An active stereo vision-based learning approach for robotic tracking, fixating and grasping control

In this paper, an active stereo vision-based learning approach is proposed for a robot to track, fixate and grasp an object in unknown environments. First, the functional mapping relationships between the joint angles of the active stereo vision system and the spatial representations of the object are derived and expressed in a three-dimensional workspace frame. Next, the self-adaptive resonance theory-based neural networks and the feedforward neural networks are used to learn the mapping relationships in a self-organized way. Then, the approach is verified by simulation using the models of an active stereo vision system which is installed in the end-effector of a robot. Finally, the simulation results confirm the effectiveness of the present approach.

A reinforcement learning approach for robot control in an unknown environment

In this paper, a control approach based on reinforcement learning is present for a robot to complete a dynamic task in an unknown environment. First, a temporal difference-based reinforcement learning algorithm and its evaluation function are used to make the robot learn with its trials and errors as well as experiences. Second, the simulation are carried out to adjust the parameters of the learning algorithm and determine an optimal policy by using the models of a robot. Last, the effectiveness of the present approach is demonstrated by balancing an inverse pendulum in the unknown environment.

An analysis of update ordering in a cluster of replicated servers

This paper analyses update ordering and its impact on the performance of a cluster of replicated servers. We propose a model for update orderings and constraints and develop a number of algorithms for implementing different ordering constraints. A performance study is then carried out to analyse the update ordering model.

Object oriented finite element analysis: a distributed approach to mesh generation

The object-oriented finite element method (OOFEM) has attracted the attention of many researchers. Compared with the traditional finite element method, OOFEM software has the advantages of maintenance and reuse. Moreover, it is easier to expand the architecture to a distributed one. In this paper, we introduce a distributed architecture of a object-oriented finite element preprocessor. A comparison between the distributed system and the centralised system shows that the former, presented in the paper, greatly improves the performance of mesh generation. Other finite element analysis modules could be expanded according to this architecture.

The design and development of an integrated system for object-oriented finite element computing

Finite Element Method (FEM) is widely used in Science and Engineering since 1960’s. The vast majority of FEM software is procedure-oriented. However, this conventional style of designing FEM software encounters problems in maintenance, reuse, and expansion of the software. Recently the object-oriented finite element method attracts the attention of lots of researchers, and now there is a growing interest in this method. In this paper, the object-oriented finite element (OOFE) is briefly introduced. Then the design and development of an integrated OOFE system is described. A comparison of the integrated OOFE system and a procedure-oriented system shows that our OOFE system has many advantages.

A web-DB model on multicast and anycast

Most of the current web-based database systems suffer from poor performance, complicated heterogeneity, and synchronization issues. In this paper, we propose a novel mechanism for web-based database system on multicast and anycast protocols to deal with these issues. In the model, we put a castway, a network interface for database server, between database server and Web server. Castway deals with the multicast and anycast requests and responses. We propose a requirement-based server selection algorithm and an atomic multicast update algorithm for data queries and synchronizations. The model is independent from the Internet environment, it can synchronise the databases efficiently and automatically. Furthermore, the model can reduce the possibility of transaction deadlocks.

A co-recommendation algorithm for web searching

This paper presents an approach called the Co-Recommendation Algorithm, which consists of the features of the recommendation rule and the co-citation algorithm. The algorithm addresses some challenges that are essential for further searching and recommendation algorithms. It does not require users to provide a lot of interactive communication. Furthermore, it supports other queries, such as keyword, URL and document investigations. When the structure is compared to other algorithms, the scalability is noticeably easier. The high online performance can be obtained as well as the repository computation, which can achieve a high group-forming accuracy using only a fraction of Web pages from a cluster.

Research on network anycast

Anycast is defined as a service in IPv6, which provides stateless best effort delivery of an anycast datagram to at least one, and preferably only one host. It is a topic of increasing interest. This paper is an attempt to gather and report on the work done on anycast. There are two main categories at present: network-layer anycast and application-layer anycast. Both involve anycast architectures, routing algorithms, metrics, applications, etc. We also present an efficient algorithm for application-layer anycast, and point out possible research directions based on our research.

The application of a reinforcement learning agent to a multi-product manufacturing facility

An intelligent agent-based scheduling system, consisting of a reinforcement learning agent and a simulation model has been developed and tested on a classic scheduling problem. The production facility studied is a multiproduct serial line subject to stochastic failure. The agent goal is to minimise total production costs, through selection of job sequence and batch size. To explore state space the agent used reinforcement learning. By applying an independent inventory control policy for each product, the agent successfully identified optimal operating policies for a real production facility.

On work in progress optimisation

In this paper, we investigate how to best optimise the level of work in progress (WIP) in a real world factory. Using a simulation model of the factory, we show that an optimum level of WIP can be attained. By systematically varying the maximum allowable level of WIP within different model runs, results show that the throughput reaches a high level very quickly and then tapers off. The production lead times, in contrast, begin at relatively low levels and increase after the optimum WIP level has been reached.

The application of ant colony systems to image texture classification

This paper presents a novel ant system based optimisation method which integrates genetic algorithms and simplex algorithms. This method is able to not only speed up the search process for solutions, but also improve the quality of the solutions. In this paper, the proposed method is applied to set up a learning model for the "tuned" mask, which is used for texture classification. Experimental results on aerial images and comparisons with genetic algorithms and genetic simplex algorithms are presented to illustrate the merit and feasibility of the proposed method.

Adding value to online privacy for consumers: remedying deficiences in online privacy policies with an holistic approach

We present findings from a longitudinal, empirical study of online privacy policies. Our research found that although online privacy policies have improved in quality and effectiveness since 2000, they still fall well short of the level of privacy assurance desired by consumers. This study has identified broad areas of deficiency in existing online privacy policies, and offers a solution in the form of an holistic framework for the development, factors and content of online privacy policies for organizations. Our study adds to existing theory in this area and, more immediately, will assist businesses concerned about the effect of privacy issues on consumer Web usage.

Sensor fusion based missile guidance

In this paper we improve the guidance system performance via sensor fusion techniques. Vision based guidance systems can be improved in performance via radar tacking or employing video tracking by unmanned jying vehicles. We also introduce an image texture gradient based image segmentation technique to identify the target in a typical surface-to-air type application with the proposed Robust Extended Kalman Filter based state estimation technique for the implementation of the Proportional Navigation guidance controlleller.

Hybrid ant colony algorithm for texture classification

We present a novel ant colony algorithm integrating genetic algorithms and simplex algorithms. This method is able to not only speed up searching process for optimal solutions, but also improve the quality of the solutions. The proposed method is applied to set up a learning model for the "tuned" mask, which is used for texture classification. Experimental results on real world images and comparisons with genetic algorithms and genetic simplex algorithms are presented to illustrate the merit and feasibility of the proposed method.

Modeling a just-in-time plant using sequencing algorithms

An Australian automotive component company plans to assemble and deliver seats to customer on just-in-time basis. The company management has decided to model operations of the seat plant to help them make decisions on capital investment and labour requirements. There are four different areas in seat assembly and delivery areas. Each area is modeled independently to optimise its operations. All four areas are then combined into one model called the plant model to model operations of seat plant from assembly to delivery. Discrete event simulation software is used to model the assembly operations of seat plant.