Abstract

Abstract
The kNN machine learning method is widely used as a classifier in Human Activity Recognition (HAR) systems. Although the kNN algorithm works similarly both online and in offline mode, the use of all training instances is much more critical online than offline due to time and memory restrictions in the online mode. Some methods propose decreasing the high computational costs of kNN by focusing, e.g., on approximate kNN solutions such as the ones relying on Locality-Sensitive Hashing (LSH). However, embedded kNN implementations also need to address the target device’s memory constraints, especially as the use of online classification needs to cope with those constraints to be practical. This paper discusses online approaches to reduce the number of training instances stored in the kNN search space. To address practical implementations of HAR systems using kNN, this paper presents simple, energy/computationally efficient, and real-time feasible schemes to maintain at runtime a maximum number of training instances stored by kNN. The proposed schemes include policies for substituting the training instances, maintaining the search space to a maximum size. Experiments in the context of HAR datasets show the efficiency of our best schemes.

Abstract

Abstract
PurposeThis paper aims to propose a translation library capable of generating robots proprietary code after their offline programming has been performed in a software application, named AdaptPack Studio, running over a robot simulation and offline programming software package.Design/methodology/approachThe translation library, named AdaptPack Studio Translator, is capable to generate proprietary code for the Asea Brown Boveri, FANUC, Keller und Knappich Augsburg and Yaskawa Motoman robot brands, after their offline programming has been performed in the AdaptPack Studio application.FindingsSimulation and real tests were performed showing an improvement in the creation, operation, modularity and flexibility of new robotic palletizing systems. In particular, it was verified that the time needed to perform these tasks significantly decreased.Practical implicationsThe design and setup of robotics palletizing systems are facilitated by an intuitive offline programming system and by a simple export command to the real robot, independent of its brand. In this way, industrial solutions can be developed faster, in this way, making companies more competitive.Originality/valueThe effort to build a robotic palletizing system is reduced by an intuitive offline programming system (AdaptPack Studio) and the capability to export command to the real robot using the AdaptPack Studio Translator. As a result, companies have an increase in competitiveness with a fast design framework. Furthermore, and to the best of the author’s knowledge, there is also no scientific publication formalizing and describing how to build the translators for industrial robot simulation and offline programming software packages, being this a pioneer publication in this area.

Abstract
The business models of banks are often seen as the result of a variety of simultaneously determined managerial choices, such as those regarding the types of activities, funding sources, level of diversification, and size. Moreover, owing to the fuzziness of data and the possibility that some banks may combine features of different business models, the use of hard clustering methods has often led to poorly identified business models. In this paper we propose a framework to deal with these challenges based on an ensemble of three unsupervised clustering methods to identify banking business models: fuzzy c-means (which allows us to handle fuzzy clustering), self-organizing maps (which yield intuitive visual representations of the clusters), and partitioning around medoids (which circumvents the presence of data outliers). We set up our analysis in the context of the European banking sector, which has seen its regulators increasingly focused on examining the business models of supervised entities in the aftermath of the twin financial crises. In our empirical application, we find evidence of four distinct banking business models and further distinguish between banks with a clearly defined business model (core banks) and others (non-core banks), as well as banks with a stable business model over time (persistent banks) and others (non-persistent banks). Our proposed framework performs well under several robustness checks related with the sample, clustering methods, and variables used.

Abstract
Irregular packing problems (also known as nesting problems)belong to the more general class of cutting and packing problems and consist of allocating a set of irregular and regular pieces to larger rectangular or irregular containers, while minimizing the waste of material or space. These problems combine the combinatorial hardness of cutting and packing problems with the computational difficulty of enforcing the geometric non-overlap and containment constraints. Unsurprisingly, nesting problems have been addressed, both in the scientific literature and in real-world applications, by means of heuristic and metaheuristic techniques. However, more recently a variety of mathematical models has been proposed for nesting problems. These models can be used either to provide optimal solutions for nesting problems or as the basis of heuristic approaches based on them (e.g. matheuristics). In both cases, better solutions are sought, with the natural economic and environmental positive impact. Different modeling options are proposed in the literature. We review these mathematical models under a common notation framework, allowing differences and similarities among them to be highlighted. Some insights on weaknesses and strengths are also provided. By building this structured review of mathematical models for nesting problems, research opportunities in the field are proposed. © 2019 Elsevier B.V.