Internal Parameters Research Articles

Muddy irrigation ditch understanding for agriculture environmental monitoring

Understanding an irrigation ditch plays an important role in intelligent agriculture environmental monitoring, especially in field environments where large chunks of ditches are particularly covered by various types of natural unstructured soil, vegetation and weeds. However, due to the diverse and unstructured muddy ditches, understanding them remains a challenge. Traditional approaches of understanding a scene from three-dimensional (3D) point clouds or multi-sensor fusion are energy intensive and computationally complex, making them quite laborious in application on a resource-constrained system. In this study, we propose a methodology to understand irrigation ditches and reconstruct them in a 3D scene, using only a resource-constrained monocular camera, without prior training. Spatial similar textures projections are extracted and clustered. Through geometric constraints of distribution and orientation, similar texture projections are refined and their corresponding surfaces are shaped. By contours and evidence lines, the ditch bottom surfaces are represented. Thus an irrigation ditch can be understood and reconstructed in a 3D environment, which can be used in agricultural automatic control system, agricultural robots, and precise agriculture. Unlike machine learning-based algorithms, the proposed method requires no prior training nor knowledge of the camera’s internal parameters such as focal length, field angle, and aperture. Additionally, pure geometric features make the presented method robust to varying illumination and colour. The percentage of incorrectly classified pixels was compared to the ground truth. Experimental results demonstrated that the approach can successfully elucidate irrigation ditches, meeting requirements in safety monitoring in an agriculture environment.

Устойчивость лессовых грунтов при сейсмических воздействиях

The results of the authors' research on the development of the stability of loess soils are presented. The internal and external factors that directly affect the stability of the oscillating slope are given. As an internal factor, the strength and deformation parameters of the soil in the body of the slope are considered, depending on the type of soil, its granulometric and mineralogical compositions, the state of density-humidity, etc. The violations of the stability of loess soils and their transition to a state of liquefaction under strong seismic influences have been studied. The research was carried out on the basis of the physico-mechanical theory of the dynamic violation of the structure of cohesive soils. The results of experiments on the study of changes in the resistance to soil shear in conditions of intense vibration are presented. Formulas are given for determining the values of dynamic pressure and soil connectivity during prolonged exposure to dynamic load.

Inline Inspection of Packaged Food Using Microwave/Terahertz Sensing—An Overview with Focus on Confectionery Products

Electromagnetic systems, in particular microwave/terahertz sensing technologies, are the newest among nondestructive sensing technologies. Currently, increased attention is pointed towards their use in various applications. Among these, food inspection stands out as a primary area due to its potential risk to human safety. As a result, substantial efforts are currently focused on utilizing microwave/terahertz imaging as a tool to enhance the efficacy of food quality assessments. This paper deals with the exploitation of microwave/terahertz imaging technology for food quality control and assessment. In particular, the work aims at reviewing the latest developments regarding the detection of internal quality parameters, such as foreign bodies, i.e., plastic, glass, and wood substances/fragments, as well as checking the completeness of the packaged food under consideration. Emphasis is placed on the (inline) inspection of wrapped/packaged food, such as chocolates, cookies, pastries, cakes, and similar confectionery products, moving along production conveyor belts. Moreover, the paper gives a recent overview of system prototypes and industrial products and highlights emerging research topics and future application directions in this area.

Development of a method for predicting barrel wear resistance when using ammunition with a long storage life

Despite the fact that a large amount of small arms ammunition has been used up over the past two years, their stocks in warehouses and arsenals in various countries are quite large. And their shelf life is 20 years or more. In addition, over time, the number of such ammunition will increase.
 Among the large number of requirements for modern small arms, one can highlight the provision of the necessary survivability of its parts and mechanisms. The problem of barrel survivability is especially important.
 One of the factors affecting barrel survivability is changes in internal ballistics parameters. In turn, these parameters change when using ammunition whose service life exceeds the warranty.
 In this regard, the task of studying the survivability indicators of small arms barrels when using post-warranty ammunition seems relevant.
 The requirements for barrel systems and factors affecting the wear of the barrel bore of powder charges during long-term storage have been determined.
 An analysis of changes in the ballistic characteristics of powder charges was carried out, which allows to state increased wear of barrels when using ammunition with long storage periods.
 The dependencies for determining the limit number of shots from a weapon sample are analyzed.
 A method for predicting barrel survivability when using long-term storage ammunition is proposed.
 Data are presented on an experimental study to determine changes in barrel survivability as a result of the use of long-term storage ammunition by estimating changes in the initial velocity of the projectile.
 Based on the results obtained, ways for further research are proposed in the direction of expanding the range of ammunition and weapons

