System (ADS)

Zhang, Yongyong; Shao, Quanxi; Zhang, Shifeng; Zhai, Xiaoyan; She, Dunxian

2016-08-01

Flow regimes (e.g., magnitude, frequency, variation, duration, timing and rating of change) play a critical role in water supply and flood control, environmental processes, as well as biodiversity and life history patterns in the aquatic ecosystem. The traditional flow magnitude-oriented calibration of hydrological model was usually inadequate to well capture all the characteristics of observed flow regimes. In this study, we simulated multiple flow regime metrics simultaneously by coupling a distributed hydrological model with an equally weighted multi-objective optimization algorithm. Two headwater watersheds in the arid Hexi Corridor were selected for the case study. Sixteen metrics were selected as optimization objectives, which could represent the major characteristics of flow regimes. Model performance was compared with that of the single objective calibration. Results showed that most metrics were better simulated by the multi-objective approach than those of the single objective calibration, especially the low and high flow magnitudes, frequency and variation, duration, maximum flow timing and rating. However, the model performance of middle flow magnitude was not significantly improved because this metric was usually well captured by single objective calibration. The timing of minimum flow was poorly predicted by both the multi-metric and single calibrations due to the uncertainties in model structure and input data. The sensitive parameter values of the hydrological model changed remarkably and the simulated hydrological processes by the multi-metric calibration became more reliable, because more flow characteristics were considered. The study is expected to provide more detailed flow information by hydrological simulation for the integrated water resources management, and to improve the simulation performances of overall flow regimes.

Multi-Objective Community Detection Based on Memetic Algorithm

PubMed Central

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels. PMID:25932646

Multi-objective community detection based on memetic algorithm.

PubMed

Wu, Peng; Pan, Li

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.

New Multi-objective Uncertainty-based Algorithm for Water Resource Models' Calibration

NASA Astrophysics Data System (ADS)

Keshavarz, Kasra; Alizadeh, Hossein

2017-04-01

Water resource models are powerful tools to support water management decision making process and are developed to deal with a broad range of issues including land use and climate change impacts analysis, water allocation, systems design and operation, waste load control and allocation, etc. These models are divided into two categories of simulation and optimization models whose calibration has been addressed in the literature where great relevant efforts in recent decades have led to two main categories of auto-calibration methods of uncertainty-based algorithms such as GLUE, MCMC and PEST and optimization-based algorithms including single-objective optimization such as SCE-UA and multi-objective optimization such as MOCOM-UA and MOSCEM-UA. Although algorithms which benefit from capabilities of both types, such as SUFI-2, were rather developed, this paper proposes a new auto-calibration algorithm which is capable of both finding optimal parameters values regarding multiple objectives like optimization-based algorithms and providing interval estimations of parameters like uncertainty-based algorithms. The algorithm is actually developed to improve quality of SUFI-2 results. Based on a single-objective, e.g. NSE and RMSE, SUFI-2 proposes a routine to find the best point and interval estimation of parameters and corresponding prediction intervals (95 PPU) of time series of interest. To assess the goodness of calibration, final results are presented using two uncertainty measures of p-factor quantifying percentage of observations covered by 95PPU and r-factor quantifying degree of uncertainty, and the analyst has to select the point and interval estimation of parameters which are actually non-dominated regarding both of the uncertainty measures. Based on the described properties of SUFI-2, two important questions are raised, answering of which are our research motivation: Given that in SUFI-2, final selection is based on the two measures or objectives and on the other hand, knowing that there is no multi-objective optimization mechanism in SUFI-2, are the final estimations Pareto-optimal? Can systematic methods be applied to select the final estimations? Dealing with these questions, a new auto-calibration algorithm was proposed where the uncertainty measures were considered as two objectives to find non-dominated interval estimations of parameters by means of coupling Monte Carlo simulation and Multi-Objective Particle Swarm Optimization. Both the proposed algorithm and SUFI-2 were applied to calibrate parameters of water resources planning model of Helleh river basin, Iran. The model is a comprehensive water quantity-quality model developed in the previous researches using WEAP software in order to analyze the impacts of different water resources management strategies including dam construction, increasing cultivation area, utilization of more efficient irrigation technologies, changing crop pattern, etc. Comparing the Pareto frontier resulted from the proposed auto-calibration algorithm with SUFI-2 results, it was revealed that the new algorithm leads to a better and also continuous Pareto frontier, even though it is more computationally expensive. Finally, Nash and Kalai-Smorodinsky bargaining methods were used to choose compromised interval estimation regarding Pareto frontier.

Multi-objective optimization of solid waste flows: environmentally sustainable strategies for municipalities.

PubMed

Minciardi, Riccardo; Paolucci, Massimo; Robba, Michela; Sacile, Roberto

2008-11-01

An approach to sustainable municipal solid waste (MSW) management is presented, with the aim of supporting the decision on the optimal flows of solid waste sent to landfill, recycling and different types of treatment plants, whose sizes are also decision variables. This problem is modeled with a non-linear, multi-objective formulation. Specifically, four objectives to be minimized have been taken into account, which are related to economic costs, unrecycled waste, sanitary landfill disposal and environmental impact (incinerator emissions). An interactive reference point procedure has been developed to support decision making; these methods are considered appropriate for multi-objective decision problems in environmental applications. In addition, interactive methods are generally preferred by decision makers as they can be directly involved in the various steps of the decision process. Some results deriving from the application of the proposed procedure are presented. The application of the procedure is exemplified by considering the interaction with two different decision makers who are assumed to be in charge of planning the MSW system in the municipality of Genova (Italy).

Novel optimization technique of isolated microgrid with hydrogen energy storage.

PubMed

Beshr, Eman Hassan; Abdelghany, Hazem; Eteiba, Mahmoud

2018-01-01

This paper presents a novel optimization technique for energy management studies of an isolated microgrid. The system is supplied by various Distributed Energy Resources (DERs), Diesel Generator (DG), a Wind Turbine Generator (WTG), Photovoltaic (PV) arrays and supported by fuel cell/electrolyzer Hydrogen storage system for short term storage. Multi-objective optimization is used through non-dominated sorting genetic algorithm to suit the load requirements under the given constraints. A novel multi-objective flower pollination algorithm is utilized to check the results. The Pros and cons of the two optimization techniques are compared and evaluated. An isolated microgrid is modelled using MATLAB software package, dispatch of active/reactive power, optimal load flow analysis with slack bus selection are carried out to be able to minimize fuel cost and line losses under realistic constraints. The performance of the system is studied and analyzed during both summer and winter conditions and three case studies are presented for each condition. The modified IEEE 15 bus system is used to validate the proposed algorithm.

Novel optimization technique of isolated microgrid with hydrogen energy storage

PubMed Central

Abdelghany, Hazem; Eteiba, Mahmoud

2018-01-01

This paper presents a novel optimization technique for energy management studies of an isolated microgrid. The system is supplied by various Distributed Energy Resources (DERs), Diesel Generator (DG), a Wind Turbine Generator (WTG), Photovoltaic (PV) arrays and supported by fuel cell/electrolyzer Hydrogen storage system for short term storage. Multi-objective optimization is used through non-dominated sorting genetic algorithm to suit the load requirements under the given constraints. A novel multi-objective flower pollination algorithm is utilized to check the results. The Pros and cons of the two optimization techniques are compared and evaluated. An isolated microgrid is modelled using MATLAB software package, dispatch of active/reactive power, optimal load flow analysis with slack bus selection are carried out to be able to minimize fuel cost and line losses under realistic constraints. The performance of the system is studied and analyzed during both summer and winter conditions and three case studies are presented for each condition. The modified IEEE 15 bus system is used to validate the proposed algorithm. PMID:29466433

Multi-Objective Flight Control for Drag Minimization and Load Alleviation of High-Aspect Ratio Flexible Wing Aircraft

NASA Technical Reports Server (NTRS)

Nguyen, Nhan; Ting, Eric; Chaparro, Daniel; Drew, Michael; Swei, Sean

2017-01-01

As aircraft wings become much more flexible due to the use of light-weight composites material, adverse aerodynamics at off-design performance can result from changes in wing shapes due to aeroelastic deflections. Increased drag, hence increased fuel burn, is a potential consequence. Without means for aeroelastic compensation, the benefit of weight reduction from the use of light-weight material could be offset by less optimal aerodynamic performance at off-design flight conditions. Performance Adaptive Aeroelastic Wing (PAAW) technology can potentially address these technical challenges for future flexible wing transports. PAAW technology leverages multi-disciplinary solutions to maximize the aerodynamic performance payoff of future adaptive wing design, while addressing simultaneously operational constraints that can prevent the optimal aerodynamic performance from being realized. These operational constraints include reduced flutter margins, increased airframe responses to gust and maneuver loads, pilot handling qualities, and ride qualities. All of these constraints while seeking the optimal aerodynamic performance present themselves as a multi-objective flight control problem. The paper presents a multi-objective flight control approach based on a drag-cognizant optimal control method. A concept of virtual control, which was previously introduced, is implemented to address the pair-wise flap motion constraints imposed by the elastomer material. This method is shown to be able to satisfy the constraints. Real-time drag minimization control is considered to be an important consideration for PAAW technology. Drag minimization control has many technical challenges such as sensing and control. An initial outline of a real-time drag minimization control has already been developed and will be further investigated in the future. A simulation study of a multi-objective flight control for a flight path angle command with aeroelastic mode suppression and drag minimization demonstrates the effectiveness of the proposed solution. In-flight structural loads are also an important consideration. As wing flexibility increases, maneuver load and gust load responses can be significant and therefore can pose safety and flight control concerns. In this paper, we will extend the multi-objective flight control framework to include load alleviation control. The study will focus initially on maneuver load minimization control, and then subsequently will address gust load alleviation control in future work.

Dielectric investigation of the sliding charge-density wave in Tl0.3MoO3

NASA Astrophysics Data System (ADS)

Ramanujachary, K. V.; Collins, B. T.; Greenblatt, M.; Gerhardt, R.; Rietman, E. A.

1988-10-01

We have investigated the low-frequency complex conductivity of the charge-density-wave condensate in Tl0.3MoO3, in the temperature range 40-90 K, by the measurement of admittance sampled in the frequency interval 5 Hz-13 MHz. The observed response can be characterized in terms of a simple Debye relaxation model with a distribution of relaxation times by analogy with the reported behavior of its isostructural analog K0.3MoO3. Despite qualitative similarities with the general trends observed in K0.3MoO3, the relaxational response in Tl0.3MoO3 differed significantly in detail. Both the mean relaxation times (τ0) and static dielectric constants (ɛ0) are shown to have Arrhenius temperature dependence with activation energies of 743 and 152 K, respectively. For applied dc biases above the threshold field (ET) for nonlinear conduction, the response shows structure at frequencies that resemble ``washboard'' characteristics of a moving charge condensate. From the values of the high-frequency real and imaginary parts of the dielectric constants, the existence of yet another relaxation process is proposed.

Improvement of pentathiophene/fullerene planar heterojunction photovoltaic cells by improving the organic films morphology through the anode buffer bilayer

NASA Astrophysics Data System (ADS)

El Jouad, Zouhair; Cattin, Linda; Martinez, Francisco; Neculqueo, Gloria; Louarn, Guy; Addou, Mohammed; Predeep, Padmanabhan; Manuvel, Jayan; Bernède, Jean-Christian

2016-05-01

Organic photovoltaic cells (OPVCs) are based on a heterojunction electron donor (ED)/electron acceptor (EA). In the present work, the electron donor which is also the absorber of light is pentathiophene. The typical cells were ITO/HTL/pentathiophene/fullerene/Alq3/Al with HTL (hole transport layer) = MoO3, CuI, MoO3/CuI. After optimisation of the pentathiophene thickness, 70 nm, the highest efficiency, 0.81%, is obtained with the bilayer MoO3/CuI as HTL. In order to understand these results the pentathiophene films deposited onto the different HTLs were characterized by scanning electron microscopy, atomic force microscopy, X-rays diffraction, optical absorption and electrical characterization. It is shown that CuI improves the conductivity of the pentathiophene layer through the modification of the film structure, while MoO3 decreases the leakage current. Using the bilayer MoO3/CuI allows cumulating the advantages of each layer. Contribution to the topical issue "Materials for Energy Harvesting, Conversion and Storage (ICOME 2015) - Elected submissions", edited by Jean-Michel Nunzi, Rachid Bennacer and Mohammed El Ganaoui

Effect of MoO3 on the synthesis of boron nitride nanotubes over Fe and Ni catalysts.

