Scheduling soft real-time jobs over dual non-real-time servers

In this paper, we consider soft real-time systems with redundant off-the-shelf processing components (e.g., CPU, disk, network), and show how applications can exploit the redundancy to improve the system's ability of meeting response time goals (soft deadlines). We consider two scheduling policies, one that evenly distributes load (Balance), and one that partitions load according to job slackness (Chop). We evaluate the effectiveness of these policies through analysis and simulation. Our results show that by intelligently distributing jobs by their slackness amount the servers, Chop can significantly improve real-time performance. ©1996 IEEE.published_or_final_versio

UAV Catapult

This document outlines the Senior Design Project proposed by Dr. Aaron Drake that was assigned to a team of Mechanical Engineering students at California Polytechnic State University, San Luis Obispo. The purpose of this project was to design, build, test, and finalize a launching system for two small, fixed wing, unmanned aerial vehicles (UAVs) owned by Dr. Drake and Cal Poly. The goal was to create a system that was both portable and reliable to use, only requiring a two-person team to use effectively in the field. The most important design requirements were determined to be the launch speed, assembly time, and storage size. Multiple propulsion methods were explored, with a pneumatic piston cylinder chosen for the preliminary design. A side clamping carriage design was selected due to the shape of the UAVs being launched. A structural prototype of the UAV carriage was constructed, and the final design was developed as a result of data obtained from the prototype. Following the creation of our final design, a manufacturing plan and design verification plan were produced to bring the concept to fruition. With these plans in place, parts were ordered, and construction began. This document will describe the background research done, the objectives of the project, the preliminary and final design, the manufacturing and testing process, difficulties and obstacles faced, our final results, and what can be improved upon in the future

A multiple objective optimization approach to the decommissioning and dismantling of a nuclear power plant.

The complexity, relevance and critical nature of the decommissioning of nuclear power plants (NPP) are of great significance in today\u27s society. Following the catastrophe in Fukushima a shift in the general public\u27s perception of NPP took place throughout the world and in Europe in particular. In this dissertation interdisciplinary methods will be discussed to identify solutions which take into account the technological complexity and organizational issues involved in the dismantling and decommissioning process of NPP. Operations research, lean management, simultaneous engineering, cost analysis, multiple-objective optimization, project management, software tools are powerful concepts and methodologies when undertaking the dismantling and decommissioning process of NPP. Besides the presentation of a wide range of terminological and methodological definitions and technical terms based on the Literature Review, in the dissertation a framework for model development of a Multiple objective optimization problem (MOOP) will discussed focusing on empirical data from a virtual NPP. The theoretical foundation of the framework is at the intersection of two successful approaches used to describe and accomplish similar complex challenges, and the integration of state-of-the-art process approaches such as lean management. The procedural conception of the model is mainly leant on the OMEGA model (International Atomic Energy Agency (IAEA) (2008)). Mathematically the model is derived from Jones et. al. (1998). Finally the application of the model using different software tools (AIMMS, MATLAB, R and SPSS) will be presented. In conclusion the work will be put into a position to venture a critical outlook and discussion for the future of the decommissioning and dismantling processes of NPP. The main goal of this dissertation is to define the requirements for the optimization of three objectives: Minimizing the total project cost, reducing the safety hazard (risk) and managing project duration. Also a description of how the programming language R and the AIMMS program interfaces with the OMEGA application and how R will be used to solve the MOOP will be given. The software Microsoft Project will be leveraged in order to model this objective

