Adaptive Torque Estimation for an IPMSM with Cross-Coupling and Parameter Variations

This paper presents a new adaptive torque estimation algorithm for an interior permanent magnet synchronous motor (IPMSM) with parameter variations and cross-coupling between d- and q-axis dynamics. All cross-coupled, time-varying, or uncertain terms that are not part of the nominal flux equations are included in two equivalent mutual inductances, which are described using the equivalent d- and q-axis back electromotive forces (EMFs). The proposed algorithm estimates the equivalent d- and q-axis back EMFs in a recursive and stability-guaranteed manner, in order to compute the equivalent mutual inductances between the d- and q-axes. Then, it provides a more accurate and adaptive torque equation by adding the correction terms obtained from the computed equivalent mutual inductances. Simulations and experiments demonstrate that torque estimation errors are remarkably reduced by capturing and compensating for the inherent cross-coupling effects and parameter variations adaptively, using the proposed algorithm.111Ysciescopu

MRBench: A Benchmark for MapReduce Framework

MapReduce is Google’s programming model for easy development of scalable parallel applications which pro-cess huge quantity of data on many clusters. Due to its conveniency and efficiency, MapReduce is used in various applications (e.g., web search services and on-line analytical processing.) However, there are only few good benchmarks to evaluate MapReduce implementa-tions by realistic testsets. In this paper, we present MRBench that is a bench-mark for evaluating MapReduce systems. MRBench fo-cuses on processing business oriented queries and con-current data modifications. To this end, we build MR-Bench to deal with large volumes of relational data and execute highly complex queries. By MRBench, users can evaluate the performance of MapReduce systems while varying environmental parameters such as data size and the number of (Map/Reduce) tasks. Our ex-tensive experimental results show that MRBench is a useful tool to benchmark the capability of answering critical business questions.

Interpretable pap smear cell representation for cervical cancer screening

Screening is critical for prevention and early detection of cervical cancer but it is time-consuming and laborious. Supervised deep convolutional neural networks have been developed to automate pap smear screening and the results are promising. However, the interest in using only normal samples to train deep neural networks has increased owing to class imbalance problems and high-labeling costs that are both prevalent in healthcare. In this study, we introduce a method to learn explainable deep cervical cell representations for pap smear cytology images based on one class classification using variational autoencoders. Findings demonstrate that a score can be calculated for cell abnormality without training models with abnormal samples and localize abnormality to interpret our results with a novel metric based on absolute difference in cross entropy in agglomerative clustering. The best model that discriminates squamous cell carcinoma (SCC) from normals gives 0.908 +- 0.003 area under operating characteristic curve (AUC) and one that discriminates high-grade epithelial lesion (HSIL) 0.920 +- 0.002 AUC. Compared to other clustering methods, our method enhances the V-measure and yields higher homogeneity scores, which more effectively isolate different abnormality regions, aiding in the interpretation of our results. Evaluation using in-house and additional open dataset show that our model can discriminate abnormality without the need of additional training of deep models.Comment: 20 pages, 6 figure

Enhancing Art Gallery Visitors’ Learning Experience using Wearable Augmented Reality: Generic Learning Outcomes Perspective

The potential of ICT-enhanced visitor learning experience is increasing with the advancement of new and emerging technologies in art gallery settings. However, studies on the visitor learning experience using wearable devices, and in particular those investigating the effects of wearable augmented reality on the learning experience within cultural heritage tourism attractions are limited. Using the Generic Learning Outcomes framework, this study aims to assess how the wearable augmented reality application enhances visitor’s learning experiences. Forty-four volunteers who were visiting an art gallery were divided into two groups, an experimental group and a control group. Following their visit to the gallery, the volunteers, who had and had not used wearable computing equipment, were interviewed, and the data were analysed using thematic analysis. Findings revealed that the wearable augmented reality application helps visitors to see connections between paintings and personalise their learning experience. However, there are some drawbacks such as lack of visitor-visitor engagement and the social acceptability

Embodiment of Wearable Augmented Reality Technology in Tourism Experiences

The increasing use of wearable devices for tourism purposes sets the stage for a critical discussion on technological mediation in tourism experience. This paper provides a theoretical reflection on the phenomenon of embodiment relation in technological mediation and then assesses the embodiment of wearable augmented reality technology in a tourism attraction. The findings suggest that technology embodiment is a multidimensional construct consisting of ownership, location, and agency. These support the concept of technology withdrawal, where technology disappears as it becomes part of human actions, and contest the interplay of subjectivity and intentionality between humans and technology in situated experiences such as tourism. It was also found that technology embodiment affects enjoyment and enhances experience with tourism attractions

Determining Visitor Engagement through Augmented Reality at Science Festivals: An Experience Economy Perspective

Augmented reality (AR) has been increasingly implemented to enhance visitor experiences, and tourism research has long understood the importance of creating memorable experiences, leading to the research era of experience economy. Although technology-enhanced visitor engagement is crucial for science festivals, research focusing on visitor engagement through AR using the experience economy perspective is limited. Therefore, the aim of this study is to examine how the educational, esthetics, escapist and entertainment experience using AR affect visitor satisfaction and memorable experience, and eventually, lead to visitor engagement with science experiences in the context of science festivals. A total of 220 data inputs were collected as part of the European City of Science festivities and Manchester Science Festival 2016 and analyzed using structural equation modelling. Findings show that the four realms of experience economy influence satisfaction and memory and, ultimately, the intention for visitor engagement with science research at science festivals. Theoretical contributions and practical implications are presented and discussed

Adaptive Torque Estimation for an IPMSM with Cross-Coupling and Parameter Variations

This paper presents a new adaptive torque estimation algorithm for an interior permanent magnet synchronous motor (IPMSM) with parameter variations and cross-coupling between d- and q-axis dynamics. All cross-coupled, time-varying, or uncertain terms that are not part of the nominal flux equations are included in two equivalent mutual inductances, which are described using the equivalent d- and q-axis back electromotive forces (EMFs). The proposed algorithm estimates the equivalent d- and q-axis back EMFs in a recursive and stability-guaranteed manner, in order to compute the equivalent mutual inductances between the d- and q-axes. Then, it provides a more accurate and adaptive torque equation by adding the correction terms obtained from the computed equivalent mutual inductances. Simulations and experiments demonstrate that torque estimation errors are remarkably reduced by capturing and compensating for the inherent cross-coupling effects and parameter variations adaptively, using the proposed algorithm