16619155
QoS-Aware and Federated Enhancement for UDDI
With more and more Web services appearing on the Web, Web serviceâ€™s discovery mechanisms become essential. UDDI is an online registry standard to facilitate the discovery of business partners and services. Service requesters can choose good performance Web services by manual test and comparison. However, this method of choosing is inefficient and costly in terms of time and money. The ability to predict the quality of service is missing in UDDI. Furthermore, UDDI registries in federated organizations should be able to share more about the service requesterâ€™s connection information to make better predictions for servicesâ€™ performances. To address these problems, we propose UX (UDDI eXtension), a system that facilitates requesters to discover services with good performance. In each enterprise domain, the requestersâ€™ QoS feedback is received and then stored in a local UX server. By sharing these experiences from all the requesters in the local domain, the system can predict the serviceâ€™s future performance. To support the discovery between different cooperation domains, we design a general federated service and then enhance the UX systemâ€™s federated discovery ability based on this federated service. The system handles the federated inquiry and provides a simple view over the whole federation. Meanwhile the UX serverâ€™s inquiry interface will still conform to the UDDI Specification.
1982773
QoS measurement issues with DAML-QoS ontology
With the industry's efforts in promoting the use of Web services, a huge number of Web services are being developed for the Web. To improve the service provider's competition advantage, it is necessary to support QoS-aware service discovery mechanism as well as the corresponding measurement system. In this paper we present a QoS measurement framework based on the DAML-QoS ontology, together with the measurement code generator. The DAML-QoS ontology is designed to specify QoS constraints for Web services. It works as a complement to DAML-S to provide QoS-aware service discovery. This paper focuses on its measurement framework to check the service provider's runtime compliance with the advertised service level. The code generator helps to generate the measurement code according to the DAML-QoS specification. This ontology level specification tries to reduce the service's integration complexity and achieve better interoperability, automation and extensibility
971957
DAML-QoS ontology for Web services
As more and more Web services are deployed, Web service's discovery mechanisms become essential. Similar services can have quite different QoS levels. For service selection and management purpose, it is necessary to explicitly, precisely, and unambiguously specify various constraints and QoS metrics for Web services descriptions. This paper provides a novel DAML-QoS ontology as a complement for DAML-S ontology to provide a better QoS metrics model. Three layers are defined together with clear role descriptions for developments. Cardinality constraints are utilized to describe the QoS property constraints. Basic profile is presented for general Web service's description and the speed startup of ontology definition. Matchmaking algorithm for QoS property constraints is presented and different matching degrees are described. When incorporated with DAML-S, multiple service levels can be described through attaching multiple QoS profiles to one service profile. Well-defined Metrics can be further utilized by measurement organizations to guarantee the promised service level.
2612705
Credit Scoring Model Based on Neural Network with Particle Swarm Optimization
Credit scoring has gained more and more attentions both in academic world and the business community today. Many modeling techniques have been developed to tackle the credit scoring tasks. This paper presents a Structure-tuning Particle Swarm Optimization (SPSO) approach for training feed-forward neural networks (NNs). The algorithm is successfully applied to a real credit problem. By simultaneously tuning the structure and connection weights of NNs, the proposed algorithm generates optimized NNs with problem-matched information processing capacity and it also eliminates some ill effects introduced by redundant input features and the corresponding redundant structure. Compared with BP and GA, SPSO can improve the pattern classification accuracy of NNs while speeding up the convergence of training process.
28673252
Dynamically Stable Walking and Kicking Gait Planning for Humanoid Soccer Robots
Humanoid dynamic walk and kick are two main technical challenges for the current Humanoid League. In this paper, we conduct a research aiming at generating dynamically stable walking and kicking gait for humanoid soccer robots with consideration of different constraints. Two methods are presented. One is synthesizing gait based on constraint equations, which has formulated gait synthesis as an optimization problem with consideration of some constraints, e.g. zero-moment point (ZMP) constraints for dynamically stable locomotion, internal forces constraints for smooth transition, geometric constraints for walking on an uneven floor and etc. The other is generating feasible gait based on human kicking motion capture data (HKMCD), which uses periodic joint motion corrections at selected joints to approximately match the desired ZMP trajectory. The effectiveness of the proposed dynamically stable gait planning approach for humanoid walking on a sloping surface and humanoid kicking on an even floor has been successfully tested on our newly developed Robo-Erectus humanoid soccer robots, which won second place in the RoboCup 2002 Humanoid Walk competition and got first place in the RoboCup 2003 Humanoid Free Performance competition.
1882154
Providing Quality of Service in IEEE 802.11 WLAN
This paper aims to provide quality of service (QoS) for IEEE 802.11 wireless local area network (WLAN). Specifically, we develop a new scheduling algorithm in link layer to support multimedia services with guaranteed QoS in WLAN. The proposed scheduling algorithm makes use of the idle system time to reduce average packet loss of real-time (RT) variable-bit-rate (VBR) services, while satisfying required transmission delay and bandwidth requirements of all RT services. Detailed analysis is given by taking into account several key statistical factors, such as service traffic model, the ratio between the polling period and the contention period (CP), burst generation probabilities. Performance is evaluated through OPNET simulation. Simulation results demonstrate that our proposed joint scheme is effective in terms of both system resource utilization and QoS provisioning
7697882
Incorporating Perception-Based Information in Reinforcement Learning Using Computing with Words
In this paper, a general fuzzy reinforcement learning (FRL) agent that can utilise not only measurement-based information but also perception-based information by means of computing with words (CW) is proposed. By introducing fuzzy numbers and their arithmetic operations and fuzzy Lyapunov synthesis in the domain of CW, a set of stable fuzzy control rules can be derived from perception-based information. Moreover, based on a neurofuzzy network architecture, the fuzzy rules can be incorporated in the FRL agent to initialise its action network, critic network and evaluation feedback module so as to improve the learning. The performance and applicability of the proposed approach are illustrated through the practical implementation of learning control of an autonomous pole-balancing mobile robot.
12769414
Robot learning with GA-based fuzzy reinforcement learning agents
Abstract How to learn from both expert knowledge and measurement-based information for a robot to acquire perception and motor skills is a challenging research topic in the field of autonomous robotic systems. For this reason, a general GA (genetic algorithm)-based fuzzy reinforcement learning (GAFRL) agent is proposed in this paper. We first characterize the robot learning problem and point out some major issues that need to be addressed in conjunction with reinforcement learning. Based on a neural fuzzy network architecture of the GAFRL agent, we then discuss how different kinds of expert knowledge and measurement-based information can be incorporated in the GAFRL agent so as to accelerate its learning. By making use of the global optimization capability of GAs, the GAFRL can solve the local minima problem in traditional actor-critic reinforcement learning. On the other hand, with the prediction capability of the critic network, GAs can evaluate the candidate solutions regularly even during the periods without external feedback from the environment. This can guide GAs to perform a more effective global search. Finally, different types of GAFRL agents are constructed and verified using the simulation model of a physical biped robot.