Agricultural Robotics

2019 ◽  
Vol 2 (1) ◽  
pp. 365-392 ◽  
Author(s):  
Stavros G. Vougioukas
Keyword(s):  
Data Driven ◽  
Farm Labor ◽  
Data Sets ◽  
Future Directions ◽  
Agricultural Robots ◽  
Benchmark Data ◽  
Labor Inputs ◽  
Sustainable Fashion ◽  
And Performance ◽  
Physical And Chemical

A key goal of contemporary agriculture is to dramatically increase production of food, feed, fiber, and biofuel products in a sustainable fashion, facing the pressure of diminishing farm labor supply. Agricultural robots can accelerate plant breeding and advance data-driven precision farming with significantly reduced labor inputs by providing task-appropriate sensing and actuation at fine spatiotemporal resolutions. This article highlights the distinctive challenges imposed on ground robots by agricultural environments, which are characterized by wide variations in environmental conditions, diversity and complexity of plant canopy structures, and intraspecies biological variation of physical and chemical characteristics and responses to the environment. Existing approaches to address these challenges are presented, along with their limitations; possible future directions are also discussed. Two key observations are that biology (breeding) and horticultural practices can reduce variabilities at the source and that publicly available benchmark data sets are needed to increase perception robustness and performance despite variability.

Data Driven Model Identification for a Chaotic Pendulum With Variable Interaction Potential

2020 ◽  
Author(s):  
Melih C. Yesilli ◽  
Firas A. Khasawneh
Keyword(s):  
Numerical Models ◽  
Model Identification ◽  
Potential Interaction ◽  
Data Driven ◽  
Estimation Methods ◽  
Total Variation Regularization ◽  
Data Sets ◽  
Derivative Estimation ◽  
Long Time ◽  
And Performance

Abstract Data driven model identification methods have grown increasingly popular due to enhancements in measuring devices and data mining. They provide a useful approach for comparing the performance of a device to the simplified model that was used in the design phase. One of the modern, popular methods for model identification is Sparse Identification of Nonlinear Dynamics (SINDy). Although this approach has been widely investigated in the literature using mostly numerical models, its applicability and performance with physical systems is still a topic of current research. In this paper we extend SINDy to identify the mathematical model of a complicated physical experiment of a chaotic pendulum with a varying potential interaction. We also test the approach using a simulated model of a nonlinear, simple pendulum. The input to the approach is a time series, and estimates of its derivatives. While the standard approach in SINDy is to use the Total Variation Regularization (TVR) for derivative estimates, we show some caveats for using this route, and we benchmark the performance of TVR against other methods for derivative estimation. Our results show that the estimated model coefficients and their resulting fit are sensitive to the selection of the TVR parameters, and that most of the available derivative estimation methods are easier to tune than TVR. We also highlight other guidelines for utilizing SINDy to avoid overfitting, and we point out that the fitted model may not yield accurate results over long time scales. We test the performance of each method for noisy data sets and provide both experimental and simulation results. We also post the files needed to build and reproduce our experiment in a public repository.

scholarly journals A Mobility Performance Assessment on Plug-in EV Battery

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Seyed Mohammad Rezvanizanian ◽  
Yixiang Huang ◽  
Jiang Chuan ◽  
Jay Lee
Keyword(s):  
State Of Charge ◽  
Data Driven ◽  
Mobility Prediction ◽  
Data Sets ◽  
Speed Profile ◽  
The Road ◽  
Mobility Performance ◽  
Benchmark Data ◽  
Battery State Of Charge ◽  
Road Slope

This paper deals with mobility prediction of LiFeMnPO4 batteries for an emission-free Electric Vehicle. The data-driven model has been developed based on empirical data from two different road types –highway and local streets –and two different driving modes – aggressive and moderate. Battery State of Charge (SoC) can be predicted on any new roads based on the trained model by selecting the drving mode. In this paper, the performance of Adaptive Recurrent Neural Network (ARNN) and regression is evaluated using two benchmark data sets. The ARNN model at first estimates the speed profile of the new road based on slope and then both slope and speed is going to be used as the input to estimate battery current and SoC. Through comparison it is found that if ARNN system is appropriately trained, it performs with better accuracy than Regression in both two road types and driving modes. The results show that prediction SoC model follows the Columb-counting SoC according to the road slope.

