Yield Visualization Based on Farm Work Information Measured by Smart Devices

This paper proposes a new approach to visualizing spatial variation of plant status in a tomato greenhouse based on farm work information operated by laborers. Farm work information consists of a farm laborer’s position and action. A farm laborer’s position is estimated based on radio wave strength measured by using a smartphone carried by the farm laborer and Bluetooth beacons placed in the greenhouse. A farm laborer’s action is recognized based on motion data measured by using smartwatches worn on both wrists of the farm laborer. As experiment, harvesting information operated by one farm laborer in a part of a tomato greenhouse is obtained, and the spatial distribution of yields in the experimental field, called a harvesting map, is visualized. The mean absolute error of the number of harvested tomatoes in each small section of the experimental field is 0.35. An interview with the farm manager shows that the harvesting map is useful for intuitively grasping the states of the greenhouse.

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Image convolution: a linear programming approach for filters design

Soft Computing ◽

10.1007/s00500-021-05783-5 ◽

2021 ◽

Author(s):

Giovanni Capobianco ◽

Carmine Cerrone ◽

Andrea Di Placido ◽

Daniel Durand ◽

Luigi Pavone ◽

...

Keyword(s):

Signal Analysis ◽

Mean Absolute Error ◽

Absolute Error ◽

Input Image ◽

Programming Approach ◽

New Approach ◽

Specific Input ◽

Convolution Filter ◽

The Mean ◽

Processing Techniques

AbstractImage analysis is a branch of signal analysis that focuses on the extraction of meaningful information from images through digital image processing techniques. Convolution is a technique used to enhance specific characteristics of an image, while deconvolution is its inverse process. In this work, we focus on the deconvolution process, defining a new approach to retrieve filters applied in the convolution phase. Given an imageIand a filtered image$$I' = f(I)$$I′=f(I), we propose three mathematical formulations that, starting fromIand$$I'$$I′, are able to identify the filter$$f'$$f′that minimizes the mean absolute error between$$I'$$I′and$$f'(I)$$f′(I). Several tests were performed to investigate the applicability of our approaches in different scenarios. The results highlight that the proposed algorithms are able to identify the filter used in the convolution phase in several cases. Alternatively, the developed approaches can be used to verify whether a specific input imageIcan be transformed into a sample image$$I'$$I′through a convolution filter while returning the desired filter as output.

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Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis

Sensors ◽

10.3390/s21113771 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3771

Author(s):

Alexey Kashevnik ◽

Walaa Othman ◽

Igor Ryabchikov ◽

Nikolay Shilov

Keyword(s):

Respiratory Rate ◽

Evaluation System ◽

Body Movement ◽

Absolute Error ◽

The Body ◽

Meditation Practice ◽

Body Parts ◽

Mental Health Training ◽

Negative Thoughts ◽

The Mean

Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application.

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Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

Drones ◽

10.3390/drones5030068 ◽

2021 ◽

Vol 5 (3) ◽

pp. 68

Author(s):

Jiwei Fan ◽

Xiaogang Yang ◽

Ruitao Lu ◽

Xueli Xie ◽

Weipeng Li

Keyword(s):

Deep Learning ◽

Autonomous Navigation ◽

Detection Method ◽

Active Role ◽

Absolute Error ◽

Face Mask ◽

Learning Technology ◽

Flight System ◽

Crowd Density ◽

The Mean

Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and poor interaction. To overcome these shortcomings, we designed an intelligent anti-epidemic patrol detection and warning flight system, which integrates UAV autonomous navigation, deep learning, intelligent voice, and other technologies. Based on the convolution neural network and deep learning technology, the system possesses a crowd density detection method and a face mask detection method, which can detect the position of dense crowds. Intelligent voice alarm technology was used to achieve an intelligent alarm system for abnormal situations, such as crowd-gathering areas and people without masks, and to carry out intelligent dissemination of epidemic prevention policies, which provides a powerful technical means for epidemic prevention and delaying their spread. To verify the superiority and feasibility of the system, high-precision online analysis was carried out for the crowd in the inspection area, and pedestrians’ faces were detected on the ground to identify whether they were wearing a mask. The experimental results show that the mean absolute error (MAE) of the crowd density detection was less than 8.4, and the mean average precision (mAP) of face mask detection was 61.42%. The system can provide convenient and accurate evaluation information for decision-makers and meets the requirements of real-time and accurate detection.

