Moments Algorithm Research Articles

An Automatic Movement Monitoring Method for Group-Housed Pigs.

Continuous movement monitoring helps quickly identify pig abnormalities, enabling immediate action to enhance pig welfare. However, continuous and precise monitoring of daily pig movement on farms remains challenging. We present an approach to automatically and precisely monitor the movement of group-housed pigs. The instance segmentation model YOLOv8m-seg was applied to detect the presence of pigs. We then applied a spatial moment algorithm to quantitatively summarize each detected pig's contour as a corresponding center point. The agglomerative clustering (AC) algorithm was subsequently used to gather the pig center points of a single frame into one point representing the group-housed pigs' position, and the movement volume was obtained by calculating the displacements of the clustered group-housed pigs' center points of consecutive frames. We employed the method to monitor the movement of group-housed pigs from April to July 2023; more than 1500 h of top-down pig videos were recorded by a surveillance camera. The F1 scores of the trained YOLOv8m-seg model during training were greater than 90% across most confidence levels, and the model achieved an mAP50-95 of 0.96. The AC algorithm performs with an average extraction time of less than 1 millisecond; this method can run efficiently on commodity hardware.

Whole-field principal angle and optical path retardation measurements using white light OPD scanning polariscope with first moment algorithm

White-light Optical Path Difference (OPD) scanning polariscope is currently an accepted technique for analyzing photoelastic specimens. While it has proven effective in overcoming limitations associated with phase-stepping and fringe scanning techniques, it is constrained when evaluating optical path retardation smaller than the light source coherence length. To address this limitation, a white-light OPD scanning polariscope, equipped with a first moment algorithm, has been introduced for the comprehensive examination of whole-field principal angle and optical path retardation. This article demonstrates the design and operational principles of the proposed polariscope, details an experimental setup for its deployment, and presents findings from practical applications of this setup. The results confirm the effectiveness and practicality of the proposed polariscope. Furthermore, these results highlight its precision, with standard deviations of 0.176° for principal angle and 6.4 nm for optical path retardation measurements.

LN: A Flexible Algorithmic Framework for Layered Queueing Network Analysis

Layered queueing networks (LQNs) are an extension of ordinary queueing networks useful to model simultaneous resource possession and stochastic call graphs in distributed systems. Existing computational algorithms for LQNs have primarily focused on mean-value analysis. However, other solution paradigms, such as normalizing constant analysis and mean-field approximation, can improve the computation of LQN mean and transient performance metrics, state probabilities, and response time distributions. Motivated by this observation, we propose the first LQN meta-solver, called LN, that allows for the dynamic selection of the performance analysis paradigm to be iteratively applied to the submodels arising from layer decomposition. We report experiments where this added flexibility helps us to reduce the LQN solution errors. We also demonstrate that the meta-solver approach eases the integration of LQNs with other formalisms, such as caching models, enabling the analysis of more general classes of layered stochastic networks. Additionally, to support the accurate evaluation of the LQN submodels, we develop novel algorithms for homogeneous queueing networks consisting of an infinite server node and a set of identical queueing stations. In particular, we propose an exact method of moment algorithms, integration techniques for normalizing constants, and a fast non-iterative mean-value analysis technique.

A Novel DOA Estimation Algorithm Based on Robust Mixed Fractional Lower-Order Correntropy in Impulsive Noise

The estimation of direction of arrival (DOA) is paramount in the realm of practical array signal processing systems. Nevertheless, traditional estimation methods often rely heavily on the Gaussian noise assumption, rendering them ineffective in achieving high-precision estimates in environments plagued by strong impulsive noise. To address this challenge, this paper introduces a novel DOA estimation algorithm that leverages mixed fractional lower-order correntropy (MFLOCR) in the context of Alpha-stable distributed impulsive noise. Correntropy is used as a measure of the similarity of the signals, using a Gaussian function to smooth extreme values and provide greater robustness against impulsive noise. By utilizing diverse kernel lengths to jointly regulate the kernel function, the concept of correntropy is expanded and implemented in the fractional lower-order moment (FLOM) algorithm for received signals. Subsequently, the MFLOCR is derived by adjusting the resulting form of correntropy. Finally, an enhanced DOA estimation algorithm is proposed that combines the MFLOCR operator with the MUSIC algorithm, specifically tailored for impulsive noise environments. Furthermore, a proof of boundedness is provided to validate the effectiveness of the proposed approach in such noisy conditions. Simulation experiments confirmed that the proposed method outperforms existing DOA estimation methods in the context of intense impulsive noise, a low generalized signal-to-noise ratio (GSNR), and a smaller number of snapshots.

