scholarly journals Data-Driven Method for High Level Rendering Pipeline Construction

2014 ◽  
pp. 191-200 ◽  
Author(s):  
Victor Krasnoproshin ◽  
Dzmitry Mazouka
Keyword(s):  
Data Driven ◽  
Pipeline Construction ◽  
High Level ◽  
Rendering Pipeline

Hybrid-Data Approach for Estimating Trip Purposes

2021 ◽  
pp. 036119812110184
Author(s):  
Xiaoling Luo ◽  
Adrian Cottam ◽  
Yao-Jan Wu ◽  
Yangsheng Jiang
Keyword(s):  
Transportation Systems ◽  
Data Driven ◽  
Trip Purpose ◽  
Additional Information ◽  
Point Of Interest ◽  
Average Accuracy ◽  
Hybrid Data ◽  
Added Benefit ◽  
Data Source ◽  
High Level

Trip purpose information plays a significant role in transportation systems. Existing trip purpose information is traditionally collected through human observation. This manual process requires many personnel and a large amount of resources. Because of this high cost, automated trip purpose estimation is more attractive from a data-driven perspective, as it could improve the efficiency of processes and save time. Therefore, a hybrid-data approach using taxi operations data and point-of-interest (POI) data to estimate trip purposes was developed in this research. POI data, an emerging data source, was incorporated because it provides a wealth of additional information for trip purpose estimation. POI data, an open dataset, has the added benefit of being readily accessible from online platforms. Several techniques were developed and compared to incorporate this POI data into the hybrid-data approach to achieve a high level of accuracy. To evaluate the performance of the approach, data from Chengdu, China, were used. The results show that the incorporation of POI information increases the average accuracy of trip purpose estimation by 28% compared with trip purpose estimation not using the POI data. These results indicate that the additional trip attributes provided by POI data can increase the accuracy of trip purpose estimation.

Damage Detection in Flexible Propeller Beam Structures by Exploiting Impact-Induced Coupled Acceleration Signals

2011 ◽  
Author(s):  
Ioannis T. Georgiou
Keyword(s):  
Data Driven ◽  
Distributed Information ◽  
Modal Expansion ◽  
Shape Difference ◽  
Spatio Temporal ◽  
Temporal Sample ◽  
High Level ◽  
Proper Orthogonal ◽  
Acceleration Signals

A local damage at the tip of a composite propeller is diagnosed by properly comparing its impact-induced free coupled dynamics to that of a pristine wooden propeller of the same size and shape. This is accomplished by creating indirectly via collocated measurements distributed information for the coupled acceleration field of the propellers. The powerful data-driven modal expansion analysis delivered by the Proper Orthogonal Decomposition (POD) Transform reveals that ensembles of impact-induced collocated coupled experimental acceleration signals are underlined by a high level of spatio-temporal coherence. Thus they furnish a valuable spatio-temporal sample of coupled response induced by a point impulse. In view of this fact, a tri-axial sensor was placed on the propeller hub to collect collocated coupled acceleration signals induced via modal hammer nondestructive impacts and thus obtained a reduced order characterization of the coupled free dynamics. This experimental data-driven analysis reveals that the in-plane unit components of the POD modes for both propellers have similar shapes-nearly identical. For the damaged propeller this POD shape-difference is quite pronounced. The shapes of the POD modes are used to compute indices of difference reflecting directly damage. At the first POD energy level, the shape-difference indices of the damaged composite propeller are quite larger than those of the pristine wooden propeller.

scholarly journals NetPyNE, a tool for data-driven multiscale modeling of brain circuits

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Salvador Dura-Bernal ◽  
Benjamin A Suter ◽  
Padraig Gleeson ◽  
Matteo Cantarelli ◽  
Adrian Quintana ◽  
...  
Keyword(s):  
Multiple Scales ◽  
Network Models ◽  
Brain Regions ◽  
Data Driven ◽  
Model Parameters ◽  
Biophysical Modeling ◽  
Neuron Network ◽  
Graphical Interfaces ◽  
Information Theoretic Measures ◽  
High Level

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis – connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena.

scholarly journals A data‐driven approach for predicting nepheline crystallization in high‐level waste glasses

2020 ◽  
Vol 103 (9) ◽  
pp. 4913-4924
Author(s):  
Irmak Sargin ◽  
Charmayne E. Lonergan ◽  
John D. Vienna ◽  
John S. McCloy ◽  
Scott P. Beckman
Keyword(s):  
Data Driven ◽  
High Level Waste ◽  
Data Driven Approach ◽  
High Level

scholarly journals THE USE OF VEHICLE DATA IN ADAS DEVELOPMENT, VERIFICATION AND FOLLOW-UP ON THE SYSTEM

2020 ◽  
Vol 1 ◽  
pp. 2551-2560
Author(s):  
J. Orlovska ◽  
C. Wickman ◽  
R. Soderberg
Keyword(s):  
Data Driven ◽  
Context Aware ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Volvo Cars ◽  
Vehicle Data ◽  
Assistance Systems ◽  
High Level

AbstractAdvanced Driver Assistance Systems (ADAS) require a high level of interaction between the driver and the system, depending on driving context at a particular moment. Context-aware ADAS evaluation based on vehicle data is the most prominent way to assess the complexity of ADAS interactions. In this study, we conducted interviews with the ADAS development team at Volvo Cars to understand the role of vehicle data in the ADAS development and evaluation. The interviews’ analysis reveals strategies for improvement of current practices for vehicle data-driven ADAS evaluation.

scholarly journals A data‐driven approach to stimulus selection reveals an image‐based representation of objects in high‐level visual areas

