scholarly journals Crop Agnostic Monitoring Driven by Deep Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Michael Halstead ◽  
Alireza Ahmadi ◽  
Claus Smitt ◽  
Oliver Schmittmann ◽  
Chris McCool
Keyword(s):  
Ground Truth ◽  
Absolute Error ◽  
Sweet Pepper ◽  
Complex Information ◽  
Yield Information ◽  
Arable Farmland ◽  
Available Technology ◽  
Object Area ◽  
The Impact ◽  
Expensive Process

Farmers require diverse and complex information to make agronomical decisions about crop management including intervention tasks. Generally, this information is gathered by farmers traversing their fields or glasshouses which is often a time consuming and potentially expensive process. In recent years, robotic platforms have gained significant traction due to advances in artificial intelligence. However, these platforms are usually tied to one setting (such as arable farmland), or algorithms are designed for a single platform. This creates a significant gap between available technology and farmer requirements. We propose a novel field agnostic monitoring technique that is able to operate on two different robots, in arable farmland or a glasshouse (horticultural setting). Instance segmentation forms the backbone of this approach from which object location and class, object area, and yield information can be obtained. In arable farmland, our segmentation network is able to estimate crop and weed at a species level and in a glasshouse we are able to estimate the sweet pepper and their ripeness. For yield information, we introduce a novel matching criterion that removes the pixel-wise constraints of previous versions. This approach is able to accurately estimate the number of fruit (sweet pepper) in a glasshouse with a normalized absolute error of 4.7% and an R2 of 0.901 with the visual ground truth. When applied to cluttered arable farmland scenes it improves on the prior approach by 50%. Finally, a qualitative analysis shows the validity of this agnostic monitoring algorithm by supplying decision enabling information to the farmer such as the impact of a low level weeding intervention scheme.

Nanosecond Photodynamics Simulations of a Cis-Trans Isomerization Are Enabled by Machine Learning

2020 ◽  
Author(s):  
Jingbai Li ◽  
Patrick Reiser ◽  
André Eberhard ◽  
Pascal Friederich ◽  
Steven Lopez
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Excited State ◽  
Adaptive Sampling ◽  
Computational Cost ◽  
Ground Truth ◽  
Absolute Error ◽  
Photochemical Reactions ◽  
Computational Techniques ◽  
Full Potential

<p>Photochemical reactions are being increasingly used to construct complex molecular architectures with mild and straightforward reaction conditions. Computational techniques are increasingly important to understand the reactivities and chemoselectivities of photochemical isomerization reactions because they offer molecular bonding information along the excited-state(s) of photodynamics. These photodynamics simulations are resource-intensive and are typically limited to 1–10 picoseconds and 1,000 trajectories due to high computational cost. Most organic photochemical reactions have excited-state lifetimes exceeding 1 picosecond, which places them outside possible computational studies. Westermeyr <i>et al.</i> demonstrated that a machine learning approach could significantly lengthen photodynamics simulation times for a model system, methylenimmonium cation (CH<sub>2</sub>NH<sub>2</sub><sup>+</sup>).</p><p>We have developed a Python-based code, Python Rapid Artificial Intelligence <i>Ab Initio</i> Molecular Dynamics (PyRAI<sup>2</sup>MD), to accomplish the unprecedented 10 ns <i>cis-trans</i> photodynamics of <i>trans</i>-hexafluoro-2-butene (CF<sub>3</sub>–CH=CH–CF<sub>3</sub>) in 3.5 days. The same simulation would take approximately 58 years with ground-truth multiconfigurational dynamics. We proposed an innovative scheme combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to effectively sample the initial data, facilitating the adaptive sampling to generate an informative and data-efficient training set with 6,232 data points. Our neural networks achieved chemical accuracy (mean absolute error of 0.032 eV). Our 4,814 trajectories reproduced the S<sub>1</sub> half-life (60.5 fs), the photochemical product ratio (<i>trans</i>: <i>cis</i> = 2.3: 1), and autonomously discovered a pathway towards a carbene. The neural networks have also shown the capability of generalizing the full potential energy surface with chemically incomplete data (<i>trans</i> → <i>cis</i> but not <i>cis</i> → <i>trans</i> pathways) that may offer future automated photochemical reaction discoveries.</p>

scholarly journals Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1432
Author(s):  
Xwégnon Ghislain Agoua ◽  
Robin Girard ◽  
Georges Kariniotakis
Keyword(s):  
Satellite Images ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Data Sources ◽  
Test Case ◽  
Temporal Data ◽  
Multiple Sources ◽  
Forecasting Error ◽  
Spatio Temporal ◽  
The Impact

