scholarly journals Smart Farming and Surveillance using a Multipurpose Robot and Machine Learning

2021 ◽  
pp. 480-487
Author(s):  
Vijay J ◽  
Syedkhadeeramed ◽  
Pragatheeshwaran K ◽  
Praveenkumar V ◽  
Sajan Kumar
Keyword(s):  
Machine Learning ◽  
Autonomous Vehicle ◽  
Rural Poverty ◽  
Farm Animals ◽  
Human Power ◽  
Performance Accuracy ◽  
Smart Farming ◽  
Overall Performance ◽  
Time Required ◽  
General Operation

Agriculture is the procedure of producing food, feed, fiber, and many other favored merchandise by cultivating favorable vegetation and raising farm animals. The exercise of agriculture is also referred to as farming. However, there are a few challenges in raising agricultural productiveness in line with the unit of land, reducing rural poverty through a socially inclusive strategy that contains each agriculture in addition to non-farm employment, ensuring that agricultural boom responds to food security wishes. Nowadays, the advancement in ingenious farming techniques is progressively enhancing the crop yield making it greater worthwhile and reduce irrigation wastages. The proposed system is to layout and increases an autonomous vehicle that can carry out various agricultural activities inclusive of digging, sowing seeds, pumping insecticides, cutting undesirable grass within the discipline, etc. The farming land is autonomously irrigated with sufficient water with the help of moisture sensors within the land. The autonomous robot's general operation and the irrigation gadget are monitored and maintained using Machine Learning (ML) algorithms. The overall operation and records are measured via the sensors are stored in the cloud for device gaining knowledge of (ML) algorithm and future references. Thereby, it increases the machine's overall performance accuracy and reduces the human power and saves the time required to cultivate the farmland

Clinical Update: Functional Capacity Evaluations—An Update

1999 ◽  
Vol 4 (5) ◽  
pp. 4-7 ◽  
Author(s):  
Laura Welch
Keyword(s):  
Job Performance ◽  
Functional Capacity ◽  
Injury Risk ◽  
Disability Evaluation ◽  
Time Period ◽  
Submaximal Effort ◽  
Lack Of Sleep ◽  
Overall Performance ◽  
Time Required ◽  
Specific Limitation

Abstract Functional capacity evaluations (FCEs) have become an important component of disability evaluation during the past 10 years to assess an individual's ability to perform the essential or specific functions of a job, both preplacement and during rehabilitation. Evaluating both job performance and physical ability is a complex assessment, and some practitioners are not yet certain that an FCE can achieve these goals. An FCE is useful only if it predicts job performance, and factors that should be assessed include overall performance; consistency of performance across similar areas of the FCE; consistency between observed behaviors during the FCE and limitations or abilities reported by the worker; objective changes (eg, blood pressure and pulse) that are appropriate relative to performance; external factors (illness, lack of sleep, or medication); and a coefficient of variation that can be measured and assessed. FCEs can identify specific movement patterns or weaknesses; measure improvement during rehabilitation; identify a specific limitation that is amenable to accommodation; and identify a worker who appears to be providing a submaximal effort. FCEs are less reliable at predicting injury risk; they cannot tell us much about endurance over a time period longer than the time required for the FCE; and the FCE may measure simple muscular functions when the job requires more complex ones.

scholarly journals Statistical and machine learning models for optimizing energy in parallel applications

2019 ◽  
Vol 33 (6) ◽  
pp. 1079-1097 ◽  
Author(s):  
Mark Endrei ◽  
Chao Jin ◽  
Minh Ngoc Dinh ◽  
David Abramson ◽  
Heidi Poxon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
High Performance ◽  
Large Scale ◽  
Energy Use ◽  
Parallel Applications ◽  
Learning Models ◽  
Trade Off ◽  
Time Required ◽  
Machine Learning Models

Rising power costs and constraints are driving a growing focus on the energy efficiency of high performance computing systems. The unique characteristics of a particular system and workload and their effect on performance and energy efficiency are typically difficult for application users to assess and to control. Settings for optimum performance and energy efficiency can also diverge, so we need to identify trade-off options that guide a suitable balance between energy use and performance. We present statistical and machine learning models that only require a small number of runs to make accurate Pareto-optimal trade-off predictions using parameters that users can control. We study model training and validation using several parallel kernels and more complex workloads, including Algebraic Multigrid (AMG), Large-scale Atomic Molecular Massively Parallel Simulator, and Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. We demonstrate that we can train the models using as few as 12 runs, with prediction error of less than 10%. Our AMG results identify trade-off options that provide up to 45% improvement in energy efficiency for around 10% performance loss. We reduce the sample measurement time required for AMG by 90%, from 13 h to 74 min.