Robots Learning Manipulation Tasks from Demonstrations and Practice

Developing personalized cognitive robots that help with everyday tasks is one of the on-going topics in robotics research. Such robots should have the capability to learn skills and perform tasks in new situations. In this thesis, we study three research problems to explore the learning methods of robots in the setting of manipulation tasks. In the first problem, we investigate hand movement learning from human demonstrations. For practical purposes, we propose a system for learning hand actions from markerless demonstrations, which are captured using the Kinect sensor. The algorithm autonomously segments an example trajectory into multiple action units, each described by a movement primitive, and forms a task-specific model. With that, similar movements for different scenarios can be generated, and performed on Baxter Robots. The second problem aims to address learning robot movement adaptation under various environmental constraints. A common approach is to adopt motion primitives to generate target motions from demonstrations. However, their generalization capability is weak for novel environments. Additionally, traditional motion generation methods do not consider versatile constraints from different users, tasks, and environments. In this work, we propose a co-active learning framework for learning to adapt the movement of robot end-effectors for manipulation tasks. It is designed to adapt the original imitation trajectories, which are learned from demonstrations, to novel situations with different constraints. The framework also considers user feedback towards the adapted trajectories, and it learns to adapt movement through human-in-the-loop interactions. Experiments on a humanoid platform validate the effectiveness of our approach. In order to further adapt robots to perform more complex manipulation tasks, as the third problem, we are investigating a framework that the robot could not only plan and execute the sequential task in a new environment, but also refine its actions by learning subgoals through re-planning/re-execution during the practice. A sequential task is naturally considered as a sequence of pre-learned action primitives, each action primitive has its own goal parameters corresponding to the subgoal. We propose a system to learn the subgoals distribution of given task model using reinforcement learning by iteratively updating the parameters in the trials. As a result, by considering the learned subgoals distribution in sequential motion planning, the proposed framework could adaptively select better subgoals to generate movements for robot to execute the task successfully. We implement the framework for the task of ''openning a microwave'' involving a sequence of primitive actions and subgoals and validate it on Baxter platform

Reservoir Flooding Optimization by Control Polynomial Approximations

In this dissertation, we provide novel parametrization procedures for water-flooding production optimization problems, using polynomial approximation techniques. The methods project the original infinite dimensional controls space into a polynomial subspace. Our contribution includes new parameterization formulations using natural polynomials, orthogonal Chebyshev polynomials and Cubic spline interpolation. We show that the proposed methods are well suited for black-box approach with stochastic global-search method as they tend to produce smooth control trajectories, while reducing the solution space size. We demonstrate their efficiency on synthetic two-dimensional problems and on a realistic 3-dimensional problem. By contributing with a new adjoint method formulation for polynomial approximation, we implemented the methods also with gradient-based algorithms. In addition to fine-scale simulation, we also performed reduced order modeling, where we demonstrated a synergistic effect when combining polynomial approximation with model order reduction, that leads to faster optimization with higher gains in terms of Net Present Value. Finally, we performed gradient-based optimization under uncertainty. We proposed a new multi-objective function with three components, one that maximizes the expected value of all realizations, and two that maximize the averages of distribution tails from both sides. The new objective provides decision makers with the flexibility to choose the amount of risk they are willing to take, while deciding on production strategy or performing reserves estimation (P10;P50;P90)

Human activity recognition for pervasive interaction

PhD ThesisThis thesis addresses the challenge of computing food preparation context in the kitchen. The automatic recognition of fine-grained human activities and food ingredients is realized through pervasive sensing which we achieve by instrumenting kitchen objects such as knives, spoons, and chopping boards with sensors. Context recognition in the kitchen lies at the heart of a broad range of real-world applications. In particular, activity and food ingredient recognition in the kitchen is an essential component for situated services such as automatic prompting services for cognitively impaired kitchen users and digital situated support for healthier eating interventions. Previous works, however, have addressed the activity recognition problem by exploring high-level-human activities using wearable sensing (i.e. worn sensors on human body) or using technologies that raise privacy concerns (i.e. computer vision). Although such approaches have yielded significant results for a number of activity recognition problems, they are not applicable to our domain of investigation, for which we argue that the technology itself must be genuinely “invisible”, thereby allowing users to perform their activities in a completely natural manner. In this thesis we describe the development of pervasive sensing technologies and algorithms for finegrained human activity and food ingredient recognition in the kitchen. After reviewing previous work on food and activity recognition we present three systems that constitute increasingly sophisticated approaches to the challenge of kitchen context recognition. Two of these systems, Slice&Dice and Classbased Threshold Dynamic Time Warping (CBT-DTW), recognize fine-grained food preparation activities. Slice&Dice is a proof-of-concept application, whereas CBT-DTW is a real-time application that also addresses the problem of recognising unknown activities. The final system, KitchenSense is a real-time context recognition framework that deals with the recognition of a more complex set of activities, and includes the recognition of food ingredients and events in the kitchen. For each system, we describe the prototyping of pervasive sensing technologies, algorithms, as well as real-world experiments and empirical evaluations that validate the proposed solutions.Vietnamese government’s 322 project, executed by the Vietnamese Ministry of Education and Training

