The Information Literacy Challenge

Abstract

Technological changes are occurring rapidly. As one result, students entering college are bringing very disparate computer skills and attitudes. Some students are reluctant to embrace new technologies; others demand electronic resources for all assignments. By considering the computer access and Internet resources available to elementary school students today, we can only imagine what our users of tomorrow will expect from libraries. Although college students may arrive at our libraries with increased computer skills, their knowledge of electronic information may be lacking. Definitions of information literacy and an overview of information literacy skills are outlined. The Digital Information Literacy program at The University of Texas at Austin, as well as the technological environment and facilities at the institution, serve as a case study for integrating information literacy skills into traditional services and partnerships.     

Control point policy optimization using genetic algorithms

This paper describes the application of an integrated Genetic Algorithm (GA)/Discrete Event Simulation model for selecting optimum values for Critical Point Policy (CPP) hedging time and buffer size parameters. The CPP is shown to perform well, when compared with the Critical Ratio priority rule, in terms of improving service levels, particularly when subject to conditions where buffer sizes and Takt times are required to be small. The technique developed involves buffer sizes being chosen by a GA according to a constraint on the total storage space available within the system. A method is described for reducing the number of variables that the GA needs to deal with, hence, improving the efficiency of the GA optimization process. The development and application work reported also provides further understanding into how and when the CPP should be applied.     

On the solution of the tracking problem for non-linear systems with MPC

This paper presents a Model Predictive Control (MPC) algorithm for non-linear systems which solves the tracking problem for asymptotically constant references. Closed-loop stability of the equilibrium and asymptotic zero-error regulation are guaranteed. The performance of the method is discussed with the classical Continuous Stirred Tank Reactor (CSTR) control application.     

Signal shaper with a distributed delay: Spectral analysis and design

Input shapers with time delays have proved useful in many applications related to controls for various oscillatory devices, for example flexible manipulators and cranes. In the paper, a novel approach for designing a zero-vibration signal shaper based on equally distributed delay is proposed. The parameter assessment of the shaper is based on the spectral approach. Various characteristics of the shaper are analyzed and compared with the classical zero-vibration shaper with a lumped delay. The analysis shows that the novel shaper is a slower, but more robust alternative to the classical shaper. Besides, the discrete implementation of the shaper is proposed and tested. It includes zero placement based parameter adjustment with the objective to preserve full compensation of the oscillatory mode by the discrete algorithm.     

电石生产用石灰质量的控制

通过麦尔兹窑生产过程阐述了石灰质量控制的要点。为保证生产出优质石灰,应对原料和燃料的杂质进行控制,同时还应选择合理的石灰石粒度、煅烧温度、压力和煅烧时间。     

Data-driven parameterization of polymer electrolyte membrane fuel cell models via simultaneous local linear structured state space identification

In order to mitigate the degradation and prolong the lifetime of polymer electrolyte membrane fuel cells, advanced, model-based control strategies are becoming indispensable. Thereby, the availability of accurate yet computationally efficient fuel cell models is of crucial importance. Associated with this is the need to efficiently parameterize a given model to a concise and cost-effective experimental data set. A challenging task due to the large number of unknown parameters and the resulting complex optimization problem. In this work, a parameterization scheme based on the simultaneous estimation of multiple structured state space models, obtained by analytic linearization of a candidate fuel cell stack model, is proposed. These local linear models have the advantage of high computational efficiency, regaining the desired flexibility required for the typically iterative task of model parameterization. Due to the analytic derivation of the local linear models, the relation to the original parameters of the non-linear model is retained. Furthermore, the local linear models enable a straight-forward parameter significance and identifiability analysis with respect to experimental data. The proposed method is demonstrated using experimental data from a 30 kW commercial polymer electrolyte membrane fuel cell stack.     

