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1 |
A novel method of fish tail fin removal for mass estimation using computer vision
یک روش جدید حذف باله دم ماهی برای تخمین جرم با استفاده از بینایی کامپیوتر-2022 Fish mass estimation is extremely important for farmers to get fish biomass information, which could be useful to
optimize daily feeding and control stocking densities and ultimately determine optimal harvest time. However,
fish tail fin mass does not contribute much to total body mass. Additionally, the tail fin of free-swimming fish is
deformed or bent for most of the time, resulting in feature measurement errors and further affecting mass
prediction accuracy by computer vision. To solve this problem, a novel non-supervised method for fish tail fin
removal was proposed to further develop mass prediction models based on ventral geometrical features without
tail fin. Firstly, fish tail fin was fully automatically removed using the Cartesian coordinate system and image
processing. Secondly, the different features were respectively extracted from fish image with and without tail fin.
Finally, the correlational relationship between fish mass and features was estimated by the Partial Least Square
(PLS). In this paper, tail fins were completely automatically removed and mass estimation model based on area
and area square has been the best tested on the test dataset with a high coefficient of determination (R2) of 0.991,
the root mean square error (RMSE) of 7.10 g, the mean absolute error (MAE) of 5.36 g and the maximum relative
error (MaxRE) of 8.46%. These findings indicated that mass prediction model without fish tail fin can more
accurately estimate fish mass than the model with tail fin, which might be extended to estimate biomass of free-
swimming fish underwater in aquaculture. keywords: برداشتن باله دم | اتوماسیون | ماهی | تخمین انبوه | بینایی کامپیوتر | Tail fin removal | Automation | Fish | Mass estimation | Computer vision |
مقاله انگلیسی |
2 |
A computer vision-based method to identify the international roughness index of highway pavements
یک روش مبتنی بر بینایی کامپیوتری برای شناسایی شاخص ناهمواری بینالمللی روسازی بزرگراه-2022 The International Roughness Index (IRI) is one of the most critical parameters in the field of pavement performance management. Traditional methods for the measurement of IRI rely on expensive instrumented vehicles and well-trained professionals. The equipment and labor costs of traditional measurement methods limit the timely updates of IRI on the pavements. In this article, a novel imaging-based Deep Neural Network (DNN) model, which can use pavement photos to directly identify the IRI values, is proposed. This model proved that it is possible to use 2-dimensional (2D) images to identify the IRI other than the typically used vertical accelerations or 3-dimensional (3D) images. Due to the fast growth in photography equipment, small and convenient sports action cameras such as the GoPro Hero series are able to capture smooth videos at a high framerate with built-in electronic image stabilization systems. These significant improvements make it not only more convenient to collect high-quality 2D images, but also easier to process them than vibrations or accelerations. In the proposed method, 15% of the imaging data were randomly selected for testing and had never been touched during the training steps. The testing results showed an averaged coefficient of determination (R square) of 0.6728 and an averaged root mean square error (RMSE) of 0.50.
keywords: شاخص بین المللی زبری | شبکه عصبی عمیق | بینایی کامپیوتر | ارزیابی وضعیت روسازی | International roughness index | Deep neural network | Computer vision | Pavement condition assessment |
مقاله انگلیسی |
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Sensible and secure IoT communication for digital twins, cyber twins, web twins
ارتباط معقول و ایمن IoT برای زوج های دیجیتال، زوج های سایبری، زوج های وب-2022 In order to effectively solve the current security problems encountered by smart wireless terminals in the digital
twin biological network, to ensure the stable and efficient operation of the wireless communication network. This
research aims to reduce the interference attack in the communication network, an interference source location
scheme based on Mobile Tracker in the communication process of the Internet of Things (IoT) is designed. Firstly,
this paper improves Attribute-Based Encryption (ABE) to meet the security and overhead requirements of digital
twin networking communication. The access control policy is used to encrypt a random key, and the symmetric
encryption scheme is used to hide the key. In addition, in the proposed interference source location technology,
the influence of observation noise is reduced based on the principle of unscented Kalman filter, and the estimated
interference source location is modified by the interference source motion model. In order to further evaluate the
performance of the method proposed as the interference source, this paper simulates the jamming attack scenario.