The efficient integration of photovoltaic (PV) production in energy systems is conditioned by the capacity to anticipate its variability, that is, the capacity to provide accurate forecasts. From the classical forecasting methods in the state of the art dealing with a single power plant, the focus has moved in recent years to spatio-temporal approaches, where geographically dispersed data are used as input to improve forecasts of a site for the horizons up to 6 h ahead. These spatio-temporal approaches provide different performances according to the data sources available but the question of the impact of each source on the actual forecasting performance is still not evaluated. In this paper, we propose a flexible spatio-temporal model to generate PV production forecasts for horizons up to 6 h ahead and we use this model to evaluate the effect of different spatial and temporal data sources on the accuracy of the forecasts. The sources considered are measurements from neighboring PV plants, local meteorological stations, Numerical Weather Predictions, and satellite images. The evaluation of the performance is carried out using a real-world test case featuring a high number of 136 PV plants. The forecasting error has been evaluated for each data source using the Mean Absolute Error and Root Mean Square Error. The results show that neighboring PV plants help to achieve around 10% reduction in forecasting error for the first three hours, followed by satellite images which help to gain an additional 3% all over the horizons up to 6 h ahead. The NWP data show no improvement for horizons up to 6 h but is essential for greater horizons.

scholarly journals A flexible ChIP-sequencing simulation toolkit

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
An Zheng ◽  
Michael Lamkin ◽  
Yutong Qiu ◽  
Kevin Ren ◽  
Alon Goren ◽  
...  
Keyword(s):  
Ground Truth ◽  
Peak Calling ◽  
Simulation Framework ◽  
Experimental Conditions ◽  
Ground Truth Data ◽  
Chip Sequencing ◽  
Genome Wide ◽  
Experimental Parameters ◽  
Differential Binding ◽  
The Impact

Abstract Background A major challenge in evaluating quantitative ChIP-seq analyses, such as peak calling and differential binding, is a lack of reliable ground truth data. Accurate simulation of ChIP-seq data can mitigate this challenge, but existing frameworks are either too cumbersome to apply genome-wide or unable to model a number of important experimental conditions in ChIP-seq. Results We present ChIPs, a toolkit for rapidly simulating ChIP-seq data using statistical models of key experimental steps. We demonstrate how ChIPs can be used for a range of applications, including benchmarking analysis tools and evaluating the impact of various experimental parameters. ChIPs is implemented as a standalone command-line program written in C++ and is available from https://github.com/gymreklab/chips. Conclusions ChIPs is an efficient ChIP-seq simulation framework that generates realistic datasets over a flexible range of experimental conditions. It can serve as an important component in various ChIP-seq analyses where ground truth data are needed.

The Effect of Vocal Alarms on Operator Mistrust

2000 ◽  
Vol 44 (22) ◽  
pp. 694-697
Author(s):  
James P. Bliss ◽  
Freida Kilpatrick
Keyword(s):  
Past Research ◽  
Primary Task ◽  
Duty To Warn ◽  
Complex Information ◽  
The Impact ◽  
Operator Workload

The use of alarms has increased in many operational areas because of increased reliance on automation and the duty to warn about system anomalies. Past research has supported the use of verbal alarms for relaying complex information. However, researchers have not considered the impact of vocal alarms on operator trust. In this research, 56 participants reacted to auditory alarms while performing a complex primary task. Half of the participants experienced vocal alarms, and the others experienced nonvocal alarms. Contrary to expectations, we noted that participants reacted to nonverbal alarms more quickly than verbal alarms. Furthermore, participants responded to verbal alarms more often than verbal alarms. We also noted that verbal alarms interfered with the primary task more than nonverbal alarms. We suggest that alarm designers alter verbal alarm onset algorithms during high operator workload.

scholarly journals Impact of ASAR soil moisture data on the MM5 precipitation forecast for the Tanaro flood event of April 2009

2011 ◽  
Vol 11 (12) ◽  
pp. 3135-3149 ◽  
Author(s):  
G. Panegrossi ◽  
R. Ferretti ◽  
L. Pulvirenti ◽  
N. Pierdicca
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Ground Truth ◽  
Rain Gauge ◽  
Precipitation Forecast ◽  
Flood Event ◽  
Strong Impact ◽  
Moisture Field ◽  
The Impact