Stability and complexity evaluation of attractors in a controllable piezoelectric Fitzhugh-Nagumo circuit

Chaotic systems have potential applications in secure communication and image encryption. The FitzHugh-Nagumo (FHN) neuron circuit model, as one of the most important models for neuron modelling, has a good chaotic discharge mode. On this basis, a controllable FHN piezoelectric neuron circuit is designed in this paper. By adjusting the state of the switch on or off, the circuit model can produce three different operating modes. The results show that the dynamic state of the system is determined by external stimuli, internal parameters of the system, and the number and nature of the external driving sources. More drivers can provide more chaos parameters. By converting different working modes, we can generate chaotic sequences with high randomness under different parameters. We found that when multiple drivers work together, the system output is more complex, and there is competition between dynamics induced by different drivers in the system. The addition of chaotic current makes the output discharge of the system produce chaotic resonance and pseudo-chaotic mode, which greatly improves the complexity of the sequence. This provides a new method for generating chaotic sequences with high randomness. Our work lays a theoretical foundation for the optimization of chaotic encryption algorithms in the future. In the future, we will further explore the effects of chaotic current stimulation with different properties. We hope that these findings will provide new insights into the security of information communications.

Mathematical analysis of the dynamics of solitary wave solutions to the time-fractional thin-film ferroelectric materials model

The thin-film ferroelectric materials equation is a fundamental mathematical model to explain the behavior of polarized electric fields in ferroelectric materials possessing unique properties such as piezoelectricity, pyroelectricity, electrostriction, hysteresis, spontaneous polarization, etc. These significant properties of ferroelectric materials are important in the progress of capacitors, non-volatile memories, electrical sensors, as well as optical and biomedical devices, among other applications. The aim of the present study is to explore applicable soliton solutions to the fractional-order model to the aforementioned equation by addressing the improved F-expansion method. Through analysis and rigorous identification, various radical solutions, including trigonometric, hyperbolic, and rational forms of the model, are established, and significant solitons are restored by setting appropriate parameter values in the analytical solutions obtained, which are demonstrated through 3D, 2D, and contour plots. We also explore the impact of fractional derivative as well as the parameters, like pressure, temperature, and others included in the model to determine the polarization effect in ferroelectric materials. This study uncovers several novel solutions along with their states in response to external or internal field parameters, which might be helpful in understanding the nature of ferroelectric materials.

Особенности аналитического описания механизма агрегатирования косилки самоходной КС-200

Author presents a mathematical connection between functional mathematical models describing the ag-gregation mechanisms of the self-propelled mower KС-200 in this article. A design feature of the KC-200 is the use of linkage mechanisms and adapter hanging mechanisms of the same links. Therefore, to design them, it is necessary to determine how changes in internal parameters, for example, the hanging mechanism, affect changes in the output parameters of the adapter hanging mechanism