The Root Mean Square Error (RMSE) value of the proposed algorithm is 0.245 m, which is better than the ErrMin
algorithm (0.313 m), and the number of observation nodes of the proposed algorithm is less than half of the
ErrMin algorithm. To sum up, satisfactory results can be achieved by taking the Jamming Signal Strength (JSS)
information as the observation value and estimating the location of the interference source and other state information based on the untracked Kalman filter algorithm. This research has significant value for the secure
communication of the digital twins in the IoT.
keywords: زوج دیجیتال | سیستم فیزیکی-سایبری | زوج وب | ارتباطات اینترنت اشیا | امنیت ارتباطات | Digital twin | Cyber-physical system | Web twins | IoT communication | Communication security |
مقاله انگلیسی |
4 |
Concurrent validity of a custom computer vision algorithm for measuring lumbar spine motion from RGB-D camera depth data
اعتبار همزمان یک الگوریتم بینایی ماشین سفارشی برای اندازه گیری حرکت ستون فقرات کمری از داده های عمق دوربین RGB-D-2021 Using RGB-D cameras as an alternative motion capture device can be advantageous for biomechanical spine motion assessments of movement quality and dysfunction due to their lower cost and complexity. In this study, we evaluated RGB-D camera performance relative to gold-standard optoelectronic motion capture equipment. Twelve healthy young adults (6M, 6F) were recruited to perform repetitive spine flexion-extension, while wearing infrared reflective marker clusters placed over their T10-T12 spinous processes and sacrum, and motion capture data were recorded simultaneously by both systems. Custom computer vision algorithms were developed to extract spine angles from depth data. Root mean square error (RMSE) was calculated for continuous Euler angles, and intra class correlation coefficients (ICC2,1) were calculated between minimum and maximum angles and range of motion in all movement planes. RMSE was low (RMSE ≤ 2.05◦ ) and reliability was good to excellent(0.849 ≤ ICC2,1 ≤ 0.979) across all movement planes. In conclusion, the proposed algorithm for tracking 3Dlumbar spine motion during a sagittal movement task from one RGB-D camera is reliable in comparison to gold- standard motion tracking equipment. Future research will investigate accuracy and validity in a wider variety of movements, and will also investigate the development of novel methods to measure spine motion without using infrared reflective markers. Keywords: RGB-D cameras | Computer vision | Depth camera | Low back pain | Movement quality |
مقاله انگلیسی |
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Color Image Enhancement based on Gamma Encoding and Histogram Equalization
بهبود تصویر رنگی بر اساس رمزگذاری گاما و یکسان سازی هیستوگرام-2021 Image Enhancement is used as a preprocessing step in many computer vision applications. It provides enhanced input for other computerized image processing methods. Many preprocessing techniques can be applied to images depending on the application domain. In this paper we are proposing an image enhancement technique for color images that can be used as preprocessing step in many computer vision applications. It can also be used as a data augmentation technique in object detection. Luminance component of images is sometimes not captured by cameras and displayed by monitors properly. To remove this drawback of devices we have used gamma encoding. Four different values of gamma are evaluated depending on the quality of images. Image is then converted into YUV Color space. Y component represents the luminance. U and V components represent color. After that Contrast Limited Adaptive Histogram Equalization is applied to the Y component to improve the contrast of the image. The results are compared with the state-of-the-art methods on the basis of Peak Signal to noise Ratio (PSNR) and Mean Square Error (MSE). Quantitative results show that proposed algorithm results in improved value of PSNR and decreased value of MSE as compared to existing methods. Qualitative comparison is also done and results show improvement over the existing techniques.© 2020 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Materials, Manufacturing and Mechanical Engineering for Sustainable Developments-2020. Keywords: Histogram | Intensity | Luminance | Contrast stretching |
مقاله انگلیسی |
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Soil color analysis based on a RGB camera and an artificial neural network towards smart irrigation: A pilot study
تجزیه و تحلیل رنگ خاک بر اساس یک دوربین RGB و یک شبکه عصبی مصنوعی برای آبیاری هوشمند: یک مطالعه آزمایشی-2021 Irrigation operations in agriculture are one of the largest water consumers in the world, and it has been increasing due to rising population and consequent increased demand for food. The development of advanced irrigation technologies based on modern techniques is of utmost necessity to ensure efficient use of water. Smart irrigation based on computer vision could help in achieving optimum water-utilization in agriculture using a highly available digital technology. This paper presents a non-contact vision system based on a standard video camera to predict the irrigation requirements for loam soils using a feed-forward back propagation neural network. The study relies on analyzing the differences in soil color captured by a video camera at different distances, times and illumination levels obtained from loam soil over four weeks of data acquisition. The proposed system used this color information as input to an artificial neural network (ANN) system to make a decision as to whether to irrigate the soil or not. The proposed system was very accurate, achieving a mean square error (MSE) of 1.616 × 10—6 (training), 1.004 × 10—5 (testing) and 1.809 × 10—5 (validation). The proposed system is simple, robust and affordable making it promising technology to support precision agriculture.