Smart Farming Using Machine Learning and IoT

2021 ◽  
pp. 13-34
Author(s):  
Alo Sen ◽  
Rahul Roy ◽  
Satya Ranjan Dash
Keyword(s):  
Machine Learning ◽  
Smart Farming

scholarly journals Feasibility and accuracy of the automated software for dynamic quantification of left ventricular and atrial volumes and function in a large unselected population

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
G Italiano ◽  
G Tamborini ◽  
V Mantegazza ◽  
V Volpato ◽  
L Fusini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Image Quality ◽  
Normal Subjects ◽  
Left Ventricular ◽  
Systolic Volume ◽  
Unselected Population ◽  
Lv Volumes ◽  
Time Required ◽  
2D Images

Abstract Funding Acknowledgements Type of funding sources: None. Objective. Preliminary studies showed the accuracy of machine learning based automated dynamic quantification of left ventricular (LV) and left atrial (LA) volumes. We aimed to evaluate the feasibility and accuracy of machine learning based automated dynamic quantification of LV and LA volumes in an unselected population. Methods. We enrolled 600 unselected patients (12% in atrial fibrillation) clinically referred for transthoracic echocardiography (2DTTE), who also underwent 3D echocardiography (3DE) imaging. LV ejection fraction (EF), LV and LA volumes were obtained from 2D images; 3D images were analysed using Dynamic Heart Model (DHM) software (Philips) resulting in LV and LA volume-time curves. A subgroup of 140 patients underwent also cardiac magnetic resonance (CMR) imaging. Average time of analysis, feasibility, and image quality were recorded and results were compared between 2DTTE, DHM and CMR. Results. The use of DHM was feasible in 522/600 cases (87%). When feasible, the boundary position was considered accurate in 335/522 patients (64%), while major (n = 38) or minor (n = 149) borders corrections were needed. The overall time required for DHM datasets was approximately 40 seconds, resulting in physiologically appearing LV and LA volume–time curves in all cases. As expected, DHM LV volumes were larger than 2D ones (end-diastolic volume: 173 ± 64 vs 142 ± 58 mL, respectively), while no differences were found for LV EF and LA volumes (EF: 55%±12 vs 56%±14; LA volume 89 ± 36 vs 89 ± 38 mL, respectively). The comparison between DHM and CMR values showed a high correlation for LV volumes (r = 0.70 and r = 0.82, p < 0.001 for end-diastolic and end-systolic volume, respectively) and an excellent correlation for EF (r= 0.82, p < 0.001) and LA volumes. Conclusions. The DHM software is feasible, accurate and quick in a large series of unselected patients, including those with suboptimal 2D images or in atrial fibrillation. Table 1 DHM quality Adjustment Feasibility Good Suboptimal Minor Major Total of patients (n, %) 522/600 (87%) 327/522 (62%) 195/522 (28%) 149/522 (29%) 38/522 (6%) Normal subjects (n, %) 39/40 (97%) 23/39 (57%) 16/39 (40%) 9/39 (21%) 1/39 (3%) Atrial Fibrillation (n, %) 59/73 (81%)* 28/59 (47%) 31/59 (53%) 15/59 (25%) 6/59 (10%) Valvular disease (n, %) 271/312 (87%) 120/271 (%) 151/271 (%) 65/271 (24%) 16/271 (6%) Coronary artery disease (n, %) 47/58 (81%)* 26/47 (46%) 21/47 (37%) 16/47 (34%) 5/47 (11%) Miscellaneous (n, %) 24/25 (96%) 18/24 (75%) 6/24 (25%) 5/24 (21%) 3/24 (12%) Feasibility of DHM, image quality and need to adjustments in global population and in each subgroup. Abstract Figure 1

scholarly journals Imitating a Safe Human Driver Behaviour in Roundabouts Through Deep Learning

2020 ◽  
Vol 19 (1) ◽  
pp. 85-88
Author(s):  
A. S. J. Cervera ◽  
F. J. Alonso ◽  
F. S. García ◽  
A. D. Alvarez
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Driver Behaviour ◽  
Safety Issues ◽  
Current State ◽  
Surrounding Environment ◽  
Human Driver ◽  
State Of The Environment