Multi-Attribution Classification Based on Lower Integral with Genetic Algorithm

2014 ◽  
Vol 668-669 ◽  
pp. 1090-1093
Author(s):  
Ai Xia Chen ◽  
Jun Hua Li
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Linear Programming Problem ◽  
Fuzzy Integral ◽  
Data Sets ◽  
Fuzzy Measures ◽  
Benchmark Data ◽  
Minimum Potential ◽  
And Performance ◽  
Set Function

Fuzzy integral has been widely used in multi-attribution classification when the interactions exist between the attributions. Because the fuzzy measure defined on the attributions represents the weights of all the attributions and the interactions between them. The lower integral is a type of fuzzy integral with respect to fuzzy measures, which represents the minimum potential of efficiency for a group of attributions with interaction. The value of lower integrals can be evaluated through solving a linear programming problem. Considering the lower integral as a classifier, this paper investigates its implementation and performance. The difficult step in the implementation is how to learn the non-additive set function used in lower integrals. And Genetic algorithm is used to solve the problem. Finally, numerical simulations on some benchmark data sets are given.

scholarly journals The Lyme and Tickborne Disease Dashboard: A map-based resource to promote public health awareness and research collaboration

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260122
Author(s):  
Frank C. Curriero ◽  
Cara Wychgram ◽  
Alison W. Rebman ◽  
Anne E. Corrigan ◽  
Anton Kvit ◽  
...  
Keyword(s):  
Public Health ◽  
Spatial Data ◽  
Scientific Collaboration ◽  
One Health ◽  
Research Collaboration ◽  
Data Driven ◽  
Data Sets ◽  
Future Directions ◽  
The Us ◽  
Spatial Data Sets

With the incidence of Lyme and other tickborne diseases on the rise in the US and globally, there is a critical need for data-driven tools that communicate the magnitude of this problem and help guide public health responses. We present the Johns Hopkins Lyme and Tickborne Disease Dashboard (https://www.hopkinslymetracker.org/), a new tool that harnesses the power of geography to raise awareness and fuel research and scientific collaboration. The dashboard is unique in applying a geographic lens to tickborne diseases, aiming not only to become a global tracker of tickborne diseases but also to contextualize their complicated geography with a comprehensive set of maps and spatial data sets representing a One Health approach. We share our experience designing and implementing the dashboard, describe the main features, and discuss current limitations and future directions.

Nanocrystals: The Preparation, Precise Control and Application Toward the Pharmaceutics and Food Industry

2018 ◽  
Vol 24 (21) ◽  
pp. 2425-2431 ◽  
Author(s):  
Cao Wu ◽  
Zhou Chen ◽  
Ya Hu ◽  
Zhiyuan Rao ◽  
Wangping Wu ◽  
...  
Keyword(s):  
Food Industry ◽  
Chemical Properties ◽  
Preparation Methods ◽  
Precise Control ◽  
Significant Process ◽  
Technology Applications ◽  
And Performance ◽  
Physical And Chemical ◽  
Analytical Technology ◽  
And Chemical Properties

Crystallization is a significant process employed to produce a wide variety of materials in pharmaceutical and food area. The control of crystal dimension, crystallinity, and shape is very important because they will affect the subsequent filtration, drying and grinding performance as well as the physical and chemical properties of the material. This review summarizes the special features of crystallization technology and the preparation methods of nanocrystals, and discusses analytical technology which is used to control crystal quality and performance. The crystallization technology applications in pharmaceutics and foods are also outlined. These illustrated examples further help us to gain a better understanding of the crystallization technology for pharmaceutics and foods.

scholarly journals Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 154
Author(s):  
Marcus Walldén ◽  
Masao Okita ◽  
Fumihiko Ino ◽  
Dimitris Drikakis ◽  
Ioannis Kokkinakis
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Sets ◽  
Output Constraints ◽  
Data Driven Approach ◽  
Scientific Simulations ◽  
Multiple Metrics ◽  
In Transit ◽  
Multiple Compression ◽  
Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

Non-Covalent Interactions Atlas Benchmark Data Sets 3: Repulsive Contacts

2021 ◽  
Vol 17 (3) ◽  
pp. 1548-1561
Author(s):  
Kristian Kříž ◽  
Martin Nováček ◽  
Jan Řezáč
Keyword(s):  
Data Sets ◽  
Benchmark Data ◽  
Non Covalent Interactions ◽  
Covalent Interactions

scholarly journals Fluorogenic Aptasensors with Small Molecules

Chemosensors ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 54
Author(s):  
Eun-Song Lee ◽  
Jeong Min Lee ◽  
Hea-Jin Kim ◽  
Young-Pil Kim
Keyword(s):  
Small Molecules ◽  
In Vitro Selection ◽  
Molecular Beacon ◽  
Future Directions ◽  
Rna Molecules ◽  
Amplification Process ◽  
Target Molecules ◽  
And Performance ◽  
Fluorescence Turn On

Aptamers are single-stranded DNA or RNA molecules that can be identified through an iterative in vitro selection–amplification process. Among them, fluorogenic aptamers in response to small molecules have been of great interest in biosensing and bioimaging due to their rapid fluorescence turn-on signals with high target specificity and low background noise. In this review, we report recent advances in fluorogenic aptasensors and their applications to in vitro diagnosis and cellular imaging. These aptasensors modulated by small molecules have been implemented in different modalities that include duplex or molecular beacon-type aptasensors, aptazymes, and fluorogen-activating aptamer reporters. We highlight the working principles, target molecules, modifications, and performance characteristics of fluorogenic aptasensors, and discuss their potential roles in the field of biosensor and bioimaging with future directions and challenges.

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