Mobile Formation Coordination and Tracking Control for Multiple Non-holonomic Vehicles

This paper addresses forward motion control for trajectory tracking and mobile formation coordination for a group of non-holonomic vehicles on SE(2). Firstly, by constructing an intermediate attitude variable which involves vehicles' position information and desired attitude, the translational and rotational control inputs are designed in two stages to solve the trajectory tracking problem. Secondly, the coordination relationships of relative positions and headings are explored thoroughly for a group of non-holonomic vehicles to maintain a mobile formation with rigid body motion constraints. We prove that, except for the cases of parallel formation and translational straight line formation, a mobile formation with strict rigid-body motion can be achieved if and only if the ratios of linear speed to angular speed for each individual vehicle are constants. Motion properties for mobile formation with weak rigid-body motion are also demonstrated. Thereafter, based on the proposed trajectory tracking approach, a distributed mobile formation control law is designed under a directed tree graph. The performance of the proposed controllers is validated by both numerical simulations and experiments

A scalable application server on Beowulf clusters : a thesis presented in partial fulfilment of the requirement for the degree of Master of Information Science at Albany, Auckland, Massey University, New Zealand

Application performance and scalability of a large distributed multi-tiered application is a core requirement for most of today's critical business applications. I have investigated the scalability of a J2EE application server using the standard ECperf benchmark application in the Massey Beowulf Clusters namely the Sisters and the Helix. My testing environment consists of Open Source software: The integrated JBoss-Tomcat as the application server and the web server, along with PostgreSQL as the database. My testing programs were run on the clustered application server, which provide replication of the Enterprise Java Bean (EJB) objects. I have completed various centralized and distributed tests using the JBoss Cluster. I concluded that clustering of the application server and web server will effectively increase the performance of the application running on them given sufficient system resources. The application performance will scale to a point where a bottleneck has occurred in the testing system, the bottleneck could be any resources included in the testing environment: the hardware, software, network and the application that is running. Performance tuning for a large-scale J2EE application is a complicated issue, which is related to the resources available. However, by carefully identifying the performance bottleneck in the system with hardware, software, network, operating system and application configuration. I can improve the performance of the J2EE applications running in a Beowulf Cluster. The software bottleneck can be solved by changing the default settings, on the other hand, hardware bottlenecks are harder unless more investment are made to purchase higher speed and capacity hardware

The mixed strategy equilibrium of the three-firm location game with discrete location choices

In the paper, we derive a symmetric MSE for the three-firm location game on the discrete strategy space. Rather than being uniformly distributed, the MSE for the game has a multimodal distribution. Our theory is more convincing to predict equilibria of three-firm location games in the real world or controlled experiments, where players face finitely many choices.mixed strategy equilibrium, multimodal distribution, discrete strategy space

Formation Shape Control Based on Distance Measurements Using Lie Bracket Approximations

We study the problem of distance-based formation control in autonomous multi-agent systems in which only distance measurements are available. This means that the target formations as well as the sensed variables are both determined by distances. We propose a fully distributed distance-only control law, which requires neither a time synchronization of the agents nor storage of measured data. The approach is applicable to point agents in the Euclidean space of arbitrary dimension. Under the assumption of infinitesimal rigidity of the target formations, we show that the proposed control law induces local uniform asymptotic stability. Our approach involves sinusoidal perturbations in order to extract information about the negative gradient direction of each agent's local potential function. An averaging analysis reveals that the gradient information originates from an approximation of Lie brackets of certain vector fields. The method is based on a recently introduced approach to the problem of extremum seeking control. We discuss the relation in the paper

Personalized neural language models for real-world query auto completion

Query auto completion (QAC) systems are a standard part of search engines in industry, helping users formulate their query. Such systems update their suggestions after the user types each character, predicting the user's intent using various signals - one of the most common being popularity. Recently, deep learning approaches have been proposed for the QAC task, to specifically address the main limitation of previous popularity-based methods: the inability to predict unseen queries. In this work we improve previous methods based on neural language modeling, with the goal of building an end-to-end system. We particularly focus on using real-world data by integrating user information for personalized suggestions when possible. We also make use of time information and study how to increase diversity in the suggestions while studying the impact on scalability. Our empirical results demonstrate a marked improvement on two separate datasets over previous best methods in both accuracy and scalability, making a step towards neural query auto-completion in production search engines.Comment: To appear in NAACL-HLT 201

Deep learning for extracting protein-protein interactions from biomedical literature

State-of-the-art methods for protein-protein interaction (PPI) extraction are primarily feature-based or kernel-based by leveraging lexical and syntactic information. But how to incorporate such knowledge in the recent deep learning methods remains an open question. In this paper, we propose a multichannel dependency-based convolutional neural network model (McDepCNN). It applies one channel to the embedding vector of each word in the sentence, and another channel to the embedding vector of the head of the corresponding word. Therefore, the model can use richer information obtained from different channels. Experiments on two public benchmarking datasets, AIMed and BioInfer, demonstrate that McDepCNN compares favorably to the state-of-the-art rich-feature and single-kernel based methods. In addition, McDepCNN achieves 24.4% relative improvement in F1-score over the state-of-the-art methods on cross-corpus evaluation and 12% improvement in F1-score over kernel-based methods on "difficult" instances. These results suggest that McDepCNN generalizes more easily over different corpora, and is capable of capturing long distance features in the sentences.Comment: Accepted for publication in Proceedings of the 2017 Workshop on Biomedical Natural Language Processing, 10 pages, 2 figures, 6 table