Static Objects Research Articles

A Preliminary Study on the Investigation of Learning Ability of Arabian Horses Through

Knowing the behavioural responses of horses while planning the training processes is crucial in constituting the appropriate training program. The purpose of the study was to examine some behavioural and physiological responses of Arabian horses participating in flat races against the reactivity to human and fear tests. Within the scope of this study, 15 female Arabian horses aged between 2 and 8 years were subjected to reactivity to passive human test, reactivity to active human test, and handling tests from reactivity to human tests and the novel surface test, the static novel object test, and the startling novel object tests from fear tests. Significant relationships (P<0.05) were detected between the agonistic behaviours of horses and maximum heart rate values in the passive human test, active human test, static novel object test, and startling novel object test. Active human test, it was determined that as the agonistic behaviours of horses increased, the distance to approach humans increased (P<0.05). The novel surface test observed that as the horses' agonistic behaviours increased, the number of attempts to cross the surface successfully also increased (P<0.01). In the handling test, increased agonistic behaviours caused the test time to prolong (P<0.01). As a result, it was determined that various processes in training were prolonged in horses with high agonistic behaviours during the tests. It has also been determined that the trainability of horses may differ within the same breed and sex. For this reason, it is recommended to determine behavioural responses and to plan horse training individually. By understanding the trainability of horses, it is possible to achieve maximum efficiency for their benefit.

A Versatile Approach for Adaptive Grid Mapping and Grid Flex-Graph Exploration with a Field-Programmable Gate Array-Based Robot Using Hardware Schemes.

Robotic exploration in dynamic and complex environments requires advanced adaptive mapping strategies to ensure accurate representation of the environments. This paper introduces an innovative grid flex-graph exploration (GFGE) algorithm designed for single-robot mapping. This hardware-scheme-based algorithm leverages a combination of quad-grid and graph structures to enhance the efficiency of both local and global mapping implemented on a field-programmable gate array (FPGA). This novel research work involved using sensor fusion to analyze a robot's behavior and flexibility in the presence of static and dynamic objects. A behavior-based grid construction algorithm was proposed for the construction of a quad-grid that represents the occupancy of frontier cells. The selection of the next exploration target in a graph-like structure was proposed using partial reconfiguration-based frontier-graph exploration approaches. The complete exploration method handles the data when updating the local map to optimize the redundant exploration of previously explored nodes. Together, the exploration handles the quadtree-like structure efficiently under dynamic and uncertain conditions with a parallel processing architecture. Integrating several algorithms into indoor robotics was a complex process, and a Xilinx-based partial reconfiguration approach was used to prevent computing difficulties when running many algorithms simultaneously. These algorithms were developed, simulated, and synthesized using the Verilog hardware description language on Zynq SoC. Experiments were carried out utilizing a robot based on a field-programmable gate array (FPGA), and the resource utilization and power consumption of the device were analyzed.

A structural description of pedestrian movement behavior in multiple surveillance videos

This study purpose of recording pedestrians' activities in all surveillance videos and then convert these records into structured descriptions to analyze a large number of surveillance videos. This paper proposes a four-stage processing method. The first stage uses convolutional neural network (CNN) to detect objects in the surveillance video and classify them. The second stage finds the correlation of pedestrians in continuous images and describes multiple objects' trajectories in the surveillance video. The third stage calculates the similarity of the pedestrian object sets of the surveillance video at different times as the basis for pedestrian re-identification. In the fourth stage, extract the walking path of the pedestrian in each surveillance video. Each pedestrian's actions in each surveillance video, including whether to stand, walking path, direction, clothing color and walking time. To construct a correlation table of pedestrian motion in multiple surveillance videos.This experiment uses two image datasets for experimentation and analysis to evaluate this method's effectiveness for pedestrian movement in multiple surveillance videos. The image datasets are the Multi-Camera Object Tracking (MCT) Challenge and PETS 2009 Benchmark Data. This experiment includes pedestrian detection, pedestrian identification, and multi-pedestrian tracking of pedestrians in single and multiple surveillance cameras. The results show that in the pedestrian detection experiment, the convolutional neural network can effectively solve the problems caused by static objects, light changes, similar colors, crowded pedestrians, and incomplete objects. In the pedestrian recognition and re-recognition experiments, the accuracy is reduced due to the influence of low-resolution surveillance videos. This detection method of the pedestrian is more accurate than traditional dynamic object detection methods. After the pedestrian tracking and re-identification are completed, the pedestrian relationships in multiple surveillance cameras are integrated, and a pedestrian behavior association table is built for surveillance videos.