PubMed

Nithya, Jeghan Shrine Maria; Pandurangan, Arumugam

2012-05-01

Synthesis of boron nitride nanotubes at reduced temperature is important for industrial manufactures. In this study boron nitride nanotubes were synthesized by thermal evaporation method using B/Fe2O3/MoO3 and B/Ni2O3/MoO3 mixtures separately with ammonia as the nitrogen source. The growth of boron nitride nanotubes occurred at 1100 degrees C, which was relatively lower than other metal oxides assisted growth processes requiring higher than 1200 degrees C. MoO3 promoted formation of B2O2 and aided boron nitride nanotubes growth at a reduced temperature. The boron nitride nanotubes with bamboo shaped, nested cone structured and straight tubes like forms were evident from the high resolution transmission electron microscopy. Metallic Fe and Ni, formed during the process, were the catalysts for the growth of boron nitride nanotubes. Their formation was established by X-ray diffraction. FT Raman showed a peak due to B-N vibration of BNNTs close to 1370 cm(-1). Hence MoO3 assisted growth of boron nitride nanotubes is advantageous, as it significantly reduced the synthesis temperature.

Interface structure and composition of MoO3/GaAs(0 0 1)

NASA Astrophysics Data System (ADS)

Sarkar, Anirban; Ashraf, Tanveer; Grafeneder, Wolfgang; Koch, Reinhold

2018-04-01

We studied growth, structure, stress, oxidation state as well as surface and interface structure and composition of thermally-evaporated thin MoO3 films on the technologically important III/V-semiconductor substrate GaAs(0 0 1). The MoO3 films grow with Mo in the 6+  oxidation state. The electrical resistance is tunable by the oxygen partial pressure during deposition from transparent insulating to semi-transparant halfmetallic. In the investigated growth temperature range (room temperature to 200 °C) no diffraction spots are detected by x-ray diffraction. However, high resolution transmission electron microscopy reveals the formation of MoO3 nanocrystal grains with diameters of 5–8 nm. At the interface a  ≈3 nm-thick intermediate layer has formed, where the single-crystal lattice of GaAs gradually transforms to the nanocrystalline MoO3 structure. This interpretation is corroborated by our in situ and real-time stress measurements evidencing a two-stage growth process as well as by elemental interface analysis revealing coexistance of Ga, As, Mo, and oxygen in a intermediate layer of 3–4 nm.

Introduction to WMOST v3 and Multi-Objective Optimization

EPA Science Inventory

Version 3 of EPA’s Watershed Management Optimization Support Tool (WMOST) will be released in early 2018 (https://www.epa.gov/exposure-assessment-models/wmost). WMOST is designed to facilitate integrated water management among communities, utilities, watershed organization...

Multi-Objective Design Of Optimal Greenhouse Gas Observation Networks

NASA Astrophysics Data System (ADS)

Lucas, D. D.; Bergmann, D. J.; Cameron-Smith, P. J.; Gard, E.; Guilderson, T. P.; Rotman, D.; Stolaroff, J. K.

2010-12-01

One of the primary scientific functions of a Greenhouse Gas Information System (GHGIS) is to infer GHG source emission rates and their uncertainties by combining measurements from an observational network with atmospheric transport modeling. Certain features of the observational networks that serve as inputs to a GHGIS --for example, sampling location and frequency-- can greatly impact the accuracy of the retrieved GHG emissions. Observation System Simulation Experiments (OSSEs) provide a framework to characterize emission uncertainties associated with a given network configuration. By minimizing these uncertainties, OSSEs can be used to determine optimal sampling strategies. Designing a real-world GHGIS observing network, however, will involve multiple, conflicting objectives; there will be trade-offs between sampling density, coverage and measurement costs. To address these issues, we have added multi-objective optimization capabilities to OSSEs. We demonstrate these capabilities by quantifying the trade-offs between retrieval error and measurement costs for a prototype GHGIS, and deriving GHG observing networks that are Pareto optimal. [LLNL-ABS-452333: This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Robust optimization of supersonic ORC nozzle guide vanes

NASA Astrophysics Data System (ADS)

Bufi, Elio A.; Cinnella, Paola

2017-03-01

An efficient Robust Optimization (RO) strategy is developed for the design of 2D supersonic Organic Rankine Cycle turbine expanders. The dense gas effects are not-negligible for this application and they are taken into account describing the thermodynamics by means of the Peng-Robinson-Stryjek-Vera equation of state. The design methodology combines an Uncertainty Quantification (UQ) loop based on a Bayesian kriging model of the system response to the uncertain parameters, used to approximate statistics (mean and variance) of the uncertain system output, a CFD solver, and a multi-objective non-dominated sorting algorithm (NSGA), also based on a Kriging surrogate of the multi-objective fitness function, along with an adaptive infill strategy for surrogate enrichment at each generation of the NSGA. The objective functions are the average and variance of the isentropic efficiency. The blade shape is parametrized by means of a Free Form Deformation (FFD) approach. The robust optimal blades are compared to the baseline design (based on the Method of Characteristics) and to a blade obtained by means of a deterministic CFD-based optimization.

Genetic algorithm-based multi-objective optimal absorber system for three-dimensional seismic structures

NASA Astrophysics Data System (ADS)

Ren, Wenjie; Li, Hongnan; Song, Gangbing; Huo, Linsheng

2009-03-01

The problem of optimizing an absorber system for three-dimensional seismic structures is addressed. The objective is to determine the number and position of absorbers to minimize the coupling effects of translation-torsion of structures at minimum cost. A procedure for a multi-objective optimization problem is developed by integrating a dominance-based selection operator and a dominance-based penalty function method. Based on the two-branch tournament genetic algorithm, the selection operator is constructed by evaluating individuals according to their dominance in one run. The technique guarantees the better performing individual winning its competition, provides a slight selection pressure toward individuals and maintains diversity in the population. Moreover, due to the evaluation for individuals in each generation being finished in one run, less computational effort is taken. Penalty function methods are generally used to transform a constrained optimization problem into an unconstrained one. The dominance-based penalty function contains necessary information on non-dominated character and infeasible position of an individual, essential for success in seeking a Pareto optimal set. The proposed approach is used to obtain a set of non-dominated designs for a six-storey three-dimensional building with shape memory alloy dampers subjected to earthquake.

Mono and multi-objective optimization techniques applied to a large range of industrial test cases using Metamodel assisted Evolutionary Algorithms

NASA Astrophysics Data System (ADS)

Fourment, Lionel; Ducloux, Richard; Marie, Stéphane; Ejday, Mohsen; Monnereau, Dominique; Massé, Thomas; Montmitonnet, Pierre

2010-06-01

The use of material processing numerical simulation allows a strategy of trial and error to improve virtual processes without incurring material costs or interrupting production and therefore save a lot of money, but it requires user time to analyze the results, adjust the operating conditions and restart the simulation. Automatic optimization is the perfect complement to simulation. Evolutionary Algorithm coupled with metamodelling makes it possible to obtain industrially relevant results on a very large range of applications within a few tens of simulations and without any specific automatic optimization technique knowledge. Ten industrial partners have been selected to cover the different area of the mechanical forging industry and provide different examples of the forming simulation tools. It aims to demonstrate that it is possible to obtain industrially relevant results on a very large range of applications within a few tens of simulations and without any specific automatic optimization technique knowledge. The large computational time is handled by a metamodel approach. It allows interpolating the objective function on the entire parameter space by only knowing the exact function values at a reduced number of "master points". Two algorithms are used: an evolution strategy combined with a Kriging metamodel and a genetic algorithm combined with a Meshless Finite Difference Method. The later approach is extended to multi-objective optimization. The set of solutions, which corresponds to the best possible compromises between the different objectives, is then computed in the same way. The population based approach allows using the parallel capabilities of the utilized computer with a high efficiency. An optimization module, fully embedded within the Forge2009 IHM, makes possible to cover all the defined examples, and the use of new multi-core hardware to compute several simulations at the same time reduces the needed time dramatically. The presented examples demonstrate the method versatility. They include billet shape optimization of a common rail, the cogging of a bar and a wire drawing problem.

Multi-objective optimization integrated with life cycle assessment for rainwater harvesting systems

NASA Astrophysics Data System (ADS)

Li, Yi; Huang, Youyi; Ye, Quanliang; Zhang, Wenlong; Meng, Fangang; Zhang, Shanxue

2018-03-01

The major limitation of optimization models applied previously for rainwater harvesting (RWH) systems is the systematic evaluation of environmental and human health impacts across all the lifecycle stages. This study integrated life cycle assessment (LCA) into a multi-objective optimization model to optimize the construction areas of green rooftops, porous pavements and green lands in Beijing of China, considering the trade-offs among 24 h-interval RWH volume (QR), stormwater runoff volume control ratio (R), economic cost (EC), and environmental impacts (EI). Eleven life cycle impact indicators were assessed with a functional unit of 10,000 m2 of RWH construction areas. The LCA results showed that green lands performed the smallest lifecycle impacts of all assessment indicators, in contrast, porous pavements showed the largest impact values except Abiotic Depletion Potential (ADP) elements. Based on the standardization results, ADP fossil was chosen as the representative indicator for the calculation of EI objective in multi-objective optimization model due to its largest value in all RWH systems lifecycle. The optimization results for QR, R, EC and EI were 238.80 million m3, 78.5%, 66.68 billion RMB Yuan, and 1.05E + 16 MJ, respectively. After the construction of optimal RWH system, 14.7% of annual domestic water consumption and 78.5% of maximum daily rainfall would be supplied and controlled in Beijing, respectively, which would make a great contribution to reduce the stress of water scarcity and water logging problems. Green lands have been the first choice for RWH in Beijing according to the capacity of rainwater harvesting and less environmental and human impacts. Porous pavements played a good role in water logging alleviation (R for 67.5%), however, did not show a large construction result in this study due to the huge ADP fossil across the lifecycle. Sensitivity analysis revealed the daily maximum precipitation to be key factor for the robustness of the results for three RWH systems construction in this study.

Optimizing regional collaborative efforts to achieve long-term discipline-specific objectives

USDA-ARS?s Scientific Manuscript database

Current funding programs focused on multi-disciplinary, multi-agency approaches to regional issues can provide opportunities to address discipline-specific advancements in scientific knowledge. Projects funded through the Agricultural Research Service, Joint Fire Science Program, and the Natural Re...

MoO3 nanoparticle anchored graphene as bifunctional agent for water purification

NASA Astrophysics Data System (ADS)

Lahan, Homen; Roy, Raju; Namsa, Nima D.; Das, Shyamal K.

2016-10-01

We report here a facile one step hydrothermal method to anchor MoO3 nanoparticles in graphene. The bifunctionality of graphene-MoO3 nanoparticles is demonstrated via dye adsorption and antibacterial activities. The nanocomposite showed excellent adsorption of methylene blue, a cationic dye, from water compared to pristine MoO3 and graphene. However, it showed negligible adsorption of methyl orange, an anionic dye. Again, the graphene-MoO3 nanoparticles exhibited bacteriostatic property against both Gram-negative (E. coli) and Gram-positive (S. aureus) bacteria.

Impact of Reduced Graphene Oxide on MoS2 Grown by Sulfurization of Sputtered MoO3 and Mo Precursor Films (Postprint)

DTIC Science & Technology

2016-05-26

AFRL-RX-WP-JA-2017-0137 IMPACT OF REDUCED GRAPHENE OXIDE ON MOS2 GROWN BY SULFURIZATION OF SPUTTERED MOO3 AND MO PRECURSOR FILMS...OXIDE ON MOS2 GROWN BY SULFURIZATION OF SPUTTERED MOO3 AND Mo PRECURSOR FILMS (POSTPRINT) 5a. CONTRACT NUMBER FA8650-11-D-5401-0008 5b. GRANT...2016. © 2016 American Vacuum Society. The U.S. Government is joint author of the work and has the right to use, modify , reproduce, release, perform

Improving the stability of subnano-MoO3/meso-SiO2 catalyst through amino-functionalization

NASA Astrophysics Data System (ADS)

Wang, Jiasheng; Wu, Wenpei; Yang, Qianfan; Wang, Wan-Hui; Bao, Ming

Subnano-MoO3 clusters (below 1nm) have excellent catalytic activity on oxidative desulfurization (ODS). However, the stability is not very satisfactory due to the leaching of MoO3 during the reaction. To enhance the stability, here we developed a method by grafting NH2 to silica. NH2 could form coordination bond with MoO3, as proved by solid state 1H NMR, which can prevent MoO3 from leaching and thus significantly enhance the stability.