Abstract. The representation of land-atmosphere interactions in weather forecast models has a strong impact on the Planetary Boundary Layer (PBL) and, in turn, on the forecast. Soil moisture is one of the key variables in land surface modelling, and an inadequate initial soil moisture field can introduce major biases in the surface heat and moisture fluxes and have a long-lasting effect on the model behaviour. Detecting the variability of soil characteristics at small scales is particularly important in mesoscale models because of the continued increase of their spatial resolution. In this paper, the high resolution soil moisture field derived from ENVISAT/ASAR observations is used to derive the soil moisture initial condition for the MM5 simulation of the Tanaro flood event of April 2009. The ASAR-derived soil moisture field shows significantly drier conditions compared to the ECMWF analysis. The impact of soil moisture on the forecast has been evaluated in terms of predicted precipitation and rain gauge data available for this event have been used as ground truth. The use of the drier, highly resolved soil moisture content (SMC) shows a significant impact on the precipitation forecast, particularly evident during the early phase of the event. The timing of the onset of the precipitation, as well as the intensity of rainfall and the location of rain/no rain areas, are better predicted. The overall accuracy of the forecast using ASAR SMC data is significantly increased during the first 30 h of simulation. The impact of initial SMC on the precipitation has been related to the change in the water vapour field in the PBL prior to the onset of the precipitation, due to surface evaporation. This study represents a first attempt to establish whether high resolution SAR-based SMC data might be useful for operational use, in anticipation of the launch of the Sentinel-1 satellite.

scholarly journals Human Gait-labeling Uncertainty and a Hybrid Model for Gait Segmentation

2021 ◽  
Author(s):  
Jiaen Wu ◽  
Henrik Maurenbrecher ◽  
Alessandro Schaer ◽  
Barna Becsek ◽  
Chris Awai Easthope ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Motion Capture ◽  
Ground Truth ◽  
Absolute Error ◽  
Human Gait ◽  
Heel Strike ◽  
Analysis Model ◽  
Flat Foot ◽  
Dynamic Time ◽  
Human Gait Analysis

<div><div><div><p>Motion capture systems are widely accepted as ground-truth for gait analysis and are used for the validation of other gait analysis systems.To date, their reliability and limitations in manual labeling of gait events have not been studied.</p><p><b>Objectives</b>: Evaluate human manual labeling uncertainty and introduce a new hybrid gait analysis model for long-term monitoring.</p><p><b>Methods</b>: Evaluate and estimate inter-labeler inconsistencies by computing the limits-of-agreement; develop a model based on dynamic time warping and convolutional neural network to identify a valid stride and eliminate non-stride data in walking inertial data collected by a wearable device; Gait events are detected within a valid stride region afterwards; This method makes the subsequent data computation more efficient and robust.</p><p><b>Results</b>: The limits of inter-labeler agreement for key</p><p>gait events of heel off, toe off, heel strike, and flat foot are 72 ms, 16 ms, 22 ms, and 80 ms, respectively; The hybrid model's classification accuracy for a stride and a non-stride are 95.16% and 84.48%, respectively; The mean absolute error for detected heel off, toe off, heel strike, and flat foot are 24 ms, 5 ms, 9 ms, and 13 ms, respectively.</p><p><b>Conclusions</b>: The results show the inherent label uncertainty and the limits of human gait labeling of motion capture data; The proposed hybrid-model's performance is comparable to that of human labelers and it is a valid model to reliably detect strides in human gait data.</p><p><b>Significance</b>: This work establishes the foundation for fully automated human gait analysis systems with performances comparable to human-labelers.</p></div></div></div>

scholarly journals Performance Evaluation of Ground AR Anchor with WebXR Device API

2021 ◽  
Vol 11 (17) ◽  
pp. 7877
Author(s):  
Daehyeon Lee ◽  
Woosung Shim ◽  
Munyong Lee ◽  
Seunghyun Lee ◽  
Kye-Dong Jung ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Mixed Reality ◽  
Ground Truth ◽  
Application Programming Interface ◽  
Absolute Error ◽  
Frame Rate ◽  
Difference Image ◽  
Match Rate ◽  
The Difference ◽  
The Web

Recently, the development of 3D graphics technology has led to various technologies being combined with reality, where a new reality is defined or studied; they are typically named by combining the name of the technology with “reality”. Representative “reality” includes Augmented Reality, Virtual Reality, Mixed Reality, and eXtended Reality (XR). In particular, research on XR in the web environment is actively being conducted. The Web eXtended Reality Device Application Programming Interface (WebXR Device API), released in 2018, allows instant deployment of XR services to any XR platform requiring only an active web browser. However, the currently released tentative version has poor stability. Therefore, in this study, the performance evaluation of WebXR Device API is performed using three experiments. A camera trajectory experiment is analyzed using ground truth, we checked the standard deviation between the ground truth and WebXR for the X, Y, and Z axes. The difference image experiment is conducted for the front, left, and right directions, which resulted in a visible difference image for each image of ground truth and WebXR, small mean absolute error, and high match rate. In the experiment for measuring the 3D rendering speed, a frame rate similar to that of real-time is obtained.

