Abstract. Activity-Based Computing  aims to capture the state of the user and its environment by exploiting heterogeneous sensors in order to provide adaptation to exogenous computing resources. When these sensors are attached to the subject's body, they permit continuous monitoring of numerous physiological signals. This has appealing use in healthcare applications, e.g. the exploitation of Ambient Intelligence (AmI) in daily activity monitoring for elderly people. In this paper, we present a system for human physical Activity Recognition (AR) using smartphone inertial sensors. As these mobile phones are limited in terms of energy and computing power, we propose a novel hardware-friendly approach for multiclass classification. This method adapts the standard Support Vector Machine (SVM) and exploits fixed-point arithmetic for computational cost reduction. A comparison with the traditional SVM shows a significant improvement in terms of computational costs while maintaining similar accuracy, which can contribute to develop more sustainable systems for AmI.
Availability, reliability and economic sustainability of naval propulsion plants are key elements to cope with because maintenance costs represent a large slice of total operational expenses. Depending on the adopted strategy, impact of maintenance on overall expenses can remarkably vary; for example, letting an asset running up until breakdown can lead to unaffordable costs. As a matter of fact, a desideratum is to progress maintenance technology of ship propulsion systems from breakdown or preventive maintenance up to more effective condition-based maintenance approaches. The central idea in condition-based maintenance is to monitor the propulsion equipment by exploiting heterogeneous sensors, enabling diagnosis and, most of all, prognosis of the propulsion system's components and of their potential future failures. The success of condition-based maintenance clearly hinges on the capability of developing effective predictive models; for this purpose, effective use of machine learning methods is proposed in this article. In particular, authors take into consideration an application of condition-based maintenance to gas turbines used for vessel propulsion, where the performance and advantages of exploiting machine learning methods in modeling the degradation of the propulsion plant over time are tested. Experiments, conducted on data generated from a sophisticated simulator of a gas turbine, mounted on a Frigate characterized by a COmbined Diesel eLectric And Gas propulsion plant type, will allow to show the effectiveness of the proposed machine learning approaches and to benchmark them in a realistic maritime application.
In-sample approaches to model selection and error estimation of support vector machines (SVMs) are not as widespread as out-of-sample methods, where part of the data is removed from the training set for validation and testing purposes, mainly because their practical application is not straightforward and the latter provide, in many cases, satisfactory results. In this paper, we survey some recent and not-so-recent results of the data-dependent structural risk minimization framework and propose a proper reformulation of the SVM learning algorithm, so that the in-sample approach can be effectively applied. The experiments, performed both on simulated and real-world datasets, show that our in-sample approach can be favorably compared to out-of-sample methods, especially in cases where the latter ones provide questionable results. In particular, when the number of samples is small compared to their dimensionality, like in classification of microarray data, our proposal can outperform conventional out-of-sample approaches such as the cross validation, the leave-one-out, or the Bootstrap methods.
In this paper we focus our attention on the long-term load forecasting problem, that is the prediction of energy consump- tion for several months ahead (up to one or more years), useful in order to ease the proper scheduling of operative conditions (such as the planning of fuel supply). While several effective techniques are available in the short-term framework, no reliable methods have been proposed for long-term predictions. For this purpose, we de- scribe in this work a new procedure, which exploits the Empirical Mode Decomposition method to disaggregate a time series into two sets of components, respectively describing the trend and the local oscillations of the energy consumption values. These sets are then used for training Support Vector Regression models. The experi- mental results, obtained both on a public-domain and on an office building dataset, allow to validate the effectiveness of the proposed method
The aim of this paper is to understand the similarities and differences in the accounting con-vergence process of the BRIC countries. The study examines the evolution of these countries’accounting systems by developing a three-dimensional framework based on the political,economic and cultural elements. Brazil and Russia merely imitate, whereas China and Indiaedit and translate the international standards (‘informed divergence’). The political aspect,supported by the national culture and ‘community’, represents the main driver, even if thethree dimensions are closely interconnected and overall, we show the current emergenceof limits of the implementation of the dominant market model
This paper provides a conceptual discussion of the bidirectional relationship between knowledge management (KM) and intellectual capital (IC) in a specific subset of knowledge-based organisations, i.e. professional sport organisations. Through the review and conceptual discussion of two relevant research themes, i.e. KM strategies for IC value creation and IC codification, this paper aims to highlight research gaps useful to future research.
The authors apply a systematic literature review method to analyse 66 management and accounting studies on KM and IC in sport organisations. Internal and external validity tests support the methodology adopted.
The authors provide a conceptual model to explain how KM strategies about IC investments can be optimal, i.e. they create value for all the stakeholders but also suboptimal, i.e. they create value only for a group of stakeholders. Next, they provide evidence of the opportunistic use of the codification associated with IC investments that impair financial reporting information transparency and mislead managers and investors.
The results are informative for managers, regulators and policymakers to mitigate the inefficiencies regarding KM and IC codification and decisions.
This study contributes to the understanding of the bidirectional relationship between KM and IC in knowledge-based organisations by focussing on professional sport organisations in which KM and IC have played an important role for a long time. It also includes future avenues for advances in managing, measuring and reporting IC.
scite is a Brooklyn-based startup that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.