Structural and optical properties of electrospun MoO3 nanowires

NASA Astrophysics Data System (ADS)

Das, Arnab Kumar; Modak, Rajkumar; Srinivasan, Ananthakrishnan

2018-05-01

Nanofibers of polyvinyl alcohol (PVA) containing ammonium molybdate were prepared by a combination of sol-gel and electrospinning techniques. Heat treatment of the as-spun composite nanofibers at 500 °C yielded MoO3 nanowires with a diameter of ˜180 nm. The product was characterized by X-ray diffraction (XRD), scanning electron microscopy, Fourier transform infrared spectroscopy and Raman spectroscopy. XRD and Raman spectra of the heat nanowires clearly show the formation of orthorhombic single phase MoO3 structure without any impurity phases.

Smart grid initialization reduces the computational complexity of multi-objective image registration based on a dual-dynamic transformation model to account for large anatomical differences

NASA Astrophysics Data System (ADS)

Bosman, Peter A. N.; Alderliesten, Tanja

2016-03-01

We recently demonstrated the strong potential of using dual-dynamic transformation models when tackling deformable image registration problems involving large anatomical differences. Dual-dynamic transformation models employ two moving grids instead of the common single moving grid for the target image (and single fixed grid for the source image). We previously employed powerful optimization algorithms to make use of the additional flexibility offered by a dual-dynamic transformation model with good results, directly obtaining insight into the trade-off between important registration objectives as a result of taking a multi-objective approach to optimization. However, optimization has so far been initialized using two regular grids, which still leaves a great potential of dual-dynamic transformation models untapped: a-priori grid alignment with image structures/areas that are expected to deform more. This allows (far) less grid points to be used, compared to using a sufficiently refined regular grid, leading to (far) more efficient optimization, or, equivalently, more accurate results using the same number of grid points. We study the implications of exploiting this potential by experimenting with two new smart grid initialization procedures: one manual expert-based and one automated image-feature-based. We consider a CT test case with large differences in bladder volume with and without a multi-resolution scheme and find a substantial benefit of using smart grid initialization.

Controllable synthesis of Bi2MoO6 nanosheets and their facet-dependent visible-light-driven photocatalytic activity

NASA Astrophysics Data System (ADS)

Yang, Zixin; Shen, Min; Dai, Ke; Zhang, Xuehao; Chen, Hao

2018-02-01

Bi2MoO6 nanosheets with exposed {010} facets were selectively synthesized through hydrothermal method by adjusting the pH value in the presence of cetyltrimethyl ammonium bromide (CTAB) as the templates. The effects of CTAB content and hydrothermal conditions on the morphologies and crystal phases of the products were determined by using X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), UV-vis diffuse reflectance spectroscopy (DRS), Fourier-transform infrared spectroscopy (FTIR), Raman spectrometry, and Brunauer-Emmett-Teller surface area analyses. It is found that Bi2MoO6 nanosheets with relatively large particle sizes (plate length 0.5-3 μm) and special anisotropic growth along the {010} plane can be obtained from an alkaline hydrothermal environment. The band gap of Bi2MoO6 can be fine-tuned from 2.30 to 2.57 eV by adjusting the pH value of hydrothermal solution. The pH value has a significant effect on the composition of hydrothermal precursors, which results in Bi2MoO6 nanosheets with different ratio of {010} faces, especially the formation of Bi2O3 in the primary stage of the hydrothermal treatment is a key factor for the exposure of {010} facets. The visible-light-driven photocatalytic activities of the Bi2MoO6 products with different ratio of {010} facets exposed are investigated through the degradation of Rhodamine B, oxytetracycline, and tetracycline. Bi2MoO6 nanosheets synthesized at pH 10.0 with highest {010} facet exposed ratio exhibited highly efficient visible light photocatalytic activity for pollutant decomposition, which can be mainly attributed to the flake structures, the crystallinity and most importantly, the exposed {010} facet which generate high concentration of rad O2-.

Distributed optimization system and method

DOEpatents

Hurtado, John E.; Dohrmann, Clark R.; Robinett, III, Rush D.

2003-06-10

A search system and method for controlling multiple agents to optimize an objective using distributed sensing and cooperative control. The search agent can be one or more physical agents, such as a robot, and can be software agents for searching cyberspace. The objective can be: chemical sources, temperature sources, radiation sources, light sources, evaders, trespassers, explosive sources, time dependent sources, time independent sources, function surfaces, maximization points, minimization points, and optimal control of a system such as a communication system, an economy, a crane, and a multi-processor computer.

Distributed Optimization System

DOEpatents

Hurtado, John E.; Dohrmann, Clark R.; Robinett, III, Rush D.

2004-11-30

A search system and method for controlling multiple agents to optimize an objective using distributed sensing and cooperative control. The search agent can be one or more physical agents, such as a robot, and can be software agents for searching cyberspace. The objective can be: chemical sources, temperature sources, radiation sources, light sources, evaders, trespassers, explosive sources, time dependent sources, time independent sources, function surfaces, maximization points, minimization points, and optimal control of a system such as a communication system, an economy, a crane, and a multi-processor computer.

Multi-objective design optimization of antenna structures using sequential domain patching with automated patch size determination

NASA Astrophysics Data System (ADS)

Koziel, Slawomir; Bekasiewicz, Adrian

2018-02-01

In this article, a simple yet efficient and reliable technique for fully automated multi-objective design optimization of antenna structures using sequential domain patching (SDP) is discussed. The optimization procedure according to SDP is a two-step process: (i) obtaining the initial set of Pareto-optimal designs representing the best possible trade-offs between considered conflicting objectives, and (ii) Pareto set refinement for yielding the optimal designs at the high-fidelity electromagnetic (EM) simulation model level. For the sake of computational efficiency, the first step is realized at the level of a low-fidelity (coarse-discretization) EM model by sequential construction and relocation of small design space segments (patches) in order to create a path connecting the extreme Pareto front designs obtained beforehand. The second stage involves response correction techniques and local response surface approximation models constructed by reusing EM simulation data acquired in the first step. A major contribution of this work is an automated procedure for determining the patch dimensions. It allows for appropriate selection of the number of patches for each geometry variable so as to ensure reliability of the optimization process while maintaining its low cost. The importance of this procedure is demonstrated by comparing it with uniform patch dimensions.

Charge transfer induced by MoO3 at boron subphthalocyanine chloride/α-sexithiophene heterojunction interface

NASA Astrophysics Data System (ADS)

Foggiatto, Alexandre L.; Sakurai, Takeaki

2018-03-01

The energy-level alignment of boron subphthalocyanine chloride (SubPc)/α-sexithiophene (6T) grown on MoO3 was investigated using ultraviolet and X-ray photoelectron spectroscopy (UPS and XPS). We demonstrated that the p-doping effect generated by the MoO3 layer can induce charge transfer at the organic-organic heterojunction interface. After the deposition of 6T on MoO3, the fermi level becomes pinned close to the 6T highest occupied molecular orbital (HOMO) level and when SubPc is deposited, owing to its tail states, charge transfer occurs in order to achieve thermodynamic equilibrium. We also demonstrated that the charge transfer can be reduced by annealing the film. We suggested that the reduction of the misalignment on the film induces a reduction in the density of gap states, which controls the charge transfer.

Organic bistable memory devices based on MoO3 nanoparticle embedded Alq3 structures.

PubMed

Abhijith, T; Kumar, T V Arun; Reddy, V S

2017-03-03

Organic bistable memory devices were fabricated by embedding a thin layer of molybdenum trioxide (MoO 3 ) between two tris-(8-hydroxyquinoline)aluminum (Alq 3 ) layers. The device exhibited excellent switching characteristics with an ON/OFF current ratio of 1.15 × 10 3 at a read voltage of 1 V. The device showed repeatable write-erase capability and good stability in both the conductance states. These conductance states are non-volatile in nature and can be obtained by applying appropriate voltage pulses. The effect of MoO 3 layer thickness and its location in the Alq 3 matrix on characteristics of the memory device was investigated. The field emission scanning electron microscopy (FE-SEM) images of the MoO 3 layer revealed the presence of isolated nanoparticles. Based on the experimental results, a mechanism has been proposed for explaining the conductance switching of fabricated devices.

Organic bistable memory devices based on MoO3 nanoparticle embedded Alq3 structures

NASA Astrophysics Data System (ADS)

Abhijith, T.; Kumar, T. V. Arun; Reddy, V. S.

2017-03-01

Organic bistable memory devices were fabricated by embedding a thin layer of molybdenum trioxide (MoO3) between two tris-(8-hydroxyquinoline)aluminum (Alq3) layers. The device exhibited excellent switching characteristics with an ON/OFF current ratio of 1.15 × 103 at a read voltage of 1 V. The device showed repeatable write-erase capability and good stability in both the conductance states. These conductance states are non-volatile in nature and can be obtained by applying appropriate voltage pulses. The effect of MoO3 layer thickness and its location in the Alq3 matrix on characteristics of the memory device was investigated. The field emission scanning electron microscopy (FE-SEM) images of the MoO3 layer revealed the presence of isolated nanoparticles. Based on the experimental results, a mechanism has been proposed for explaining the conductance switching of fabricated devices.

Alteration of architecture of MoO₃ nanostructures on arbitrary substrates: growth kinetics, spectroscopic and gas sensing properties.

PubMed

Illyaskutty, Navas; Sreedhar, Sreeja; Sanal Kumar, G; Kohler, Heinz; Schwotzer, Matthias; Natzeck, Carsten; Pillai, V P Mahadevan

2014-11-21

MoO3 nanostructures have been grown in thin film form on five different substrates by RF magnetron sputtering and subsequent annealing; non-aligned nanorods, aligned nanorods, bundled nanowires, vertical nanorods and nanoslabs are formed respectively on the glass, quartz, wafer, alumina and sapphire substrates. The nanostructures formed on these substrates are characterized by AFM, SEM, GIXRD, XPS, micro-Raman, diffuse reflectance and photoluminescence spectroscopy. A detailed growth model for morphology alteration with respect to substrates has been discussed by considering various aspects such as surface roughness, lattice parameters and the thermal expansion coefficient, of both substrates and MoO3. The present study developed a strategy for the choice of substrates to materialize different types MoO3 nanostructures for future thin film applications. The gas sensing tests point towards using these MoO3 nanostructures as principal detection elements in gas sensors.

Estimation of the discharges of the multiple water level stations by multi-objective optimization

NASA Astrophysics Data System (ADS)

Matsumoto, Kazuhiro; Miyamoto, Mamoru; Yamakage, Yuzuru; Tsuda, Morimasa; Yanami, Hitoshi; Anai, Hirokazu; Iwami, Yoichi

2016-04-01

This presentation shows two aspects of the parameter identification to estimate the discharges of the multiple water level stations by multi-objective optimization. One is how to adjust the parameters to estimate the discharges accurately. The other is which optimization algorithms are suitable for the parameter identification. Regarding the previous studies, there is a study that minimizes the weighted error of the discharges of the multiple water level stations by single-objective optimization. On the other hand, there are some studies that minimize the multiple error assessment functions of the discharge of a single water level station by multi-objective optimization. This presentation features to simultaneously minimize the errors of the discharges of the multiple water level stations by multi-objective optimization. Abe River basin in Japan is targeted. The basin area is 567.0km2. There are thirteen rainfall stations and three water level stations. Nine flood events are investigated. They occurred from 2005 to 2012 and the maximum discharges exceed 1,000m3/s. The discharges are calculated with PWRI distributed hydrological model. The basin is partitioned into the meshes of 500m x 500m. Two-layer tanks are placed on each mesh. Fourteen parameters are adjusted to estimate the discharges accurately. Twelve of them are the hydrological parameters and two of them are the parameters of the initial water levels of the tanks. Three objective functions are the mean squared errors between the observed and calculated discharges at the water level stations. Latin Hypercube sampling is one of the uniformly sampling algorithms. The discharges are calculated with respect to the parameter values sampled by a simplified version of Latin Hypercube sampling. The observed discharge is surrounded by the calculated discharges. It suggests that it might be possible to estimate the discharge accurately by adjusting the parameters. In a sense, it is true that the discharge of a water level station can be accurately estimated by setting the parameter values optimized to the responding water level station. However, there are some cases that the calculated discharge by setting the parameter values optimized to one water level station does not meet the observed discharge at another water level station. It is important to estimate the discharges of all the water level stations in some degree of accuracy. It turns out to be possible to select the parameter values from the pareto optimal solutions by the condition that all the normalized errors by the minimum error of the responding water level station are under 3. The optimization performance of five implementations of the algorithms and a simplified version of Latin Hypercube sampling are compared. Five implementations are NSGA2 and PAES of an optimization software inspyred and MCO_NSGA2R, MOPSOCD and NSGA2R_NSGA2R of a statistical software R. NSGA2, PAES and MOPSOCD are the optimization algorithms of a genetic algorithm, an evolution strategy and a particle swarm optimization respectively. The number of the evaluations of the objective functions is 10,000. Two implementations of NSGA2 of R outperform the others. They are promising to be suitable for the parameter identification of PWRI distributed hydrological model.

