Real Images Research Articles

Increase the operational reliability of the electric drive of the weaving machine

The main purpose of this research work is to analyze malfunctions, power consumption, engine overheating and vibrations based on the loadings of electrical circuits through artificial neural networks. The reliability of artificial intelligence systems was proven on the basis of a model-based system depending on the task, and the obtained values were experimentally compared in the electrical operation of existing equipment in general industrial enterprises. An imitation model of the real object was developed. A concept of increasing productivity was set up to identify malfunctions, in contrast to the existing annular method. The article developed an algorithm for increasing the operational reliability of the electrical operation of the weaving machine on the basis of integrated indicators of excitation to determine the probability of failure of electrical operation. The article proposes the possibility of directly processing the diagnostic energy parameters through artificial neural networks. An experimental combination of signals resulted in a model based on input power and torque, and was based on an asynchronous motorized electrical circuit. It has been proven that intellectual reliability can be increased by 3-5% compared to operational reliability in traditional methods.

Demonstration of computer vision for void characterisation of 3D-printed continuous carbon fibre composites

Microstructural void formation during the processing of continuous fibre-reinforced polymer composites are a significant limitation of processes such as 3D printing, as voids inhibit resultant mechanical properties. Traditional optical microscopy approaches to void characterisation are tedious and are prone to errors due to non-ideal but realistic image conditions, including low contrast typical of microscopic images. This paper proposes a novel application of automated computer vision image processing techniques (i.e. a contrast-limited adaptive histogram equalisation (CLAHE) algorithm and Otsu's method) for detailed void characterisation of continuous fibre-reinforced composites. Microstructural void fraction was subsequently determined for both unidirectional and bi-axial laminates of a variety of thicknesses. The void characterisation by computer vision was shown to produce comparable results with manual contrast/brightness control, but with significantly less standard deviation. The computer vision software used in void characterisation is made freely available online at https://github.com/Xiao-ying/VoidDetector.

GAN Based Multi-Class Skin Disease Classification: Deep Learning Approach

Abstract: Skin diseases pose significant diagnostic and treatment challenges due to their diverse and complex manifestations. Convolutional neural networks (CNNs) have demonstrated superior capabilities in image classification tasks, including skin disease identification. However, the performance of CNN models depends heavily on the quality and quantity of training data, which often suffers from limitations such as imbalance and sparsity. This project proposes an approach new approach to address these challenges by integrating generative adversarial networks (GANs). ) with CNN for multi-class skin disease classification. The GAN-based system aims to improve the diversity and quantity of the training dataset by generating synthetic images of various skin conditions. Through an adversarial training process, the generator network learns to generate realistic images of skin diseases, while the discriminator network distinguishes between real and synthetic data. Figures The synthetic images generated by the GAN are then combined with the real dataset to train the model CNN. specially designed to classify skin diseases.

Enhancing computational thinking and spatial reasoning skills in gamification programming learning: A comparative study of tangible, block and paper‐and‐pencil tools

AbstractTangible programming tools have become a mainstream teaching aid in gamification programming learning (GPL) due to their interactivity and ability to enhance novice learners' computational thinking and spatial reasoning skills. However, comparing the relative efficacy of different programming tools that simultaneously support these skills was not adequately explored. This study designed and evaluated three programming tools: the tangible programming tool (TPG), which uses real touchable objects; the block programming tool (BPG), which employs virtual programming blocks and 3D game scenarios; and the paper‐and‐pencil programming tool (PPG), which uses paper and pen to draw. The study involved 112 seventh‐grade students from three natural classes: Class A (TPG, n1=37), Class B (BPG, n2=38), and Class C (PPG, n3=37). These students completed four gamification programming tasks and CT skills, spatial reasoning skills, enjoyment, cognitive load and GPL task list measurements. The results indicated that the tangible programming tool led to lower cognitive load, significant improvement in spatial reasoning skills and better abstraction and problem decomposition skills. The block programming tool provided a more enjoyable experience and facilitated students' algorithm design and efficiency. The paper‐and‐pencil programming tool was found to be less effective in improving spatial reasoning skills. This study's findings can help programming educators cultivate students' thinking skills and improve their learning experience by effectively selecting the most appropriate programming tools.

