Search results for "Machine learning"

Artificial neural networks for neutron/ γ discrimination in the neutron detectors of NEDA

2020

Three different Artificial Neural Network architectures have been applied to perform neutron/? discrimination in NEDA based on waveform and time-of-flight information. Using the coincident ?-rays from AGATA, we have been able to measure and compare on real data the performances of the Artificial Neural Networks as classifiers. While the general performances are quite similar for the data set we used, differences, in particular related to the computing times, have been highlighted. One of the Artificial Neural Network architecture has also been found more robust to time misalignment of the waveforms. Such a feature is of great interest for online processing of waveforms. Narodowe Centrum Nau…

Assessment of Proton Direct Ionization for the Radiation Hardness Assurance of Deep Submicron SRAMs Used in Space Applications

2021

Proton direct ionization from low-energy protons has been shown to have a potentially significant impact on the accuracy of prediction methods used to calculate the upset rates of memory devices in space applications for state-of-the-art deep sub-micron technologies. The general approach nowadays is to consider a safety margin to apply over the upset rate computed from high-energy proton and heavy ion experimental data. The data reported here present a challenge to this approach. Different upset rate prediction methods are used and compared in order to establish the impact of proton direct ionization on the total upset rate. No matter the method employed the findings suggest that proton dir…

A comparison between industrial experts' and novices' haptic perceptual organization: a tool to identify descriptors of the handle of fabrics

2004

Abstract In descriptive analysis, the establishing of the list of attributes is crucial. Attributes should account for consumers' perceptions and be understood by professionals for efficient communication. This work was aimed at identifying the most appropriate attributes for fabric description from the terminology associated with both experts' and novices' haptic perceptual spaces. Eleven industrial experts and two groups of novices (20 males and 20 females) evaluated 26 clothing fabrics. They performed (1) a free-sorting task based on haptic similarities, (2) a description of the previously formed groups, and (3) a hedonic rating task for each fabric. The perceptual organization was simil…

Classification and retrieval on macroinvertebrate image databases

2011

Aquatic ecosystems are continuously threatened by a growing number of human induced changes. Macroinvertebrate biomonitoring is particularly efficient in pinpointing the cause-effect structure between slow and subtle changes and their detrimental consequences in aquatic ecosystems. The greatest obstacle to implementing efficient biomonitoring is currently the cost-intensive human expert taxonomic identification of samples. While there is evidence that automated recognition techniques can match human taxa identification accuracy at greatly reduced costs, so far the development of automated identification techniques for aquatic organisms has been minimal. In this paper, we focus on advancing …

Wireless Interference Estimation Using Machine Learning in a Robotic Force-Seeking Scenario

2019

Cyber-physical systems are systems governed by the laws of physics that are tightly controlled by computer-based algorithms and network-based sensing and actuation. Wireless communication technology is envisioned to play a primary role in conducting the information flows within such systems. A practical industrial wireless use case involving a robot manipulator control system, an integrated wireless force-torque sensor, and a remote vision-based observer is constructed and the performance of the cyber-physical system is examined. By using readings from the remote observer, an estimation system is developed using machine learning regression techniques. We demonstrate the practicality of comb…

Towards personalized screening for hepatocellular carcinoma: Still not there

2020

In patients with HCV-related cirrhosis the annual risk of hepatocellular carcinoma (HCC) is 2–4%.1 However, with the advent of highly effective and well tolerated direct-acting antivirals...

Project Selection by Constrained Fuzzy AHP

2004

The selection of a project among a set of possible alternatives is a difficult task decision makers have to face. Difficulties in selecting a project arise because of the different goals involved and because of the large number of attributes to consider. Our approach is based upon a fuzzy extension of the Analytic Hierarchy Process (AHP). This paper focuses on the constraints that have to be considered within fuzzy AHP in order to take in account all the available information. This study demonstrates that by considering all the information deriving from the constraints better results in terms of certainty and reliability can be achieved.

Visual Information Fidelity with better Vision Models and better Mutual Information Estimates

2021

Retrieving Quantum Information with Active Learning

2019

Active learning is a machine learning method aiming at optimal design for model training. At variance with supervised learning, which labels all samples, active learning provides an improved model by labeling samples with maximal uncertainty according to the estimation model. Here, we propose the use of active learning for efficient quantum information retrieval, which is a crucial task in the design of quantum experiments. Meanwhile, when dealing with large data output, we employ active learning for the sake of classification with minimal cost in fidelity loss. Indeed, labeling only 5% samples, we achieve almost 90% rate estimation. The introduction of active learning methods in the data a…

On Optimizing Locally Linear Nearest Neighbour Reconstructions Using Prototype Reduction Schemes

2010

This paper concerns the use of Prototype Reduction Schemes (PRS) to optimize the computations involved in typical k-Nearest Neighbor (k-NN) rules. These rules have been successfully used for decades in statistical Pattern Recognition (PR) applications, and have numerous applications because of their known error bounds. For a given data point of unknown identity, the k-NN possesses the phenomenon that it combines the information about the samples from a priori target classes (values) of selected neighbors to, for example, predict the target class of the tested sample. Recently, an implementation of the k-NN, named as the Locally Linear Reconstruction (LLR) [11], has been proposed. The salien…