Beyond Preservation: Heritage as an Educational Practice Process at 141 Neil Road, Singapore

Over the past two decades, the concept of heritage has been significantly broadened, with an increasing number of scholars viewing it not as a set of static objects with fixed meanings but as a social process consciously endowed with commemorative functions through human creation. This research illustrates and empirically supports this perspective through a case study. Initially, the paper reviews the development of the heritage concept through the literature review and elaborates on the idea of “heritage as process”. Subsequently, it examines Singapore’s enriching exploration and successful implementation of heritage conservation, particularly since 2000, when the local government shifted from preserving historical buildings to a comprehensive conservation strategy that embraces a sense of place, identity, and memory and encourages a bottom-up participatory approach. Finally, this paper takes the transformation of the century-old townhouse at 141 Neil Road in Singapore as its focus. After being renovated into an urban architectural heritage conservation laboratory, the house has gradually become a place of heritage on Neil Road by training professionals’ construction skills, transforming its function, introducing technological explorations, and enhancing public interaction. This process has developed the abilities of heritage conservation professionals in desktop research, reuse design, and practical construction skills while simultaneously promoting community participation and heightening awareness of heritage conservation among local residents. Through daily practices, this historical townhouse gradually became a place of heritage on Neil Road, exemplifying the research theme that “heritage is a comprehensive and dynamic practice encompassing social, cultural, and technological dimensions”. From being preserved for renovation in 2020 to being approved as a “Heritage Show House” by the Urban Redevelopment Authority (URA) in 2023, the case of 141 Neil Road offers a new perspective in the heritage conservation field that “heritage is always in the process of becoming, rather than a constant given”.

Spatial and temporal motion characterization for x-ray CT.

Motion induced image artifacts have been the focus of many investigations for x-ray computed tomography (CT). Methodologies of combating patient motion include the use of gating devices to optimize the data acquisition, reduction in patient scan time via faster gantry rotation and large detector coverage, and the development of advanced reconstruction and post-processing algorithms to minimize motion artifacts. Previously proposed approaches are generally "global" in nature in that motion is characterized for the entire image. It is well known, however, that the presence of motion artifact in a CT image is highly nonuniform. When there is a lack of automated and quantitative local measure indicating the presence and the severity of motion artifacts in a local region, the quality of the reconstructed images depends heavily on the CT operator's rigor and experience. Even when an operator is informed of the presence of motion, little information is provided about the nature of the motion artifact to understand its relevance to the clinical task at hand. In this paper, we propose an image-space spatial- and temporal-consistency metric (CM) to detect and characterize the local motion. In a non-rigid human organ, such as the lung, there are many small and rigid objects (target objects), such as blood vessels and nodules, distributed throughout the organ. If motion can be characterized for these target objects, we obtain a complete motion map for the organ. To accomplish this, a preliminary image reconstruction is carried out to identify the target objects and establish region-of-interests for consistency-metric calculation. The CM is then obtained based on the backprojected intensity difference between the object region and its circular background. For a stationary object, the accumulation of this quantity over views is linear. When a target object moves, nonlinear behavior exhibits and a quantitative measure of linearity indicates the severity of motion. Extensive computer simulation was utilized to confirm the validity of the theory. These tests stress the sensitivity of the proposed CM to the target object size, object shape, in-plane motion, cross-plane motion, cone-beam effect, and complex background. Results confirm that the proposed approach is robust under different testing conditions. The proposed CM is further validated using a cardiac scan of a swine, and the proposed CM correlates well with the visual inspection of the artifact in the reconstructed images. In this paper, we have demonstrated the efficacy of the proposed CM for motion detection. Unlike previously proposed approaches where the consistency condition is derived for the entire image or the entire imaging volume, the proposed metric is well localized so that different zones in a patient anatomy can be individually characterized. In addition, the proposed CM provides a quantitative measure on a view-by-view basis so that the severity of motion is consistently estimated over time. Such information can be used to optimize the image reconstruction process and minimize the motion artifact.