Roundabouts provide safe and fast circulation as well as many environmental advantages, but drivers adopting unsafe behaviours while circulating through them may cause safety issues, provoking accidents. In this paper we propose a way of training an autonomous vehicle in order to behave in a human and safe way when entering a roundabout. By placing a number of cameras in our vehicle and processing their video feeds through a series of algorithms, including Machine Learning, we can build a representation of the state of the surrounding environment. Then, we use another set of Deep Learning algorithms to analyze the data and determine the safest way of circulating through a roundabout given the current state of the environment, including nearby vehicles with their estimated positions, speeds and accelerations. By watching multiple attempts of a human entering a roundabout with both safe and unsafe behaviours, our second set of algorithms can learn to mimic the human’s good attempts and act in the same way as him, which is key to a safe implementation of autonomous vehicles. This work details the series of steps that we took, from building the representation of our environment to acting according to it in order to attain safe entry into single lane roundabouts.

scholarly journals Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models

2021 ◽  
Author(s):  
Abigail Enders ◽  
Nicole North ◽  
Chase Fensore ◽  
Juan Velez-Alvarez ◽  
Heather Allen
Keyword(s):  
Machine Learning ◽  
Gas Phase ◽  
Functional Groups ◽  
Functional Group ◽  
Spectroscopic Method ◽  
Group Identification ◽  
Spectroscopic Technique ◽  
Ftir Spectra ◽  
Spectral Interpretation ◽  
Time Required

<p>Fourier Transform Infrared Spectroscopy (FTIR) is a ubiquitous spectroscopic technique. Spectral interpretation is a time-consuming process, but it yields important information about functional groups present in compounds and in complex substances. We develop a generalizable model via a machine learning (ML) algorithm using Convolutional Neural Networks (CNNs) to identify the presence of functional groups in gas phase FTIR spectra. The ML models will reduce the amount of time required to analyze functional groups and facilitate interpretation of FTIR spectra. Through web scraping, we acquire intensity-frequency data from 8728 gas phase organic molecules within the NIST spectral database and transform the data into images. We successfully train models for 15 of the most common organic functional groups, which we then determine via identification from previously untrained spectra. These models serve to expand the application of FTIR measurements for facile analysis of organic samples. Our approach was done such that we have broad functional group models that inference in tandem to provide full interpretation of a spectrum. We present the first implementation of ML using image-based CNNs for predicting functional groups from a spectroscopic method.</p>

scholarly journals On the Development of Autonomous Vehicle Safety Distance by an RSS Model Based on a Variable Focus Function Camera

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6733
Author(s):  
Min-Joong Kim ◽  
Sung-Hun Yu ◽  
Tong-Hyun Kim ◽  
Joo-Uk Kim ◽  
Young-Min Kim
Keyword(s):  
Autonomous Vehicles ◽  
Focal Length ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Cruise Control ◽  
Radar Sensors ◽  
Safety And Reliability ◽  
Time Required ◽  
Variable Focus ◽  
Camera Sensors

Today, a lot of research on autonomous driving technology is being conducted, and various vehicles with autonomous driving functions, such as ACC (adaptive cruise control) are being released. The autonomous vehicle recognizes obstacles ahead by the fusion of data from various sensors, such as lidar and radar sensors, including camera sensors. As the number of vehicles equipped with such autonomous driving functions increases, securing safety and reliability is a big issue. Recently, Mobileye proposed the RSS (responsibility-sensitive safety) model, which is a white box mathematical model, to secure the safety of autonomous vehicles and clarify responsibility in the case of an accident. In this paper, a method of applying the RSS model to a variable focus function camera that can cover the recognition range of a lidar sensor and a radar sensor with a single camera sensor is considered. The variables of the RSS model suitable for the variable focus function camera were defined, the variable values were determined, and the safe distances for each velocity were derived by applying the determined variable values. In addition, as a result of considering the time required to obtain the data, and the time required to change the focal length of the camera, it was confirmed that the response time obtained using the derived safe distance was a valid result.

Fast and Intelligent Antenna Design Optimization using Machine Learning

2021 ◽  
Vol 35 (11) ◽  
pp. 1350-1351
Author(s):  
Gopinath Gampala ◽  
C. J. Reddy
Keyword(s):  
Machine Learning ◽  
Mathematical Model ◽  
Design Optimization ◽  
Antenna Design ◽  
Convergence Criteria ◽  
Time Required

Traditional antenna optimization solves the modified version of the original antenna design for each iteration. Thus, the total time required to optimize a given antenna design is highly dependent on the convergence criteria of the selected algorithm and the time taken for each iteration. The use of machine learning enables the antenna designer to generate trained mathematical model that replicates the original antenna design and then apply optimization on the trained model. Use of trained model allows to run thousands of optimization iterations in a span of few seconds.

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