Multi objective optimization model for minimizing production cost and environmental impact in CNC turning process

NASA Astrophysics Data System (ADS)

Widhiarso, Wahyu; Rosyidi, Cucuk Nur

2018-02-01

Minimizing production cost in a manufacturing company will increase the profit of the company. The cutting parameters will affect total processing time which then will affect the production cost of machining process. Besides affecting the production cost and processing time, the cutting parameters will also affect the environment. An optimization model is needed to determine the optimum cutting parameters. In this paper, we develop an optimization model to minimize the production cost and the environmental impact in CNC turning process. The model is used a multi objective optimization. Cutting speed and feed rate are served as the decision variables. Constraints considered are cutting speed, feed rate, cutting force, output power, and surface roughness. The environmental impact is converted from the environmental burden by using eco-indicator 99. Numerical example is given to show the implementation of the model and solved using OptQuest of Oracle Crystal Ball software. The results of optimization indicate that the model can be used to optimize the cutting parameters to minimize the production cost and the environmental impact.

Optimal Reference Strain Structure for Studying Dynamic Responses of Flexible Rockets

NASA Technical Reports Server (NTRS)

Tsushima, Natsuki; Su, Weihua; Wolf, Michael G.; Griffin, Edwin D.; Dumoulin, Marie P.

2017-01-01

In the proposed paper, the optimal design of reference strain structures (RSS) will be performed targeting for the accurate observation of the dynamic bending and torsion deformation of a flexible rocket. It will provide the detailed description of the finite-element (FE) model of a notional flexible rocket created in MSC.Patran. The RSS will be attached longitudinally along the side of the rocket and to track the deformation of the thin-walled structure under external loads. An integrated surrogate-based multi-objective optimization approach will be developed to find the optimal design of the RSS using the FE model. The Kriging method will be used to construct the surrogate model. For the data sampling and the performance evaluation, static/transient analyses will be performed with MSC.Natran/Patran. The multi-objective optimization will be solved with NSGA-II to minimize the difference between the strains of the launch vehicle and RSS. Finally, the performance of the optimal RSS will be evaluated by checking its strain-tracking capability in different numerical simulations of the flexible rocket.

A multi-objective optimization approach accurately resolves protein domain architectures

PubMed Central

Bernardes, J.S.; Vieira, F.R.J.; Zaverucha, G.; Carbone, A.

2016-01-01

Motivation: Given a protein sequence and a number of potential domains matching it, what are the domain content and the most likely domain architecture for the sequence? This problem is of fundamental importance in protein annotation, constituting one of the main steps of all predictive annotation strategies. On the other hand, when potential domains are several and in conflict because of overlapping domain boundaries, finding a solution for the problem might become difficult. An accurate prediction of the domain architecture of a multi-domain protein provides important information for function prediction, comparative genomics and molecular evolution. Results: We developed DAMA (Domain Annotation by a Multi-objective Approach), a novel approach that identifies architectures through a multi-objective optimization algorithm combining scores of domain matches, previously observed multi-domain co-occurrence and domain overlapping. DAMA has been validated on a known benchmark dataset based on CATH structural domain assignments and on the set of Plasmodium falciparum proteins. When compared with existing tools on both datasets, it outperforms all of them. Availability and implementation: DAMA software is implemented in C++ and the source code can be found at http://www.lcqb.upmc.fr/DAMA. Contact: juliana.silva_bernardes@upmc.fr or alessandra.carbone@lip6.fr Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26458889

Topology Optimization - Engineering Contribution to Architectural Design

NASA Astrophysics Data System (ADS)

Tajs-Zielińska, Katarzyna; Bochenek, Bogdan

2017-10-01

The idea of the topology optimization is to find within a considered design domain the distribution of material that is optimal in some sense. Material, during optimization process, is redistributed and parts that are not necessary from objective point of view are removed. The result is a solid/void structure, for which an objective function is minimized. This paper presents an application of topology optimization to multi-material structures. The design domain defined by shape of a structure is divided into sub-regions, for which different materials are assigned. During design process material is relocated, but only within selected region. The proposed idea has been inspired by architectural designs like multi-material facades of buildings. The effectiveness of topology optimization is determined by proper choice of numerical optimization algorithm. This paper utilises very efficient heuristic method called Cellular Automata. Cellular Automata are mathematical, discrete idealization of a physical systems. Engineering implementation of Cellular Automata requires decomposition of the design domain into a uniform lattice of cells. It is assumed, that the interaction between cells takes place only within the neighbouring cells. The interaction is governed by simple, local update rules, which are based on heuristics or physical laws. The numerical studies show, that this method can be attractive alternative to traditional gradient-based algorithms. The proposed approach is evaluated by selected numerical examples of multi-material bridge structures, for which various material configurations are examined. The numerical studies demonstrated a significant influence the material sub-regions location on the final topologies. The influence of assumed volume fraction on final topologies for multi-material structures is also observed and discussed. The results of numerical calculations show, that this approach produces different results as compared with classical one-material problems.

Genetic Algorithm Optimizes Q-LAW Control Parameters

NASA Technical Reports Server (NTRS)

Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard

2008-01-01

A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

Balancing exploration, uncertainty and computational demands in many objective reservoir optimization

NASA Astrophysics Data System (ADS)

Zatarain Salazar, Jazmin; Reed, Patrick M.; Quinn, Julianne D.; Giuliani, Matteo; Castelletti, Andrea

2017-11-01

Reservoir operations are central to our ability to manage river basin systems serving conflicting multi-sectoral demands under increasingly uncertain futures. These challenges motivate the need for new solution strategies capable of effectively and efficiently discovering the multi-sectoral tradeoffs that are inherent to alternative reservoir operation policies. Evolutionary many-objective direct policy search (EMODPS) is gaining importance in this context due to its capability of addressing multiple objectives and its flexibility in incorporating multiple sources of uncertainties. This simulation-optimization framework has high potential for addressing the complexities of water resources management, and it can benefit from current advances in parallel computing and meta-heuristics. This study contributes a diagnostic assessment of state-of-the-art parallel strategies for the auto-adaptive Borg Multi Objective Evolutionary Algorithm (MOEA) to support EMODPS. Our analysis focuses on the Lower Susquehanna River Basin (LSRB) system where multiple sectoral demands from hydropower production, urban water supply, recreation and environmental flows need to be balanced. Using EMODPS with different parallel configurations of the Borg MOEA, we optimize operating policies over different size ensembles of synthetic streamflows and evaporation rates. As we increase the ensemble size, we increase the statistical fidelity of our objective function evaluations at the cost of higher computational demands. This study demonstrates how to overcome the mathematical and computational barriers associated with capturing uncertainties in stochastic multiobjective reservoir control optimization, where parallel algorithmic search serves to reduce the wall-clock time in discovering high quality representations of key operational tradeoffs. Our results show that emerging self-adaptive parallelization schemes exploiting cooperative search populations are crucial. Such strategies provide a promising new set of tools for effectively balancing exploration, uncertainty, and computational demands when using EMODPS.

Collaborative real-time scheduling of multiple PTZ cameras for multiple object tracking in video surveillance

NASA Astrophysics Data System (ADS)

Liu, Yu-Che; Huang, Chung-Lin

2013-03-01

This paper proposes a multi-PTZ-camera control mechanism to acquire close-up imagery of human objects in a surveillance system. The control algorithm is based on the output of multi-camera, multi-target tracking. Three main concerns of the algorithm are (1) the imagery of human object's face for biometric purposes, (2) the optimal video quality of the human objects, and (3) minimum hand-off time. Here, we define an objective function based on the expected capture conditions such as the camera-subject distance, pan tile angles of capture, face visibility and others. Such objective function serves to effectively balance the number of captures per subject and quality of captures. In the experiments, we demonstrate the performance of the system which operates in real-time under real world conditions on three PTZ cameras.

CQPSO scheduling algorithm for heterogeneous multi-core DAG task model

NASA Astrophysics Data System (ADS)

Zhai, Wenzheng; Hu, Yue-Li; Ran, Feng

2017-07-01

Efficient task scheduling is critical to achieve high performance in a heterogeneous multi-core computing environment. The paper focuses on the heterogeneous multi-core directed acyclic graph (DAG) task model and proposes a novel task scheduling method based on an improved chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm. A task priority scheduling list was built. A processor with minimum cumulative earliest finish time (EFT) was acted as the object of the first task assignment. The task precedence relationships were satisfied and the total execution time of all tasks was minimized. The experimental results show that the proposed algorithm has the advantage of optimization abilities, simple and feasible, fast convergence, and can be applied to the task scheduling optimization for other heterogeneous and distributed environment.

Multicomponent pre-stack seismic waveform inversion in transversely isotropic media using a non-dominated sorting genetic algorithm

NASA Astrophysics Data System (ADS)

Padhi, Amit; Mallick, Subhashis

2014-03-01

Inversion of band- and offset-limited single component (P wave) seismic data does not provide robust estimates of subsurface elastic parameters and density. Multicomponent seismic data can, in principle, circumvent this limitation but adds to the complexity of the inversion algorithm because it requires simultaneous optimization of multiple objective functions, one for each data component. In seismology, these multiple objectives are typically handled by constructing a single objective given as a weighted sum of the objectives of individual data components and sometimes with additional regularization terms reflecting their interdependence; which is then followed by a single objective optimization. Multi-objective problems, inclusive of the multicomponent seismic inversion are however non-linear. They have non-unique solutions, known as the Pareto-optimal solutions. Therefore, casting such problems as a single objective optimization provides one out of the entire set of the Pareto-optimal solutions, which in turn, may be biased by the choice of the weights. To handle multiple objectives, it is thus appropriate to treat the objective as a vector and simultaneously optimize each of its components so that the entire Pareto-optimal set of solutions could be estimated. This paper proposes such a novel multi-objective methodology using a non-dominated sorting genetic algorithm for waveform inversion of multicomponent seismic data. The applicability of the method is demonstrated using synthetic data generated from multilayer models based on a real well log. We document that the proposed method can reliably extract subsurface elastic parameters and density from multicomponent seismic data both when the subsurface is considered isotropic and transversely isotropic with a vertical symmetry axis. We also compute approximate uncertainty values in the derived parameters. Although we restrict our inversion applications to horizontally stratified models, we outline a practical procedure of extending the method to approximately include local dips for each source-receiver offset pair. Finally, the applicability of the proposed method is not just limited to seismic inversion but it could be used to invert different data types not only requiring multiple objectives but also multiple physics to describe them.

Multi-objective optimization of laser-scribed micro grooves on AZO conductive thin film using Data Envelopment Analysis

NASA Astrophysics Data System (ADS)

Kuo, Chung-Feng Jeffrey; Quang Vu, Huy; Gunawan, Dewantoro; Lan, Wei-Luen

2012-09-01

Laser scribing process has been considered as an effective approach for surface texturization on thin film solar cell. In this study, a systematic method for optimizing multi-objective process parameters of fiber laser system was proposed to achieve excellent quality characteristics, such as the minimum scribing line width, the flattest trough bottom, and the least processing edge surface bumps for increasing incident light absorption of thin film solar cell. First, the Taguchi method (TM) obtained useful statistical information through the orthogonal array with relatively fewer experiments. However, TM is only appropriate to optimize single-objective problems and has to rely on engineering judgment for solving multi-objective problems that can cause uncertainty to some degree. The back-propagation neural network (BPNN) and data envelopment analysis (DEA) were utilized to estimate the incomplete data and derive the optimal process parameters of laser scribing system. In addition, analysis of variance (ANOVA) method was also applied to identify the significant factors which have the greatest effects on the quality of scribing process; in other words, by putting more emphasis on these controllable and profound factors, the quality characteristics of the scribed thin film could be effectively enhanced. The experiments were carried out on ZnO:Al (AZO) transparent conductive thin film with a thickness of 500 nm and the results proved that the proposed approach yields better anticipated improvements than that of the TM which is only superior in improving one quality while sacrificing the other qualities. The results of confirmation experiments have showed the reliability of the proposed method.

