Monitoring Moment-to-Moment Operator Workload Using Task Load and System-State Information

Multi-touch computer inputs allow users to interact with a virtual environment through the use of gesture commands on a monitor instead of a mouse and keyboard. This style of input is easy for the human mind to adapt to because gestures directly reflect how one interacts with the natural environment. This paper presents and assesses a personal-computer-based unmanned aerial vehicle ground control station that utilizes multi-touch gesture inputs and system reconfigurability to enhance operator performance. The system was developed at Ryerson University’s Mixed-Reality Immersive Motion Simulation Laboratory using commercial-off-the-shelf Presagis software. The ground control station was then evaluated using NASA’s task load index to determine if the inclusion of multi-touch gestures and reconfigurability provided an improvement in operator workload over the more traditional style of mouse and keyboard inputs. To conduct this assessment, participants were tasked with flying a simulated aircraft through a specified number of waypoints, and had to utilize a payload controller within a predetermined area. The task load index results from these flight tests have initially shown that the developed touch-capable ground control station improved operator workload while reducing the impact of all six related human factors.

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Reconstructing dynamic molecular states from single-cell time series

Journal of The Royal Society Interface ◽

10.1098/rsif.2016.0533 ◽

2016 ◽

Vol 13 (122) ◽

pp. 20160533 ◽

Cited By ~ 3

Author(s):

Lirong Huang ◽

Loic Pauleve ◽

Christoph Zechner ◽

Michael Unger ◽

Anders S. Hansen ◽

...

Keyword(s):

Single Cell ◽

Experimental Techniques ◽

Protein Abundance ◽

System State ◽

Expression Systems ◽

Full System ◽

State Information ◽

Infinite Dimensional ◽

Consistent Manner ◽

Cell Data

The notion of state for a system is prevalent in the quantitative sciences and refers to the minimal system summary sufficient to describe the time evolution of the system in a self-consistent manner. This is a prerequisite for a principled understanding of the inner workings of a system. Owing to the complexity of intracellular processes, experimental techniques that can retrieve a sufficient summary are beyond our reach. For the case of stochastic biomolecular reaction networks, we show how to convert the partial state information accessible by experimental techniques into a full system state using mathematical analysis together with a computational model. This is intimately related to the notion of conditional Markov processes and we introduce the posterior master equation and derive novel approximations to the corresponding infinite-dimensional posterior moment dynamics. We exemplify this state reconstruction approach using both in silico data and single-cell data from two gene expression systems in Saccharomyces cerevisiae , where we reconstruct the dynamic promoter and mRNA states from noisy protein abundance measurements.

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Distributed Parameter State Estimation for the Gray–Scott Reaction-Diffusion Model

Systems ◽

10.3390/systems9040071 ◽

2021 ◽

Vol 9 (4) ◽

pp. 71

Author(s):

Petro Feketa ◽

Alexander Schaum ◽

Thomas Meurer

Keyword(s):

Finite Number ◽

Exponential Convergence ◽

Reaction Diffusion ◽

Distributed Parameter ◽

System State ◽

State Information ◽

One Dimensional ◽

Reaction Diffusion Model ◽

Complete State ◽

The One

A constructive approach is provided for the reconstruction of stationary and non-stationary patterns in the one-dimensional Gray-Scott model, utilizing measurements of the system state at a finite number of locations. Relations between the parameters of the model and the density of the sensor locations are derived that ensure the exponential convergence of the estimated state to the original one. The designed observer is capable of tracking a variety of complex spatiotemporal behaviors and self-replicating patterns. The theoretical findings are illustrated in particular numerical case studies. The results of the paper can be used for the synchronization analysis of the master–slave configuration of two identical Gray–Scott models coupled via a finite number of spatial points and can also be exploited for the purposes of feedback control applications in which the complete state information is required.

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Using system state information for adaptive state polling policy in distributed load balancing

Proceedings of IEEE International Symposium on Parallel Algorithms Architecture Synthesis ◽

10.1109/aispas.1997.581652 ◽

2002 ◽

Author(s):

Gil-Haeng Lee

Keyword(s):

Load Balancing ◽

System State ◽

State Information ◽

Distributed Load

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Subjective Workload Assessment during 48 Continuous Hours of LOS-F-H Operations

Proceedings of the Human Factors Society Annual Meeting ◽

10.1177/154193128903301620 ◽

1989 ◽

Vol 33 (16) ◽

pp. 1129-1133 ◽

Cited By ~ 2

Author(s):

Susan G. Hill ◽

James C. Byers ◽

Allen L. Zaklad ◽

Richard E. Christ

Keyword(s):

Factor Analysis ◽

Motion Sickness ◽

Rating Scales ◽

Time Factor ◽

Subjective Rating ◽

Regression Analyses ◽

Task Load ◽

Subjective Workload ◽

Operator Workload

Two operator workload (OWL) subjective rating scales were used to obtain judgments of workload during 48 hours of operation. The Task Load Index (TLX) and Overall Workload (OW) scales were administered to two crews during 48-hour operations. A 16-item symptoms ratings scale was also administered to investigate motion sickness and other physical ailments. Results indicated that workload increases across time. Factor analysis on the symptoms found three significant: (1) Heat; (2) Eyestrain/Headache; and (3) Allergy/Dust. Regression analyses suggest that OWL scores can be described as a combination of hour into mission and job being performed. These findings are discussed in the context of a methodology for assessing OWL.

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A Framework for OpenCL Task Scheduling on Heterogeneous Multicores

Parallel Processing Letters ◽

10.1142/s0129626417500086 ◽

2017 ◽

Vol 27 (03n04) ◽

pp. 1750008 ◽

Cited By ~ 2

Author(s):

Anirban Ghose ◽

Lokesh Dokara ◽

Soumyajit Dey ◽

Pabitra Mitra

Keyword(s):

State Of The Art ◽

System State ◽

State Information ◽

Heterogeneous Multicore ◽

Heterogeneous Platform ◽

Scheduling Strategy ◽

Current State ◽

Multiple Devices ◽

Scheduling Strategies ◽

Programming Interfaces

We present an intelligent scheduling framework which takes as input a set of OpenCL kernels and distributes the workload across multiple CPUs and GPUs in a heterogeneous multicore platform. The framework relies on a Machine Learning (ML) based frontend that analyzes static program features of OpenCL kernels and predicts the ratio in which kernels are to be distributed across CPUs and GPUs. The framework provides such static analysis information along with system state information like runtime availability details of computing cores using well defined programming interfaces. Such interfaces are to be utilized by a user specified scheduling strategy. Given such a scheduling strategy, the framework generates device specific binaries and dispatches them across multiple devices in the heterogeneous platform as per the strategy. We test our scheduling framework extensively using different OpenCL task mixes of varying sizes and computational nature. Along with the scheduling framework, we propose a set of novel partition-aware scheduling strategies for heterogeneous multicores. Our proposed approach yields considerably better results in terms of schedule makespan when compared with the current state of the art ML based methods for scheduling of OpenCL workloads across heterogeneous multicores.

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