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In ordinal classification problems, the class value exhibits a natural order. Usually, these problems are solved as multiclass classification problems while discarding the ordering form of the class. Recently, several research studies have proposed novel methods for ordinal classification problems while aiming to predict a predefined objective value. These methods are based on modification of the splitting criteria of decision tree-based algorithms. These methods consider the ordinal nature of the data and the magnitude of the potential classification error from a predefined predicted objective. This research aims to consolidate these methods and generalize them for any decision tree-based method while constructing decision trees according to any predefined objective value calculated from the training dataset. Furthermore, to evaluate the proposed method, a comprehensive experimental study is performed based on known ordinal datasets. The proposed method is found to significantly outperform its counterpart nonordinal models. This observation confirms that ordering information benefits ordinal methods and improves their performance. Additionally, the ordinal decision tree-based methods achieved competitive performance compared to state-of-the-art ordinal techniques. The results are validated with various performance measures that are commonly used for ordinal and non-ordinal classification problems. [ABSTRACT FROM AUTHOR]

This paper presents a comparative study of entropy estimation in a large-alphabet regime. A variety of entropy estimators have been proposed over the years, where each estimator is designed for a different setup with its own strengths and caveats. As a consequence, no estimator is known to be universally better than the others. This work addresses this gap by comparing twenty-one entropy estimators in the studied regime, starting with the simplest plug-in estimator and leading up to the most recent neural network-based and polynomial approximate estimators. Our findings show that the estimators' performance highly depends on the underlying distribution. Specifically, we distinguish between three types of distributions, ranging from uniform to degenerate distributions. For each class of distribution, we recommend the most suitable estimator. Further, we propose a sample-dependent approach, which again considers three classes of distribution, and report the top-performing estimators in each class. This approach provides a data-dependent framework for choosing the desired estimator in practical setups. [ABSTRACT FROM AUTHOR]

The paper addresses the problem of distinguishing the leading agents in the group. The problem is considered in the framework of classification problems, where the agents in the group select the items with respect to certain properties. The suggested method of distinguishing the leading agents utilizes the connectivity between the agents and the Rokhlin distance between the subgroups of the agents. The method is illustrated by numerical examples. The method can be useful in considering the division of labor in swarm dynamics and in the analysis of the data fusion in the tasks based on the wisdom of the crowd techniques. [ABSTRACT FROM AUTHOR]

In this paper, we propose a solution for the problem of searching for multiple targets by a group of mobile agents with sensing errors of the first and the second types. The agents' goal is to plan the search and follow its trajectories that lead to target detection in minimal time. Relying on real sensors' properties, we assume that the agents can detect the targets in various directions and distances; however, they are exposed to first- and second-type statistical errors. Furthermore, we assume that the agents in the group have errorless communication with each other. No central station or coordinating agent is assumed to control the search. Thus, the search follows a fully distributed decision-making process, in which each agent plans its path independently based on the information about the targets, which is collected independently or received from the other agents. The suggested solution includes two algorithms: the Distributed Expected Information Gain (DEIG) algorithm, which implements dynamic Voronoi partitioning of the search space and plans the paths by maximizing the expected one-step look-ahead information per region, and the Collective Q-max (CQM) algorithm, which finds the shortest paths of the agents in the group by maximizing the cumulative information about the targets' locations using deep Q-learning techniques. The developed algorithms are compared against previously developed reactive and learning methods, such as the greedy centralized Expected Information Gain (EIG) method. It is demonstrated that these algorithms, specifically the Collective Q-max algorithm, considerably outperform existing solutions. In particular, the proposed algorithms improve the results by 20% to 100% under different scenarios of noisy environments and sensors' sensitivity. [ABSTRACT FROM AUTHOR]

