Understanding customers' holistic perception of switches in automotive human–machine interfaces

For successful new product development, it is necessary to understand the customers' holistic experience of the product beyond traditional task completion, and acceptance measures. This paper describes research in which ninety-eight UK owners of luxury saloons assessed the feel of push-switches in five luxury saloon cars both in context (in-car) and out of context (on a bench). A combination of hedonic data (i.e. a measure of ‘liking’), qualitative data and semantic differential data was collected. It was found that customers are clearly able to differentiate between switches based on the degree of liking for the samples' perceived haptic qualities, and that the assessment environment had a statistically significant effect, but that it was not universal. A factor analysis has shown that perceived characteristics of switch haptics can be explained by three independent factors defined as ‘Image’, ‘Build Quality’, and ‘Clickiness’. Preliminary steps have also been taken towards identifying whether existing theoretical frameworks for user experience may be applicable to automotive human–machine interfaces

Web Supervision System of a Freight Elevator

Nowadays, automation and industrial control is an area in which there are innovations ev- ery day in terms of process digitalization, equipment interconnection and human-machine interaction, which results in a constant learning and adaptation to new technologies and methodologies developed. With this comes the responsibility to keep systems robust and prepared for eventual failures, while moving towards an increasing dependence on remote communication between different controllers and different processes. This fact leads to the need to create supervision and monitoring tools capable of detecting and transmitting existing failures, while ensuring that the system continues to operate with the same stability and performance. Therefore, in this work it is proposed the development of a supervisory tool based on industrial automation that has a fault detection component and a human-machine interface in order to incorporate all the essential features of an industrial supervisor. Using industrial programming languages for Programmable Logic Controllers, it was possible to develop an algorithm that is based on inference mechanisms to identify potential faults in the system, which are then transmitted to the user in an interface that can be accessed either locally or remotely via the Web.Nos dias de hoje, a automação e controlo industrial é uma área onde existe todos os dias inovações ao nível da digitalização de processos, da interconexão de equipamentos e na interação Homem-máquina, o que resulta numa constante aprendizagem e adaptação às novas tecnologias e metodologias desenvolvidas. Com isto, vem a responsabilidade de manter os sistemas robustos e preparados para eventuais falhas, ao mesmo tempo que se avança no sentido da cada vez maior dependência da comunicação remota entre diferentes controladores e diferentes processos. Este facto leva a que tenham de ser criadas ferramentas de supervisão e monitorização capazes de detetar e transmitir as falhas existentes, enquanto se garante que o sistema continua em funcionamento garantindo a mesma estabilidade e performance. Assim, neste trabalho é proposto o desenvolvimento de uma ferramenta de supervisão baseada em automação industrial que possua uma componente de deteção de falhas e uma interface Homem-máquina de forma a incorporar todas as funcionalidades essenciais de um supervisor industrial. Recorrendo a linguagens de programação industrial para controladores lógicos programáveis, foi possível desenvolver um algoritmo que se baseia em mecanismos de inferência para identificar potenciais avarias no sistema que são posteriormente transmitidas ao utilizador numa interface que pode ser acedida quer localmente, quer remotamente via Web

Pathological Brain Detection Using Weiner Filtering, 2D-Discrete Wavelet Transform, Probabilistic PCA, and Random Subspace Ensemble Classifier

Accurate diagnosis of pathological brain images is important for patient care, particularly in the early phase of the disease. Although numerous studies have used machine-learning techniques for the computer-aided diagnosis (CAD) of pathological brain, previous methods encountered challenges in terms of the diagnostic efficiency owing to deficiencies in the choice of proper filtering techniques, neuroimaging biomarkers, and limited learning models. Magnetic resonance imaging (MRI) is capable of providing enhanced information regarding the soft tissues, and therefore MR images are included in the proposed approach. In this study, we propose a new model that includes Wiener filtering for noise reduction, 2D-discrete wavelet transform (2D-DWT) for feature extraction, probabilistic principal component analysis (PPCA) for dimensionality reduction, and a random subspace ensemble (RSE) classifier along with the K-nearest neighbors (KNN) algorithm as a base classifier to classify brain images as pathological or normal ones. The proposed methods provide a significant improvement in classification results when compared to other studies. Based on 5×5 cross-validation (CV), the proposed method outperforms 21 state-of-the-art algorithms in terms of classification accuracy, sensitivity, and specificity for all four datasets used in the study

Prerequisites for Affective Signal Processing (ASP) - Part V: A response to comments and suggestions

In four papers, a set of eleven prerequisites for affective signal processing (ASP) were identified (van den Broek et al., 2010): validation, triangulation, a physiology-driven approach, contributions of the signal processing community, identification of users, theoretical specification, integration of biosignals, physical characteristics, historical perspective, temporal construction, and real-world baselines. Additionally, a review (in two parts) of affective computing was provided. Initiated by the reactions on these four papers, we now present: i) an extension of the review, ii) a post-hoc analysis based on the eleven prerequisites of Picard et al.(2001), and iii) a more detailed discussion and illustrations of temporal aspects with ASP