Design of Optimal Control Systems in the Frequency Domain by the Functional of the Generalized Work

The paper considers the problem of analytical design of optimal controllers (ADOC) for one-dimensional linear stationary objects according to the functional of generalized work (FGW) A. A. Krasovsky. The use of FGW in comparison with the quadratic performance functional greatly simplifies the calculation of the optimal controller — the calculation of its matrix of coefficients mainly consists in solving the linear matrix Lyapunov equation, which, in contrast to the nonlinear matrix Riccati equation, fundamentally reduces the amount of calculations. In addition, the use of FGW provides the best stability margins of the designed system in terms of amplitude and phase. This work is devoted to the development of a method for solving the ADOC problem A. A. Krasovsky in the frequency (complex) domain, which reduces the determination of the transfer function coefficients of the optimal controller for an object of order n to the solution of the corresponding system of n linear algebraic equations. In this regard, the proposed method for solving the ADOC problem by A. A. Krasovsky differs by a much smaller amount of calculations in comparison with the standard method, in which the Lyapunov equation is solved with the desired matrix of dimensions n * n. The proposed method for the synthesis of optimal control systems, which has an analytical nature, became the basis for solving the inverse problem ADOC A. A. Krasovsky, which consists in determining the values of the weight coefficients of the FGW, which provide the given primary quality indicators of the synthesized control system. Using its relations, a relatively simple method for calculating the FGW coefficients based on the given values of the error coefficients for the designed dynamic system has been developed.

PBDB-T/Pentacene-Based Organic Optoelectronic Synaptic Transistor with Adjustable Critical Flicker Fusion Frequency for Dynamic Vision.

In the era of the Internet of Things and the rapid progress of artificial intelligence, there is a growing demand for advanced dynamic vision systems. Vision systems are no longer confined to static object detection and recognition, as the detection and recognition of moving objects are becoming increasingly important. To meet the requirements for more precise and efficient dynamic vision, the development of adaptive multimodal motion detection devices becomes imperative. Inspired by the varied response rates in biological vision, we introduce the concept of critical flicker fusion frequency (cFFF) and develop an organic optoelectronic synaptic transistor with adjustable cFFF. In situ Kelvin probe force microscopy analysis reveals that light signal recognition in this device originates from charge transfer in the poly[(2,6-(4,8-bis(5-(2-ethylhexyl)thiophen-2-yl)benzo[1,2-b:4,5-b']dithiophene)-co-(1,3-di(5-thiophene-2-yl)-5,7-bis(2-ethylhexyl)-benzo[1,2-c:4,5-c']dithiophene-4,8-dione)] (PBDB-T)/pentacene heterojunction, which can be effectively modulated by gate voltage. Building upon this, we implement different cFFF within a single device to facilitate the detection and recognition of objects moving at different speeds. This approach allows for resource allocation during dynamic detection, resulting in a reduction in power consumption. Our research holds great potential for enhancing the capabilities of dynamic visual systems.

