Search results for "Neural"

Toward Self-Supervised Feature Learning for Online Diagnosis of Multiple Faults in Electric Powertrains

2021

This article proposes a novel online fault diagnosis scheme for industrial powertrains without using historical faulty or labeled training data. The proposed method combines a one-class support vector machine (SVM) based anomaly detection and supervised convolutional neural network (CNN) algorithms to online detect multiple faults and fault severities under variable speeds and loads. The one-class SVM algorithm is to derive a score for defining faults or health classes in the first stage, and the resulting health classes are used as the training data for the CNN-based classifier in the second stage. Within this framework, the self-supervised learning of the proposed CNN algorithm allows the…

No Reservations Required: Achieving Fairness between Wi-Fi and NR-U with Self-Deferral Only

2021

Wireless technologies coexisting in unlicensed bands should receive a fair share of the available channel resources, even when they use different access methods. We consider the problem of coexistence between Wi-Fi and New Radio Unlicensed (NR-U) nodes, which employ, respectively, a random and scheduled access scheme. The latter typically resorts to reservation signals (RSs), which allow keeping the control of the channel until the start of the next synchronized slot. This mechanism, although effective for increasing the channel access opportunities of scheduled-based nodes, is also a waste of channel resources. We investigate alternative solutions, based on self-deferral only. We built ana…

Gravitational-wave parameter inference using Deep Learning

2021

We explore machine learning methods to detect gravitational waves (GW) from binary black hole (BBH) mergers using deep learning (DL) algorithms. The DL networks are trained with gravitational waveforms obtained from BBH mergers with component masses randomly sampled in the range from 5 to 100 solar masses and luminosity distances from 100 Mpc to, at least, 2000 Mpc. The GW signal waveforms are injected in public data from the O2 run of the Advanced LIGO and Advanced Virgo detectors, in time windows that do not coincide with those of known detected signals, and the data from each detector in the Advanced LIGO and Advanced Virgo network is combined into a unique RGB image. We show that a clas…

Integration of Gravitational Torques in Cerebellar Pathways Allows for the Dynamic Inverse Computation of Vertical Pointing Movements of a Robot Arm

2008

BackgroundSeveral authors suggested that gravitational forces are centrally represented in the brain for planning, control and sensorimotor predictions of movements. Furthermore, some studies proposed that the cerebellum computes the inverse dynamics (internal inverse model) whereas others suggested that it computes sensorimotor predictions (internal forward model).Methodology/principal findingsThis study proposes a model of cerebellar pathways deduced from both biological and physical constraints. The model learns the dynamic inverse computation of the effect of gravitational torques from its sensorimotor predictions without calculating an explicit inverse computation. By using supervised …

Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment

2021

Several scoring systems have been devised to objectively predict survival for patients with intrahepatic cholangiocellular carcinoma (ICC) and support treatment stratification, but they have failed external validation. The aim of the present study was to improve prognostication using an artificial intelligence-based approach. We retrospectively identified 417 patients with ICC who were referred to our tertiary care center between 1997 and 2018. Of these, 293 met the inclusion criteria. Established risk factors served as input nodes for an artificial neural network (ANN). We compared the performance of the trained model to the most widely used conventional scoring system, the Fudan score. Pr…

A neural multi-agent based system for smart html pages retrieval

2003

A neural based multi-agent system for smart HTML page retrieval is presented. The system is based on the EalphaNet architecture, a neural network capable of learning the activation function of its hidden units and having good generalization capabilities. System goal is to retrieve documents satisfying a query and dealing with a specific topic. The system has been developed using the basic features supplied by the Jade platform for agent creation, coordination and control. The system is composed of four agents: the trainer agent, the neural classifier mobile agent, the interface agent, and the librarian agent. The sub-symbolic knowledge of the neural classifier mobile agent is automatically …

2020

Abstract A person-centered approach was used to identify the profiles of symptoms of psychological ill-being among Finnish upper secondary education students (N = 2889); to examine whether gender and educational track (i.e., academic or vocational) are associated with these profiles; and to investigate the role of profiles in school dropout intentions. Using latent profile analysis, one asymptomatic profile (normative, 79.2%) and three symptomatic profiles (internalizing symptoms, 9.1%; externalizing symptoms, 9.1%; and comorbid symptoms, 2.6%) were identified. Boys in the vocational track were overrepresented in the externalizing-symptoms profile, whereas girls in both tracks were overrepr…

Automatic Segmentation Using a Hybrid Dense Network Integrated With an 3D-Atrous Spatial Pyramid Pooling Module for Computed Tomography (CT) Imaging

2020

Computed tomography (CT) with a contrast-enhanced imaging technique is extensively proposed for the assessment and segmentation of multiple organs, especially organs at risk. It is an important factor involved in the decision making in clinical applications. Automatic segmentation and extraction of abdominal organs, such as thoracic organs at risk, from CT images are challenging tasks due to the low contrast of pixel values surrounding other organs. Various deep learning models based on 2D and 3D convolutional neural networks have been proposed for the segmentation of medical images because of their automatic feature extraction capability based on large labeled datasets. In this paper, we p…

Action Recognition based on Hierarchical Self-Organizing Maps

2014

We propose a hierarchical neural architecture able to recognise observed human actions. Each layer in the architecture represents increasingly complex human activity features. The first layer consists of a SOM which performs dimensionality reduction and clustering of the feature space. It represents the dynamics of the stream of posture frames in action sequences as activity trajectories over time. The second layer in the hierarchy consists of another SOM which clusters the activity trajectories of the first-layer SOM and thus it learns to represent action prototypes independent of how long the activity trajectories last. The third layer of the hierarchy consists of a neural network that le…

Dynamic anomalies at the glass transition of organic van der Waals liquids

1993

Abstract The paper discusses the question of whether there is a characteristic temperature T c above the calorimetric glass transition temperature T g . Mode-coupling theory (MCT) predicts a crossover from liquid- to solid-like dynamics at T c . Neutron scattering and gradient NMR experiments have been carried out to test MCT using the molecular van der Waals liquid ortho -terphenyl as a model system. A significant anomaly of the Debye—Waller factor and a “decoupling” of self-diffusion from viscosity support the MCT predictions. A critical discussion of the relevance of such tests and of the limitations of neutron scattering is presented.