Super-resolution reconstruction of single image for latent features

AbstractSingle-image super-resolution (SISR) typically focuses on restoring various degraded low-resolution (LR) images to a single high-resolution (HR) image. However, during SISR tasks, it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture features. This challenge can lead to issues such as model collapse, lack of rich details and texture features in the reconstructed HR images, and excessive time consumption for model sampling. To address these problems, this paper proposes a Latent Feature-oriented Diffusion Probability Model (LDDPM). First, we designed a conditional encoder capable of effectively encoding LR images, reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed images. We then employed a normalized flow and multimodal adversarial training, learning from complex multimodal distributions, to model the denoising distribution. Doing so boosts the generative modeling capabilities within a minimal number of sampling steps. Experimental comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics, providing a fresh perspective for tackling SISR tasks.

Recall of Odorous Objects in Virtual Reality

The aim was to investigate how the congruence of odors and visual objects in virtual reality (VR) affects later memory recall of the objects. Participants (N = 30) interacted with 12 objects in VR. The interaction was varied by odor congruency (i.e., the odor matched the object’s visual appearance, the odor did not match the object’s visual appearance, or the object had no odor); odor quality (i.e., an authentic or a synthetic odor); and interaction type (i.e., participants could look and manipulate or could only look at objects). After interacting with the 12 objects, incidental memory performance was measured with a free recall task. In addition, the participants rated the pleasantness and arousal of the interaction with each object. The results showed that the participants remembered significantly more objects with congruent odors than objects with incongruent odors or odorless objects. Furthermore, interaction with congruent objects was rated significantly more pleasant and relaxed than interaction with incongruent objects. Odor quality and interaction type did not have significant effects on recall or emotional ratings. These results can be utilized in the development of multisensory VR applications.

ARCHIGENIUS - VR MAZE SOLVING USING A* ALGORIHTM

Game is one of the entertainment media that is rapidly growing. On this day, playing games is one way to spare time for refreshing. One of the popular games this year is an action game such as First-Person Shooter (FPS). Besides, the FPS game genre has developed rapidly to make it more interesting for the player. Non-player Character (NPC) has an essential function in artificially and automatically to make a game more immersive and consisting of various kinds. Also, from observation, adding challenges when playing, namely enemy characters, can make a player more enjoyable. Besides, one of modern technology to develop innovations for supporting a game is called virtual reality. So, in this research, the FPS game is built by adding NPC elements with virtual reality technology. Also, the gameplay in this game is a survival maze. Based on the evaluation, this game application can display objects in a virtual reality smoothly, and all feature in this game can display and run effectively. Besides, the respondents also give good feedback for user testing which has average score 4.66 from several users. So, the conclusion from this research is this game can make the FPS game more interesting for users by adding NPC, 3D element, and virtual reality. Keywords - First-person Game, Virtual Reality, Survival Maze, Non-Player Character, virtual maze environment.

DMOT-SLAM: Visual SLAM in Dynamic Environments with Moving Object Tracking

Abstract Visual simultaneous localization and mapping (SLAM) in dynamic environments has received significant attention in recent years, and accurate segmentation of real dynamic objects is the key to enhancing the accuracy of pose estimation in such environments. In this study, we propose a visual SLAM approach based on ORB-SLAM3, namely DMOT-SLAM, which can accurately estimate the camera’s pose in dynamic environments while tracking the trajectories of moving objects. We introduce a spatial point correlation constraint and combine it with instance segmentation and epipolar constraint to identify dynamic objects. We integrate the proposed motion check method into DeepSort, an object tracking algorithm, to facilitate inter-frame tracking of dynamic objects. This integration not only enhances the stability of dynamic features detection but also enables the estimation of global motion trajectories for dynamic objects and the construction of object-level semi-dense semantic maps. We evaluate our approach on the public TUM, Bonn, and KITTI dataset, and the results show that our approach has a significant improvement over ORB-SLAM3 in dynamic scenes and performs better compared to other state-of-the-art SLAM approaches. Moreover, experiments in real-world scenarios further substantiate the effectiveness of our approach.

