A new heuristic to solve the one-warehouse N-retailer problem

We deal with a multi-echelon inventory system in which one warehouse supplies an item to multiple retailers. Customer demand arrives at each retailer at a constant rate. The retailers replenish their inventories from the warehouse that in turn orders from an outside supplier. It is assumed that shortages are not allowed and lead times are negligible. The goal is to determine replenishment policies that minimize the overall cost in the system. We develop a heuristic to compute efficient policies, which also can easily be used in a spreadsheet application. The main idea consists of finding a balance between the replenishment and the inventory holding costs at each installation. This new heuristic we compare with two other approaches proposed in the literature; the computational studies show that in most of the instances generated the new method provides lower costs.     

Passivity based control of a distributed PEM fuel cell model

This work considers the controlled load change of proton exchange membrane (PEM) fuel cells. Due to the intrinsic nonlinearities of fuel cells, load changes are quite challenging. In the case of a low temperature PEM fuel cell, there is the possibility of undesired liquid water formation. Most available control concepts are heuristic linear controller structures based on a perfectly mixed fuel cell model. In this work a nonlinear controller for one-dimensional spatially distributed model of a PEM fuel cell is presented. The fuel cell model is derived from first principles. The concept of passivity is used to design the controller. A suitable control Lyapunov function is chosen and passivity of the fuel cell is shown. A state-feedback law is derived that can guarantee stability of the closed-loop system over a wide range of operation conditions. In order to make the feedback law applicable to fuel cells with limited measurement information an observer is designed. In a final step the state-feedback law and the observer are combined to an output-feedback controller.     

Improving component selection and monitoring with controlled experimentation and automated measurements

Context: A number of approaches have been proposed for the general problem of software component evaluation and selection. Most approaches come from the field of Component-Based Software Development (CBSD), tackle the problem of Commercial-off-the-shelf component selection and use goal-oriented requirements modelling and multi-criteria decision making techniques. Evaluation of the suitability of components is carried out largely manually and partly relies on subjective judgement. However, in dynamic, distributed environments with high demands for transparent selection processes leading to trustworthy, auditable decisions, subjective judgements and vendor claims are not considered sufficient. Furthermore, continuous monitoring and re-evaluation of components after integration is sometimes needed.Objective: This paper describes how an evidence-based approach to component evaluation can improve repeatability and reproducibility of component selection under the following conditions: (1) Functional homogeneity of candidate components and (2) High number of components and selection problem instances.Method: Our evaluation and selection method and tool empirically evaluate candidate components in controlled experiments by applying automated measurements. By analysing the differences to system-development-oriented scenarios, the paper shows how the process of utility analysis can be tailored to fit the problem space, and describes a method geared towards automated evaluation in an empirical setting. We describe tool support and a framework for automated measurements.We further present a taxonomy of decision criteria for the described scenario and discuss the data collection means needed for each category of criteria.Results: To evaluate our approach, we discuss a series of case studies in the area of digital preservation. We analyse the criteria defined in these case studies, classify them according to the taxonomy, and discuss the quantitative coverage of automated measurements.Conclusion: The results of the analysis show that an automated measurement, evaluation and selection framework is necessary and feasible to ensure trusted and repeatable decisions.     

Nonlinear pressure control of self-supplied variable displacement axial piston pumps

The present paper deals with the pressure control of self-supplied variable displacement axial piston pumps subject to fast changing, unknown loads. First, the setup of the system and the mathematical model are described. As the pump is self-supplied, the mathematical model exhibits a switching right-hand side which makes the control design a challenging task. A nonlinear two degrees-of-freedom control strategy, comprising a feedforward and a feedback control, in combination with a load estimator is proposed for the pressure control. The proof of the stability of the overall closed-loop system is based on Lyapunov's theory. The performance of the control concept is verified by means of experiments. The results show that the proposed control concept has an excellent and robust behavior.     

Highly efficient switched‐mode transmitter using a current mode class‐D RF amplifier

This article presents a switched‐mode transmitter architecture using a current mode class‐D (CMCD) amplifier. To achieve high average efficiency for a modulated signal the envelope of the complex baseband signal is transformed into pulses such that the CMCD amplifier is operated either at its peak efficiency or completely switched off. The CMCD amplifier has been designed based on single‐tone active harmonic load‐pull measurements to achieve a power‐added efficiency (PAE) of 61.5% with 25 W output power at 900 MHz using LDMOS FETs. Removing the losses of the demodulation filter and of the amplifier a 10% higher efficiency than in an ideal class‐B amplifier can be obtained for burst‐mode operation with a peak‐to‐average power ratio of 10 dB. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

Facets of the Fully Mixed Nash Equilibrium Conjecture

In this work, we continue the study of the many facets of the Fully Mixed Nash Equilibrium Conjecture, henceforth abbreviated as the FMNE\mathsf{FMNE} Conjecture, in selfish routing for the special case of n identical users over two (identical) parallel links. We introduce a new measure of Social Cost, defined as the expectation of the square of the maximum congestion on a link; we call it Quadratic Maximum Social Cost. A Nash equilibrium is a stable state where no user can improve her (expected) latency by switching her mixed strategy; a worst-case Nash equilibrium is one that maximizes Quadratic Maximum Social Cost. In the fully mixed Nash equilibrium, all mixed strategies achieve full support.     

Modeling and control of the doubly fed induction generator wind turbine

The present paper deals with the modeling of wind turbine generation systems. The model of a doubly fed induction generator, along with the corresponding converter, crow bar protection and electrical grid is described. The different level control strategies both in normal operation and under voltage dig conditions are discussed, including speed control, torque and reactive power control for the rotor-side converter, reactive and DC voltage control for the grid-side converter and the corresponding current loops control. The results obtained with simulations are compared to experimental data obtained from voltage sags provoked to real wind turbines.     

A novel HMM-based learning framework for improving dynamic wireless push system performance

A new machine learning framework is introduced in this paper, based on the hidden Markov model (HMM), designed to provide scheduling in dynamic wireless push systems. In realistic wireless systems, the clients’ intentions change dynamically; hence a cognitive scheduling scheme is needed to estimate the desirability of the connected clients. The proposed scheduling scheme is enhanced with self-organized HMMs, supporting the network with an estimated expectation of the clients’ intentions, since the system’s environment characteristics alter dynamically and the base station (server side) has no a priori knowledge of such changes. Compared to the original pure scheme, the proposed machine learning framework succeeds in predicting the clients’ information desires and overcomes the limitation of the original static scheme, in terms of mean delay and system efficiency.