FORMATION OF TWO-PHASE ZONES DURING INTERNAL OXIDATION OF BINARY ALLOYS

In the paper the formation patterns of two-phase regions during the internal oxidation process in low-alloyed binary alloys is studied. The goal of the research is the creation of a mathematical model for the internal oxidation process, which is to describe the kinetics of formation and evolution of internal oxidation zones, the study of the patterns governing the two-phase zone formation during internal oxidation, the study of the space-time dynamics of the structural parameters of the two-phase area, namely the number of the oxide particles, their average radius and the phase volume, at different stages of the internal oxidation zone evolution. The two-phase process of diffusion saturation with light components applied to binary alloys is analyzed theoretically. The conditions for formation of disperse oxide particles are formulated. A mathematical model for the formation of two-phase areas as well as the numerical solution for the system of equations describing the kinet- ics of formation and evolution of the two-phase area for different values of the process parameters is proposed. The modeling and experiments demonstrate that the distribution of structural parameters of the two-phase area has complex, non-monotonous nature. The influence of the main parameters of the process on the kinetics of the formation of the two-phase area in a lightly-alloyed binary nickel-based alloy plate is also determined. All stages of the plate’s oxidation process are studied – from the inception of the particles to the coalescence of second-stage particles. The results of the work could be used for developing new technologies in thermal and chemical-thermal treatments for disperse strengthening of materials using internal oxidation, as a physical basis for seeking ways of providing certain distributions of the internal oxidation zone structural parameters, which are required to give the al- loy a necessary set of physical and mechanical properties. The comparison of the modeling and experimental results shows a good correlation, which allows them to be recommended for developing new technologies in disperse strengthening, new heat-resistant steels, disperse-strengthened magnetic materials and electrotechnical alloys.

Compound fault diagnosis of rolling bearings based on AVMD and IMOMEDA

The intricate nature of compound fault diagnosis in rolling bearings during nonstationary operations poses a challenge. To address this, a novel technique combines adaptive variational mode decomposition (AVMD) with improved multipoint optimal minimum entropy deconvolution adjustment (IMOMEDA). The compound fault signal is isolated through AVMD, with internal parameters obtained via a new indicator termed integrated fault-impact measure index guiding the improved dung beetle optimizer. An adaptive selection method, using a weight factor, chooses the intrinsic mode function containing principal fault data. IMOMEDA whose key parameters are determined by a novel combinatorial strategy is then employed to deconvolute selected fault components, enhancing periodic fault impulses by removing complex interferences and ambient noise. The deconvoluted signal undergoes enhanced envelope spectrum processing to extract fault frequencies and identify fault types. Numerical simulations and experimental data confirm the method’s effectiveness and feasibility for compound faults diagnosis of rolling bearings, showcasing its superiority over existing techniques.

Peridynamic correspondence model with strain gradient elasticity for microstructure dependent size effects

This study presents a peridynamic (PD) correspondence model with strain gradient elasticity (SGE) to capture size effect on strength of nano- and micro-scale structures. The classical elasticity theory lacking a length scale parameter does not account for the microstructure-dependent size effects. Strain Gradient (SG) elasticity is an extension of the classical elasticity with a length scale parameter(s) which can be linked to the microstructure of the material. Peridynamic theory introduces damage into the constitutive relations in a natural way by allowing for interactions of a material point within its horizon which is referred to as the internal length parameter. The correspondence model of the PD theory permits the incorporation of the constitutive relationship of SG theory. Therefore, the present approach combines the effects of PD and SGE length scale parameters on the stiffness and strength of the material. The resulting equations present two length parameters: the horizon of a material point in PD theory and the characteristic length in SGE theory. The PD equation of motion with SGE (PDSG) is free of spatial derivatives and allows for the imposition of classical and nonclassical boundary conditions. The PDSG is first applied to investigate the deformation response of a one-dimensional nanoscale film subjected to a quasi-static tensile load and a dynamic load through an initial constant strain. The static response is compared with the analytical solution for varying ratio of SG length parameter to PD horizon. The dynamic response is compared with a computational solution while satisfying the nonclassical boundary conditions. Subsequently, the PDSG is applied to study the deformation response of a two-dimensional nanoscale film subjected to a quasi-static axial load and a specified tangential end displacement. These two cases are considered under special displacement constraints to compare with the available analytical solutions. Subsequently, a two-dimensional nanoscale film with or without an internal crack is subjected to uniform tensile loading. Finally, crack propagation is simulated under incremental displacement loading. The results capture the analytical predictions and the expected increase in stiffness with increasing ratio of SG length parameter to PD horizon.