Анализ методов получения и обработки данных для формирования 3D-модели генерального плана объекта недвижимости

In the context of increased urbanization and limited land resources, the efficient vertical planning (including underground and airspace) of the territory, taking into account the existing edifices, as well as the design of new facilities to provide the population with the necessary housing (apartment blocks), is of key importance. Implementation of this tendency is impossible without description of the area with metric 3D models (information ones), which should also be reflected in the master plans of real estate objects (in the design of new buildings). The authors provide practical research on selecting optimal methods of getting information and processing it to form a 3D model of capital construction projects (using the example of an apartment block with protruding structures). For obtaining the data (x, y, H) on the position of the real estate object in space, it is proposed to use a combined or photogrammetric method (depending on the specific survey conditions). The authors also concluded the necessity of improving the legal framework regarding the data format for 3D modeling of real estate using domestic (RF) software

STRUCTURE AND CHARACTERISTICS OF THE FIRST MODULE IN THE EDUCATIONAL COMPONENT “INDIVIDUAL RESEARCH WORK” FOR EDUCATIONAL PROFESSIONAL PROGRAM “MEDICINE”

The proportion of skills, abilities, and competencies pertaining to the scientific method of understanding reality—comprising principles, rules, norms, and methodologies for acquiring and organizing new knowledge about both objective and subjective realities—represents the scientific aptitude within the educational framework. A robust scientific aptitude within the educational process is a vital prerequisite for students in specific fields of study (specializations) to receive education of requisite quality. The Department of Pathophysiology at Poltava State Medical University is actively engaged in not only fundamental medical and preclinical research but also in ongoing scientific and methodological endeavors aimed at enhancing the educational process departmentally and university-wide. The educational component ‘Individual Research Work” within the educational professional program “Medicine”, developed by the departmental faculty, serves to elevate educational standards by augmenting the scientific aptitude within the educational process. This study aimed to analyze and delineate the structure of the initial module within the educational component “Individual Research Work” of the educational and professional program “Medicine”. In preparing the manuscript of the article, the authors employed several general theoretical research methods, including analysis, synthesis, comparison of concepts, abstraction, and generalization. Module No. 1, titled “Modeling of Typical Pathological Processes in Various Organs and Systems”, within the educational component “Individual Research Work”, spans 120 hours and is structured as follows: 8 lecture hours, 32 hours of practical classes, and 80 hours designated for independent study of module topics. The thematic plan encompasses the exploration of the following topics: 1. Modelling inflammatory processes in various organs; 2. Modelling disturbances in the cell cycle (hypo- and hyperbiotic processes); 3. Modelling systemic metabolic disorders; 4. Modelling functional disorders across various organs and systems. The ultimate outcome of Module No. 1 entails students selecting a topic and experimental model conducive to their research pursuit. The structure and content of this initial module within the educational component “Individual Research Work”, developed by the Department of Pathophysiology at Poltava State Medical University, align with the standards of contemporary higher medical education. The study, along with its inaugural module, is poised to enhance the quality of education for aspiring individuals enrolled in the educational and professional program “Medicine”.

The Emergence of Consciousness in a Physical Universe

Consciousness appears so mysterious and hard to formulate within physical sciences because the present day scientific thinking excludes an element of reality and a general mechanics of its processing from its consideration. The primary missing element is the reality of information in the physical universe as an intrinsic causal correlate of observable physical states. Moreover, there exists a general formalism of information processing that is universally applicable to the processing resulting from each physical interaction. As shown, the formalism further enables a general mechanism to construct arbitrary structured and abstract semantics or object description in modular hierarchy as well as a powerful mechanism of population coding to represent arbitrary precision and variation in object description resolving the combinatorial problem. Here, a semantic value, or simply semantics, is equivalent (\(\equiv\)) to the content of information of causal correlation, and treated as a value to enable its formal processing. The primary motive here is to lay down a formal account of information (semantic) processing that leads to bridging the conceptual gap between the objectively observable elements in nature and the subjective consciousness. It is shown that the qualities we associate with consciousness are causally correlated semantics of relation that a represented agency holds with other objects within a dynamically evolving semantic structure, where the state of the population of physical systems (neurons) correlating with the structure holds causal powers to effect appropriate behavior. Since the information (semantic value) arises from natural causal dependence, the correlation based consciousness forms an undeniable reality of existence. It is derived here how a semantic value equivalent to ‘a self as an observer of objects and controller of actions’ is constructed. If the semantic components of a conscious experience, such as the self, the objects of experience, and the relation of experience attributing the self as the owner or experiencer, causally correlate with a system’s state having causal influence in action, then it suffices to bridge the gap between the objective reality and the subjective consciousness. That is, the semantic value corresponding to the thoughts and senses is the reality of nature the semantics of self relates to as the owner. Moreover, the semantics of ‘self as an observer and controller of action’ is itself shown to form a part of observed objects giving rise to self awareness.

