Search results for "Machine"

Inferring Learning Strategies from Cultural Frequency Data

2015

Social learning has been identified as one of the fundamentals of culture and therefore the understanding of why and how individuals use social information presents one of the big questions in cultural evolution. To date much of the theoretical work on social learning has been done in isolation of data. Evolutionary models often provide important insight into which social learning strategies are expected to have evolved but cannot tell us which strategies human populations actually use. In this chapter we explore how much information about the underlying learning strategies can be extracted by analysing the temporal occurrence or usage patterns of different cultural variants in a population…

Set Membership (In) Validation of nonlinear positive models for biological systems

2006

The complexity of biology needs quantitative tools in order to support and validate biologists intuition and traditional qualitative descriptions. In this paper, Nonlinear Positive models with constraints for biological systems are validated/invalidated in a worst-case deterministic setting. These models are usefull for the analysis of the DNA and RNA evolution and for the description of the population dynamics of viruses and bacteria. The conditional central estimate and the Uncertainty Intervals are determined in order to validate/invalidate the model. The effectiveness of the proposed procedure has been illustrated by means of simulation experiments.

Smartphone data analysis for human activity recognition

2017

In recent years, the percentage of the population owning a smartphone has increased significantly. These devices provide the user with more and more functions, so that anyone is encouraged to carry one during the day, implicitly producing that can be analysed to infer knowledge of the user’s context. In this work we present a novel framework for Human Activity Recognition (HAR) using smartphone data captured by means of embedded triaxial accelerometer and gyroscope sensors. Some statistics over the captured sensor data are computed to model each activity, then real-time classification is performed by means of an efficient supervised learning technique. The system we propose also adopts a …

A Method Based on Multi-source Feature Detection for Counting People in Crowded Areas

2019

We propose a crowd counting method for multisource feature fusion. Image features are extracted from multiple sources, and the population is estimated by image feature extraction and texture feature analysis, along with for crowd image edge detection. We count people in high-density still images. For instance, in the city’s squares, sports fields, subway stations, etc. Our approach uses a still image taken by a camera on a drone to appraise the count in the population density image, using a kind of sources of information: HOG, LBP, CANNY. We furnish separate estimates of counts and other statistical measurements through several types of sources. Support vector machine SVM, classification an…

Anomaly Detection in Dynamic Social Systems Using Weak Estimators

2009

Anomaly detection involves identifying observationsthat deviate from the normal behavior of a system. One ofthe ways to achieve this is by identifying the phenomena thatcharacterize “normal” observations. Subsequently, based on thecharacteristics of data learned from the “normal” observations,new observations are classified as being either “normal” or not.Most state-of-the-art approaches, especially those which belongto the family parameterized statistical schemes, work under theassumption that the underlying distributions of the observationsare stationary. That is, they assume that the distributions thatare learned during the training (or learning) phase, thoughunknown, are not time-varyin…

On the mechanical stability and out-of-plane dynamics of a travelling panel submerged in axially flowing ideal fluid : a study into paper production …

2011

Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias

2022

In order to determine the site of origin (SOO) in outflow tract ventricular arrhythmias (OTVAs) before an ablation procedure, several algorithms based on manual identification of electrocardiogram (ECG) features, have been developed. However, the reported accuracy decreases when tested with different datasets. Machine learning algorithms can automatize the process and improve generalization, but their performance is hampered by the lack of large enough OTVA databases. We propose the use of detailed electrophysiological simulations of OTVAs to train a machine learning classification model to predict the ventricular origin of the SOO of ectopic beats. We generated a synthetic database of 12-l…

Absolute of Relative? A New Approach to Building Feature Vectors For Emotion Tracking In Music

2013

It is believed that violation of or conformity to expectancy when listening to music is one of the main sources of musical emotion. To address this, we test a new way of building feature vectors and representing features within the vector for the machine learning approach to continuous emotion tracking systems. Instead of looking at the absolute values for specific features, we concentrate on the average value of that feature across the whole song and the difference between that and the absolute value for a particular sample. To test this “relative” representation, we used a corpus of popular music with continuous labels on the arousalvalence space. The model consists of a Support Vector Re…

A magnetostrictive generator for sensors network

2017

In this paper we present a vibration harvesting electric power generator based on magnetostrictive effect for sensors network in hazardous area and we validate it experimentally. The generator has been designed by using Dynamic Preisach hysteresis Model (DPM). DPM is a development of classical Preisach Model which is able to include dynamical features in the mathematical model of hysteresis. We measure the output power capability of the generator and we estimate its power density generation capability.

Classification of Solutions to the Minimum Energy Problem in One Dimensional Sensor Networks

2016

We classify of the minimum energy problem in one dimensional wireless sensor networks for the data transmission cost matrix which is a power function of the distance between transmitter and receiver with any real exponent. We show, how these solutions can be utilized to solve the minimum energy problem for the data transmission cost matrix which is a linear combination of two power functions. We define the minimum energy problem in terms of the sensors signal power, transmission time and capacities of transmission channels. We prove, that for the point-to-point data transmission method utilized by the sensors in the physical layer, when the transmitter adjust the power of its radio signal t…