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Workshop on "Control issues in the micro / nano - world".

International audienceDuring the last decade, the need of systems with micro/nanometers accuracy and fast dynamics has been growing rapidly. Such systems occur in applications including 1) micromanipulation of biological cells, 2) micrassembly of MEMS/MOEMS, 3) micro/nanosensors for environmental monitoring, 4) nanometer resolution imaging and metrology (AFM and SEM). The scale and requirement of such systems present a number of challenges to the control system design that will be addressed in this workshop. Working in the micro/nano-world involves displacements from nanometers to tens of microns. Because of this precision requirement, environmental conditions such as temperature, humidity, vibration, could generate noise and disturbance that are in the same range as the displacements of interest. The so-called smart materials, e.g., piezoceramics, magnetostrictive, shape memory, electroactive polymer, have been used for actuation or sensing in the micro/nano-world. They allow high resolution positioning as compared to hinges based systems. However, these materials exhibit hysteresis nonlinearity, and in the case of piezoelectric materials, drifts (called creep) in response to constant inputs In the case of oscillating micro/nano-structures (cantilever, tube), these nonlinearities and vibrations strongly decrease their performances. Many MEMS and NEMS applications involve gripping, feeding, or sorting, operations, where sensor feedback is necessary for their execution. Sensors that are readily available, e.g., interferometer, triangulation laser, and machine vision, are bulky and expensive. Sensors that are compact in size and convenient for packaging, e.g., strain gage, piezoceramic charge sensor, etc., have limited performance or robustness. To account for these difficulties, new control oriented techniques are emerging, such as[d the combination of two or more ‘packageable' sensors , the use of feedforward control technique which does not require sensors, and the use of robust controllers which account the sensor characteristics. The aim of this workshop is to provide a forum for specialists to present and overview the different approaches of control system design for the micro/nano-world and to initiate collaborations and joint projects

The Role of Entanglement in Quantum Communication, and Analysis of the Detection Loophole

Entanglement is a feature at the heart of quantum information. Its enablement of unusual correlations between particles drives a new wave of communication and computation. This thesis explores some of the ways in which the tools for studying entanglement can be used to quantify the transmission of quantum information, and compares the use of different techniques. We begin this thesis by expanding the technique of teleportation simulation, which adds noise to the entangled resource state to mimic channel effects. By introducing classical noise in the communication step, we show it is possible to simulate more than just Pauli channels using teleportation. This new class is characterised, and studied in detail for a particular resource state, leading to a family of simulable channels named “Pauli-Damping channels” whose properties are analysed. Also introduced are a new family of quantum states, “phase Werner” states, whose entanglement properties relate to the interesting conjecture of bound entangled states with a negative partial transpose. Holevo-Werner channels, to which these states are connected, are shown to be teleportation covariant. We exploit this to present several interesting results, including the optimal estimation of the channel-defining parameter. The minimal binary-discrimination error for Holevo-Werner channels is bounded for the first time with the analytical form of the quantum Chernoff bound. We also consider the secret key capacity of these channels, showing how different entanglement measures provide a better upper bound for different regions of these channels. Finally, a method for generating new Bell inequalities is presented, exploiting nonphysical probability distributions to obtain new inequalities. Tens of thousands of new inequivalent inequalities are generated, and their usefulness in closing the detection loophole for imperfect detectors is examined, with comparison to the current optimal construction. Two candidate Bell inequalities which may equal or beat the best construction are presented