Firstly, definition and methods of reliability assignment are introduced in this paper. And the nonlinear programming based on Lagrange multipliers method is described briefly. Meanwhile the calculation principle and ...
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ISBN:
(纸本)9781479900305
Firstly, definition and methods of reliability assignment are introduced in this paper. And the nonlinear programming based on Lagrange multipliers method is described briefly. Meanwhile the calculation principle and mathematic model are proposed. Secondly, determining the reliability index is discussed in the paper, and the reliability block diagram and allocation model are given. Base on the discussion before and the fight control with longitudinal pitch motion, the feasibility of the nonlinear programming method is needed to verify. Lastly, the allocated system reliability is bigger than the predicted threshold reliability parameter, which verifies that the used allocation method is feasible and reasonable.
A class of semistrictly G-preinvex functions and optimality in nonlinear programming are further discussed. Firstly, the relationships between semistrictly G-preinvex functions and G-preinvex functions are further dis...
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A class of semistrictly G-preinvex functions and optimality in nonlinear programming are further discussed. Firstly, the relationships between semistrictly G-preinvex functions and G-preinvex functions are further discussed. Then, two interesting properties of semistrictly G-preinvexity are given. Finally, two optimality results for nonlinear programming problems are obtained under the assumption of semistrict G-preinvexity. The obtained results are new and different from the corresponding ones in the literature. Some examples are given to illustrate our results.
In today's business environment, many fresh food companies have complex supply networks to distribute their products. For example, agricultural products are distributed through a multiechelon supply chain which in...
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In today's business environment, many fresh food companies have complex supply networks to distribute their products. For example, agricultural products are distributed through a multiechelon supply chain which includes agricultural association, agricultural produce marketing corporations (APMCs), markets, and so forth. In this paper a fresh produce supply network model is designed to determine the optimal service area for APMCs, the replenishment cycle time of APMCs, and the freshness-keeping effort, while maximizing the total profit. The objective is to address the integrated facility location, inventory allocation, and freshness-keeping effort problems. This paper develops an algorithm to solve the nonlinear problem, provides numerical analysis to illustrate the proposed solution procedure, and discusses the effects of various system parameters on the decisions and total profits. A real case of an agricultural product supply chain in Taiwan is used to verify the model. Results of this study can serve as a reference for business managers and administrators.
Interval-valued univex functions are introduced for differentiable programming problems. Optimality and duality results are derived for a class of generalized convex optimization problems with interval-valued univex f...
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Interval-valued univex functions are introduced for differentiable programming problems. Optimality and duality results are derived for a class of generalized convex optimization problems with interval-valued univex functions.
This paper presents a novel adaptable energy management system (EMS) for smart buildings. In this framework we model the energy consumption of a living unit, and its energy exchanges with the surroundings. A unit is a...
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This paper presents a novel adaptable energy management system (EMS) for smart buildings. In this framework we model the energy consumption of a living unit, and its energy exchanges with the surroundings. A unit is a well delimited space inside a building, for which the information on electrical consumption is known. We assume that the unit has no energy exchanges with its neighbors. Examples of units are a household, an office, a restaurant or a gym. We explicitly consider the impact of the outside environment and design features such as building orientation, automatic shading, and double facade. We formulate this problem as a nonlinear optimization model in which the living unit minimizes a performance function subject to the energy flows from and toward the unit as well as the building-specific features. It is solved using off-the-shelf solvers. We present computational experiments to validate the proposed approach, considering different objective functions and several building configurations. The experiments show that our approach enhances the unit's performance and also provides demand flexibility for the grid. We demonstrate that for heating a unit in Montreal, Canada, there are periods where the EMS alone can lower the electricity cost by up to 26% and the energy consumption by up to 14%. If the EMS is combined with smart design features, the electricity cost of heating can be lowered by up to 35%, the cost of cooling by up to 97%, and the energy consumption by up to 49%.
In this paper we propose a framework for Model-based Sequential Optimal Design of Experiments to assist experimenters involved in Vapor-Liquid equilibrium characterization studies to systematically construct thermodyn...
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In this paper we propose a framework for Model-based Sequential Optimal Design of Experiments to assist experimenters involved in Vapor-Liquid equilibrium characterization studies to systematically construct thermodynamically consistent models. The approach uses an initial continuous optimal design obtained via semidefinite programming, and then iterates between two stages (i) model fitting using the information available;and (ii) identification of the next experiment, so that the information content in data is maximized. The procedure stops when the number of experiments reaches the maximum for the experimental program or the dissimilarity between the parameter estimates during two consecutive iterations is below a given threshold. This methodology is exemplified with the D-optimal design of isobaric experiments, for characterizing binary mixtures using the NRTL and UNIQUAC thermodynamic models for liquid phase. Significant reductions of the confidence regions for the parameters are achieved compared with experimental plans where the observations are uniformly distributed over the domain. (C) 2020 Elsevier Ltd. All rights reserved.