Seeing Through Walls with Wi-Fi: Wi Vi Technology and its Security Implications

he technology known as WI- FI, or wireless networking, allows users to connect to the internet wirelessly “Wi-Fi" sis the acronym for “WIRELESS FIDELITY. “Data is transferred between transmitters and receivers using Wi-Fi waves. WI-VI (WIRELESS VISION) is a new technology introduced by WI-FI. WIVI is a device that uses WI-FI signals to see through any object on the wall. In a closed room, we can detect the number of moving humans. In the future, we will concentrate on detecting more than three moving objects using WI-FI signals. WI- VI can detect people's gestures and relative location via the WI-FI signal. WIVI technology can be utilized in the tax department to detect black money and weapons, as well as for security purpose. Wivi is used for defense at home. Wivi technology can help us with door-face detection. Within a limited radius of no more than 1000 square feet, Wi-Fi is linked to Wi-Fi. Wi-Vi technology can also be used to assist in paramedic circumstances, like a land slide. Wi-Fi, a popular technology, is nothing more than an information channel between transmitter and receiver. We demonstrate in this work that Wi-Fi can also extend our senses, allowing us to view moving objects across walls and behind closed doors. So, can Wi-Fi signals be used to identify persons in a closed room based on their Relative location? Yes, we Through the use of a technology known as Wi-Vi, things in Confined rooms can be identified. Simple movements can also be identified and combined behind the wall to express messages. This introduces two major changes. Initially, MIMO interference nulling is used to reduce static object reflections and focus on moving objects. It then demonstrates how to follow a human by treating. Keyword- Wi-Fi Sensing, Through-Wall Imaging, MIMO Interference Nulling, Object Detection, Moving Object, wivi, Detection, Human Gesture Recognition, Indoor Localization

Alignment and fusion for adaptive domain nighttime semantic segmentation

In the field of autonomous driving technology, both daytime and nighttime scenes are common. However, due to the poor illumination and difficulty in manual annotation of nighttime images, semantic segmentation of nighttime scenes is more challenging compared to daytime scenes. Therefore, achieving significant progress in nighttime semantic segmentation would greatly enhance the effectiveness of the application of autonomous driving scenarios. In this work, we investigate the problem of domain-adaptive nighttime semantic segmentation (DANSS). The problem aims to learn semantic segmentation in nighttime scenes by leveraging a labeled Cityscapes dataset and unlabeled but roughly aligned day-night image pairs. To address this, we propose an Align and Fusion Network (AAFnet), a network for adaptive domain nighttime semantic segmentation. AAFnet utilizes a novel DAFormer as the backbone to separately compute features for dynamic and static objects during the training process. It also employs methods such as small category sampling and image blending to improve learning effectiveness. Additionally, we propose utilizing local image patches to enhance the results during the testing process. Experimental results demonstrate a significant improvement of 10.4% compared to the previous method, DANNet. Extensive experiments conducted on the Dark Zurich dataset and Nightdriving dataset validate the effectiveness of the proposed approach. Our method outperforms previous backbones and achieves top-ranking results.

How to ‘repair’ repair? A conceptual model to escalate repair as urgent or important

Globally, the volume of clothing waste is now catastrophic in scale. Further, the manufacturing sector of fast fashion garments will soon produce over 100 billion garments per year. Fast fashion promotes wastefulness – during clothing production, laundering and the quick discard of used and even new clothing to landfills throughout the world. This article presents a model for the mechanism of repair to become activated as ‘urgent’ and ‘important’. Firstly, repair can be activated by the vector of trauma. Urgency comes through the exigencies of two life experiences, when trauma follows natural disaster and/or climate change. As summarized in the section on the environmental costs from fashion overproduction, natural disasters and climate change have impacted severely on global communities. In addition, trauma occurs when people’s lives are ‘disenfranchised’ outside the norms of social well-being. These marginal communities include refugees, migrant workers, trafficked slaves, prisoners and discrimination by race, colour, gender, sexual identity, religion and more. Secondly, repair can be activated through the vector of value. Clothing is cherished and repaired because of initial cost, sentimentality and family history. Here clothing encapsulates a process of nostalgia; from a mindset of ‘casting back’ where garments are indelibly linked to memories. This conceptual model for ‘repairing’ repair is not lightly proposed. Complications can arise when repair is delegated to ‘women’s work’ that undermines feminist theories. A model can be useful, but the key to any model’s efficacy is its application to real-world activities. Fast fashion further relocates clothing to static objects, as photos viewed quickly on social media. It is postulated here that this analysis of repair as urgent and important, when driven by vectors of trauma and value offers a re-evaluation of how to relocate repair as a vital pathway to lessen global fast fashion overproduction and overconsumption.

