Multi-agent quality of experience control

In the framework of the Future Internet, the aim of the Quality of Experience (QoE) Control functionalities is to track the personalized desired QoE level of the applications. The paper proposes to perform such a task by dynamically selecting the most appropriate Classes of Service (among the ones supported by the network), this selection being driven by a novel heuristic Multi-Agent Reinforcement Learning (MARL) algorithm. The paper shows that such an approach offers the opportunity to cope with some practical implementation problems: in particular, it allows to face the so-called “curse of dimensionality” of MARL algorithms, thus achieving satisfactory performance results even in the presence of several hundreds of Agents

Operational Decision-Making in Healthcare Using Control Charts

The primary objective of this thesis was to design a framework supplemented with guidelines for the healthcare managers to select an appropriate type of control chart for operational decision-making. A systematic literature review was conducted to gauge the extent to which control charts were being used in a healthcare setting for clinical decision making and operational decision-making purposes. The findings showed that the application of control charts was almost equal for the clinical decision-making sector and the operational decision-making sector. On further analysis, the ability of control charts to function as a standalone tool was affirmed by the vast majority of studies where it was deployed as a primary tool for quality improvement purposes. The framework contains some prerequisites with regards to data collection and construction of control charts. Also, the metrics involved are clearly identified: Quality, Financial, Volume and Utilization; and subsequently defined. The guidelines were created keeping the metric and possible scenario/s that can be associated with it into consideration. These guidelines would save the healthcare managers their time and significantly reduce the chances of selecting an inappropriate type of control chart. Potential operational areas for the usage of control charts are also discussed in the thesis. In order to demonstrate the way in which the prescribed framework can be implemented in a real-life hospital environment, a regional hospital was chosen and the yearly rate of Surgical Site Infections (SSI) for colon surgery was monitored using an appropriate control chart which was selected by following the guidelines outlined in the framework

Variable Switching Point Model Predictive Control for DC-Link Voltage Regulation of Back-to-Back Converters

In this paper, a novel control method for back-toback converters used in grid-to-motor connections is explored. To increase the robustness of low DC-link capacitances, a control method based on variable switching point model predictive control is proposed. While previous model predictive control methods for the back-to-back converter selected a certain switching state to fulfill all control goals, we use the switching time in addition to the switching state in order to minimise deviations from the target voltage. Choosing a variable switching point provides an additional degree of freedom to the control framework and allows the system to cope with the large number of control variables. In this case, the variable switching point is used to minimize the effects of low DC-link capacitances on the system. This can either be achieved by selecting a switching point that yields low DClink capacitor charging or by selecting a switching point that aims to keep the DC-link voltage close to the reference. The proposed method is verified through numerical simulations and hardware-in-the-loop (HIL) experiments and compared to existing approaches. The results show that it is possible to control the DClink using only the switching point of the converter

Optimal Test Access Mechanism (TAM) for Reducing Test Application Time of Core-Based SOCs

[[abstract]]In this paper, we propose an algorithm based on a framework of reconfigurable multiple scan chains for system-on-chip to minimize test application time. The control signal combination causes the computing time increasing exponentially, and the algorithm we proposed introduces a heuristic control signal selecting method to solve this serious problem. We also minimize the test application time by using the balancing method to assign registers into multiple scan chains. The results show that it could significantly reduces both the test application time and the computation time.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]紙

Local False Discovery Rate Based Methods for Multiple Testing of One-Way Classified Hypotheses

This paper continues the line of research initiated in \cite{Liu:Sarkar:Zhao:2016} on developing a novel framework for multiple testing of hypotheses grouped in a one-way classified form using hypothesis-specific local false discovery rates (Lfdr's). It is built on an extension of the standard two-class mixture model from single to multiple groups, defining hypothesis-specific Lfdr as a function of the conditional Lfdr for the hypothesis given that it is within a significant group and the Lfdr for the group itself and involving a new parameter that measures grouping effect. This definition captures the underlying group structure for the hypotheses belonging to a group more effectively than the standard two-class mixture model. Two new Lfdr based methods, possessing meaningful optimalities, are produced in their oracle forms. One, designed to control false discoveries across the entire collection of hypotheses, is proposed as a powerful alternative to simply pooling all the hypotheses into a single group and using commonly used Lfdr based method under the standard single-group two-class mixture model. The other is proposed as an Lfdr analog of the method of \cite{Benjamini:Bogomolov:2014} for selective inference. It controls Lfdr based measure of false discoveries associated with selecting groups concurrently with controlling the average of within-group false discovery proportions across the selected groups. Simulation studies and real-data application show that our proposed methods are often more powerful than their relevant competitors.Comment: 26 pages, 17 figure

A Framework for Meta-heuristic Parameter Performance Prediction Using Fitness Landscape Analysis and Machine Learning

The behaviour of an optimization algorithm when attempting to solve a problem depends on the values assigned to its control parameters. For an algorithm to obtain desirable performance, its control parameter values must be chosen based on the current problem. Despite being necessary for optimal performance, selecting appropriate control parameter values is time-consuming, computationally expensive, and challenging. As the quantity of control parameters increases, so does the time complexity associated with searching for practical values, which often overshadows addressing the problem at hand, limiting the efficiency of an algorithm. As primarily recognized by the no free lunch theorem, there is no one-size-fits-all to problem-solving; hence from understanding a problem, a tailored approach can substantially help solve it. To predict the performance of control parameter configurations in unseen environments, this thesis crafts an intelligent generalizable framework leveraging machine learning classification and quantitative characteristics about the problem in question. The proposed parameter performance classifier (PPC) framework is extensively explored by training 84 high-accuracy classifiers comprised of multiple sampling methods, fitness types, and binning strategies. Furthermore, the novel framework is utilized in constructing a new parameter-free particle swarm optimization (PSO) variant called PPC-PSO that effectively eliminates the computational cost of parameter tuning, yields competitive performance amongst other leading methodologies across 99 benchmark functions, and is highly accessible to researchers and practitioners. The success of PPC-PSO shows excellent promise for the applicability of the PPC framework in making many more robust parameter-free meta-heuristic algorithms in the future with incredible generalization capabilities