Besides electricity, many industrial production processes require other energy sources such as steam or pressure. To transform primary or secondary energy sources into the required energy sources, manufacturing compan...
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Besides electricity, many industrial production processes require other energy sources such as steam or pressure. To transform primary or secondary energy sources into the required energy sources, manufacturing companies often operate their own on-site energy conversion system. Most important parameters determining a conversion systems' overall degree of efficiency are the dimension of its conversion units (CUs) and the design point (nominal load) at which a CU operates with maximum efficiency. In addition, conversion efficiencies at part load are particularly important because strongly varying energy demands from production processes frequently forcing an operation different from the nominal load. The (mostly) nonlinear relationship between part load operation and conversion efficiency makes an adequate consideration of this relationship essential. To address these factors, we present a new conversion system design approach aligned for manufacturing companies. To maximize energy efficiency, we propose a heuristic and a mixed-integer nonlinear program. In the experimental analysis, we analyze different types of companies, several CU parameter settings, the influence of nonlinear and linear efficiency modelling, and the influence of different machine scheduling objectives on the conversion system design. The results show that all these factors can remarkably influence the design and the energy efficiency of a conversion system.
The concepts of preinvex and invex are extended to the interval-valued functions. Under the assumption of invexity, the Karush-Kuhn-Tucker optimality sufficient and necessary conditions for interval-valued nonlinear p...
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The concepts of preinvex and invex are extended to the interval-valued functions. Under the assumption of invexity, the Karush-Kuhn-Tucker optimality sufficient and necessary conditions for interval-valued nonlinear programming problems are derived. Based on the concepts of having no duality gap in weak and strong sense, the Wolfe duality theorems for the invex interval-valued nonlinear programming problems are proposed in this paper.
With renewed interest in lunar exploration and the upcoming deployment of the lunar space station, the Lunar Orbital Platform-Gateway (LOP-G), a scientific community, is focusing on the design of a lander to bring peo...
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With renewed interest in lunar exploration and the upcoming deployment of the lunar space station, the Lunar Orbital Platform-Gateway (LOP-G), a scientific community, is focusing on the design of a lander to bring people back to the lunar surface. This work focuses on optimizing two aspects of the lunar lander concurrently: the mission architecture and the vehicle design, often treated independently in the literature. A methodology is introduced to enumerate and preliminarily rank all possible mission architectures. The best mission architectures are then coupled with a multidisciplinary design optimization process by modeling the various components of the spacecraft and optimizing over a set of design parameters. The need for fast computational models, particularly in trajectory optimization, resulted in an analytical approximation of gravitational losses. This work resulted in a hierarchy of mission architectures that are ranked according to the average mass necessary to perform the mission. This work is intended to help a system engineer designing a lunar lander in choosing the best number of vehicles, the number of reuses, and the mission profile for his/her mission requirements.
In this paper, we study the impact of the presence of byzantine sensors on the reduced-rank linear least squares (LS) estimator. A sensor network with N sensors makes observations of the physical phenomenon and transm...
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ISBN:
(纸本)9781728140841
In this paper, we study the impact of the presence of byzantine sensors on the reduced-rank linear least squares (LS) estimator. A sensor network with N sensors makes observations of the physical phenomenon and transmits them to a fusion center which computes the LS estimate of the parameter of interest. It is well-known that rank reduction exploits the bias-variance tradeoff in the full-rank estimator by putting higher priority on highly informative content of the data. The low-rank LS estimator is constructed using this highly informative content, while the remaining data can be discarded without affecting the overall performance of the estimator. We consider the scenario where a fraction 0 <= alpha <= 1 of the N sensors are subject to data falsification attack from byzantine sensors, wherein an intruder injects a higher noise power (compared to the unattacked sensors) to the measurements of the attacked sensors. Our main contribution is an analytical characterization of the impact of data falsification attack of the above type on the performance of reduced-rank LS estimator. In particular, we show how optimally prioritizing the highly informative content of the data gets affected in the presence of attacks. A surprising result is that, under sensor attacks, when the elements of the data matrix are all positive the error performance of the low-rank estimator experiences a phenomenon wherein the estimate of the mean-squared error comprises negative components. A complex nonlinear programming-based recipe is known to exist that resolves this undesirable effect;however, the phenomenon is oftentimes considered very objectionable in the statistical literature. On the other hand, to our advantage this effect can serve to detect cyber attacks on sensor systems. Numerical results are presented to complement the theoretical findings of the paper.
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