Security Threat Object Recognition System

Security Threat object recognition has emerged as a very advanced topic in the field of video surveillance community. Due to need of system has been proposed for video monitoring or surveillance system for public and private places to protect againt thept and recognise the suspicious object which is harmful for the society and the people. Because of these systems' complexity, researchers frequently handle the various stages of analysis—such as foreground segmentation, stationary object recognition, and abandonment validation—independently. The impact of each level of improvement on the overall performance of the system has not been investigated, despite the fact that each stage has seen gains. This is because the advancements are rarely applied across the entire pipeline. This study presents a thorough assessment of the state-of-the-art methods for each stage and formalizes the framework used by systems for abandoned object detection. Additionally, we develop a multi-configuration system that enables the selection of many options for every step, with the goal of identifying the combination that yields the best results. The scientific community has internet access to this multi-configuration. Keywords: Object detection, Surveillance, Threat Detection, Computer Vision, Deep Learning, YOLO, Image analysis, CNN, R-CNN, SSD

ContactGen: Contact-Guided Interactive 3D Human Generation for Partners

Among various interactions between humans, such as eye contact and gestures, physical interactions by contact can act as an essential moment in understanding human behaviors. Inspired by this fact, given a 3D partner human with the desired interaction label, we introduce a new task of 3D human generation in terms of physical contact. Unlike previous works of interacting with static objects or scenes, a given partner human can have diverse poses and different contact regions according to the type of interaction. To handle this challenge, we propose a novel method of generating interactive 3D humans for a given partner human based on a guided diffusion framework (ContactGen in short). Specifically, we newly present a contact prediction module that adaptively estimates potential contact regions between two input humans according to the interaction label. Using the estimated potential contact regions as complementary guidances, we dynamically enforce ContactGen to generate interactive 3D humans for a given partner human within a guided diffusion model. We demonstrate ContactGen on the CHI3D dataset, where our method generates physically plausible and diverse poses compared to comparison methods.

Moving and Static Faces, Bodies, Objects, and Scenes Are Differentially Represented across the Three Visual Pathways.

Models of human cortex propose the existence of neuroanatomical pathways specialized for different behavioral functions. These pathways include a ventral pathway for object recognition, a dorsal pathway for performing visually guided physical actions, and a recently proposed third pathway for social perception. In the current study, we tested the hypothesis that different categories of moving stimuli are differentially processed across the dorsal and third pathways according to their behavioral implications. Human participants (n = 30) were scanned with fMRI while viewing moving and static stimuli from four categories (faces, bodies, scenes, and objects). A whole-brain group analysis showed that moving bodies and moving objects increased neural responses in the bilateral posterior parietal cortex, parts of the dorsal pathway. By contrast, moving faces and moving bodies increased neural responses, the superior temporal sulcus, part of the third pathway. This pattern of results was also supported by a separate ROI analysis showing that moving stimuli produced more robust neural responses for all visual object categories, particularly in lateral and dorsal brain areas. Our results suggest that dynamic naturalistic stimuli from different categories are routed in specific visual pathways that process dissociable behavioral functions.