Keywords: Smart irrigation | Computer vision system | RGB color analysis | Artificial neural network | Feed-forward back propagation neural network |
مقاله انگلیسی |
7 |
AI-based Reference Ankle Joint Torque Trajectory Generation for Robotic Gait Assistance: First Steps
تولید مسیر حرکت گشتاور مفصل مچ پا مبتنی بر هوش مصنوعی برای کمک به راه رفتن رباتیک: اولین قدم ها-2020 Robotic-based gait rehabilitation and assistance
have been growing to augment and to recover motor function in
subjects with lower limb impairments. There is interest in
developing user-oriented control strategies to provide
personalized assistance. However, it is still needed to set the
healthy user-oriented reference joint trajectories, namely,
reference ankle joint torque, that would be desired under healthy
conditions. Considering the potential of Artificial Intelligence (AI)
algorithms to model nonlinear relationships of the walking
motion, this study implements and compares two offline AI-based
regression models (Multilayer Perceptron and Long-Short Term
Memory-LSTM) to generate healthy reference ankle joint torques
oriented to subjects with a body height ranging from 1.51 to 1.83
m, body mass from 52.0 to 83.7 kg and walking in a flat surface
with a walking speed from 1.0 to 4.0 km/h. The best results were
achieved for the LSTM, reaching a Goodness of Fit and a
Normalized Root Mean Square Error of 79.6 % and 4.31 %,
respectively. The findings showed that the implemented LSTM
has the potential to be integrated into control architectures of
robotic assistive devices to accurately estimate healthy useroriented
reference ankle joint torque trajectories, which are
needed in personalized and Assist-As-Needed conditions. Future
challenges involve the exploration of other regression models and
the reference torque prediction for remaining lower limb joints,
considering a wider range of body masses, heights, walking speeds,
and locomotion modes. Keywords: Ankle Joint Torque Prediction | Artificial Intelligence | Control Strategies | Regression Models | Robotic Gait Rehabilitation |
مقاله انگلیسی |
8 |
Comparison of the impacts of empirical power-law dispersion schemes on simulations of pollutant dispersion during different atmospheric conditions
مقایسه تأثیر برنامه های پراکندگی قانون تجربی قدرت در شبیه سازی پراکندگی آلاینده در شرایط جوی مختلف-2020 Accurate and rapid predictions of air pollutant dispersion are important for effective emergency responses after
sudden air pollution accidents (SAPA). Notably, dispersion parameters (σ) are the key variables that influence the
simulation accuracy of dispersion models. Empirical dispersion schemes based on power-law formulas are
probably appropriate choices for simulations in SAPA because of the requirement for only routine meteorological
data. However, performance comparisons of different schemes are lacking. In this study, the performances during
simulations of air pollutant dispersion of four typical empirical parameterised schemes, i.e. BRIGGS, SMITH,
Pasquill-Gifford, and Chinese National Standard (CNS), were investigated based on the GAUSSIAN plume model
with datasets for the classic Prairie Grass experiments, 1956. The performances when simulating peak and
overall concentrations in different Pasquill atmospheric stability classes (A, B, C, D, E, F) were quantitatively
analysed through different statistical approaches. Results showed that the performances of four schemes for peak
and overall concentrations were basically consistent. Scheme CNS in unstable atmospheric conditions (A, B, and
C) performed significantly better than the others according to performance criteria, which included the lowest
mean of absolute value of fractional biases, lowest normalised mean square errors, and largest mean values of the
fraction within a factor of two when predicting peak and overall concentrations, respectively. Schemes BRIGGS
and P-G exhibited slightly better performances during the neutral condition (D) followed by scheme CNS.
