Online change detection for energy-efficient mobilec crowdsensing

Mobile crowdsensing is power hungry since it requires continuously and simultaneously sensing, processing and uploading fused data from various sensor types including motion sensors and environment sensors. Realizing that being able to pinpoint change points of contexts enables energy-efficient mobile crowdsensing, we modify histogram-based techniques to efficiently detect changes, which has less computational complexity and performs better than the conventional techniques. To evaluate our proposed technique, we conducted experiments on real audio databases comprising 200 sound tracks. We also compare our change detection with multivariate normal distribution and one-class support vector machine. The results show that our proposed technique is more practical for mobile crowdsensing. For example, we show that it is possible to save 80% resource compared to standard continuous sensing while remaining detection sensitivity above 95%. This work enables energy-efficient mobile crowdsensing applications by adapting to contexts

Unambiguous detection of nitrated explosive vapours by fluorescence quenching of dendrimer films

Unambiguous and selective standoff (non-contact) infield detection of nitro-containingexplosives and taggants is an important goal but difficult to achieve with standard analyticaltechniques. Oxidative fluorescence quenching is emerging as a high sensitivity method fordetecting such materials but is prone to false positives—everyday items such as perfumeselicit similar responses. Here we report thin films of light-emitting dendrimers that detectvapours of explosives and taggants selectively—fluorescence quenching is not observed for arange of common interferents. Using a combination of neutron reflectometry, quartz crystalmicrobalance and photophysical measurements we show that the origin of the selectivity isprimarily electronic and not the diffusion kinetics of the analyte or its distribution in the film.The results are a major advance in the development of sensing materials for the standoffdetection of nitro-based explosive vapours, and deliver significant insights into the physicalprocesses that govern the sensing efficacy

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)

International audienceThis paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machinelearning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain

Simulating a virtual machining model in an agent-based model for advanced analytics

International audienceMonitoring the performance of manufacturing equipment is critical to ensure the efficiency of manufacturing processes. Machine-monitoring data allows measuring manufacturing equipment efficiency. However, acquiring real and useful machine-monitoring data is expensive and time consuming. An alternative method of getting data is to generate machine-monitoring data using simulation. The simulation data mimic operations and operational failure. In addition, the data can also be used to fill in real data sets with missing values from real-time data collection. The mimicking of real manufacturing systems in computer-based systems is called “virtual manufacturing”. The computer-based systems execute the manufacturing system models that represent real manufacturing systems. In this paper, we introduce a virtual machining model of milling operations. We developed a prototype virtual machining model that represents 3-axis milling operations. This model is a digital mock-up of a real milling machine; it can generate machine-monitoring data from a process plan. The prototype model provides energy consumption data based on physics-based equations. The model uses the standard interfaces of Step-compliant data interface for Numeric Controls and MTConnect to represent process plan and machine-monitoring data, respectively. With machine-monitoring data for a given process plan, manufacturing engineers can anticipate the impact of a modification in their actual manufacturing systems. This paper describes also how the virtual machining model is integrated into an agent-based model in a simulation environment. While facilitating the use of the virtual machining model, the agent-based model also contributes to the generation of more complex manufacturing system models, such as a virtual shop-floor model. The paper describes initial building steps towards a shop-floor model. Aggregating the data generated during the execution of a virtual shop-floor model allows one to take advantage of data analytics techniques to predict performance at the shop-floor level

BIM and PLM: Comparing and learning from changes to professional practice across sectors

This paper explores the effects of PLM and BIM on professional practice. It draws on existing literature documenting the experiences of both communities of practice to explain shifts in professional boundaries. A review of case study based literature compares the nature of changes triggered by PLM and BIM relative to the new activities, roles/responsibilities and knowledge competencies, and supply chain relationships. The paper synthesises these changes and reflects PLM and BIM experiences against each other so as to contrast the continuing evolution of professional practice and lessons learned