13570447
Paradigm shift and the integrated feedback approach
With the increased competition in today's world market, emphasis has been on the ability to shift from an existing paradigm to a new paradigm so as to create new opportunities and to gain new market. A successful paradigm shift is dependent on two factors: (1) the ability to pinpoint the inherent weaknesses in the existing paradigm, (2) the ability to find a paradigm that can replace the old paradigm. In this paper, we show how the integrated feedback approach is able to address these two concerns. The integrated feedback approach was proposed to integrate the design phase with the downstream activities so as to achieve a design that is better for assembly. The approach operates in two phases: an evaluation phase and a redesign suggestion generation phase. A number of objective criteria have been proposed to evaluate a given design from the functional perspective, from the assembly plan perspective, and from the tolerance perspective. Through the evaluation process, weaknesses in the design are identified and techniques for generating feasible redesign suggestions are examined. By encouraging greater participation from the designer, a systematic aid to paradigm shifting can be obtained. A prototype system implementing the integrated feedback approach has been developed on a Sun Sparcstation with graphics simulation on a Silicon Graphics Iris workstation. A real life product, the telephone, is used as an illustration.
33340433
An evolutionary approach to the job-shop scheduling problem
This paper focuses on the heuristic hybridization and the genetic search as a methodology to develop a computationally efficient heuristic for the job-shop scheduling problem (JSP). In order to adapt the JSP to a genetic algorithm (GA), the ASGPL (Active-Schedule Generation with a Priority-List) algorithm with a hopping scheme was proposed, and using a GA, an iterative schedule improvement procedure called EVIS (Evolutionary Intracell Scheduler) was designed. The genetic search in EVIS was parallelized with a model of subpopulations and migration. Without implementing any problem-tailored heuristic for the job-shop scheduling problem, EVIS was able to find optimal solutions to a number of different problem instances in reasonable computation time.<<ETX>>
41633235
Automatic generation of error recovery knowledge through learned reactivity
Develops a new method for automatic generation of error recovery procedures in the assembly workcell. Current reactive systems have integrated planning and learning capabilities to allow them to augment their store of reactive behaviors whenever they are forced to plan. The cycle of evaluation and reaction used by these systems implicitly detects and corrects any errors made in prior behavior execution. By capitalizing on this analogy to error recovery, the authors are able to gain the benefits of a reactive system for use in error recovery while minimizing the perception and evaluation burden that is inherent in those approaches. The authors present a system that uses the new learning reactive paradigm to quickly correct known errors and autonomously learn to recover new errors without human intervention.<<ETX>>
17815023
Experimental study on adaptive control of underwater robots
The control of underwater robots presents a number of unique and formidable challenges. Underwater robot dynamics are highly nonlinear, coupled, and time-varying, and subject to hydrodynamic uncertainties and external disturbances such as current. Unlike land mobile robots, underwater robots cannot use GPS. Most popular underwater positioning sensors are sonar-based, such as long-base line. However, autonomous processing of sonar measurements is plagued by noise, drop-outs, missed detection, false reading, poor resolution, etc. The paper presents a new adaptive control of underwater robots with sonar-based position measurements. Experimental results show robustness of the control system in the presence of unmodelled dynamics and various noise.
1173830
A genetic-algorithm-based neighbor-selection strategy for hybrid peer-to-peer networks
BitTorrent is a popular, open-source, hybrid peer-to-peer content distribution system that is conducive for distribution of large-volume contents. In this paper, we propose a genetic-algorithm-based neighbor-selection strategy for hybrid peer-to-peer networks, which enhances the decision process performed at the tracker for transfer coordination. We also investigate how the strategy affects system throughput and distribution efficiency as well as peer contributions. We show through computer simulations that by increasing content availability to the clients from their immediate neighbors, it can significantly improve the system performance without trading off users' satisfaction. The proposed strategy can significantly improve the efficiency of distribution, especially for low-connectivity peers, and it is suitable to deploy for online decisions
30064492
Genetic reinforcement learning approach to the machine scheduling problem
This paper focuses on the development of a learning-based heuristic for the machine scheduling problem, which automatically captures the search control knowledge or the common features of good schedules while generating a number of schedules. Defining states and actions of the machine shop, the machine scheduling problem is transformed into a problem of reinforcement learning (RL) in which a learner or a scheduler will learn to select the right action at each state of the machine shop, using the reward from a schedule evaluator for executing the action. Implementing the proposed reinforcement learning with a genetic algorithm results in the genetic reinforcement learning (GRL) approach to the machine scheduling problem. Although the learning-based heuristic has the overhead of acquiring knowledge on the problem, it can be easily adapted for a wide variety of machine scheduling problems due to the weak dependence on the problem structures and objectives. A GRL-based scheduler, called EVIS (Evolutionary Intracell Scheduler), has been developed and applied to various classes of machine scheduling problems, such as the job-shop scheduling, the flow-shop scheduling and the open-shop scheduling problems, and even the processor scheduling problem, the performance evaluation of EVIS with a number of different problem instances has shown that the learning-based heuristic is robust and its performance is comparable with that of other problem-specific heuristics or search-oriented heuristics in the quality of solutions.