scholarly journals PENGARUH KRIB TERHADAP KECEPATAN ALIRAN PADA SUNGAI KRUENG ACEH

2020 ◽  
Vol 3 (4) ◽  
pp. 292-301
Author(s):  
Sari Rezeki ◽  
Eldina Fatimah ◽  
Masimin Masimin
Keyword(s):  
Absolute Error ◽  
Flow Speed ◽  
The Public ◽  
Public Facilities ◽  
Speed Distribution ◽  
Surface Water Modeling ◽  
The Impact ◽  
Downstream Area ◽  
Large Discharge ◽  
System Program

Krueng Aceh River is one of the rivers that has a large discharge crossing two administrative regions, namely Banda Aceh City and Aceh Besar District. One of the problems in Krueng Aceh river, precisely around the area of Pango fly over towards the downstream area is the high flow speed distribution at the turn of the river. The impact of the bridge pillar on the river turn results in changes in the cross section of the river endangering the public facilities in front of it. Based on this analysis, it is necessary to control and secure the river, namely by placing the Groyne. The purpose of this study is to obtain a speed distribution that occurs from placing Groyne construction. The methodology used in this study with hydrodynamic numerical modeling approach is by using the Surface Water Modeling System program (SMS 11.2). Calibrating with the parameter n = 0.025 has obtained an absolute error value of 0.039 in cross 1 and 0.051 in cross 2. Based on the analysis of 20 scenarios with 7 m and 9 m distance variations, 5-unit and 3-unit Groyne variations, and the variations in perpendicular angle and 10°, 30° (degrees) towards the downstream and the upstream area, as well as the flow speed with the same number of Groyne and distance variations, the result shows that (V7 m V9 m à 5 unit) and (V7 m V9 m à 3 unit). The simulation results show that the more the number of Groyne there are, the more negative the impact on the downstream area becomes, the more narrow the Groyne, the higher the flow speed value increases. From the 20 scenarios, we obtained a Groyne scenario that is in accordance with the field conditions, namely the Groyne scenario with a distance of 7 m, 3-unit cribs, and a Groyne placement angle of 30° towards upstream area (GUb3L7). The result of the velocity distribution observation shows that the scenario of GUb3L7 Existing (without pillars)

Educating Students with Chronic Illness

2020 ◽  
pp. 578-597
Author(s):  
Margaret Tseng ◽  
Rebecca Magee Pluta
Keyword(s):  
United States ◽  
Middle School ◽  
Chronic Illness ◽  
Civil Rights ◽  
The United States ◽  
Middle School Student ◽  
Physical Location ◽  
Available Technology ◽  
The Impact

Students with chronic illness have historically received an education via home and hospital instruction during their absences. This instruction is significantly inferior in both quality and quantity when compared with the educational experience of students able to attend school. This case study details the experiences of a middle school student in the mid-Atlantic Region of the United States whose chronic illness presented unique and multifaceted challenges that could not be met by her district's inflexible policies and disconnected resources. This case illuminates the need for schools to break away from the traditional administrative special education mold when responding to the challenges of educating frequently absent students with chronic illness. The educational Civil Rights of these students can be preserved, however, by utilizing affordable, available technology to minimize the impact of frequently missed classes, provide continuity of instruction and allow educational access regardless of a student's physical location during their absences from school.

AI and AR

2021 ◽  
pp. 216-231
Author(s):  
Anita M. Cassard ◽  
Brian W. Sloboda
Keyword(s):  
Artificial Intelligence ◽  
Distance Learning ◽  
Augmented Reality ◽  
Learning Environments ◽  
Virtual Learning Environments ◽  
Learning Assessment ◽  
New Learning Environments ◽  
New Learning ◽  
Available Technology ◽  
The Impact

This chapter presents some of the possibilities and approaches that are used in the application of AI (artificial intelligence) and AR (augmented reality) in the new learning environments. AI will add another dimension to distance learning or eLearning that in some cases already includes AR (augmented reality) virtual learning environments. Because of this advent in available technology and the impact it will have on learning, assessment of newly structured parameters and their impact on student outcomes is crucial when measuring student learning. For some of us there might be a concern about the domination of AI as seen in the movie The Terminator, but we can take ease in the notion that it is not only AI versus humans. A new version of human augmented intelligence (HI) is being developed as we speak.

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