Wina Sentosa Bottled Water Distribution System Using Web-Based Distribution Requirement Planning and Trend Moment Algorithms

This research focuses on the problem of PT Anugrah Wina Sentosa, which is a producer of bottled drinking water in Central Sulawesi. The company faces challenges in organizing and improving the efficiency of the distribution of bottled drinking water products. Problems include distribution management that has not been optimized and distribution arrangements at various outlets that have not been mapped. The research objective is to implement a web-based distribution system with the Distribution Requirement Planning (DRP) algorithm and Trend Moment Algorithm to see the results of sales and distribution predictions. The application development method uses Waterfall with DRP Algorithm and Trend Moment method. The test results show that the system can manage input, edit, and delete data and run DRP calculations as a whole or per outlet. With an error value of 1.71%, the trend moment forecasting system proved to be very accurate in forecasting sales of drinking water products. Thus, the implementation of a web-based distribution system can improve production efficiency and facilitate stock management and product distribution at PT Anugrah Wina Sentosa.

Linearising anhysteretic magnetisation curves: A novel algorithm for finding simulation parameters and magnetic moments

This paper proposes a new method for determining the simulation parameters of the Jiles–Atherton Model used to simulate the first magnetisation curve and hysteresis loop in ferromagnetic materials. The Jiles–Atherton Model is an important tool in engineering applications due to its relatively simple differential formulation. However, determining the simulation parameters for the anhysteretic curve is challenging. Several methods have been proposed, primarily based on mathematical aspects of the anhysteretic and first magnetisation curves and hysteresis loops. This paper focuses on finding the magnetic moments of the material, which are used to define the simulation parameters for its anhysteretic curve. The proposed method involves using the susceptibility of the material and a linear approximation of a paramagnet to find the magnetic moments. The simulation parameters can then be found based on the magnetic moments. The method is validated theoretically and experimentally and offers a more physical approach to finding simulation parameters for the anhysteretic curve and a simplified way of determining the magnetic moments of the material.

Derivation of long duration geographically-centred areal reduction factors for catchment design rainfall estimation in South Africa

Study regionSouth Africa. Study focusThe use of Areal Reduction Factors (ARFs) to convert average design point rainfall into areal catchment design rainfall is regarded as fundamental input to event-based deterministic design flood estimation methods. The ARF methods currently used in South Africa are outdated and were neither developed nor verified using local data. This paper presents the development and assessment of a regionalised approach to estimate long duration (≥ 1-day) geographically-centred ARFs using daily rainfall data representative of the 78 homogeneous long duration rainfall clusters associated with the Regional Linear Moment Algorithm and Scale Invariance (RLMA&SI) regionalisation scheme in South Africa. In order to meet the recommended World Meteorological Organisation rainfall station density criteria, the latter clusters were merged into 46 clusters. Subsequently, long duration geographically-centred sample ARFs were estimated using 2 053 circular catchments and 1 779 daily rainfall stations. New hydrological insights for the regionFive ARF regions were deduced from the 46 clusters and a single regional empirical ARF equation, with unique regional calibration coefficients, was derived. The ARF methodology developed and the subsequent findings are not only new to the South African flood hydrology community and practice, but are also probabilistically correct given that the ARF estimates decrease with an increasing catchment area and decrease with a decreasing storm duration and return period, unlike many other methods used internationally.

Face Duplication Identifier Using Artificial Nerves

The facial recognition system develops a basic identity verification system based on the natural features of human faces. The study included duplicate passport identification, which checks each person's facial accuracy through a sample of facial data. The data used in this study were 180 face samples at the training stage and 30 face samples at the testing stage. The face sample taken is a forward-facing face that is not obstructed by an object. Face image recognition in this study combines GLCM method, color moment, shape extraction and backpropagation algorithm. The test process recognition rate is 78.83%.