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A new simpler approach to measure the strength of toe plantar flexion requiring no mechanical restraint with a light-weight device

Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine ◽

10.1177/09544119211002932 ◽

2021 ◽

pp. 095441192110029

Author(s):

Yuko Komuro ◽

Yuji Ohta

Keyword(s):

Plantar Flexion ◽

Mean Value ◽

Simple Approach ◽

Light Weight ◽

New Approach ◽

Mean Values ◽

Mechanical Restraint ◽

Significant Difference ◽

Ankle Joints ◽

The Mean

Conventionally, the strength of toe plantar flexion (STPF) is measured in a seated position, in which not only the target toe joints but also the knee and particularly ankle joints, are usually restrained. We have developed an approach for the measurement of STPF which does not involve restraint and considers the interactions of adjacent joints of the lower extremities. This study aimed to evaluate this new approach and comparing with the seated approach. A thin, light-weight, rigid plate was attached to the sole of the foot in order to immobilize the toe area. Participants were 13 healthy young women (mean age: 24 ± 4 years). For measurement of STPF with the new approach, participants were instructed to stand, raise the device-wearing leg slightly, plantar flex the ankle, and push the sensor sheet with the toes to exert STPF. The sensor sheet of the F-scan II system was inserted between the foot sole and the plate. For measurement with the seated approach, participants were instructed to sit and push the sensor with the toes. They were required to maintain the hip, knee, and ankle joints at 90°. The mean values of maximum STPF of the 13 participants obtained with each approach were compared. There was no significant difference in mean value of maximum STPF when the two approaches were compared (new: 59 ± 23 N, seated: 47 ± 33 N). The coefficient of variation of maximum STPF was smaller for data obtained with the new approach (new: 39%, seated: 70%). Our simple approach enables measurement of STPF without the need for the restraints that are required for the conventional seated approach. These results suggest that the new approach is a valid method for measurement of STPF.

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Explanation Plus Prediction—The Logical Focus of Project Management Research

Project Management Journal ◽

10.1177/8756972821999945 ◽

2021 ◽

pp. 875697282199994

Author(s):

Joseph F. Hair ◽

Marko Sarstedt

Keyword(s):

Project Management ◽

Statistical Models ◽

Predictive Power ◽

Mean Absolute Error ◽

Explanatory Power ◽

Absolute Error ◽

Model Parameters ◽

Mean Square ◽

Management Research ◽

The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

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An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach

Sensors ◽

10.3390/s21051867 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1867

Author(s):

Tasbiraha Athaya ◽

Sunwoong Choi

Keyword(s):

Blood Pressure ◽

Cardiovascular Health ◽

Estimation Method ◽

Absolute Error ◽

British Hypertension Society ◽

Non Invasive ◽

Arterial Blood ◽

Blood Pressure Waveform ◽

The Mean ◽

Mimic Iii

Blood pressure (BP) monitoring has significant importance in the treatment of hypertension and different cardiovascular health diseases. As photoplethysmogram (PPG) signals can be recorded non-invasively, research has been highly conducted to measure BP using PPG recently. In this paper, we propose a U-net deep learning architecture that uses fingertip PPG signal as input to estimate arterial BP (ABP) waveform non-invasively. From this waveform, we have also measured systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP). The proposed method was evaluated on a subset of 100 subjects from two publicly available databases: MIMIC and MIMIC-III. The predicted ABP waveforms correlated highly with the reference waveforms and we have obtained an average Pearson’s correlation coefficient of 0.993. The mean absolute error is 3.68 ± 4.42 mmHg for SBP, 1.97 ± 2.92 mmHg for DBP, and 2.17 ± 3.06 mmHg for MAP which satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed method is an efficient process to estimate ABP waveform directly using fingertip PPG.

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A Multi-Channel Uncertainty-Aware Multi-Resolution Network for MR to CT Synthesis

Applied Sciences ◽

10.3390/app11041667 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1667

Author(s):

Kerstin Klaser ◽

Pedro Borges ◽

Richard Shaw ◽

Marta Ranzini ◽

Marc Modat ◽

...

Keyword(s):

Medical Image Analysis ◽

Absolute Error ◽

Magnetic Resonance Images ◽

The Body ◽

Whole Body ◽

Image Size ◽

Body Images ◽

Image Translation ◽

The Mean ◽

Synthetic Ct

Synthesising computed tomography (CT) images from magnetic resonance images (MRI) plays an important role in the field of medical image analysis, both for quantification and diagnostic purposes. Convolutional neural networks (CNNs) have achieved state-of-the-art results in image-to-image translation for brain applications. However, synthesising whole-body images remains largely uncharted territory, involving many challenges, including large image size and limited field of view, complex spatial context, and anatomical differences between images acquired at different times. We propose the use of an uncertainty-aware multi-channel multi-resolution 3D cascade network specifically aiming for whole-body MR to CT synthesis. The Mean Absolute Error on the synthetic CT generated with the MultiResunc network (73.90 HU) is compared to multiple baseline CNNs like 3D U-Net (92.89 HU), HighRes3DNet (89.05 HU) and deep boosted regression (77.58 HU) and shows superior synthesis performance. We ultimately exploit the extrapolation properties of the MultiRes networks on sub-regions of the body.

