16116503
Evolutionary programming techniques for constrained optimization problems
Two evolutionary programming (EP) methods are proposed for handling nonlinear constrained optimization problems. The first, a hybrid EP, is useful when addressing heavily constrained optimization problems both in terms of computational efficiency and solution accuracy. But this method offers an exact solution only if both the mathematical form of the objective function to be minimized/maximized and its gradient are known. The second method, a two-phase EP (TPEP) removes these restrictions. The first phase uses the standard EP, while an EP formulation of the augmented Lagrangian method is employed in the second phase. Through the use of Lagrange multipliers and by gradually placing emphasis on violated constraints in the objective function whenever the best solution does not fulfill the constraints, the trial solutions are driven to the optimal point where all constraints are satisfied. Simulations indicate that the TPEP achieves an exact global solution without gradient information, with less computation time than the other optimization methods studied here, for general constrained optimization problems.
14570877
Genetic Elevator Group Control
In a tall building with multiple elevators, it is a notoriously di cult task to control the elevators in the most e cient manner [1].The system proposed in this paper contains two key ideas. First, it continuously generates dispatch functions by adaptation to changes in passenger tra c. Second, it tries to reduce passengers' waiting time by multiple elevator allocation when it is expected that one elevator cannot serve all the passengers for a hall call at a oor.
15382159
Hybrid recursive energy-based method for robust optical flow on large motion fields
We present a new reliable hybrid recursive method for optical flow estimation. The method efficiently combines the advantage of discrete motion estimation and optical flow estimation in a recursive block-to-pixel estimation scheme. Integrated local and global approaches using the robust statistic of anisotropic diffusion remove outliers from the estimated motion field. We separately describe the process with two frameworks i.e. an incremental updating framework and a robust energy minimization framework. With robust error norms of Perona and Marik anisotropic diffusion, the formulation usually leads to non-convex optimization problems. Thus, the solution has many local minima, and convergence to the global minima is not guaranteed. Our hybrid recursive energy-based method employs a hierarchical block-to-pixel estimation concept to prevent this problem. The experimental results prove the excellent performance on several large motion fields.
1514813
Temporal Query Processing Using Spatially-Partitioned Method
In this paper, we propose a general spatiallypartitioned temporal operation model, called 3D-TOM, and partition-based join algorithms for various types of temporal join in the proposed model. The temporal operators addressed in this paper include overlap, precede, follow, and equal. Some factors which a ect the performance of the proposed method are also discussed.
5105796
Two-stage normalization using background intensities in cDNA microarray data
BackgroundIn the microarray experiment, many undesirable systematic variations are commonly observed. Normalization is the process of removing such variation that affects the measured gene expression levels. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization. One major source of variation is the background intensities. Recently, some methods have been employed for correcting the background intensities. However, all these methods focus on defining signal intensities appropriately from foreground and background intensities in the image analysis. Although a number of normalization methods have been proposed, no systematic methods have been proposed using the background intensities in the normalization process.ResultsIn this paper, we propose a two-stage method adjusting for the effect of background intensities in the normalization process. The first stage fits a regression model to adjust for the effect of background intensities and the second stage applies the usual normalization method such as a nonlinear LOWESS method to the background-adjusted intensities. In order to carry out the two-stage normalization method, we consider nine different background measures and investigate their performances in normalization. The performance of two-stage normalization is compared to those of global median normalization as well as intensity dependent nonlinear LOWESS normalization. We use the variability among the replicated slides to compare performance of normalization methods.ConclusionsFor the selected background measures, the proposed two-stage normalization method performs better than global or intensity dependent nonlinear LOWESS normalization method. Especially, when there is a strong relationship between the background intensity and the signal intensity, the proposed method performs much better. Regardless of background correction methods used in the image analysis, the proposed two-stage normalization method can be applicable as long as both signal intensity and background intensity are available.
6014517
The Real-Time Development and Deployment of a Cooperative Multi-UAV System
This paper presents a systems view of the Autonomous Nav- igation and Sensing Experimental Research (ANSER) project. ANSER is one of the most advanced UAV projects of its kind, which is aimed at demonstrating Decentralised Data Fusion (DDF) and Simultaneous Localisation and Map (SLAM) building on multiple cooperative UAVs. The demonstration of DDF and SLAM require both navigation and terrain sensors to be carried onboard by the UAVs. These include an INS/GPS navigation system, millimeter wave (MMW) radar, and both a single vision node and a vision node augmented with a laser system. This paper presents the implementation of the algorithms, sensors and platforms, along with their interaction to address this demonstration. Furthermore, the paper will present the high fidelity real time simulator which is implemented to thoroughly test all algorithms and sensors before actual implementation in the environment.
11496281
A Transformational Analysis of the EBL Utility Problem
Efficiency is a major concern for all problem solving systems. One way of achieving efficiency is the application of learning techniques to speed up problem solving. Accordingly, there has been considerable amount of research on applying explanation-based learning (EBL)(Mitchell, Keller, & Kedar-Cabelli 1986) techniques to problem solving. However, EBL is known to suffer from the utility problem, where the cost of using the learned knowledge overwhelms its benefit. We show that how the cost increase of a learned rule in an EBL system can be analyzed by characterizing the learning process as a sequence of transformations from a problem solving episode to a learned rule. The analysis of how the cost changes through the transformations can be a useful tool for revealing the sources of cost increase in the learning system. We focus on the Soar problem solving system which uses a variant of EBL called chunking(Rosenbloom et al. 1991). The chunking process has been decomposed into a sequence of transformations from the problem solving to a chunk (learned rule). By analyzing these transformations, we have identified a set of sources which can make the output chunk expensive. The set of sources and the proposed solutions are :