An MILP-based cross-layer optimization for a multi-reader arbitration in the UHF RFID system.

PubMed

Choi, Jinchul; Lee, Chaewoo

2011-01-01

In RFID systems, the performance of each reader such as interrogation range and tag recognition rate may suffer from interferences from other readers. Since the reader interference can be mitigated by output signal power control, spectral and/or temporal separation among readers, the system performance depends on how to adapt the various reader arbitration metrics such as time, frequency, and output power to the system environment. However, complexity and difficulty of the optimization problem increase with respect to the variety of the arbitration metrics. Thus, most proposals in previous study have been suggested to primarily prevent the reader collision with consideration of one or two arbitration metrics. In this paper, we propose a novel cross-layer optimization design based on the concept of combining time division, frequency division, and power control not only to solve the reader interference problem, but also to achieve the multiple objectives such as minimum interrogation delay, maximum reader utilization, and energy efficiency. Based on the priority of the multiple objectives, our cross-layer design optimizes the system sequentially by means of the mixed-integer linear programming. In spite of the multi-stage optimization, the optimization design is formulated as a concise single mathematical form by properly assigning a weight to each objective. Numerical results demonstrate the effectiveness of the proposed optimization design.

An MILP-Based Cross-Layer Optimization for a Multi-Reader Arbitration in the UHF RFID System

PubMed Central

Choi, Jinchul; Lee, Chaewoo

2011-01-01

In RFID systems, the performance of each reader such as interrogation range and tag recognition rate may suffer from interferences from other readers. Since the reader interference can be mitigated by output signal power control, spectral and/or temporal separation among readers, the system performance depends on how to adapt the various reader arbitration metrics such as time, frequency, and output power to the system environment. However, complexity and difficulty of the optimization problem increase with respect to the variety of the arbitration metrics. Thus, most proposals in previous study have been suggested to primarily prevent the reader collision with consideration of one or two arbitration metrics. In this paper, we propose a novel cross-layer optimization design based on the concept of combining time division, frequency division, and power control not only to solve the reader interference problem, but also to achieve the multiple objectives such as minimum interrogation delay, maximum reader utilization, and energy efficiency. Based on the priority of the multiple objectives, our cross-layer design optimizes the system sequentially by means of the mixed-integer linear programming. In spite of the multi-stage optimization, the optimization design is formulated as a concise single mathematical form by properly assigning a weight to each objective. Numerical results demonstrate the effectiveness of the proposed optimization design. PMID:22163743

Cognitive Nonlinear Radar

DTIC Science & Technology

2013-01-01

intelligently selecting waveform parameters using adaptive algorithms. The adaptive algorithms optimize the waveform parameters based on (1) the EM...the environment. 15. SUBJECT TERMS cognitive radar, adaptive sensing, spectrum sensing, multi-objective optimization, genetic algorithms, machine...detection and classification block diagram. .........................................................6 Figure 5. Genetic algorithm block diagram

Parana Basin Structure from Multi-Objective Inversion of Surface Wave and Receiver Function by Competent Genetic Algorithm

NASA Astrophysics Data System (ADS)

An, M.; Assumpcao, M.

2003-12-01

The joint inversion of receiver function and surface wave is an effective way to diminish the influences of the strong tradeoff among parameters and the different sensitivity to the model parameters in their respective inversions, but the inversion problem becomes more complex. Multi-objective problems can be much more complicated than single-objective inversion in the model selection and optimization. If objectives are involved and conflicting, models can be ordered only partially. In this case, Pareto-optimal preference should be used to select solutions. On the other hand, the inversion to get only a few optimal solutions can not deal properly with the strong tradeoff between parameters, the uncertainties in the observation, the geophysical complexities and even the incompetency of the inversion technique. The effective way is to retrieve the geophysical information statistically from many acceptable solutions, which requires more competent global algorithms. Competent genetic algorithms recently proposed are far superior to the conventional genetic algorithm and can solve hard problems quickly, reliably and accurately. In this work we used one of competent genetic algorithms, Bayesian Optimization Algorithm as the main inverse procedure. This algorithm uses Bayesian networks to draw out inherited information and can use Pareto-optimal preference in the inversion. With this algorithm, the lithospheric structure of Paran"› basin is inverted to fit both the observations of inter-station surface wave dispersion and receiver function.

Stochastic multi-objective model for optimal energy exchange optimization of networked microgrids with presence of renewable generation under risk-based strategies.

PubMed

Gazijahani, Farhad Samadi; Ravadanegh, Sajad Najafi; Salehi, Javad

2018-02-01

The inherent volatility and unpredictable nature of renewable generations and load demand pose considerable challenges for energy exchange optimization of microgrids (MG). To address these challenges, this paper proposes a new risk-based multi-objective energy exchange optimization for networked MGs from economic and reliability standpoints under load consumption and renewable power generation uncertainties. In so doing, three various risk-based strategies are distinguished by using conditional value at risk (CVaR) approach. The proposed model is specified as a two-distinct objective function. The first function minimizes the operation and maintenance costs, cost of power transaction between upstream network and MGs as well as power loss cost, whereas the second function minimizes the energy not supplied (ENS) value. Furthermore, the stochastic scenario-based approach is incorporated into the approach in order to handle the uncertainty. Also, Kantorovich distance scenario reduction method has been implemented to reduce the computational burden. Finally, non-dominated sorting genetic algorithm (NSGAII) is applied to minimize the objective functions simultaneously and the best solution is extracted by fuzzy satisfying method with respect to risk-based strategies. To indicate the performance of the proposed model, it is performed on the modified IEEE 33-bus distribution system and the obtained results show that the presented approach can be considered as an efficient tool for optimal energy exchange optimization of MGs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Synthesis of nanometre-thick MoO3 sheets

NASA Astrophysics Data System (ADS)

Kalantar-Zadeh, Kourosh; Tang, Jianshi; Wang, Minsheng; Wang, Kang L.; Shailos, Alexandros; Galatsis, Kosmas; Kojima, Robert; Strong, Veronica; Lech, Andrew; Wlodarski, Wojtek; Kaner, Richard B.

2010-03-01

The formation of MoO3 sheets of nanoscale thickness is described. They are made from several fundamental sheets of orthorhombic α-MoO3, which can be processed in large quantities via a low cost synthesis route that combines thermal evaporation and mechanical exfoliation. These fundamental sheets consist of double-layers of linked distorted MoO6 octahedra. Atomic force microscopy (AFM) measurements show that the minimum resolvable thickness of these sheets is 1.4 nm which is equivalent to the thickness of two double-layers within one unit cell of the α-MoO3 crystal.

Fabrication of Pt nanoparticles decorated Gd-doped Bi2MoO6 nanosheets: Design, radicals regulating and mechanism of Gd/Pt-Bi2MoO6 photocatalyst

NASA Astrophysics Data System (ADS)

Li, Hongda; Li, Wenjun; Wang, Fangzhi; Liu, Xintong; Ren, Chaojun; Miao, Xiao

2018-01-01

A new Pt nanoparticles decorated Gd-doped Bi2MoO6 photocatalyst was synthesized by the hydrothermal process and in-situ reduction method. The crystal structure, morphology, chemical state and optical property of the obtained photocatalysts were investigated. The activities of photocatalysts were also evaluated by the degradation of Rhodamine B, Tetracyclines and 4-Chlorophenol under visible light irradiation, and the results indicated that the Gd/Pt co-modified Bi2MoO6 sample shows better photocatalytic activity. Meanwhile, the results of trapping experiments and Electron Spin Resonance (ESR) spectra demonstrated that the rad OH radicals can be formed by doping of Gd3+ ions, and the addition of Pt was conducive to the producing of more • O2- and rad OH radicals. Also the results from the degradation of 4-chlorophenol implied that the formed rad OH radicals in the system of Gd/Pt-BMO possess stronger oxidizability than • O2- radicals for degrading the special organics which are difficult to be mineralized. Additionally, the mechanism about the excellent photocatalytic activity of Gd/Pt co-modified Bi2MoO6 was also discussed.

Ultrasensitive Surface-Enhanced Raman Spectroscopy Detection Based on Amorphous Molybdenum Oxide Quantum Dots.

PubMed

Li, Hao; Xu, Qun; Wang, Xuzhe; Liu, Wei

2018-06-07

Surface-enhanced Raman spectroscopy (SERS) based on plasmonic semiconductive material has been proved to be an efficient tool to detect trace of substances, while the relatively weak plasmon resonance compared with noble metal materials restricts its practical application. Herein, for the first time a facile method to fabricate amorphous H x MoO 3 quantum dots with tunable plasmon resonance is developed by a controlled oxidization route. The as-prepared amorphous H x MoO 3 quantum dots show tunable plasmon resonance in the region of visible and near-infrared light. Moreover, the tunability induced by SC CO 2 is analyzed by a molecule kinetic theory combined with a molecular thermodynamic model. More importantly, the ultrahigh enhancement factor of amorphous H x MoO 3 quantum dots detecting on methyl blue can be up to 9.5 × 10 5 with expending the limit of detection to 10 -9 m. Such a remarkable porperty can also be found in this H x MoO 3 -based sensor with Rh6G and RhB as probe molecules, suggesting that the amorphous H x MoO 3 quantum dot is an efficient candidate for SERS on molecule detection in high precision. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Analysis of the Multi Strategy Goal Programming for Micro-Grid Based on Dynamic ant Genetic Algorithm

NASA Astrophysics Data System (ADS)

Qiu, J. P.; Niu, D. X.

Micro-grid is one of the key technologies of the future energy supplies. Take economic planning. reliability, and environmental protection of micro grid as a basis for the analysis of multi-strategy objective programming problems for micro grid which contains wind power, solar power, and battery and micro gas turbine. Establish the mathematical model of each power generation characteristics and energy dissipation. and change micro grid planning multi-objective function under different operating strategies to a single objective model based on AHP method. Example analysis shows that in combination with dynamic ant mixed genetic algorithm can get the optimal power output of this model.

Investigation of trunk muscle activities during lifting using a multi-objective optimization-based model and intelligent optimization algorithms.

PubMed

Ghiasi, Mohammad Sadegh; Arjmand, Navid; Boroushaki, Mehrdad; Farahmand, Farzam

2016-03-01

A six-degree-of-freedom musculoskeletal model of the lumbar spine was developed to predict the activity of trunk muscles during light, moderate and heavy lifting tasks in standing posture. The model was formulated into a multi-objective optimization problem, minimizing the sum of the cubed muscle stresses and maximizing the spinal stability index. Two intelligent optimization algorithms, i.e., the vector evaluated particle swarm optimization (VEPSO) and nondominated sorting genetic algorithm (NSGA), were employed to solve the optimization problem. The optimal solution for each task was then found in the way that the corresponding in vivo intradiscal pressure could be reproduced. Results indicated that both algorithms predicted co-activity in the antagonistic abdominal muscles, as well as an increase in the stability index when going from the light to the heavy task. For all of the light, moderate and heavy tasks, the muscles' activities predictions of the VEPSO and the NSGA were generally consistent and in the same order of the in vivo electromyography data. The proposed methodology is thought to provide improved estimations for muscle activities by considering the spinal stability and incorporating the in vivo intradiscal pressure data.

Three-Dimensional Reconstruction from Single Image Base on Combination of CNN and Multi-Spectral Photometric Stereo.

PubMed

Lu, Liang; Qi, Lin; Luo, Yisong; Jiao, Hengchao; Dong, Junyu

2018-03-02

Multi-spectral photometric stereo can recover pixel-wise surface normal from a single RGB image. The difficulty lies in that the intensity in each channel is the tangle of illumination, albedo and camera response; thus, an initial estimate of the normal is required in optimization-based solutions. In this paper, we propose to make a rough depth estimation using the deep convolutional neural network (CNN) instead of using depth sensors or binocular stereo devices. Since high-resolution ground-truth data is expensive to obtain, we designed a network and trained it with rendered images of synthetic 3D objects. We use the model to predict initial normal of real-world objects and iteratively optimize the fine-scale geometry in the multi-spectral photometric stereo framework. The experimental results illustrate the improvement of the proposed method compared with existing methods.

