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

Grey Wolf Optimizer

Data Mining Practical Machine Learning Tools and Techniques

The Whale Optimization Algorithm

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

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Differential Evolution: A Survey of the State-of-the-Art

Archery is one of these sports in which the athletes repeat the same body postures over and over again. This means that tiny wrong habits could cause serious long-term health injuries. Consequently, learning a correct shooting technique is very important for both beginner archers and elite athletes. In this work, we present a system that uses machine learning to automatically detect anomalous postures and return to the archer a shooting score, that works by giving the archer a feedback on his own body configuration. We use a neural network to analyze images of archers during the firing and return the place of their different body joints. With this information, the system can detect wrong postures which might lead to injuries. This feedback is very important to the archer when learning the shooting technique. In addition, the system is not intrusive for the archer, so she/he can fire arrows freely. Preliminary results show the usefulness of the system, which is able to detect 4 spine misalignment and 4 raised elbow analyzing only 9 shots.

Intelligent System for the Reduction of Injuries in Archery

Smart city initiatives have emerged to mitigate the negative effects of a very fast growth of urban areas. A number of universities are applying smart city solutions to face similar challenges in their campuses. In this study, we analyze the possibility of using low cost sensors based on detecting wireless signals of light commodity devices to track the movement of the members of the university community. This tracking information will help the university managers to provide the users with smart services. The first insight is that there were not detected barely movements through the campus during late-night/early morning hours (from 0:00H to 6:00H). In turn, the number of human flows sensed in a given direction is similar to the ones in the opposite one. The analysis of the sensed data has shown that the most mobility occurs during the opening and finishing school hours, as expected. Finally, we observed that the sensors are able to detect vehicular mobility.

Smart Campus Human Tracking: The Case of University of Málaga

Nowadays, city streets are populated not only by private vehicles but also by public transport, fleets of workers, and deliveries. Since each vehicle class has a maximum cargo capacity, we study in this article how authorities could improve the road traffic by endorsing long term policies to change the different vehicle proportions: sedans, minivans, full size vans, trucks, and motorbikes, without losing the ability of moving cargo throughout the city. We have performed our study in a realistic scenario (map, road traffic characteristics, and number of vehicles) of the city of Malaga and captured the many details into the SUMO microsimulator. After analyzing the relationship between travel times, emissions, and fuel consumption, we have defined a multiobjective optimization problem to be solved, so as to minimize these city metrics. Our results provide a scientific evidence that we can improve the delivery of goods in the city by reducing the number of heavy duty vehicles and fostering the use of vans instead.

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An Intelligent Advisor for City Traffic Policies

The Vehicle Routing Problem (VRP) is a hard combinatorial problem with numerous industrial applications.Among the large number of extensions to the canonical VRP, we study the Capacitated VRP (CVRP), whichis mainly characterized by using vehicles of the same capacity. A cellular Genetic Algorithm (cGA) |a kind ofdecentralized population based heuristic| is used for solving CVRP, improving several of the best existing resultsso far in the literature. Our study shows a high performance in terms of the quality of the solutions found and thenumber of function evaluations (eﬁort). Key words: Cellular Genetic Algorithm, Hybrid Genetic Algorithm, Vehicle Routing Problem 1. IntroductionThis paper is devoted to solve the CapacitatedVRP(CVRP),anextensionofVRP[1]inwhichallvehicleshavethesamecapacity.Formally,CVRPisdeﬂned on an undirected graph G = (V; ~ E~ ) where ~V = fv 0 ;v 1 ;:::;v n g is a vertex set and E ~ = f ( v i ;v j ) =v i ;v j 2 ~V;i < j g is an edge set. Vertex v 0 stands for the

ComputingNineNewBest-So-FarSolutionsforCapacitatedVRP withaCellularGeneticAlgorithm

Consider a random sample X1, X2,…, Xn, from a normal population with unknown mean and standard deviation. Only the sample size, mean and range are recorded and it is necessary to estimate the unknown population mean and standard deviation. In this paper the estimation of the mean and standard deviation is made from a Bayesian perspective by using a Markov Chain Monte Carlo (MCMC) algorithm to simulate samples from the intractable joint posterior distribution of the mean and standard deviation. The proposed methodology is applied to simulated and real data. The real data refers to the sugar content (oBRIX level) of orange juice produced in different countries.

Enrique Alba

Papers

Intelligent System for the Reduction of Injuries in Archery

Smart Campus Human Tracking: The Case of University of Málaga

An Intelligent Advisor for City Traffic Policies

ComputingNineNewBest-So-FarSolutionsforCapacitatedVRP withaCellularGeneticAlgorithm

Bayesian inference for the mean and standard deviation of a normal population when only the sample size, mean and range are observed