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Dynamic modeling of nutrient use and individual requirements of lactating sows1

Journal of Animal Science ◽

10.1093/jas/skz167 ◽

2019 ◽

Vol 97 (7) ◽

pp. 2822-2836 ◽

Cited By ~ 5

Author(s):

Raphaël Gauthier ◽

Christine Largouët ◽

Charlotte Gaillard ◽

Laetitia Cloutier ◽

Frédéric Guay ◽

...

Keyword(s):

Feed Intake ◽

Average Daily Gain ◽

Individual Performance ◽

Maintenance Cost ◽

Nutrient Requirements ◽

Modeling Approach ◽

Ex Post ◽

Nutrient Use ◽

The Mean ◽

On Farm

AbstractNutrient requirements of sows during lactation are related mainly to their milk yield and feed intake, and vary greatly among individuals. In practice, nutrient requirements are generally determined at the population level based on average performance. The objective of the present modeling approach was to explore the variability in nutrient requirements among sows by combining current knowledge about nutrient use with on-farm data available on sows at farrowing [parity, BW, backfat thickness (BT)] and their individual performance (litter size, litter average daily gain, daily sow feed intake) to estimate nutrient requirements. The approach was tested on a database of 1,450 lactations from 2 farms. The effects of farm (A, B), week of lactation (W1: week 1, W2: week 2, W3+: week 3 and beyond), and parity (P1: 1, P2: 2, P3+: 3 and beyond) on sow performance and their nutrient requirements were evaluated. The mean daily ME requirement was strongly correlated with litter growth (R2 = 0.95; P < 0.001) and varied slightly according to sow BW, which influenced the maintenance cost. The mean daily standardized ileal digestible (SID) lysine requirement was influenced by farm, week of lactation, and parity. Variability in SID lysine requirement per kg feed was related mainly to feed intake (R2 = 0.51; P < 0.001) and, to a smaller extent, litter growth (R2 = 0.27; P < 0.001). It was lowest in W1 (7.0 g/kg), greatest in W2 (7.9 g/kg), and intermediate in W3+ (7.5 g/kg; P < 0.001) because milk production increased faster than feed intake capacity did. It was lower for P3+ (6.7 g/kg) and P2 sows (7.3 g/kg) than P1 sows (8.3 g/kg) due to the greater feed intake of multiparous sows. The SID lysine requirement per kg of feed was met for 80% of sows when supplies were 112 and 120% of the mean population requirement on farm A and B, respectively, indicating higher variability in requirements on farm B. Other amino acid and mineral requirements were influenced in the same way as SID lysine. The present modeling approach allows to capture individual variability in the performance of sows and litters according to farm, stage of lactation, and parity. It is an initial step in the development of new types of models able to process historical farm data (e.g., for ex post assessment of nutrient requirements) and real-time data (e.g., to control precision feeding).

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Visualizing the Variance of a Random Variable

Open Systems & Information Dynamics ◽

10.1142/s1230161211000054 ◽

2011 ◽

Vol 18 (01) ◽

pp. 71-85

Author(s):

Fabrizio Cacciafesta

Keyword(s):

Stochastic Dominance ◽

Mean Absolute Error ◽

Random Variable ◽

Absolute Error ◽

Second Order ◽

Taylor Formula ◽

Order Stochastic Dominance ◽

The Mean ◽

Second Order Stochastic Dominance ◽

Options Theory

We provide a simple way to visualize the variance and the mean absolute error of a random variable with finite mean. Some application to options theory and to second order stochastic dominance is given: we show, among other, that the "call-put parity" may be seen as a Taylor formula.

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Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

Sustainability ◽

10.3390/su10124863 ◽

2018 ◽

Vol 10 (12) ◽

pp. 4863 ◽

Cited By ~ 6

Author(s):

Chao Huang ◽

Longpeng Cao ◽

Nanxin Peng ◽

Sijia Li ◽

Jing Zhang ◽

...

Keyword(s):

Power Plants ◽

Mean Squared Error ◽

Absolute Error ◽

Multilayer Perception ◽

Squared Error ◽

The Mean ◽

Effectiveness And Efficiency ◽

Mlp Network ◽

Grid Operation ◽

Better Than

Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

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