Absolute phase retrieval based on spatial ternary phase coding with circular fringe projection

Fringe projection profilometry (FPP) based on temporal phase unwrapping (TPU) plays a vital role due to its high precision and fast speed in three-dimensional (3D) measurements. However, a fundamental contradiction arises between the necessity for numerous auxiliary patterns and the goal of enhancing measurement efficiency, particularly when ensuring a big codeword space for fringe order. Thus, a spatial ternary phase coding (STPC) method is proposed, which achieves a big codeword space by projecting just one auxiliary coding pattern while ensuring a robust decoding. By equally dividing each fringe period into several segments, ternary coding of fringe order is accomplished using an arccosine operation to code three distinct phase values within each segment. Meanwhile, circular grating is adopted due to its excellent performance in phase sensitivity, which offers a higher measuring accuracy in 3D reconstruction. Accordingly, a robust TPU is achieved by combining the introduced phase unwrapping scheme and the circle center-cosine theorem (CCCT) strategy. Furthermore, high-efficiency measurements are attained through projecting three phase-shifting circular gratings and one STPC grating, rendering it suitable for real-time measurements. Experimental results with various static objects and real-time scene demonstrate the feasibility and validity of the proposed method.

ENHANCING URBAN PATHFINDING FOR PEDESTRIANS THROUGH FUSION OF MLS AND HMLS DATA

Abstract. Pedestrian pathfinding is crucial for enhancing pedestrian mobility in urban environments. In this research a method to generate navigation graphs based on Mobile Laser Scanning (MLS) and Handheld Laser Scanning (HMLS) data fusion is developed. The input data comprises a 2-kilometer urban street network that integrates both MLS and HMLS data, effectively mitigating sidewalk occlusions caused mainly by parked vehicles. The proposed method encompasses the following steps: (1) Deep Learning semantic segmentation of urban ground elements and navigation-related obstacles, (2) Static vs. Dynamic object differentiation to replicate conditions of unobstructed passage. (3) Sidewalk enrichment with inclination, preservation status, and width. (4) Physical accessibility estimation between sidewalks and crosswalks incorporating curb information. And (5) navigation graph computation based on the enhanced sidewalk data, crosswalks, with accurate node location and connections. Routes were calculated within a Geographical Information System, and results ensure that pedestrians can navigate in urban environments with precision and efficiency.

Nanomaterials in 4D Printing: Expanding the Frontiers of Advanced Manufacturing.

As an innovative technology, four-dimentional (4D) printing is built upon the principles of three-dimentional (3D) printing with an additional dimension: time. While traditional 3D printing creates static objects, 4D printing generates "responsive 3D printed structures", enabling them to transform or self-assemble in response to external stimuli. Due to the dynamic nature, 4D printing has demonstrated tremendous potential in a range of industries, encompassing aerospace, healthcare, and intelligent devices. Nanotechnology has gained considerable attention owing to the exceptional properties and functions of nanomaterials. Incorporating nanomaterials into an intelligent matrix enhances the physiochemical properties of 4D printed constructs, introducing novel functions. This review provides a comprehensive overview of current applications of nanomaterials in 4D printing, exploring their synergistic potential to create dynamic and responsive structures. Nanomaterials play diverse roles as rheology modifiers, mechanical enhancers, function introducers, and more. The overarching goal of this review is to inspire researchers to delve into the vast potential of nanomaterial-enabled 4D printing, propelling advancements in this rapidly evolving field.

Hybrid star within [formula omitted] gravity

The purpose of this work is to investigate some interesting features of a static anisotropic relativistic stellar object composed of two different types of fluid distributions typically termed as quark matter (QM) and ordinary baryonic matter (OBM) together with Krori–Barua type (KB) ansatz in the regime of modified f(G) gravity, where G being the Gauss–Bonnet invariant term. In order to explain the correlation between pressure and matter density for the quark matter distribution within the compact object, we have taken into consideration the well-known MIT bag equation of state (EoS) whereas there is a simple linear correlation between pressure and matter density for ordinary baryonic matter. Furthermore, using graphical representations for varying parameters, the physical credibility of our obtained solutions has been intensively examined by regularity checking of the metric coefficients and matter variables, energy conditions, mass function, and causality conditions. For these analyses, we consider a particular compact stellar candidate 4U 1538-52. Finally, we found that the resulting outcome depicts the viability of the considered hybrid stellar model.