39638713
Robust H∞ fuzzy static output feedback control of T-S fuzzy systems with parametric uncertainties
This paper addresses robust H"~ fuzzy static output feedback control problem for T-S fuzzy systems with time-varying norm-bounded uncertainties. Sufficient conditions for synthesis of a fuzzy static output feedback controller for T-S fuzzy systems are derived in terms of a set of linear matrix inequalities (LMIs). In comparison with the existing literatures, the proposed approach not only simplifies the design procedure but also achieves a better H"~ performance. Three drawbacks existing in the previous papers such as coordinate transformation, same output matrices and BMI problem have been eliminated. The effectiveness of the proposed design method is demonstrated by an example for the control of a truck-trailer system.
14420598
A component-based approach for JavaCard runtime system development
In recent years a lot of research efforts have been directed towards component-based approach in the development of embedded operating systems. This allows the system to easily adapt itself to different hardware chips and various embedded application subsystems. Most of the existing research on component-based embedded system development is directed towards adapting the system to different hardware chips. However, this approach is not sufficient to port the system from one embedded application to another. In this research we will address the importance of component-based design at the application domain and runtime environment level of embedded systems. Since JavaCard is gaining importance in SmartCard embedded systems, we will design a component-based Java Card Runtime Environment and Application Subsystem. Our approach will enable easy configuration of the system to support Java Card, EMV-compliant card and various other smartcard embedded applications. Our research will demonstrate that the component-based approach allows the embedded operating system to easily port itself to other resource-constrained devices such as cell phones and other embedded system application with relative ease.
6076905
Finite-horizon scheduling of radar dwells with online template construction
Timing constraints for radar tasks are usually specified in terms of the minimum and maximum temporal distance between successive radar dwells. We utilize the idea of feasible intervals for dealing with the temporal distance constraints. In order to increase the freedom that the scheduler can offer a high-level resource manager, we introduce a technique for nesting and interleaving dwells online while accounting for the energy constraint that radar systems need to satisfy. Further, in radar systems, the task set changes frequently and we advocate the use of finite horizon scheduling in order to avoid the pessimism inherent in schedulers that assume a task will execute forever. The combination of feasible intervals and online dwell packing allows modular schedule updates whereby portions of a schedule can be altered without affecting the entire schedule, hence reducing the complexity of the scheduler. Through extensive simulations we validate our claims of providing greater scheduling flexibility without compromising on performance when compared with earlier work based on templates constructed offline. We also evaluate the impact of two parameters in our scheduling approach: the template length (or the extent of dwell nesting and interleaving) and the length of the finite horizon.
13953434
A design of multi‐layered location registration areas in microcellular systems
Multi‐layered location registration areas are considered to minimize the control signals at boundary cells in the microcellular systems. The decision of multi‐layered location registration (LR) areas is formulated as an integer programming problem by examining the regional subscriber densities, traffic flows and call arrival rates. By relaxing the constraints of forward and reverse control channels the problem is decomposed into a number of subproblems each of which corresponds to a layer. Each subproblem is solved via a modified graph partitioning algorithm. Feasible LR areas are obtained by considering the two relaxed constraints. Computational experiments are performed in a system with 80 microcells to investigate the performance of the proposed multi‐layer scheme. The simulation results indicate that the two‐layer scheme generally outperforms the one‐layer and three‐layer methods in view of control signals.
18817570
Long-term location data management for distributed moving object databases
To handle the extreme situation that must manage positional information of a very large volume, at least millions, of moving objects, a cluster-based scalable distributed computing system architecture, called the GALIS, which consists of multiple data processors, each dedicated to keeping records relevant to a different geographical zone and a different time zone, was proposed. In this paper, we propose a valid time management and time-zone shifting scheme, which are essential in realizing the long-term location data subsystem of GALIS, but missed in our previous prototype development. We explain how to manage valid time of moving objects to avoid ambiguity of location information. We also describe time-zone shifting algorithm, with three variations, such as RT-TZS, B-TZS and TPB-TZS. Through experiments related with query processing time and CPU utilization, we show the efficiency of the proposed time-zone shifting schemes