Search results for "Application"
showing 10 items of 5559 documents
Adaptive Vehicle Mode Monitoring Using Embedded Devices with Accelerometers
2012
Monitoring of specific attributes such as vehicle speed and fuel consumption as well as cargo safety is an important problem for transport domain. This task is performed using specific multiagent monitoring systems. To ensure secure operation of such systems they should have autonomous and adaptive behaviour.
Robotic Platform for Automated Search and Rescue Missions of Humans
2013
We present a novel type of model incorporating a special remote life signals sensing optical system on top of a controllable robotic platform. The remote sensing system consists of a laser and a camera. By properly adapting our optics and by applying a proper image processing algorithm we can sense within the field of view, illuminated by the laser and imaged by the camera, the heartbeats and the blood pulse pressure of subjects (even several simultaneously). The task is to use the developed robotic system for search and rescue mission such as saving survivals from a fire.
Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)
2017
International audience; This 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 pred…
Implementation and Deployment Evaluation of the DMAMAC Protocol for Wireless Sensor Actuator Networks
2016
Abstract The increased application of wireless technologies including Wireless Sensor Actuator Networks (WSAN) in industry has given rise to a plethora of protocol designs. These designs target metrics ranging from energy efficiency to real-time constraints. Protocol design typically starts with a requirements specification, and continues with analytic and model-based simulation analysis. State-of- the-art network simulators provide extensive physical environment emulation, but still have limitations due to model abstractions. Deployment testing on actual hardware is therefore vital in order to validate implementability and usability in the real environment. The contribution of this article…
Analysis and Visualization of Product Memory Layout in IP-XACT
2017
Modern ASIC and FPGA based embedded products use model based design, in which both hardware and software are developed in parallel. Previously HW was completed first and the information handed over to SW team, typically in the form of register tables. The information was even manually copied to SW code, making any changes error-prone and laborious. IP-XACT is the most feasible standard to model HW also for the SW needs. The HW design connectivity and overall memory layout may change due to component instantiations, configurations and conditional operation states, which makes it difficult to create register tables even for documentation. Current register design tools fall short in serving th…
A methodology to assess the intrinsic discriminative ability of a distance function and its interplay with clustering algorithms for microarray data …
2013
Abstract Background Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from statistics to computer science. Following Handl et al., it can be summarized as a three step process: (1) choice of a distance function; (2) choice of a clustering algorithm; (3) choice of a validation method. Although such a purist approach to clustering is hardly seen in many areas of science, genomic data require that level of attention, if inferences made from cluster analysis have to be of some relevance to biomedical research. Results A procedure is proposed for the assessment of the discriminative ability of a distance functi…
Review on Modeling and Control of Flexible Link Manipulators
2020
This paper presents a review of dynamic modeling techniques and various control schemes to control flexible link manipulators (FLMs) that were studied in recent literature. The advantages and complexities associated with the FLMs are discussed briefly. A survey of the reported studies is carried out based on the method used for modeling link flexibility and obtaining equations of motion of the FLMs. The control techniques are reviewed by classifying them into two main categories: model-based and model-free control schemes. The merits and limitations of different modeling and control methods are highlighted.
Automated Uncertainty Quantification Through Information Fusion in Manufacturing Processes
2017
International audience; Evaluation of key performance indicators (KPIs) such as energy consumption is essential for decision-making during the design and operation of smart manufacturing systems. The measurements of KPIs are strongly affected by several uncertainty sources such as input material uncertainty, the inherent variability in the manufacturing process, model uncertainty, and the uncertainty in the sensor measurements of operational data. A comprehensive understanding of the uncertainty sources and their effect on the KPIs is required to make the manufacturing processes more efficient. Towards this objective, this paper proposed an automated methodology to generate a hierarchical B…
Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
2019
In this paper, we present an auto-encoder-based machine learning framework for self organizing networks (SON). Traditional machine learning approaches, for example, K Nearest Neighbor, lack the ability to be precisely predictive. Therefore, they can not be extended for sequential data in the true sense because they require a batch of data to be trained on. In this work, we explore artificial neural network-based approaches like the autoencoders (AE) and propose a framework. The proposed framework provides an advantage over traditional machine learning approaches in terms of accuracy and the capability to be extended with other methods. The paper provides an assessment of the application of …
The Elephant in the Machine: Proposing a New Metric of Data Reliability and its Application to a Medical Case to Assess Classification Reliability
2020
In this paper, we present and discuss a novel reliability metric to quantify the extent a ground truth, generated in multi-rater settings, as a reliable basis for the training and validation of machine learning predictive models. To define this metric, three dimensions are taken into account: agreement (that is, how much a group of raters mutually agree on a single case)