A hybrid cloud controller for vertical memory elasticity: A control-theoretic approach

Web-facing applications are expected to provide certain performance guarantees despite dynamic and continuous workload changes. As a result, application owners are using cloud computing as it offers the ability to dynamically provision computing resources (e.g., memory, CPU) in response to changes in workload demands to meet performance targets and eliminates upfront costs. Horizontal, vertical, and the combination of the two are the possible dimensions that cloud application can be scaled in terms of the allocated resources. In vertical elasticity as the focus of this work, the size of virtual machines (VMs) can be adjusted in terms of allocated computing resources according to the runtime workload. A commonly used vertical resource elasticity approach is realized by deciding based on resource utilization, named capacity-based. While a new trend is to use the application performance as a decision making criterion, and such an approach is named performance-based. This paper discusses these two approaches and proposes a novel hybrid elasticity approach that takes into account both the application performance and the resource utilization to leverage the benefits of both approaches. The proposed approach is used in realizing vertical elasticity of memory (named as vertical memory elasticity), where the allocated memory of the VM is auto-scaled at runtime. To this aim, we use control theory to synthesize a feedback controller that meets the application performance constraints by auto-scaling the allocated memory, i.e., applying vertical memory elasticity. Different from the existing vertical resource elasticity approaches, the novelty of our work lies in utilizing both the memory utilization and application response time as decision making criteria. To verify the resource efficiency and the ability of the controller in handling unexpected workloads, we have implemented the controller on top of the Xen hypervisor and performed a series of experiments using the RUBBoS interactive benchmark application, under synthetic and real workloads including Wikipedia and FIFA. The results reveal that the hybrid controller meets the application performance target with better performance stability (i.e., lower standard deviation of response time), while achieving a high memory utilization (close to 83%), and allocating less memory compared to all other baseline controllers.     

Dynamics and control of a thermally self-sustaining energy storage system using integrated solid oxide cells for an islanded building

A solid oxide cell-based energy system is proposed for a solar-powered stand-alone building. The system is comprised of a 5 kW_el solid oxide fuel cell (SOFC), a 9.5 kW_el solid oxide electrolysis cell (SOEC), and the required balance of plant. The SOFC supplies: 1- building demand in the absence of sufficient solar power, 2- heat for SOEC in endothermic and standby modes. Thermal integration of SOFC and SOEC is implemented through a network of heat exchangers, combined with set of control algorithms. Two control strategies were implemented to actuate the SOFC in response to endothermic heat demands of SOEC by manipulating: 1- electric power, 2- fuel utilization. The results of dynamic simulation of system for two scenarios (sunny day and cloudy day) showed successful compliance of temperature constraints with both methods. Manipulation of fuel utilization, however, resulted in better system performance in terms of efficiency and H₂ balance.     

不确定性X-Y定位平台PID控制方法研究

X-Y定位平台属于强耦合非线性系统,且由于结构复杂性及外界干扰,当前测量水平难以获得其精确的数学模型,提出基于自适应神经网络的PID控制策略。考虑到X-Y定位平台系统精确的数学模型难以获得,利用神经网络良好的学习能力来逼近系统未知非线性模型,设计神经网络控制器;为了保证神经网络在学习的初期阶段的控制精度,设计PID控制器来进行辅助补偿控制;为加快学习速度,提高运动控制的实时性,设计变学习率的优化算法来实现神经网络权值的在线调整。试验结果验证了所提控制方法的有效性。     

The current state of control loop performance monitoring – A survey of application in industry

Control loop performance monitoring (CPM) in industrial production processes is an established area of research for which many methods to detect malfunctioning loops have been developed. However, it is unclear which methods are successful in an industrial environment. Often, there are additional aspects such as organizational issues, data availability and access that can compromise the use of CPM. In this paper, we are reporting on the results of a survey amongst CPM users. The survey takes stock of existing methods and their use in industry as well as which faults are most frequent and can be detected. Organizational as well as implementation issues are investigated and discussed. This paper aims to identify open research topics and the direction of development of CPM in industrial production processes.