Dipole Moments and Transition Dipole Moments Calculated by Pair-Density Functional Theory with State Interaction

We develop response-function algorithms for dipole moments and transition dipole moments for compressed multistate pair-density functional theory (CMS-PDFT). We use the method of undetermined Lagrange multipliers to derive analytical expressions and validate them using numerical differentiation. We test the accuracy of the magnitudes of predicted ground-state and excited-state dipole moments, the orientations of these dipole moments, and the orientation of transition dipole moments by comparison to experimental data. We show that CMS-PDFT has good accuracy for these quantities, and we also show that, unlike methods that neglect state interaction, CMS-PDFT yields correct behavior for the dipole moment curves in the vicinity of conical intersections. This work, therefore, opens the door to molecular dynamic simulations in strong electric fields, and we envision that CMS-PDFT can now be used to discover chemical reactions that can be controlled by an oriented external electric field upon photoexcitation of the reactants.

A Powerful Zero-Watermarking Algorithm for Copyright Protection of Color Images Based on Quaternion Radial Fractional Hahn Moments and Artificial Bee Colony Algorithm

A Powerful Zero-Watermarking Algorithm for Copyright Protection of Color Images Based on Quaternion Radial Fractional Hahn Moments and Artificial Bee Colony Algorithm

A Comparative Study of Structural Deformation Test Based on Edge Detection and Digital Image Correlation.

Digital image-correlation (DIC) algorithms rely heavily on the accuracy of the initial values provided by whole-pixel search algorithms for structural displacement monitoring. When the measured displacement is too large or exceeds the search domain, the calculation time and memory consumption of the DIC algorithm will increase greatly, and even fail to obtain the correct result. The paper introduced two edge-detection algorithms, Canny and Zernike moments in digital image-processing (DIP) technology, to perform geometric fitting and sub-pixel positioning on the specific pattern target pasted on the measurement position, and to obtain the structural displacement according to the change of the target position before and after deformation. This paper compared the difference between edge detection and DIC in accuracy and calculation speed through numerical simulation, laboratory, and field tests. The study demonstrated that the structural displacement test based on edge detection is slightly inferior to the DIC algorithm in terms of accuracy and stability. As the search domain of the DIC algorithm becomes larger, its calculation speed decreases sharply, and is obviously slower than the Canny and Zernike moment algorithms.

Development of welding discontinuity identification system using statistical texture feature extraction and ANN classification on digital radiographic image

Discontinuity in welds is one of the causes of the quality of a connection in the material decreases function. Undamaged test with radiographic method is one of the tests to see the quality of a weld. The test results are radiograph images and evaluated by a radiographer. So this research is designed by optimizing a system to help the work of a radiography expert in identifying discontinuities by utilizing the Matlab Application. On this system uses the method of characteristic extraction and classification of neural networks (AAN). The system uses a characteristic extraction method with geometric invariant moment (GIM) algorithms and a gray level co-occurenece matrix (GLCM) as identification values used in the classification process. The calcification process uses a backpropagation-type multilayer Artificial Neural Network. The types of discontinuities used as data in this system are incompleted of penetration, crack, wormhole, and distributed porosity using a total of 800 datasets of radiograph imagery data. This data sharing is organized using k fold cross validation. The study conducted 15 experiments in system testing to prove the truth in identifying. The results of the experiment resulted in the highest average performance score reaching 93.33%

Generalized gamma distribution based on the Bayesian approach with application to investment modelling

The Generalized Gamma Distribution (GGD) is one of the most popular distributions in analyzing real lifetime datasets. Estimating the parameters of a high dimensional probability distribution is challenging due to the complexities associated with the resulting objectives function. When traditional estimation techniques fail due to complexity in the model objectives function, other powerful computational approaches are employed. In this study, a Bayesian approach to Generalized Gamma Distribution (GGD) based on Markov Chain Monte-Carlo (MCMC) has been employed to estimate model parameters. This study considers the Bayesian approach to GGD parameters using the Adaptive Rejection Metropolis Sampling (ARMS) technique of random variable generation within the Gibbs sampler. The MCMC approach has been used for estimating the multi-dimensional objectives function distribution. The results of the ARMS were compared to the existing Simulated annealing (SA) algorithm and Method of Moment (MM) based on modified internal rate of return data (MIRR). The performances of various derived estimators were recorded using the Markov chain Monte Carlo simulation technique for different sample sizes. The study reveals that ARMS's performance is marginally better than the existing SA and MA approaches. The efficiency of ARMS does not require a larger sample size as the SA does, in the case of simulated data. The performances of ARMS and SA are similar comparing them to the MM as an initial assumption in the case of real MIRR data. However, ARMS gives an acceptable estimated parameter for the different sample sizes due to its ability to evaluate the conditional distributions easily and sample from them exactly.