Three-Dimensional Reconstruction from Single Image Base on Combination of CNN and Multi-Spectral Photometric Stereo

PubMed Central

Lu, Liang; Qi, Lin; Luo, Yisong; Jiao, Hengchao; Dong, Junyu

2018-01-01

Multi-spectral photometric stereo can recover pixel-wise surface normal from a single RGB image. The difficulty lies in that the intensity in each channel is the tangle of illumination, albedo and camera response; thus, an initial estimate of the normal is required in optimization-based solutions. In this paper, we propose to make a rough depth estimation using the deep convolutional neural network (CNN) instead of using depth sensors or binocular stereo devices. Since high-resolution ground-truth data is expensive to obtain, we designed a network and trained it with rendered images of synthetic 3D objects. We use the model to predict initial normal of real-world objects and iteratively optimize the fine-scale geometry in the multi-spectral photometric stereo framework. The experimental results illustrate the improvement of the proposed method compared with existing methods. PMID:29498703

Multi-response optimization of T300/epoxy prepreg tape-wound cylinder by grey relational analysis coupled with the response surface method

NASA Astrophysics Data System (ADS)

Kang, Chao; Shi, Yaoyao; He, Xiaodong; Yu, Tao; Deng, Bo; Zhang, Hongji; Sun, Pengcheng; Zhang, Wenbin

2017-09-01

This study investigates the multi-objective optimization of quality characteristics for a T300/epoxy prepreg tape-wound cylinder. The method integrates the Taguchi method, grey relational analysis (GRA) and response surface methodology, and is adopted to improve tensile strength and reduce residual stress. In the winding process, the main process parameters involving winding tension, pressure, temperature and speed are selected to evaluate the parametric influences on tensile strength and residual stress. Experiments are conducted using the Box-Behnken design. Based on principal component analysis, the grey relational grades are properly established to convert multi-responses into an individual objective problem. Then the response surface method is used to build a second-order model of grey relational grade and predict the optimum parameters. The predictive accuracy of the developed model is proved by two test experiments with a low prediction error of less than 7%. The following process parameters, namely winding tension 124.29 N, pressure 2000 N, temperature 40 °C and speed 10.65 rpm, have the highest grey relational grade and give better quality characteristics in terms of tensile strength and residual stress. The confirmation experiment shows that better results are obtained with GRA improved by the proposed method than with ordinary GRA. The proposed method is proved to be feasible and can be applied to optimize the multi-objective problem in the filament winding process.

"Hexagonal molybdenum trioxide"--known for 100 years and still a fount of new discoveries.

PubMed

Lunk, Hans-Joachim; Hartl, Hans; Hartl, Monika A; Fait, Martin J G; Shenderovich, Ilya G; Feist, Michael; Frisk, Timothy A; Daemen, Luke L; Mauder, Daniel; Eckelt, Reinhard; Gurinov, Andrey A

2010-10-18

In 1906, the preparation of “molybdic acid hydrate” was published by Arthur Rosenheim. Over the past 40 years, a multitude of isostructural compounds, which exist within a wide phase range of the system MoO3−NH3−H2O, have been published. The reported molecular formulas of “hexagonal molybdenum oxide” varied from MoO3 to MoO3·0.33NH3 to MoO3·nH2O (0.09 ≤ n ≤ 0.69) to MoO3·mNH3·nH2O (0.09 ≤ m ≤ 0.20; 0.18 ≤ n ≤ 0.60). Samples, prepared by the acidification route were investigated using thermal analysis coupled online to a mass spectrometer for evolved gas analysis, X-ray powder diffraction, Fourier transform infrared, Raman, magic-angle-spinning 1H- and 15N NMR spectroscopy, and incoherent inelastic neutron scattering. A comprehensive characterization of these samples will lead to a better understanding of their structure and physical properties as well as uncover the underlying relationship between the various compositions. The synthesized polymeric parent samples can be represented by the structural formula (NH4)(x∞)(3)[Mo(y square 1−y)O(3y)(OH)(x)(H2O)(m−n)]·nH2O with 0.10 ≤ x ≤ 0.14, 0.84 ≤ y ≤ 0.88, and m + n ≥ 3 − x − 3y. The X-ray study of a selected monocrystal confirmed the presence of the well-known 3D framework of edge- and corner-sharing MoO6 octahedra. The colorless monocrystal crystallizes in the hexagonal system with space group P6(3)/m, Z = 6, and unit cell parameters of a = 10.527(1) Å, c = 3.7245(7) Å, V = 357.44(8) Å3, and ρ = 3.73 g·cm(−3). The structure of the prepared monocrystal can best be described by the structural formula (NH4)(0.13∞)(3)[Mo(0.86 square 0.14)O2.58(OH)0.13(H2O)(0.29−n)]·nH2O, which is consistent with the existence of one vacancy (square) for six molybdenum sites. The sample MoO3·0.326NH3·0.343H2O, prepared by the ammoniation of a partially dehydrated MoO3·0.170NH3·0.153H2O with dry gaseous ammonia, accommodates NH3 in the hexagonal tunnels, in addition to [NH4]+ cations and H2O. The “chimie douce” reaction of MoO3·0.155NH3·0.440H2O with a 1:1 mixture of NO/NO2 at 100 °C resulted in the synthesis of MoO3·0.539H2O. This material is of great interest as a host of various molecules and cations.

Person-Environment Congruence: A Response to the Moos Perspective.

ERIC Educational Resources Information Center

Walsh, W. Bruce

1987-01-01

Describes Moos' conceptual framework which delineates how perceptions of collective human environments (social climates) influence behavior, with congruence achieved when individuals adapt their preferences in selecting environments, as underemphasizing the role of individual variables and the actual environment. Advocates continual analysis of…

Robustness of mission plans for unmanned aircraft

NASA Astrophysics Data System (ADS)

Niendorf, Moritz

This thesis studies the robustness of optimal mission plans for unmanned aircraft. Mission planning typically involves tactical planning and path planning. Tactical planning refers to task scheduling and in multi aircraft scenarios also includes establishing a communication topology. Path planning refers to computing a feasible and collision-free trajectory. For a prototypical mission planning problem, the traveling salesman problem on a weighted graph, the robustness of an optimal tour is analyzed with respect to changes to the edge costs. Specifically, the stability region of an optimal tour is obtained, i.e., the set of all edge cost perturbations for which that tour is optimal. The exact stability region of solutions to variants of the traveling salesman problems is obtained from a linear programming relaxation of an auxiliary problem. Edge cost tolerances and edge criticalities are derived from the stability region. For Euclidean traveling salesman problems, robustness with respect to perturbations to vertex locations is considered and safe radii and vertex criticalities are introduced. For weighted-sum multi-objective problems, stability regions with respect to changes in the objectives, weights, and simultaneous changes are given. Most critical weight perturbations are derived. Computing exact stability regions is intractable for large instances. Therefore, tractable approximations are desirable. The stability region of solutions to relaxations of the traveling salesman problem give under approximations and sets of tours give over approximations. The application of these results to the two-neighborhood and the minimum 1-tree relaxation are discussed. Bounds on edge cost tolerances and approximate criticalities are obtainable likewise. A minimum spanning tree is an optimal communication topology for minimizing the cumulative transmission power in multi aircraft missions. The stability region of a minimum spanning tree is given and tolerances, stability balls, and criticalities are derived. This analysis is extended to Euclidean minimum spanning trees. This thesis aims at enabling increased mission performance by providing means of assessing the robustness and optimality of a mission and methods for identifying critical elements. Examples of the application to mission planning in contested environments, cargo aircraft mission planning, multi-objective mission planning, and planning optimal communication topologies for teams of unmanned aircraft are given.

Multi-objective optimization of hole characteristics during pulsed Nd:YAG laser microdrilling of gamma-titanium aluminide alloy sheet

NASA Astrophysics Data System (ADS)

Biswas, R.; Kuar, A. S.; Mitra, S.

2014-09-01

Nd:YAG laser microdrilled holes on gamma-titanium aluminide, a newly developed alloy having wide applications in turbine blades, engine valves, cases, metal cutting tools, missile components, nuclear fuel and biomedical engineering, are important from the dimensional accuracy and quality of hole point of view. Keeping this in mind, a central composite design (CCD) based on response surface methodology (RSM) is employed for multi-objective optimization of pulsed Nd:YAG laser microdrilling operation on gamma-titanium aluminide alloy sheet to achieve optimum hole characteristics within existing resources. The three characteristics such as hole diameter at entry, hole diameter at exit and hole taper have been considered for simultaneous optimization. The individual optimization of all three responses has also been carried out. The input parameters considered are lamp current, pulse frequency, assist air pressure and thickness of the job. The responses at predicted optimum parameter level are in good agreement with the results of confirmation experiments conducted for verification tests.

A spatial multi-objective optimization model for sustainable urban wastewater system layout planning.

PubMed

Dong, X; Zeng, S; Chen, J

2012-01-01

Design of a sustainable city has changed the traditional centralized urban wastewater system towards a decentralized or clustering one. Note that there is considerable spatial variability of the factors that affect urban drainage performance including urban catchment characteristics. The potential options are numerous for planning the layout of an urban wastewater system, which are associated with different costs and local environmental impacts. There is thus a need to develop an approach to find the optimal spatial layout for collecting, treating, reusing and discharging the municipal wastewater of a city. In this study, a spatial multi-objective optimization model, called Urban wastewateR system Layout model (URL), was developed. It is solved by a genetic algorithm embedding Monte Carlo sampling and a series of graph algorithms. This model was illustrated by a case study in a newly developing urban area in Beijing, China. Five optimized system layouts were recommended to the local municipality for further detailed design.

Calculating and controlling the error of discrete representations of Pareto surfaces in convex multi-criteria optimization.

PubMed

Craft, David

2010-10-01

A discrete set of points and their convex combinations can serve as a sparse representation of the Pareto surface in multiple objective convex optimization. We develop a method to evaluate the quality of such a representation, and show by example that in multiple objective radiotherapy planning, the number of Pareto optimal solutions needed to represent Pareto surfaces of up to five dimensions grows at most linearly with the number of objectives. The method described is also applicable to the representation of convex sets. Copyright © 2009 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Hydrogen Doping into MoO3 Supports toward Modulated Metal-Support Interactions and Efficient Furfural Hydrogenation on Iridium Nanocatalysts.

PubMed

Xie, Lifang; Chen, Ting; Chan, Hang Cheong; Shu, Yijin; Gao, Qingsheng

2018-03-16

As promising supports, reducible metal oxides afford strong metal-support interactions to achieve efficient catalysis, which relies on their band states and surface stoichiometry. In this study, in situ and controlled hydrogen doping (H doping) by means of H 2 spillover was employed to engineer the metal-support interactions in hydrogenated MoO x -supported Ir (Ir/H-MoO x ) catalysts and thus promote furfural hydrogenation to furfuryl alcohol. By easily varying the reduction temperature, the resulting H doping in a controlled manner tailors low-valence Mo species (Mo 5+ and Mo 4+ ) on H-MoO x supports, thereby promoting charge redistribution on Ir and H-MoO x interfaces. This further leads to clear differences in H 2 chemisorption on Ir, which illustrates its potential for catalytic hydrogenation. As expected, the optimal Ir/H-MoO x with controlled H doping afforded high activity (turnover frequency: 4.62 min -1 ) and selectivity (>99 %) in furfural hydrogenation under mild conditions (T=30 °C, PH2 =2 MPa), which means it performs among the best of current catalysts. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Multi Dimensional Honey Bee Foraging Algorithm Based on Optimal Energy Consumption

NASA Astrophysics Data System (ADS)

Saritha, R.; Vinod Chandra, S. S.

2017-10-01

In this paper a new nature inspired algorithm is proposed based on natural foraging behavior of multi-dimensional honey bee colonies. This method handles issues that arise when food is shared from multiple sources by multiple swarms at multiple destinations. The self organizing nature of natural honey bee swarms in multiple colonies is based on the principle of energy consumption. Swarms of multiple colonies select a food source to optimally fulfill the requirements of its colonies. This is based on the energy requirement for transporting food between a source and destination. Minimum use of energy leads to maximizing profit in each colony. The mathematical model proposed here is based on this principle. This has been successfully evaluated by applying it on multi-objective transportation problem for optimizing cost and time. The algorithm optimizes the needs at each destination in linear time.