2019 ◽  
Vol 40 (16) ◽  
pp. 4716-4731 ◽  
Author(s):  
David D. Coggan ◽  
Afrodite Giannakopoulou ◽  
Sanah Ali ◽  
Burcu Goz ◽  
David M. Watson ◽  
...  
Keyword(s):  
Data Driven ◽  
Stimulus Selection ◽  
Visual Areas ◽  
Data Driven Approach ◽  
High Level

The Vanderbilt PICC Service: Program, Procedural, and Patient Outcomes Successes

2005 ◽  
Vol 10 (4) ◽  
pp. 183-192 ◽  
Author(s):  
Doug Burns
Keyword(s):  
Patient Outcomes ◽  
Medical Center ◽  
Nursing Program ◽  
Data Driven ◽  
Complication Rates ◽  
Advanced Practice ◽  
The Past ◽  
Data Driven Approach ◽  
High Level ◽  
Vanderbilt University

Abstract Since its inception in early 2000, Vanderbilt University's Peripherally Inserted Central Catheter (PICC) Service has experienced a high level of success as measured by high proficiency rates and increasing patient procedures each year, low complication rates during and after PICC placements, and an increasing scope of influence within the Vanderbilt University Medical Center and Children's Hospital, the surrounding community, and in the Southeastern United States. Primary drivers of the PICC Service's continuing success include consistent applications of technique and technology, a data-driven approach to assessing the program's progress, and appropriately managing customers' expectations and needs. Over the past five years, data were collected on more than 12,500 PICC placements performed in this specialized nursing program. Retrospective analyses of the data demonstrate an increasing rate of successful placements (from 87.2% to 92.4%) since the program's inception in 2000 to late 2004. Furthermore, the choice of PICC technology has had a significant impact on the odds for occlusion or infection. The Vanderbilt PICC Service provides a model by which other programs can be established, maintained, and expanded into advanced practice.

scholarly journals Automated movement assessment in stroke rehabilitation

2021 ◽  
Author(s):  
Tamim Ahmed ◽  
Kowshik Thopalli ◽  
Thanassis Rikakis ◽  
Pavan Turaga ◽  
Aisling Kelliher ◽  
...  
Keyword(s):  
Upper Extremity ◽  
Hierarchical Model ◽  
Stroke Rehabilitation ◽  
Expert Knowledge ◽  
Low Cost ◽  
Data Driven ◽  
Task Completion ◽  
Convolutional Network ◽  
Rgb Images ◽  
High Level

We are developing a system for long-term Semi-Automated Rehabilitation At the Home (SARAH) that relies on low-cost and unobtrusive video-based sensing. We present a cyber-human methodology used by the SARAH system for automated assessment of upper extremity stroke rehabilitation at the home. We propose a hierarchical model for automatically segmenting stroke survivor's movements and generating training task performance assessment scores during rehabilitation. The hierarchical model fuses expert therapist knowledge-based approaches with data-driven techniques. The expert knowledge is more observable in the higher layers of the hierarchy (task and segment) and therefore more accessible to algorithms incorporating high-level constraints relating to activity structure (i.e. type and order of segments per task). We utilize an HMM and a Decision Tree model to connect these high-level priors to data-driven analysis. The lower layers (RGB images and raw kinematics) need to be addressed primarily through data-driven techniques. We use a transformer-based architecture operating on low-level action features (tracking of individual body joints and objects) and a Multi-Stage Temporal Convolutional Network(MS-TCN) operating on raw RGB images. We develop a sequence combining these complementary algorithms effectively, thus encoding the information from different layers of the movement hierarchy. Through this combination, we produce robust segmentation and task assessment results on noisy, variable, and limited data, which is characteristic of low-cost video capture of rehabilitation at the home. Our proposed approach achieves 85% accuracy in per-frame labeling, 99% accuracy in segment classification, and 93% accuracy in task completion assessment. Although the methodology proposed in this paper applies to upper extremity rehabilitation using the SARAH system, it can potentially be used, with minor alterations, to assist automation in many other movement rehabilitation contexts (i.e. lower extremity training for neurological accidents).

scholarly journals COVID-19 Contact Tracing: Challenges and Future Directions

2020 ◽  
Author(s):  
Mohammad Jabed Morshed Chowdhury ◽  
Md Sadek Ferdous ◽  
Kamanashis Biswas ◽  
Niaz Chowdhury ◽  
Vallipuram Muthukkumarasamy
Keyword(s):  
Personal Data ◽  
Human Memory ◽  
Data Driven ◽  
Contact Tracing ◽  
Future Directions ◽  
Significant Challenge ◽  
Innovative Solution ◽  
Public Health Officials ◽  
Novel Coronavirus ◽  
High Level

Contact tracing has become a vital tool for public health officials to effectively combat the spread of new diseases, such asthe novel coronavirus disease COVID-19. Contact tracing is not new to epidemiologist rather, it used manual or semi-manualapproaches that are incredibly time-consuming, costly and inefficient. It mostly relies on human memory while scalabilityis a significant challenge in tackling pandemics. The unprecedented health and socio-economic impacts led researchersand practitioners around the world to search for technology-based approaches for providing scalable and timely answers.Smartphones and associated digital technologies have the potential to provide a better approach due to their high level ofpenetration, coupled with mobility. While data-driven solutions are extremely powerful, the fear among citizens is thatinformation like location or proximity associated with other personal data and can be weaponised by the states to enforcesurveillance. Low adoption rate of such apps due to the lack of trust questioned the efficacy and demanded researchers tofind innovative solution for building digital-trust, and appropriately balancing privacy and accuracy of data. In this paper,we have critically reviewed such protocols and apps to identify the strength and weakness of each approach. Finally, wehave penned down our recommendations to make the future contact tracing mechanisms more universally inter-operable andprivacy-preserving.

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