The application of deep learning in autonomous driving

Autonomous driving technology is currently a globally prominent subject, with its relevance and impact readily apparent. Leveraging advanced sensors, sophisticated algorithms, and state-of-the-art computer vision techniques, autonomous vehicles can autonomously navigate, mitigate traffic accidents, and alleviate urban congestion. Furthermore, this technology is poised to accelerate innovation within the automotive sector, drive industrial advancement, and enhance people's convenience and safety in their travel experiences. The importance of autonomous driving technology is not only reflected in its own advantages, but also in its ability to lead the development direction of intelligent transportation in the future, helping to promote the development of intelligent transportation systems, realize the intelligent interconnection between vehicles and vehicles, vehicles and road infrastructure, and further enhance the safety and convenience of traffic travel. Therefore, autonomous driving technology is a significant innovation that will bring a better and more convenient future for mankind. In this study, a method based on sensor internal and external parameter calibration transformation matrix, road target detection algorithm, automatic vehicle detection method, pedestrian intention prediction technology and automatic pedestrian recognition system are analyzed, which are applied to object detection and obstacle avoidance. This article provides a good overview of the field of autonomous driving.

Model Predictive Current Control of Permanent Magnet Synchronous Motor Based on Sliding‐Mode Disturbance Observer

A nonlinear multivariable highly coupled system that is susceptible to both internal and external perturbations is the permanent magnet synchronous motor. A new reaching law‐based control approach that combines a sliding‐mode disturbance observer and an enhanced two‐vector model predictive current control is suggested in order to enhance the dynamic performance of the permanent magnet synchronous motor and its robustness to system disturbances. First, a new reaching law is developed in this paper to resolve the conflict between the traditional reaching law's chattering of the system and the reaching time of the sliding‐mode surface. This new reaching law not only makes the system's response more rapid but also reduces chattering; second, taking into account the possibility of systems with internal parameter uptake and external load disturbance, a sliding‐mode disturbance observer based on t is developed. Finally, the q‐axis current obtained from the speed sliding‐mode control rate is added to the model predictive current control, and an optimal duty cycle model predictive current control based on space vector modulation is designed because the traditional two‐vector model predictive current control is to optimize the duty cycle and voltage vectors separately, and the switching frequency is not fixed. The method suggested in this research may successfully suppress the sliding‐mode control system chattering and increase the resilience and dynamic response performance of the speed control system, according to simulation findings compared to the traditional reaching law and traditional duty cycle. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Stop! Planner Time: Metareasoning for Probabilistic Planning Using Learned Performance Profiles

The metareasoning framework aims to enable autonomous agents to factor in planning costs when making decisions. In this work, we develop the first non-myopic metareasoning algorithm for planning with Markov decision processes. Our method learns the behaviour of anytime probabilistic planning algorithms from performance data. Specifically, we propose a novel model for metareasoning, based on contextual performance profiles that predict the value of the planner's current solution given the time spent planning, the state of the planning algorithm's internal parameters, and the difficulty of the planning problem being solved. This model removes the need to assume that the current solution quality is always known, broadening the class of metareasoning problems that can be addressed. We then employ deep reinforcement learning to learn a policy that decides, at each timestep, whether to continue planning or start executing the current plan, and how to set hyperparameters of the planner to enhance its performance. We demonstrate our algorithm's ability to perform effective metareasoning in two domains.

Microscopic Factors Affecting the Performance of Pervious Concrete.

The impacts of various aggregate particle sizes and cement contents on the internal structure of pervious concrete were investigated. Accordingly, test blocks with different aggregate particle sizes and cement contents were dissected and photographed. Subsequently, the captured images were processed using the ImageJ software (1.53i) to analyze the profiles of the test blocks and identify the internal mesoscopic parameters of the pervious concrete. This study discusses the relationship between microscopic parameters and macroscopic factors based on experimental results. It also fits functional equations linking the permeability coefficient with pore parameters, matrix parameters, and compressive strength. The results indicated that, as the aggregate size increased, the internal pore diameter of the pervious concrete increased, whereas the total area and width of the cement matrix decreased. This resulted in a low permeability coefficient and high compressive strength of the test block. Increasing the cement content in pervious concrete reduced the porosity and increased the width and area of the internal matrix. Consequently, the permeability coefficient decreased, and the compressive strength of the test block increased.