Novel Approach to Facilitating Tradeoff Multi-Objective Grouping Optimization

ERIC Educational Resources Information Center

Lin, Yu-Shih; Chang, Yi-Chun; Chu, Chih-Ping

2016-01-01

The grouping problem is critical in collaborative learning (CL) because of the complexity and difficulty in adequate grouping, based on various grouping criteria and numerous learners. Previous studies have paid attention to certain research questions, and the consideration for a number of learner characteristics has arisen. Such a multi-objective…

Antimicrobial and biocorrosion-resistant MoO3-SiO2 nanocomposite coating prepared by double cathode glow discharge technique

NASA Astrophysics Data System (ADS)

Xu, Jiang; Sun, Teng Teng; Jiang, Shuyun; Munroe, Paul; Xie, Zong-Han

2018-07-01

In this investigation, a MoO3-SiO2 nanocomposite coating was developed on a 316L stainless steel (SS) substrate by double-cathode glow discharge deposition. Chemical valence states, phase composition and microstructure features of the nanocomposite coating were studied using X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), scanning electron microscopy (SEM) and transmission electron microscopy (TEM). It was found that the nanocomposite coating was composed of a mixture of crystalline MoO3 and amorphous phases, in which amorphous SiO2 phase was embedded between the hexagonal-structured MoO3 grains with an average grain size of ∼8.4 nm. Nanoindentation and scratch tests, together with SEM and TEM observation of locally deformed regions, indicated that the nanocomposite coating exhibited high load-bearing capacity due to a combination of high hardness and good adhesion. Contact angle measurements suggested that the nanocomposite coating was more hydrophobic than uncoated 316L SS. The anti-bacterial activity of the MoO3-SiO2 nanocomposite coating against two bacterial strains (E. coli and S. aureus) was determined by the spread plate method. This showed that both bacterial strains exposed to the coating suffered a significant loss of viability. The influences of sulfate-reducing bacteria (SRB) on the electrochemical behavior of the MoO3-SiO2 nanocomposite coating in modified Postgate's C seawater (PCS) medium were investigated through potentiodynamic polarization and electrochemical impedance spectroscopy (EIS). The electrochemical tests revealed that the coating had a greater resistance to microbiologically influenced corrosion induced by SRB than uncoated 316L SS. This was corroborated by electrochemical testing (potentiodynamic polarization and EIS), in conjunction with SEM observations of the corroded surfaces.

Effect of zirconia morphology on sulfur-resistant methanation performance of MoO3/ZrO2 catalyst

NASA Astrophysics Data System (ADS)

Liu, Chen; Wang, Weihan; Xu, Yan; Li, Zhenhua; Wang, Baowei; Ma, Xinbin

2018-05-01

Two kinds of ZrO2 support with different morphologies were prepared by facile solvothermal method in different solvents. The obtained two supports showed monoclinic zirconia (m-ZrO2) and tetragonal zirconia (t-ZrO2) phase with similar crystalline size. Their supported Mo-based catalysts were prepared by impregnation method and the effect of zirconia morphology on the performance of sulfur-resistant methanation was examined. The results indicated that the MoO3/m-ZrO2 has higher CO conversion than the MoO3/t-ZrO2 catalyst. Characterizations by XRD, Raman, H2-TPR and IR confirmed that the m-ZrO2 is superior to t-ZrO2 for dispersing molybdenum species. In addition, the MoO3/m-ZrO2 catalyst has weaker interaction between support and active Mo speices than the MoO3/t-ZrO2 catalyst, which facilitates to forming active species of nanocrystalline MoS2 layers for sulfur-resistant methanation. The weaker interaction of molybdenum species with m-ZrO2 is related with the more covalent character of the Zrsbnd O bond and more oxygen defective structure of m-ZrO2. A larger number of Lewis acid centers appear on the surface of m-ZrO2, which verified the substantial vacancies on m-ZrO2 exposing coordinately unsaturated Zr3+ and Zr4+ cations. Meanwhile, the less Lewis acid of t-ZrO2 result in stronger interaction between support and molybdenum species and trigger crystalline phase MoO3 and Mosbnd Osbnd Zr linkages.

Synthesis, structure, and catalytic performance in cyclooctene epoxidation of a molybdenum oxide/bipyridine hybrid material: {[MoO3(bipy)][MoO3(H2O)]}n.

PubMed

Abrantes, Marta; Amarante, Tatiana R; Antunes, Margarida M; Gago, Sandra; Paz, Filipe A Almeida; Margiolaki, Irene; Rodrigues, Alírio E; Pillinger, Martyn; Valente, Anabela A; Gonçalves, Isabel S

2010-08-02

The reaction of [MoO(2)Cl(2)(bipy)] (1) (bipy = 2,2'-bipyridine) with water in a Teflon-lined stainless steel autoclave (100 degrees C, 19 h), in an open reflux system with oil bath heating (12 h) or in a microwave synthesis system (120 degrees C, 4 h), gave the molybdenum oxide/bipyridine hybrid material {[MoO(3)(bipy)][MoO(3)(H(2)O)]}(n) (2) as a microcrystalline powder in yields of 72-92%. The crystal structure of 2 determined from synchrotron X-ray powder diffraction data is composed of two distinct neutral one-dimensional polymers: an organic-inorganic polymer, [MoO(3)(bipy)](n), and a purely inorganic chain, [MoO(3)(H(2)O)](n), which are interconnected by O-H...O hydrogen bonding interactions. Compound 2 is a moderately active, stable, and selective catalyst for the epoxidation of cis-cyclooctene at 55 degrees C with tert-butylhydroperoxide (tBuOOH, 5.5 M in decane or 70% aqueous) as the oxidant. Biphasic solid-liquid or triphasic solid-organic-aqueous mixtures are formed, and 1,2-epoxycyclooctane is the only reaction product. When n-hexane is employed as a cosolvent and tBuOOH(decane) is the oxidant, the catalytic reaction is heterogeneous in nature, and the solid catalyst can be recycled and reused without a loss of activity. For comparison, the catalytic performance of the precursor 1 was also investigated. The IR spectra of solids recovered after catalysis indicate that 1 transforms into the organic-inorganic polymer [MoO(3)(bipy)] when the oxidant is tBuOOH(decane) and compound 2 when the oxidant is 70% aqueous tBuOOH.

Three-dimensional nitrogen and sulfur co-doped holey-reduced graphene oxide frameworks anchored with MoO2 nanodots for advanced rechargeable lithium-ion batteries.

PubMed

Pei, Jie; Geng, Hongbo; Ang, Huixiang; Zhang, Lingling; Wei, Huaixin; Cao, Xueqin; Zheng, Junwei; Gu, Hongwei

2018-07-20

In this manuscript, we synthesize a porous three-dimensional anode material consisting of molybdenum dioxide nanodots anchored on nitrogen (N)/sulfur (S) co-doped reduced graphene oxide (GO) (3D MoO 2 /NP-NSG) through hydrothermal, lyophilization and thermal treatment. First, the NP-NSG is formed via hydrothermal treatment using graphene oxide, hydrogen peroxide (H 2 O 2 ), and thiourea as the co-dopant for N and S, followed by calcination of the N/S co-doped GO in the presence of ammonium molybdate tetrahydrate to obtain the 3D MoO 2 /NP-NSG product. This novel material exhibits a series of out-bound electrochemical performances, such as superior conductivity, high specific capacity, and excellent stability. As an anode for lithium-ion batteries (LIBs), the MoO 2 /NP-NSG electrode has a high initial specific capacity (1376 mAh g -1 ), good cycling performance (1250 mAh g -1 after 100 cycles at a current density of 0.2 A g -1 ), and outstanding Coulombic efficiency (99% after 450 cycles at a current density of 1 A g -1 ). Remarkably, the MoO 2 /NP-NSG battery exhibits exceedingly good rate capacities of 1021, 965, 891, 760, 649, 500 and 425 mAh g -1 at different current densities of 200, 500, 1000, 2000, 3000, 4000 and 5000 mA g -1 , respectively. The superb electrochemical performance is owed to the high porosity of the 3D architecture, the synergistic effect contribution from N and S co-doped in the reduced graphene oxide (rGO), and the uniform distribution of MoO 2 nanodots on the rGO surface.

Three-dimensional nitrogen and sulfur co-doped holey-reduced graphene oxide frameworks anchored with MoO2 nanodots for advanced rechargeable lithium-ion batteries

NASA Astrophysics Data System (ADS)

Pei, Jie; Geng, Hongbo; Ang, Huixiang; Zhang, Lingling; Wei, Huaixin; Cao, Xueqin; Zheng, Junwei; Gu, Hongwei

2018-07-01

In this manuscript, we synthesize a porous three-dimensional anode material consisting of molybdenum dioxide nanodots anchored on nitrogen (N)/sulfur (S) co-doped reduced graphene oxide (GO) (3D MoO2/NP-NSG) through hydrothermal, lyophilization and thermal treatment. First, the NP-NSG is formed via hydrothermal treatment using graphene oxide, hydrogen peroxide (H2O2), and thiourea as the co-dopant for N and S, followed by calcination of the N/S co-doped GO in the presence of ammonium molybdate tetrahydrate to obtain the 3D MoO2/NP-NSG product. This novel material exhibits a series of out-bound electrochemical performances, such as superior conductivity, high specific capacity, and excellent stability. As an anode for lithium-ion batteries (LIBs), the MoO2/NP-NSG electrode has a high initial specific capacity (1376 mAh g‑1), good cycling performance (1250 mAh g‑1 after 100 cycles at a current density of 0.2 A g‑1), and outstanding Coulombic efficiency (99% after 450 cycles at a current density of 1 A g‑1). Remarkably, the MoO2/NP-NSG battery exhibits exceedingly good rate capacities of 1021, 965, 891, 760, 649, 500 and 425 mAh g‑1 at different current densities of 200, 500, 1000, 2000, 3000, 4000 and 5000 mA g‑1, respectively. The superb electrochemical performance is owed to the high porosity of the 3D architecture, the synergistic effect contribution from N and S co-doped in the reduced graphene oxide (rGO), and the uniform distribution of MoO2 nanodots on the rGO surface.

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters

PubMed Central

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme. PMID:27362762

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters.

PubMed

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme.

4E analysis and multi objective optimization of a micro gas turbine and solid oxide fuel cell hybrid combined heat and power system

NASA Astrophysics Data System (ADS)

Sanaye, Sepehr; Katebi, Arash

2014-02-01

Energy, exergy, economic and environmental (4E) analysis and optimization of a hybrid solid oxide fuel cell and micro gas turbine (SOFC-MGT) system for use as combined generation of heat and power (CHP) is investigated in this paper. The hybrid system is modeled and performance related results are validated using available data in literature. Then a multi-objective optimization approach based on genetic algorithm is incorporated. Eight system design parameters are selected for the optimization procedure. System exergy efficiency and total cost rate (including capital or investment cost, operational cost and penalty cost of environmental emissions) are the two objectives. The effects of fuel unit cost, capital investment and system power output on optimum design parameters are also investigated. It is observed that the most sensitive and important design parameter in the hybrid system is fuel cell current density which has a significant effect on the balance between system cost and efficiency. The selected design point from the Pareto distribution of optimization results indicates a total system exergy efficiency of 60.7%, with estimated electrical energy cost 0.057 kW-1 h-1, and payback period of about 6.3 years for the investment.

An EGR performance evaluation and decision-making approach based on grey theory and grey entropy analysis

PubMed Central

2018-01-01

Exhaust gas recirculation (EGR) is one of the main methods of reducing NOX emissions and has been widely used in marine diesel engines. This paper proposes an optimized comprehensive assessment method based on multi-objective grey situation decision theory, grey relation theory and grey entropy analysis to evaluate the performance and optimize rate determination of EGR, which currently lack clear theoretical guidance. First, multi-objective grey situation decision theory is used to establish the initial decision-making model according to the main EGR parameters. The optimal compromise between diesel engine combustion and emission performance is transformed into a decision-making target weight problem. After establishing the initial model and considering the characteristics of EGR under different conditions, an optimized target weight algorithm based on grey relation theory and grey entropy analysis is applied to generate the comprehensive evaluation and decision-making model. Finally, the proposed method is successfully applied to a TBD234V12 turbocharged diesel engine, and the results clearly illustrate the feasibility of the proposed method for providing theoretical support and a reference for further EGR optimization. PMID:29377956

An EGR performance evaluation and decision-making approach based on grey theory and grey entropy analysis.