Fabric Image Retrieval Algorithm based on Fractal Coding and Zernike Moment under Wavelet Transform

Abstract: To help the workers retrieve the same or similar in textile companies, a fabric image retrieval algorithm based on fractal coding and Zernike moment under wavelet transform is proposed, which can quickly and accurately retrieve images similar to the fabric image to be retrieved from the fabric image database. Firstly, the low-frequency component is obtained by wavelet transform, and the transformed low-frequency sub-image is fractal encoded to obtain the coding parameters. Then, the Zernike moment of the low-frequency sub-image is calculated. The fractal coding parameters and Zernike moment under wavelet transform are combined as the fabric image retrieval characteristic. The algorithm overcomes the problems of low retrieval accuracy and time-consuming of direct feature extraction under single feature. Compared with the basic fractal algorithm (BFIC), the joint orthogonal fractal parameters with the improved Hu invariant moment algorithm (HVKF) and the sparse fractal image compression

A Novel Zernike Moment-Based Real-Time Head Pose and Gaze Estimation Framework for Accuracy-Sensitive Applications.

A real-time head pose and gaze estimation (HPGE) algorithm has excellent potential for technological advancements either in human-machine or human-robot interactions. For example, in high-accuracy advent applications such as Driver's Assistance System (DAS), HPGE plays a crucial role in omitting accidents and road hazards. In this paper, the authors propose a new hybrid framework for improved estimation by combining both the appearance and geometric-based conventional methods to extract local and global features. Therefore, the Zernike moments algorithm has been prominent in extracting rotation, scale, and illumination invariant features. Later, conventional discriminant algorithms were used to classify the head poses and gaze direction. Furthermore, the experiments were performed on standard datasets and real-time images to analyze the accuracy of the proposed algorithm. As a result, the proposed framework has immediately estimated the range of direction changes under different illumination conditions. We obtained an accuracy of ~85%; the average response time was 21.52 and 7.483 ms for estimating head poses and gaze, respectively, independent of illumination, background, and occlusion. The proposed method is promising for future developments of a robust system that is invariant even to blurring conditions and thus reaching much more significant performance enhancement.

Optimal Design of a Five-Bar Planar Manipulator and Its Controller by Using Different Algorithms for Minimum Shaking Forces and Moments for the Largest Trajectory in a Usable Workspace

In this paper, a structural design and controller optimization process for a five-bar planar manipulator are studied using three different population-based optimization techniques: particle swarm optimization, genetic algorithm, and differential evolution. First, the desired kinematic properties of the manipulator, such as the position, velocity, and acceleration of the endpoint, are determined using inverse kinematics. Then, an optimization problem is created to minimize the shaking force and moments, and the desired kinematic quantities are implemented as constraints. All the link properties of the manipulator are defined as design variables, and the optimization results are obtained. The results show that it is possible to significantly reduce the shaking force and moment significantly thanks to the optimal design parameters. Finally, the controller is optimized to find the best PID gains considering the forward kinematics of the manipulator. It is observed that the shaking force and shaking moment can be reduced by 99% and 54%, respectively, which has a very positive effect on the accuracy of the trajectory tracking. Moreover, the performances of the optimization methods are compared by using the same number of iterations in the calculations, and thus, it can be seen that the GA method achieves the best results compared to the others. Therefore, the results of this study are of utmost importance for a manufacturer, who wants to design a five-bar planar manipulator and its controller.

