This panel position paper addresses issues and challenges of resource management (facilitating adaptive and automatic information fusion) and identifies the necessary interaction with Levels 2 and 3 components of &quo...
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ISBN:
(纸本)9781424409532
This panel position paper addresses issues and challenges of resource management (facilitating adaptive and automatic information fusion) and identifies the necessary interaction with Levels 2 and 3 components of "information fusion''. Starting with the definitions of fusion levels, implicit and explicit interactions among the levels are examined from several perspectives: identifying commensurate measures to model interactions, selection of objective functions at each level or globally;representation of optimum decision making under uncertainty, effects of local optimization at each fusion level vs. joint optimization;representation of interactions among fusion levels by the perceptual reasoning machine paradigm-based adaptive anticipatory planning and control model, and identifying research directions.
Conventionally, a revenue function, a cost function and a profit function are selected to be the objective function for Aggregate Production Planning (APP) problems. The theory of constraints (TOC) alternative conside...
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ISBN:
(纸本)9781424400225
Conventionally, a revenue function, a cost function and a profit function are selected to be the objective function for Aggregate Production Planning (APP) problems. The theory of constraints (TOC) alternative consideration argues that instead of measuring by cost, factory should evaluate their performance by throughput. Even though, there are a lot of research works on formulations of APP problems, there has been no investigation, which formulation is the most appropriate for APP problems. In this research, the investigation of the formulation of existing APP problems is done. In order to clarify the difference of each objective function, a simple case study has been used to compare the performances of the APP problem with revenue, cost, and profit objective functions when resource constraints (limited processing time) are not included and included in the model. For the profit objective function, two formulations are also compared: profit objective function by TOC and profit objective function by linear programming. From the results, it can be shown that setting the objective function of an APP problem is very important because it may lead to a wrong decision in production planning.
In this paper we will discuss the multilevel structure of global optimization problems. Such problems can often be seen at different levels, the number of which varies from problem to problem. At each level different ...
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In this paper we will discuss the multilevel structure of global optimization problems. Such problems can often be seen at different levels, the number of which varies from problem to problem. At each level different objects are observed, but all levels display a similar structure. The number of levels which can be recognized for a given optimization problem represents a more complete measure of the difficulty of the problem with respect to the standard measure given by the total number of local minima. Moreover, the subdivision in levels will also suggest the introduction of appropriate tools, which will be different for each level but, in accordance with the fact that all levels display a similar structure, will all be based on a common concept namely that of local move. Some computational experiments will reveal the effectiveness of such tools.
The problem of selecting appropriate objective functions for the identification of a lumped conceptual rainfall runoff model is investigated, focusing on the value of the model in an operational setting. A probability...
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The problem of selecting appropriate objective functions for the identification of a lumped conceptual rainfall runoff model is investigated, focusing on the value of the model in an operational setting. A probability-distributed soil moisture model is coupled with a linear parallel routing scheme, and conditioned on rainfall-runoff observations from three catchments in the southeast of England. Using an abstraction control problem, which requires accurate simulation of the intermediate flow range, it is shown that using the traditional RMSE fit criterion, produces operationally sub-optimal predictions. This is true in the identification period, when applied to a testing period, and to proxy catchment data. Using a second case study of the Leaf River in Mississippi (USA), where the focus changes to predicting flood peaks over a specified threshold, also suggests that the relevant flood threshold should govern the objective function choice. It is concluded that, due to limitations in the structure of the employed model, it would be counter-productive to try to achieve a good all-round representation of the rainfall-runoff processes, and that a more empirical approach to identification may be preferred for specific forecasting problems. This leaves us with the question of how far hydrological realism should be sacrificed in favour of purpose-driven objective functions.
The paper analyses the three main variables categories that are structurally characterizing the material laser processing: the influence factors, the process variables and the objective functions. These variables are ...
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The paper analyses the three main variables categories that are structurally characterizing the material laser processing: the influence factors, the process variables and the objective functions. These variables are divided in subsets determined by the system's structure, operating procedures and objectives of technological transformations. (c) 2004 Elsevier Ltd. All rights reserved.
The effectiveness of traditional CAD optimization algorithms is proportional to the accuracy of the targeted objective functions. However, behavioral synthesis tools are not used in isolation; they form a strongly con...
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ISBN:
(纸本)9781581134612
The effectiveness of traditional CAD optimization algorithms is proportional to the accuracy of the targeted objective functions. However, behavioral synthesis tools are not used in isolation; they form a strongly connected design flow where each tool optimizes its own objective function without considering the consequences on the optimization goals of the subsequently applied tools. Therefore, efforts to optimize one aspect of a design often have unforeseen negative impacts on other phases of the design process.
Multisensor-multitarget sensor management is viewed as a problem in nonlinear control theory. This paper applies newly developed theories for sensor management based on a Bayesian control-theoretic foundation. Finite-...
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ISBN:
(纸本)0819453528
Multisensor-multitarget sensor management is viewed as a problem in nonlinear control theory. This paper applies newly developed theories for sensor management based on a Bayesian control-theoretic foundation. Finite-Set-Statistics (FISST) and the Bayes recursive filter for the entire multisensor-multitarget system are used with information-theoretic objective functions in the development of the sensor management algorithms. The theoretical analysis indicate that some of these objective functions lead to potentially tractable sensor management algorithms when used in conjunction with MHC (multi-hypothesis correlator)-like algorithms. We show examples of such algorithms, and present an evaluation of their performance against multisensor-multitarget scenarios. This sensor management formulation also allows for the incorporation of target preference, and experiments demonstrating the performance of sensor management with target preference will be presented.
In this work a system based on genetic algorithms (CA) and active shape models (ASM) is presented that, in a two-step approach, is able to accurately find a particular object within the image. The aim of the first sta...
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In this work a system based on genetic algorithms (CA) and active shape models (ASM) is presented that, in a two-step approach, is able to accurately find a particular object within the image. The aim of the first stage is to approximately locate the object in order to serve as an initialization for the second. Following a method similar to that used by other authors, a model of the desired shape (cows in lateral position) is constructed using PDM, and later the search within the image is carried out based on instances of that model, and using genetic algorithm techniques, for which several objective functions are suggested. In the second stage we attempt to make a fine adjustment of our instance to the object in the image, starting from the result of the previous process, Consequently, we propose two different techniques compared afterwards: deformable contours (specifically active shape models) and a restricted genetic search. Finally, the system is tested over a database of 309 animal images, and results are presented.
Concave functions play a fundamental role in the structure of and minimization of badness-of-fit functions used in data analysis when extreme values of either parameters or data need to be penalized. This paper summar...
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Concave functions play a fundamental role in the structure of and minimization of badness-of-fit functions used in data analysis when extreme values of either parameters or data need to be penalized. This paper summarizes my findings about this role. I also describe three examples where concave functions are useful: building measures of badnessof-fit, building robust M-estimators, and building clustering objective functions. Finally, using the common thread of concavity, all three will be combined to build a comprehensive, flexible procedure for robust cluster analysis.
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