THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Although uncertainty about structures of environmental models (conceptual uncertainty) is often acknowledged to be the main source of uncertainty in model predictions, it is rarely considered in environmental modelling. Rather, formal uncertainty analyses have traditionally focused on model parameters and input data as the principal source of uncertainty in model predictions. The traditional approach to model uncertainty analysis, which considers only a single conceptual model, may fail to adequately sample the relevant space of plausible conceptual models. As such, it is prone to modelling bias and underestimation of predictive uncertainty. In this paper we review a range of strategies for assessing structural uncertainties in models. The existing strategies fall into two categories depending on whether field data are available for the predicted variable of interest. To date, most research has focussed on situations where inferences on the accuracy of a model structure can be made directly on the basis of field data. This corresponds to a situation of ‘interpolation’. However, in many cases environmental models are used for ‘extrapolation’; that is, beyond the situation and the field data available for calibration. In the present paper, a framework is presented for assessing the predictive uncertainties of environmental models used for extrapolation. It involves the use of multiple conceptual models, assessment of their pedigree and reflection on the extent to which the sampled models adequately represent the space of plausible models. � 2005 Elsevier Ltd. All rights reserved.

A Framework for Dealing With Uncertainty due to Model Structure Error

A survey of hybrid metaheuristics for the resource-constrained project scheduling problem

An Iterative Learning Control (ILC) method with Extended State Observer (ESO) is proposed to enhance the tracking precision of telescope. Telescope systems usually suffer some uncertain nonlinear disturbances, such as nonlinear friction and unknown disturbances. Thereby, to ensure the tracking precision, the ESO which can estimate system states (including parts of uncertain nonlinear disturbances) is introduced. The nonlinear system is converted to an approximate linear system by making use of the ESO. Besides, to make further improvement on the tracking precision, we make use of the ILC method which can find an ideal control signal by the process of iterative learning. Furthermore, this control method theoretically guarantees a prescribed tracking performance and final tracking accuracy. Finally, a few comparative experimental results show that the proposed control method has excellent performance for reducing the tracking error of telescope system.

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Iterative Learning Control with Extended State Observer for Telescope System

Article history: Received: July 5, 2017 Received in revised format: October 10, 2017 Accepted: January 4, 2018 Available online: January 4, 2018 The project scheduling problem is both practically and theoretically of paramount importance. From the practical perspective, improvement of project scheduling as a critical part of project management process can lead to successful project completion and significantly decrease of the relevant costs. From the theoretical perspective, project scheduling is regarded as one of the interesting optimization issues, which has attracted the attention of many researchers in the area of operations research. Therefore, the project scheduling issue has been significantly evaluated over time and has been developed from various aspects. In this research, the topics related to Resource-Constrained Project Scheduling Problem (RCPSP) are reviewed, recent developments in this field are evaluated, and the results are presented for future studies. In this regard, first, the standard problem of RCPSP is expressed and related developments are presented from four aspects of resources, characteristics of activities, type of objective functions, and availability level of information. Following that, details about 216 articles conducted on RCPSP during 1980-2017 are expressed. At the end, in line with the statistics obtained from the evaluation of previous articles, suggestions are made for the future studies in order to help the development of new issues in this area. © 2018 by the authors; licensee Growing Science, Canada.

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Resource-constrained project scheduling problem: review of past and recent developments

In this article, the genetic algorithm (GA) and fully informed particle swarm (FIPS) are hybridized for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) with minimization of project makespan as the objective subject to resource and precedence constraints. In the proposed hybrid genetic algorithm–fully informed particle swarm algorithm (HGFA), FIPS is a popular variant of the particle swarm optimization algorithm. A random key and the related mode list representation schemes are used as encoding schemes, and the multi-mode serial schedule generation scheme (MSSGS) is considered as the decoding procedure. Furthermore, the existing mode improvement procedure in the literature is modified. The results show that the proposed mode improvement procedure remarkably improves the project makespan. Comparing the results of the proposed HGFA with other approaches using the well-known PSPLIB benchmark sets validates the effectiveness of the proposed algorithm to solve the MRCPSP.

Hybridization of genetic algorithm and fully informed particle swarm for solving the multi-mode resource-constrained project scheduling problem

This paper deals with implementation of an optimal linear controller for a laboratory liquid four-tank system. A discrete linear model is used for modeling a class of MIMO nonlinear systems. It is shown that this model can identify these nonlinear systems with any desired accuracy, as a result the designed controller is accurate. The global stability condition is studied. The advantage of the proposed method against other controllers is its simplicity in design, there is no need for physical equations of the nonlinear system, and it is more accurate than other proposed linear methods. We present a step-by-step description of all steps leading towards the derivation and implementation of such a controller. The proposed method is tested on an experimental four tank benchmark process.

Optimal controller design using discrete linear model for a four tank benchmark process.

Identification of robust Gaussian Process Regression with noisy input using EM algorithm

The dual neural extended Kalman filter (DNEKF) is proposed in this article to compensate for model inaccuracies and violations of noise assumption in the EKF-based multirate sensor fusion. The DNEKF employs two neural networks to improve process state and output predictions through simultaneous state and parameter estimations using state vector augmentation. Taking advantage of frequent but less accurate measurements and infrequent but more accurate measurements provides a multirate parameter update strategy for neural networks training in the proposed approach. Two numerical examples and one industrial application demonstrate the effectiveness of the proposed method to compensate for inadequate process knowledge to improve multirate sensor fusion performance.

Dual Neural Extended Kalman Filtering Approach for Multirate Sensor Data Fusion

This paper presents an evolutionary programming (EP)-based approach to solving the resource-constrained project scheduling problem (RCPSP), a well-known NP-hard problem in scheduling, with minimization of project duration as the objective subject to precedence and resource constraints. The individual representation of EP for the problem is based on random keys. The serial generation scheme is used in the decoding scheme to generate the project plan. Experimental analyses are presented to investigate the performance of the proposed EP-based methodology, including comparison of the four variants of EP, namely, CEP, FEP, MCEP and IMCEP, with each other and GA to find the best variant of EP for the RCPSP, and comparison of this best variant of EP (MCEP) with other approaches using the J30 standard instances set in PSPLIB. The computational results validate the effectiveness of the proposed algorithm.

https://link.springer.com/content/pdf/10.1057%2Fjors.2012.69.pdf

Yousef Alipouri

Papers

Hybridization of genetic algorithm and fully informed particle swarm for solving the multi-mode resource-constrained project scheduling problem

Optimal controller design using discrete linear model for a four tank benchmark process.

Identification of robust Gaussian Process Regression with noisy input using EM algorithm

Dual Neural Extended Kalman Filtering Approach for Multirate Sensor Data Fusion

Solving resource-constrained project scheduling problem with evolutionary programming