ADAPTIVE IDENTIFICATION OF NON-STATIONARY AIRCRAFT, ASSESSMENT OF THEIR STABILITY

For the most part, various processes are multi-link inertial systems that have a certain non-stationarity to one degree or another. The presence of inertia leads to the fact that it is customary to consider processes as dynamic objects. Such dynamic non-stationary objects can be described by models, both in continuous time (for example, differential equations) and in discrete time (for example, difference equations). Ultimately, the choice of a discrete or continuous form of representation of a non-stationary object is determined by which mathematical apparatus is considered more appropriate by the researcher.
 In some cases, it is possible to consider the process as a static object, which allows to significantly simplify the analysis of such a non-stationary object. However, replacing a dynamic object with a static model is not always possible and leads to an uncontrolled error at the output.
 Non-stationarity is an important characteristic of the process as an object of management and is caused primarily by changes in process properties, under the influence of changes in environmental conditions, aging and wear of equipment, etc. Non-stationarity of the process significantly complicates the analysis and synthesis of control systems of this kind of objects, since the theory for control systems stationary in time is not applicable for non- stationary systems.
 Depending on the set goal and management tasks, process models can have different forms of presentation.
 The article examines various forms of presentation and research of non-stationary dynamic objects and their parameters from the standpoint of synthesis of the circuit of assessment and management of various processes, in particular the flight of an aircraft.
 The results of the analysis of the accuracy of identification of non-stationary objects, the adaptation of systems when combining or distributing in time the stages of "learning" and "management" in conditions of stochastic uncertainty are presented. The conditions for obtaining reliable estimates of the current values of object parameters during their measurement, processing and transmission in communication channels in real time are determined.

A Review of Intel Real Sense-Based Obstacle Detection for Unmanned Ground Vehicles

Abstract: The process of obstacle detection involves the identification of obstacles that lie ahead in the route, Intel RealSense camera has been a prominent solution for identifying real-time applications in recent years. Autonomous Unmanned Ground Vehicle(UGVs) face a difficult time in detecting obstacles because of stationary objects, moving objects and adverse weather conditions. We need to get an accurate distance between the UGVs and obstacles, So Intel RealSense cameras use stereo vision to calculate the depth data. Based on D415, D435 and D455 Intel RealSense depth sensors, we have examined and compared various obstacle detection methods.

МОДЕЛЮВАННЯ ХАРАКТЕРИСТИК ПРОЦЕСІВ У БІОГАЗОВИХ УСТАНОВКАХ НА ОСНОВІ АНАЛІЗУ ІНТЕРВАЛЬНИХ ДАНИХ

The problems of the research presented in the work relate to mathematical modeling in order to reflect the relationship between the main characteristic of the process and the factors that affect it, as well as the dynamics of the main characteristic of the process, which is determined by the acidity of the substrate in the bioreactor. To build mathematical models of both types, it is proposed to use the methods of parametric and structural identification of models of static objects and discrete models of object dynamics based on the analysis of interval data. An universal approach based on metaheuristic optimization algorithms is proposed and substantiated for the identification of both types of models. These methods, in turn, use mechanisms of self-organization and self-adaptation in the process of finding an optimal or quasi-optimal solution. In particular, the work uses computational algorithms built on the basis of artificial bee colony algorithms. The method is implemented using data presented in interval form. The proposed universal method was tested on the construction of a mathematical model that reflects the dependence between the pH of the fermentation medium and the volume of the loaded bio-raw material in the form of its dry and liquid parts, the temperature in the bioreactor and the humidity of the dry part of the bio-raw material. Another mathematical model built in the work reflects the dynamics of the acidity indicator pH of the fermentation medium depending on the ratio of the mass of the loaded dry bio-raw material to the volume of the loaded liquid bio-raw material Both obtained interval mathematical models can be applied to control processes in biogas plants.