A Comparative Study of Artificial Intelligence Algorithms Used for an Accurate Image Geometric Dimensions Recognition

The process of distinguishing objects in digital images and recognizing them is a basic process in AI, in order to distinguish the object and determine its features. It has many uses in various engineering and medical fields. There is a great difficulty in recognizing digital images and distinguishing objects in them. This paper discusses some basic artificial intelligence techniques based on the geometric dimensions of the object in the image in order to recognize these objects and distinguish them from the background of the image. Where there is great difficulty in separating the object from its background, this paper presents the most accurate methods of programmatic application using Matlab environment to identify components in the image, its distinction, and its characteristics. The paper provides a comparison between these AI methods and chooses the most accurate one. AI recognition methods use various algorithms including algorithms based on color density and algorithms that depend on geometric dimensions such as Hue moment algorithms, Haralik features algorithms, and Zernike moment algorithms. In this paper these algorithms were applied to a group of images to extract the features of the object and the best one will be chosen with the aid of Matlab. The most accurate recognition process is chosen through building a digital library that contains many pictures and training the program on these images to recognize and distinguish the object in the image.

An Improved Canny-Zernike Subpixel Detection Algorithm

This paper proposed a new subpixel detection method that detects subpixel edges directly, as opposed to the previous method, which requires crossing the entire image. It shows superior subpixel detection on the edge directly, which enhances subpixel edge detection speed significantly. In order to overcome the problem of noise reduction, this paper employs bilateral filtering. The method first performed coarse localization with the improved operator to determine the coordinates and gradient direction of the edge points. Then, the Zernike moment algorithm was used for subpixel repositioning of edge points. Finally, subpixel level edge positioning of the image is obtained. The detection algorithm is used to identify the edges of large-size workpieces, and the results reveal that the approach has superior positioning accuracy, noise immunity, and fast detection speed.

Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms

The intelligent damage detection of catenary insulators is one of the key steps in maintaining the safe and stable operation of railway traction power supply systems. However, traditional deep learning algorithms need to train a large number of images with damage features, which are hard to obtain; and feature-matching algorithms have limitations in anti-complex background interference, affecting the accuracy of damage detection. The current work proposes a method that combines deep learning and Zernike moment algorithms. The Mask R-CNN algorithm is firstly used to identify the catenary insulators to realize the region proposal of the insulators. After image preprocessing, the Zernike moment algorithm is used to replace the existing Hu moment algorithm to extract more detailed insulator contour features, then the similarity value and its standard deviation are further calculated, so as to complete the damage detection of the catenary insulator. The experimental results show that the mean average precision of insulator identification can reach 96.4%, and the Zernike moment algorithm has an accuracy of 93.36% in judging the damage of insulators. Compared with the existing Hu moment algorithm, the accuracy is increased by 10.94%, which provides a new method for the automatic detection of damaged insulators in catenary and even other scenarios.

A novel particle subgrid scale modeling of large eddy simulation for swirling particle-laden turbulent flow

How to describe the effects of subgrid scale (SGS) gas flow on particle dynamics in large eddy simulation (LES) is a challenge for modeling and simulation of particle-laden swirling turbulent flow. SGS four-way coupling interaction mechanisms of gas-to-particle, particle-to-gas, and particle-particle collision have not been considered fully. A novel particle SGS kinetic turbulent energy-granular temperature (SGS-kp-θp) model is proposed firstly to model this effect on particle motions and dispersions. Second-order moment algorithm to establish the anisotropic two-phase Reynolds stress equations and their closure correlations is utilized. Validations by experiment results and Moissette (MOB) particle dispersion model built-in OpenFoam software are performed with better agreements for mean and fluctuation velocities. Anisotropic behaviors of Reynolds stresses and particle collisions are distinctively, smaller-scale particle fluctuations originating from particle collisions lead to the redistribution and dissipation of Reynolds stresses. Power spectrum density is utilized to analyze statistically the particle number density fluctuations and Reynolds stresses, dominant frequencies are roughly 2.5 Hz and 1.0 Hz marking the internal and the corner recirculation regions. Compared to those of inlet flow regions, normal and tangential Reynolds stresses at development flow regions approximately increased by twice and decreased by 3.0 times. Modeling strategy for multiphase interactions are of great importance for capturing particle dynamics in accurate.