Research on Image Stitching Technology for Road Surface Cracks

Image stitching refers to the combination of multiple images with a certain over lappingarea rate acquired by the image acquisition device into one image, which has a full picture of each image. Image stitching technology is an important part in the field of digital image processing. With the increasing requirements for image quality and the limited resolution of ordinary cameras, it is impossible to obtain large angle of view and clear image at the same time. In this paper, the key technologies involved in pavement crack image stitching are intensively studied, including image feature point detection and matching, internal and external camera parameter estimation and optimization, and image fusion. The work done in this paper and the main innovations include: (1)Two main feature detection and description algorithms, SIFT algorithm and ORBalgorithm, are studied. In view of the fact that the SIFT algorithm has redundancy when the Gaussian pyramid is established, this paper appropriately reduces the number of groups and maintains the number and stability of feature point detection. Combined with the advantages and disadvantages of the improved SIFT algorithm and ORB algorithm, and the performance of the two algorithms in the detection of pavement cracks, the improvedSIF'T algorithm is used as the feature point detection algorithm, and rBRlEF algorithm for describing the feature points in the ORB algorithm is used as the feature point description algorithm. The description algorithm is called the BSIF'T algorithm. Experiments show that the robustness of BSIFT algorithm is similar to that of SIFT algorithm, which is better than ORB algorithm and faster than SIF'T algorithm. (2)Image fiusion algorithms are studied, including best seam-line search and image fusion. Aiming at the ghost phenomenon that may occur in overlapping regions, this paper proposes a best seam-line search algorithm based on distance transform.

Novel fusion strategy for image fusion using rescue hunt optimization-based modified guidance model

ABSTRACT A new method for image fusion introduces an inventive strategy for amalgamating data from multiple images, resulting in the creation of a single, improved output image. This approach aims to address the limitations and challenges associated with conventional fusion techniques, paving the way for improved results in various applications. In this research, a novel approach for image fusion is presented, featuring a rescue hunt optimisation-based modified guidance model (RHO-based MG model). The methodology leverages the non-subsampled contourlet transform (NSCT) and non-subsampled shear let transform (NSST) to construct the fusion transform using two input images, typically infrared and visual images. By hybridising high-frequency (HF) and low-frequency (LF) bands from both types of images, the fusion model generates the final HF and LF bands. A distinctive modified guidance strategy is employed in the development of these bands. The proposed approach utilises a rescue hunt algorithm developed through the combination of search and rescue optimisation (SARO) and grey wolf optimisation (GWO) behaviours. In image fusion, optimisation fine-tunes VGG-19 and RESNET 18 models to improve their ability to combine multiple images effectively. This process involves adjusting the models’ internal parameters using optimisation techniques. By analysing the features in different input images, these models learn to extract meaningful information and create a fused image that retains the important details from each source. This strategy is further enhanced by integrating the VGG-19 and RESNET 18 models. The fused image is composed of combined HF and LF bands, with the final result obtained through an inverse hybrid transform. Experimental results, conducted on a dataset of 25 images, demonstrate the effectiveness of the approach with metrics average gradient (AG), edge intensity (EI), PSNR, RMSE, SSIM, and variance attained the values of 24.71, 236.67, 54.96 dB, 0.22, 63.16, and 0.11, respectively. This innovative method offers a promising direction for enhancing image fusion quality and is highlighted by its unique integration of optimisation and guidance strategies.