Remote learning has been an increasingly growing field in the last two decades. The Internet development, as well as the increase in PC's capabilities and bandwidth capacity, has made remote learning through the internet a convenient learning preference, leading to a variety of new interfaces and methods. In this work, we consider a remote learning interface, developed in a Computer Integrated Manufacturing (CIM) Laboratory, and evaluate the contribution of different interface components to the overall performance and learning ability of end users. The evaluated components are the control method of the robotic arm and the use of a three-dimensional simulation tool before and during the execution of a robotic task. An experiment is designed and executed, comparing alternative interface designs for remote learning of robotic operation. A teleoperation task was given to 120 engineering students through five semesters. The number of steps required for completing the task, the number of errors during the execution, and the improvement rate during the execution were measured and analyzed. The results provide guidelines for a better design of an interface for remote learning of robotic operation. The main contribution of this paper is in the introduction of a new teaching tool for laboratories and the supplied guidelines for an efficient design of such tools. (Contains 14 figures.)

This work proposes an approach to remote learning of robotic cells based on internet and simulation tools. The proposed approach, which integrates remote-learning and tele-operation into a generic scheme, is designed to enable students and developers to set-up and manipulate a robotic cell remotely. Its implementation is based on a dedicated website that supports a full three-dimensional simulation and a manual online control of the cell. The website provides high flexibility for making changes in the design of the work area, independent of the user's location. The website can be used to further integrate full-scale remote learning labs in collaboration with other organizations. (Contains 7 figures and 2 tables.)

The method of robust design has long been used for the design of systems that are insensitive to noises. In this paper it is demonstrated how this approach can be used to obtain a robust eco-design (ecological design). In a case study, robust design principles are applied to the design of a factory smokestack, using the Gaussian Plume Model (GPM). The GPM is a well-known model for describing pollutant dispersal from a point source, subject to various atmospheric conditions. In this research, the mean-square-error (MSE) of the accumulated and the maximum pollution values around a given target are defined as the performance measures and used to adjust the design parameters. Both analytical and numerical approaches are used to evaluate the MSE measures over the design space. It is demonstrated how to use the non-linearity in the GPM to reach a low MSE value that produces a cheaper design configuration. The differences between the manufacturer viewpoint and the environmentalist viewpoint with respect to the considered eco-design problem are discussed and analyzed. Keywords: Ecological design, green manufacturing, air pollution, clean production, 1. Introduction: Eco-design and quality engineering The concept of eco-design, often referred to as cleaner production or design for the environment, describes a design process which takes into consideration the [...]

We suggest an experimentation strategy for the robust design of empirically fitted models. The suggested approach is used to design experiments that minimize the variance of the optimal robust solution. The new design-of-experiment optimality criterion, termed [V.sub.s]-optimal, prioritizes the estimation of a model's coefficients, such that the variance of the optimal solution is minimized by the performed experiments. It is discussed how the proposed criterion is related to known optimality criteria. We present an analytical formulation of the suggested approach for linear models and a numerical procedure for higher-order or nonpolynomial models. In comparison with conventional robust-design methods, our approach provides more information on the robust solution by numerically generating its multidimensional distribution. Moreover, in a case study, the proposed approach results in a better robust solution in comparison with these standard methods., 1. Introduction In the last three decades, the Taguchi method of robust design has been widely applied to the design of various systems. In many cases, the exact underlying relationship [...]

In certain types of processes, verification of the quality of the output units is possible only after the entire batch has been processed. We develop a model that prescribes which units should be inspected and how the units that were not inspected should be disposed of, in order to minimize the expected sum of inspection costs and disposition error costs, for processes that are subject to random failure and recovery. The model is based on a dynamic programming algorithm that has a low computational complexity. The study also includes a sensitivity analysis under a variety of cost and probability scenarios, supplemented by an analysis of the smallest batch that requires inspection, the expected number of inspections, and the performance of an easy to implement heuristic., 1. Introduction Most researchers and practitioners in the field of production management agree that process improvement contributes more to product quality than the inspection of process output. However, quality inspection, [...]