Schemes SMITH and CNS demonstrated slight merits in predicting concentrations compared to the other schemes
during stable conditions (E and F). As a whole, scheme CNS generally performed well for the different atmospheric
stability classes. These analysis results can help to fill in the data gaps and improve our understanding of
the influence of typical power-law function schemes on simulations of air pollutant dispersion. The results are
expected to provide scientific support for air pollution predictions, especially during emergency responses to
SAPA. Keywords: Empirical power-law dispersion schemes | Atmospheric stability | Performance evaluation | Statistical analysis | Emergency response | Sudden air pollution accidents |
مقاله انگلیسی |
9 |
Development of a chemometric-assisted spectrophotometric method for quantitative simultaneous determination of Amlodipine and Valsartan in commercial tablet
توسعه یک روش اسپکتروفتومتری با کمک شیمیایی برای تعیین کمی همزمان آملودیپین و والرسارتان در قرص تجاری-2020 In this study, two drugs named Amlodipine (AML) and Valsartan (VAL) related to the high blood
pressure were simultaneously determined in synthetic mixtures and Valzomix tablet. For this
purpose, the chemometric-assisted spectrophotometric method was developed without any prepreparation.
Artificial intelligence techniques, including artificial neural network (ANN) and
least squares support vector machine (LS-SVM) as chemometrics procedures were proposed.
Feed-forward back-propagation neural network (FFBP-NN) with two different algorithms, containing
Levenberg–Marquardt (LM) and gradient descent with momentum and adaptive learning
rate backpropagation (GDX) was applied. To select the best model, several layers and neurons
were investigated. The results revealed that layer = 5 with 6 neurons and layer = 2 with 10
neurons had lower mean square error (MSE) (1.41 × 10−24, 1.16 × 10-23) for AML and VAL,
respectively. In the LS-SVM method, gamma (γ) and sigma (σ) parameters were optimized. γ and
σ were obtained 50, 30 and 40, 40 with the root mean square error (RMSE) of 0.4290 and 0.5598
for AML and VAL, respectively. Analysis of the pharmaceutical formulation was evaluated
through the chemometrics methods and high-performance liquid chromatography (HPLC) as a
reference technique. The obtained results were statistically compared with each other using the
one-way analysis of variance (ANOVA) test. There were no significant differences between them
and the proposed method was satisfactory for estimating the components of the Valzomix tablet. Keywords: Spectrophotometry | Amlodipine | Valsartan | Artificial neural network | Least squares support vector machine |
مقاله انگلیسی |
10 |
Power law scaling model predicts N2O emissions along the Upper Mississippi River basin
مدل مقیاس گذاری توان پیش بینی انتشار N2O در امتداد حوضه بالا می سی سی پی رودخانه-2020 Nitrous oxide (N2O) iswidely recognized as one of themost important greenhouse gases, and responsible for stratospheric
ozone destruction. A significant fraction of N2O emissions to the atmosphere is from rivers. Reliable
catchment-scale estimates of these emissions require both high-resolution field data and suitable models able to
capture the main processes controlling nitrogen transformation within surface and subsurface riverine environments.
Thus, this investigation tests and validates a recently proposed parsimonious and effective model to predict
riverineN2Ofluxes withmeasurements taken along themain stemof the UpperMississippi River (UMR). Themodel
parameterizesN2Oemissions bymeans of two denitrification Damköhler numbers; one accounting for processes occurring
within the hyporheic and benthic zones, and the other one within the water column, as a function of river
size. Its performance was assessed with several statistical quantitative indexes such as: Absolute Error (AE), Nash-
Sutcliffe efficiency (NSE), percent bias (PBIAS), and ratio of the root mean square error to the standard deviation
of measured data (RSR). Comparison of predicted N2O gradients between water and air (ΔN2O) with those quantified
from fieldmeasurements validates the predictive performance of themodel and allow extending previous findings
to large river networks including highly regulated rivers with cascade reservoirs and locks. Results show the
major role played by the water column processes in contributing to N2O emissions in large rivers. Consequently,
N2O productions along the UMR, characterized by regulated flows and large channel size, occur chiefly within this
surficial riverine compartment, where the suspended particles may create anoxic microsites, which favor
denitrification. Keywords: Nitrous oxide emissions | River network | Upper Mississippi River |
مقاله انگلیسی |