Modeling Robot “Psycho-Physical” State and Reactions – A New Option in Human–Robot Communication Part 2: Modeling and Simulation

This part of the paper examines numerically the possibility of modeling robot fatigue being representative of a human psychophysical state that can be applied to robots. Temperatures of driving motors are suggested as analogs to fatigue in muscles. Simulation of robot behavior is performed on a typical human task, namely handwriting. Three phases of task execution, characteristic for humans, are observed, i.e. regular motion, reconfiguration after symptoms of fatigue, and degeneration caused by the too long, hard work.     

Optimization of the Temperature-Time Curve for the Curing Process of Thermoset Matrix Composites

An intelligent optimization model aiming at off-line or pre-series optimization of the thermal curing cycle of polymer matrix composites is proposed and discussed. The computational procedure is based on the coupling of a finite element thermochemical process model, dynamic artificial neural networks and genetic algorithms. Objective of the optimization routine is the maximization of the composite degree of cure by the definition of the autoclave temperature. Obtained outcomes evidenced the capability of the method as well as its efficiency with respect to hard computing or experimental procedures.     

A new concept of the SCARA robot

Based on the SCARA concept, accepted worldwide, this paper considers the possibility of realizing SCARA with a full circle working area. The basis of the approach is a mechanism with two eccentrically positioned rotating discs instead several tools (hands) is also facilitated. This paper analyzes the advantages of the proposed solution and the possibilities of its realization.     

Iterative algorithm to compute the maximal and stabilising solutions of a general class of discrete-time Riccati-type equations

In this article an iterative method to compute the maximal solution and the stabilising solution, respectively, of a wide class of discrete-time nonlinear equations on the linear space of symmetric matrices is proposed. The class of discrete-time nonlinear equations under consideration contains, as special cases, different types of discrete-time Riccati equations involved in various control problems for discrete-time stochastic systems. This article may be viewed as an addendum of the work of Dragan and Morozan (Dragan, V. and Morozan, T. (2009), ‘A Class of Discrete Time Generalized Riccati Equations’, Journal of Difference Equations and Applications, first published on 11 December 2009 (iFirst), doi: 10.1080/10236190802389381) where necessary and sufficient conditions for the existence of the maximal solution and stabilising solution of this kind of discrete-time nonlinear equations are given. The aim of this article is to provide a procedure for numerical computation of the maximal solution and the stabilising solution, respectively, simpler than the method based on the Newton–Kantorovich algorithm.     

Challenges and opportunities of multimodal data in human learning: The computer science students' perspective

Multimodal data have the potential to explore emerging learning practices that extend human cognitive capacities. A critical issue stretching in many multimodal learning analytics (MLA) systems and studies is the current focus aimed at supporting researchers to model learner behaviours, rather than directly supporting learners. Moreover, many MLA systems are designed and deployed without learners' involvement. We argue that in order to create MLA interfaces that directly support learning, we need to gain an expanded understanding of how multimodal data can support learners' authentic needs. We present a qualitative study in which 40 computer science students were tracked in an authentic learning activity using wearable and static sensors. Our findings outline learners' curated representations about multimodal data and the non-technical challenges in using these data in their learning practice. The paper discusses 10 dimensions that can serve as guidelines for researchers and designers to create effective and ethically aware student-facing MLA innovations.     

Particle Swarm Optimization and Pure Adaptive Search in Finish Goods’ Inventory Management

Abstract

Inventory management deals with a tradeoff between the benefits of keeping stocks of goods that allows fulfillment of the customer’s demand, and the cost of carrying inventory. Inventory control techniques are very important components and the most organizations can substantially reduce their costs associated with the flow of materials. This paper presents new inventory management model based on particle swarm optimization and pure adaptive search global optimization algorithm in production-inventory system. The proposed model is focusing on planned level of demand for finished goods, production and raw materials cost, production capacity as the norm, change of the production cost and inventory capital cost, all of which are typical factors in automobile manufacture industry. The model determines different factors such as the minimizing inventory quantity, minimizing inventory value, and minimizing production cost based on demand for production items. The model is tested with original real-world dataset obtained from the automotive company Lear from US and its factory in Novi Sad, Serbia.     

Neural network prediction of disc brake performance

An automotive brake's performance results from the complex interrelated phenomena occurring at the contact of the friction pair. These complex braking phenomena are mostly affected by the tribochemical properties of the friction material's ingredients, the brake disc properties, and the brake's operating regimes. In this paper, the synergistic effects of the friction material's properties, defined by its composition and manufacturing conditions, and the brake's operating regimes on the disc brake factor C variation have been modelled by means of artificial neural networks. The influences of 26 input parameters, determined by the friction material composition (18 ingredients), its manufacturing conditions (5 parameters), and the brake's operating regimes (3 parameters) on the brake factor C variation, have been predicted. The neural model of the disc brake cold performance has been developed by training 18 different neural network architectures with the five different learning algorithms. The optimal neural model of disc brake operation has been shown to be valid for predicting the brake factor C variation of the cold disc brake over a wide range of brake's operating regimes and for different types of friction material.     

Detection of Damaged Tool in Injection Molding Process with Acoustic Emission

ABSTRACT

The measurement of acoustic emission (AE) signals during injection molding of polypropylene with new and damaged mold is presented. The damaged injection mold was fitted with a steel insert with cracks induced by laser surface heat treatment. Two resonant piezoelectric AE sensors were attached to the mold via AE waveguides. To improve the mold integrity prediction with smaller defects, AE signal frequency characteristics and a measure of AE signal amplitude probability distribution are implemented. A 5-dimensional feature vector with real-valued explanatory variables is proposed, providing the defining points in an appropriate multidimensional space to characterize the state of injection molding tool. Feature vectors are classified with neural network pattern recognition. The results confirm that presented AE technique offers characterizing the integrity of molds also with resonant sensors.