Low-fat microwaved peanut snacks production: Effect of defatting treatment on structural characteristics, texture, color, and nutrition

This study develops low-fat microwaved peanut snacks (LMPS) using partially defatted peanuts (PDP) with different defatting ratios, catering to people’s pursuit of healthy, low-fat cuisine. The effects of defatting treatment on the structural characteristics, texture, color, and nutrient composition of LMPS were comprehensively explored. The structural characteristics of LMPS were characterized using X-ray micro-computed tomography (Micro-CT) and scanning electron microscope (SEM). The results demonstrated that the porosity, pore number, pore volume, brightness, brittleness, protein content, and total sugar content of LMPS all significantly increased (P<0.05) with the increase in the defatting ratio. At the micro level, porous structure, cell wall rupture, and loss of intracellular material could be observed in LMPS after defatting treatments. LMPS made from PDP with a defatting ratio of 64.44% had the highest internal pore structural parameters (porosity 59%, pore number 85.3×105, pore volume 68.23 mm3), the brightest color (L* 78.39±0.39), the best brittleness (3.64±0.21) mm–1), and the best nutrition (high protein content, (34.02±0.38)%; high total sugar content, (17.45±0.59)%; low-fat content, (27.58±0.85)%). The study provides a theoretical basis for the quality improvement of LMPS.

Developing a quality assessment model (QAM) using logical prediction: Binary validation

This study focuses on evaluating the quality of competency transfer through various assessment methods and results, considering diverse stakeholder perspectives. The research aims to introduce an innovative approach for validating assessment outcomes, leveraging predicted sub-measurements, and transforming Boolean parameters' symbols into a binary coding system. This transformation simplifies the validation process by employing logical equations. The study's sample involves the adaptation of a competency transfer model, which combines internal parameters with the novel logical assessment method. The research findings indicate that the binary 2x system effectively simplifies quantitative and qualitative data representation within the validation process. This system facilitates the early detection of potentially ambiguous results, enabling the creation of validation procedures grounded in organizational cultural dimensions, outcomes, reports, and assessments. The proposed Quality Assessment Model (QAM) serves as a powerful tool for prediction, enhancing the quality of both quantitative and qualitative data outcomes. This approach generates distinct values, precise predictive measurements, and valuable result quality suitable for informed decision-making in various contexts. Ultimately, the study contributes to the advancement of assessment methodologies, enabling stakeholders to make more accurate and reliable judgments based on the quality of competency transfer.

Concurrent validation of the resistance intensity scale for exercise for monitoring velocity-based training with elastic bands

The aim was to evaluate the concurrent validity and reliability of the Resistance Intensity Scale for Exercise [RISE], which uses verbal descriptors, to quantify the intensity in velocity-based training with elastic bands. Eighteen trained volunteers performed parallel squats at maximum speed at 40%, 55%, 70%, and 85%1RM in four sessions, two for familiarization and two for reliability. Each set was stopped at a 10% intra-set velocity loss. Participants reported the perceived effort (easy-low-moderate-hard-maximal) at the first and last repetition. The concurrent validation was conducted with external load (i.e., mean propulsive velocity, weight, repetitions, and maximum power) and internal load parameters (i.e., heart rate). Participants' relative strength was calculated to assess its influence on the dependent variable. Acceptable concurrent validity and reliability (ICC>0.77, CV<21%) were observed, with the perceived effort being appropriate to differentiate between intensities and not being influenced by the participants’ relative strength (p = 0.88). A categorical linear regression showed significant (p < 0.001) associations between the RISE scores and the weight, repetitions, and mean propulsive velocity (r = 0.43–0.63). The findings certify the usefulness of the perceived exertion for quantifying the intensity during velocity-based training with elastic bands. The perceived exertion of the first and last repetition favors a proper dosage of the training load.

Development of beam elements for nonlinear analysis of reinforced concrete frames subjected to fire

A beam element was developed for nonlinear analysis of three-dimensional reinforced concrete frames subjected to fire. The formulation considers three critical aspects: the coupling between axial and bending deformations, thermal elongation, and the slenderness effect caused by fire. The formulation incorporates a Jacobian matrix between the internal forces and strain parameters to enhance the computational efficiency. Nonlinear analyses of the structures were conducted at ambient and elevated temperatures to verify the proposed beam elements. To statistically validate the accuracy, we analyzed 82 columns exposed to ambient temperature and 74 columns exposed to elevated temperatures, and compared them with the experimental results. The applicability of the proposed analysis method was demonstrated through the analysis of three-dimensional reinforced concrete frames. The beam element can be used for the performance-based fire design of reinforced concrete building structures.