1268203
Quantum-Inspired Evolutionary Algorithm-Based Face Verification
Face verification is considered to be the main part of the face detection system. To detect human faces in images, face candidates are extracted and face verification is performed. This paper proposes a new face verification algorithm using Quantum-inspired Evolutionary Algorithm (QEA). The proposed verification system is based on Principal Components Analysis (PCA). Although PCA related algorithms have shown outstanding performance, the problem lies in the selection of eigenvectors. They may not be the optimal ones for representing the face features. Moreover, a threshold value should be selected properly considering the verification rate and false alarm rate. To solve these problems, QEA is employed to find out the optimal distance measure under the predetermined threshold value which distinguishes between face images and non-face images. The proposed verification system is tested on the AR face database and the results are compared with the previous works to show the improvement in performance.
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.
39277510
Lagrangian-Based Evolutionary Programming for Constrained Optimization
In this paper, Lagrangian-based evolutionary programming, Evolian is proposed for the general constrained optimization problem, which incorporates the concept of (1) multi-phase optimization process and (2) constraint scaling techniques to resolve the ill-conditioning problem. In each phase of Evolian, the typical EP is performed using augmented Lagrangian objective function with a penalty parameter fixed. If there is no improvement in the best objective function in one phase, another phase is performed after scaling the constraints, and updating the Lagrange multipliers and penalty parameter. Simulation results indicate that Evolian gives outperforming or at least reasonable solutions for multivariable heavily constrained optimization problem compared to other several evolutionary computation based methods.
9661446
Normalization for neural network in genetic search
Jung-Hwan Kim School of Computer Science and Engineering Seoul National University Shilim-dong, Kwanak-gu, Seoul, 151-742 Korea aram@soar.snu.ac.kr Sung-Soon Choi School of Computer Science and Engineering Seoul National University Shilim-dong, Kwanak-gu, Seoul, 151-742 Korea sschoi@soar.snu.ac.kr Byung-Ro Moon School of Computer Science and Engineering Seoul National University Shilim-dong, Kwanak-gu, Seoul, 151-742 Korea moon@soar.snu.ac.kr
10913816
A Web Services-enabled marketplace architecture for negotiation process management
As the eBusiness environment becomes more pervasive and dynamic, negotiations between companies are required more frequently than ever. Despite its potential value and the progress in research, the adoption of negotiation systems has been slow in practice. We believe one reason for this is insufficient consideration of process management aspects such as process design, description, and deployment. Business negotiations must be approached from the process management perspective since they take place in the context of corporate processes such as procurement or sales.In this paper, we study system support and automation of business-to-business (B2B) negotiations from the process management perspective. We propose a Web Services-enabled marketplace architecture for negotiation process management and refine it by adding pattern-based process composition. We validate the concept by implementing the proposed architecture using BPEL4WS and evaluating it from various perspectives.
12515507
Using content-addressable networks for load balancing in desktop grids
Desktop grids have evolved to combine Peer-to-Peer and Grid computing techniques to improve the robustness, reliability and scalability of job execution infrastructures. However, efficiently matching incoming jobs to available system resources and achieving good load balance in a fully decentralized and heterogeneous computing environment is a challenging problem. In this paper, we extend our prior work with a new decentralized algorithm for maintaining approximate global load information, and a job pushing mechanism that uses the global information to push jobs towards underutilized portions of the system. The resulting system more effectively balances load and improves overall system throughput. Through a comparative analysis of experimental results across different system configurations and job profiles, performed via simulation, we show that our system can reliably execute Grid applications on a distributed set of resources both with low cost and with good load balance.
1198499
A new base station receiver for increasing diversity order in a CDMA cellular system
A new base station receiver is proposed and analyzed for a code-division multiple-access (CDMA) cellular system. The proposed receiver can achieve remarkable diversity gain by increasing diversity order with reasonable cost and complexity. From the numerical results, it is confirmed that the proposed receiver structure can be a practical solution for enhancing reverse-link capacity and improving performance in CDMA cellular system operations. The result in the letter can find its applications to legacy IS-95/cdma2000 1x base stations with simple modifications.
8536996
Goal and scenario based domain requirements analysis environment
Identifying and representing domain requirements among products in a product family are crucial activities for a successful software reuse. The domain requirements should be not only identified based on the business goal, which drives marketing plan, product plan, and differences among products, but also represented as familiar notations in order to support developing a particular product in the product family. Thus, our proposal is to identify the domain requirements through goals and scenarios, and represent them as variable use cases for a product family. Especially, for identification of the domain requirements, we propose four abstraction levels of requirements in a product family, and goal and scenario modeling. For representation of them, variable use case model is suggested, and also the use case transfer rules are proposed so as to bridge the gap between the identification and representation activity. The paper illustrates the application of the approach within a supporting tool using the HIS (Home Integration System) example.
11308682
Techniques for improving the accuracy of geometric-programming based analog circuit design optimization
We present techniques for improving the accuracy of geometric-programming (GP) based analog circuit design optimization. We describe major sources of discrepancies between the results from optimization and simulation, and propose several methods to reduce the error. Device modeling based on convex piecewise-linear (PWL) function fitting is introduced to create accurate active and passive device models. We also show that in selected cases GP can enable nonconvex constraints such as bias constraints using monotonicity, which help reduce the error. Lastly, we suggest a simple method to take the modeling error into account in GP optimization, which results in a robust design over the inherent errors in GP device models. Two-stage operational amplifier and on-chip spiral inductor designs are given as examples to demonstrate the presented ideas.