PubMed

Zu, Xianghuan; Yang, Chuanlei; Wang, Hechun; Wang, Yinyan

2018-01-01

Exhaust gas recirculation (EGR) is one of the main methods of reducing NOX emissions and has been widely used in marine diesel engines. This paper proposes an optimized comprehensive assessment method based on multi-objective grey situation decision theory, grey relation theory and grey entropy analysis to evaluate the performance and optimize rate determination of EGR, which currently lack clear theoretical guidance. First, multi-objective grey situation decision theory is used to establish the initial decision-making model according to the main EGR parameters. The optimal compromise between diesel engine combustion and emission performance is transformed into a decision-making target weight problem. After establishing the initial model and considering the characteristics of EGR under different conditions, an optimized target weight algorithm based on grey relation theory and grey entropy analysis is applied to generate the comprehensive evaluation and decision-making model. Finally, the proposed method is successfully applied to a TBD234V12 turbocharged diesel engine, and the results clearly illustrate the feasibility of the proposed method for providing theoretical support and a reference for further EGR optimization.

Thermofluid Analysis of Magnetocaloric Refrigeration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abdelaziz, Omar; Gluesenkamp, Kyle R; Vineyard, Edward Allan

While there have been extensive studies on thermofluid characteristics of different magnetocaloric refrigeration systems, a conclusive optimization study using non-dimensional parameters which can be applied to a generic system has not been reported yet. In this study, a numerical model has been developed for optimization of active magnetic refrigerator (AMR). This model is computationally efficient and robust, making it appropriate for running the thousands of simulations required for parametric study and optimization. The governing equations have been non-dimensionalized and numerically solved using finite difference method. A parametric study on a wide range of non-dimensional numbers has been performed. While themore » goal of AMR systems is to improve the performance of competitive parameters including COP, cooling capacity and temperature span, new parameters called AMR performance index-1 have been introduced in order to perform multi objective optimization and simultaneously exploit all these parameters. The multi-objective optimization is carried out for a wide range of the non-dimensional parameters. The results of this study will provide general guidelines for designing high performance AMR systems.« less

Multi-objective optimization for an automated and simultaneous phase and baseline correction of NMR spectral data

NASA Astrophysics Data System (ADS)

Sawall, Mathias; von Harbou, Erik; Moog, Annekathrin; Behrens, Richard; Schröder, Henning; Simoneau, Joël; Steimers, Ellen; Neymeyr, Klaus

2018-04-01

Spectral data preprocessing is an integral and sometimes inevitable part of chemometric analyses. For Nuclear Magnetic Resonance (NMR) spectra a possible first preprocessing step is a phase correction which is applied to the Fourier transformed free induction decay (FID) signal. This preprocessing step can be followed by a separate baseline correction step. Especially if series of high-resolution spectra are considered, then automated and computationally fast preprocessing routines are desirable. A new method is suggested that applies the phase and the baseline corrections simultaneously in an automated form without manual input, which distinguishes this work from other approaches. The underlying multi-objective optimization or Pareto optimization provides improved results compared to consecutively applied correction steps. The optimization process uses an objective function which applies strong penalty constraints and weaker regularization conditions. The new method includes an approach for the detection of zero baseline regions. The baseline correction uses a modified Whittaker smoother. The functionality of the new method is demonstrated for experimental NMR spectra. The results are verified against gravimetric data. The method is compared to alternative preprocessing tools. Additionally, the simultaneous correction method is compared to a consecutive application of the two correction steps.

Pattern recognition with composite correlation filters designed with multi-object combinatorial optimization

DOE PAGES

Awwal, Abdul; Diaz-Ramirez, Victor H.; Cuevas, Andres; ...

2014-10-23

Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Furthermore, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, formore » a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.« less

Pattern recognition with composite correlation filters designed with multi-object combinatorial optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Awwal, Abdul; Diaz-Ramirez, Victor H.; Cuevas, Andres

Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Furthermore, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, formore » a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.« less

Multi-objective optimization on laser solder jet bonding process in head gimbal assembly using the response surface methodology

NASA Astrophysics Data System (ADS)

Deeying, J.; Asawarungsaengkul, K.; Chutima, P.

2018-01-01

This paper aims to investigate the effect of laser solder jet bonding parameters to the solder joints in Head Gimbal Assembly. Laser solder jet bonding utilizes the fiber laser to melt solder ball in capillary. The molten solder is transferred to two bonding pads by nitrogen gas. The response surface methodology have been used to investigate the effects of laser energy, wait time, nitrogen gas pressure, and focal position on the shear strength of solder joints and the change of pitch static attitude (PSA). The response surface methodology is employed to establish the reliable mathematical relationships between the laser soldering parameters and desired responses. Then, multi-objective optimization is conducted to determine the optimal process parameters that can enhance the joint shear strength and minimize the change of PSA. The validation test confirms that the predicted value has good agreement with the actual value.

Optimal Appearance Model for Visual Tracking

PubMed Central

Wang, Yuru; Jiang, Longkui; Liu, Qiaoyuan; Yin, Minghao

2016-01-01

Many studies argue that integrating multiple cues in an adaptive way increases tracking performance. However, what is the definition of adaptiveness and how to realize it remains an open issue. On the premise that the model with optimal discriminative ability is also optimal for tracking the target, this work realizes adaptiveness and robustness through the optimization of multi-cue integration models. Specifically, based on prior knowledge and current observation, a set of discrete samples are generated to approximate the foreground and background distribution. With the goal of optimizing the classification margin, an objective function is defined, and the appearance model is optimized by introducing optimization algorithms. The proposed optimized appearance model framework is embedded into a particle filter for a field test, and it is demonstrated to be robust against various kinds of complex tracking conditions. This model is general and can be easily extended to other parameterized multi-cue models. PMID:26789639

76 FR 67238 - Self-Regulatory Organizations; BATS Exchange, Inc.; Order Approving Proposed Rule Change by BATS...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-31

...'' or ``MOO'' order, ``Limit-On-Open'' or ``LOO'' order, and a ``Late-Limit-On-Open'' or ``LLOO'' order... Rule 11.23(a)(8) defines ``Eligible Auction Orders'' as any MOO, LOO, LLOO, MOC, LOC, or LLOC order...

MoO3/nano-Si heterostructure based highly sensitive and acetone selective sensor prototype: a key to non-invasive detection of diabetes.

PubMed

Dwivedi, Priyanka; Dhanekar, Saakshi; Das, Samaresh

2018-07-06

This paper presents the development of an extremely sensitive and selective acetone sensor prototype which can be used as a platform for non-invasive diabetes detection through exhaled human breath. The miniaturized sensors were produced in high yield with the use of standard microfabrication processes. The sensors were based on a heterostructure composed of MoO 3 and nano-porous silicon (NPS). Features like acetone selective, enhanced sensor response and 0.5 ppm detection limit were observed upon introduction of MoO 3 on the NPS. The sensors were found to be repeatable and stable for almost 1 year, as tested under humid conditions at room temperature. It was inferred that the interface resistance of MoO 3 and NPS played a key role in the sensing mechanism. With the use of breath analysis and lab-on-chip, medical diagnosis procedures can be simplified and provide solutions for point-of-care testing.

Determination of Mo- and Ca-isotope ratios in Ca100MoO4 crystal for AMoRE-I experiment

NASA Astrophysics Data System (ADS)

Karki, S.; Aryal, P.; Kim, H. J.; Kim, Y. D.; Park, H. K.

2018-01-01

The first phase of the AMoRE (Advanced Mo-based Rare process Experiment) is to search for neutrinoless double-beta decay of 100Mo with calcium molybdate (Ca100MoO4) crystals enriched in 100Mo and depleted in 48Ca using a cryogenic technique at Yangyang underground laboratory in Korea. It is important to know 100Mo- and 48Ca-isotope ratios in Ca100MoO4 crystal to estimate half-life of 100Mo decays and to 2 νββ background from 48Ca. We employed the ICP-MS (Inductive Coupled Plasma Mass Spectrometer) to measure 100Mo- and 48Ca-isotope ratios in Ca100MoO4 crystal. The measured results for 100Mo- and 48Ca-isotope ratios in the crystal are (94 . 6 ± 2 . 8) % and (0 . 00211 ± 0 . 00006) %, respectively, where errors are included both statistical and systematic uncertainties.

Thermal desorption of dimethyl methylphosphonate from MoO 3

DOE PAGES

Head, Ashley R.; Tang, Xin; Hicks, Zachary; ...

2017-03-03

Organophosphonates are used as chemical warfare agents, pesticides, and corrosion inhibitors. New materials for the sorption, detection, and decomposition of these compounds are urgently needed. To facilitate materials and application innovation, a better understanding of the interactions between organophosphonates and surfaces is required. To this end, we have used diffuse reflectance infrared Fourier transform spectroscopy to investigate the adsorption geometry of dimethyl methylphosphonate (DMMP) on MoO 3, a material used in chemical warfare agent filtration devices. We further applied ambient pressure X-ray photoelectron spectroscopy and temperature programmed desorption to study the adsorption and desorption of DMMP. While DMMP adsorbs intactmore » on MoO 3, desorption depends on coverage and partial pressure. At low coverages under UHV conditions, the intact adsorption is reversible. Decomposition occurs with higher coverages, as evidenced by PCH x and PO x decomposition products on the MoO 3 surface. Heating under mTorr partial pressures of DMMP results in product accumulation.« less

Molybdenum modified phosphate glasses studied by 31P MAS NMR and Raman spectroscopy.

PubMed

Szumera, Magdalena

2015-02-25

Glasses have been synthesized in the system P2O5-SiO2-K2O-MgO-CaO modified by addition of MoO3. Glasses were prepared by conventional fusion method from 40 g batches. The influence of Mo-cations on the analysed glass structure was investigated by means of Raman and (31)P MAS-NMR techniques. It has been found that molybdate units can form Mo[MoO4/MoO6]-O-P and/or Mo[MoO4/MoO6]-O-Si bonds with non-bridging oxygens atoms of Q2 methaphosphate units, resulting in the transformation of chain methaphosphate structure into pyrophosphate and finally into orthophosphate structure. It has been also found that increasing amount of MoO3 in the structure of investigated glasses causes their gradual depolymerization and molybdenum ions in the analysed glass matrix act as modifying cations. Copyright © 2014 Elsevier B.V. All rights reserved.

Optimized Multi-Spectral Filter Array Based Imaging of Natural Scenes.

PubMed

Li, Yuqi; Majumder, Aditi; Zhang, Hao; Gopi, M

2018-04-12

Multi-spectral imaging using a camera with more than three channels is an efficient method to acquire and reconstruct spectral data and is used extensively in tasks like object recognition, relighted rendering, and color constancy. Recently developed methods are used to only guide content-dependent filter selection where the set of spectral reflectances to be recovered are known a priori. We present the first content-independent spectral imaging pipeline that allows optimal selection of multiple channels. We also present algorithms for optimal placement of the channels in the color filter array yielding an efficient demosaicing order resulting in accurate spectral recovery of natural reflectance functions. These reflectance functions have the property that their power spectrum statistically exhibits a power-law behavior. Using this property, we propose power-law based error descriptors that are minimized to optimize the imaging pipeline. We extensively verify our models and optimizations using large sets of commercially available wide-band filters to demonstrate the greater accuracy and efficiency of our multi-spectral imaging pipeline over existing methods.

Optimized Multi-Spectral Filter Array Based Imaging of Natural Scenes

PubMed Central

Li, Yuqi; Majumder, Aditi; Zhang, Hao; Gopi, M.

2018-01-01

Multi-spectral imaging using a camera with more than three channels is an efficient method to acquire and reconstruct spectral data and is used extensively in tasks like object recognition, relighted rendering, and color constancy. Recently developed methods are used to only guide content-dependent filter selection where the set of spectral reflectances to be recovered are known a priori. We present the first content-independent spectral imaging pipeline that allows optimal selection of multiple channels. We also present algorithms for optimal placement of the channels in the color filter array yielding an efficient demosaicing order resulting in accurate spectral recovery of natural reflectance functions. These reflectance functions have the property that their power spectrum statistically exhibits a power-law behavior. Using this property, we propose power-law based error descriptors that are minimized to optimize the imaging pipeline. We extensively verify our models and optimizations using large sets of commercially available wide-band filters to demonstrate the greater accuracy and efficiency of our multi-spectral imaging pipeline over existing methods. PMID:29649114