Поэтика вещи в героическом эпосе «Джангар»

Introduction. The article is devoted to the consideration of the poetics of things in the epic tradition of “Dzhangar” of the Kalmyks of Russia and the Oirats of Xinjiang. The image of the world of things that surround the heroes of the epic serves not only as a material background, they (objects, things) are necessary for the actions of the character and the expression of his individuality. The research material is the texts of the epic songs of the Xinjiang-Oirat and Kalmyk versions of “Dzhangar”. The purpose of the article is to consider the objective-material world in the heroic epic “Dzhangar”. Results. Consideration of the objective-material world in the Kalmyk and Xinjiang-Oirat versions of “Dzhangar” showed that in the lyrics a thing is one of the elements characterizing the image of a hero; in the depiction of an epic picture of the world, its heroes, characters, a thing acquires a symbolic meaning. The name of the epic country Bumba is associatively connected with a functioning object — a “treasure vessel”, the semantic field of which is its connecting link. The palace preserves the experience of the nomadic life of the Oirats and Kalmyks, their material way of life, as an objective reality of the epic space. The epic palace of Dzhangar, being a kind of field of attraction, gathers the heroic family at the banquet table, where the ruler of Bumba chooses the hero to go on a military campaign. The items of horse equipment and weapons of the hero in the epic “Dzhangar” are correlated with the objective realities with the help of which the hero moves in the epic space, undergoes difficult trials and achieves the goal of the military campaign. Things that are correlated with objects of material culture perform their main functions and are associated with the peculiarities of the worldview of the traditional culture of the Mongolian peoples.

Поэтика вещи в калмыцких сказках о животных

The article examines the poetics of things in Kalmyk fairy tales about animals. The research material included both published and unpublished texts of Kalmyk fairy tales about animals in the original and translated into Russian, as well as legends and traditions. Despite the fact that when studying Kalmyk fairy tales about animals related to the genre, plot, composition, system of characters, and linguistic means, researchers touched upon such material culture to one degree or another, the objective world of such fairy tales did not become the object and subject of a separate study. Results. In representative Kalmyk tales about animals, object realities related to different spheres of material culture were found: types of housing and its decoration, non-residential buildings, tools and materials, household utensils, weapons and hunting tools, musical instruments, vehicles, etc. One of them is used by people and animals, others - only by people. Some objective realities are created by animals. In the plot collisions of fairy tales, things are used for one purpose or another by people and animals or only by people, while the same thing can have different functions, for example, attack and defense. In the only case, the thing is endowed with the magical ability of the donor. Some things reveal only the objective background against which certain events occur. Subject realities have both a universal and local, ethnic character.

Real time object detection with data variation

Real time object detection with data variation

Early Childhood Care and Education: A Systematic Literature Review in Indian Context

Early Childhood Care and Education (ECCE) is often regarded as A key element of universal education. This study delves into the landscape of Early Childhood Care and Education (ECCE) in India through a systematic review of literature. After extensive attempts, several nations throughout the world are making strides toward securing ECCE. Despite this, the majority of them were unable to reach or compromise on this aim owing to insufficient resources, bad planning, and weak strategies for implementation, particularly in a large and populated nation such as India. The development of quality standards and curriculum frameworks, plans, acts, constitutional amendments, schemes, and flagship programmes like, District Primary Education Programme (DPEP), Integrated Child Development Services (ICDS) and Sarva Shiksha Abhiyan are just a few of the initiatives the Indian government has launched to improve quality. It has resulted in both anticipated and unexpected concerns and obstacles due to India's compromise on the non- negotiable ECCE quality requirements. Consequently, the government's objective of guaranteeing quality in ECCE was not fully achieved. However, recent research indicates significant advancements in quality improvements. Of course, there are unsatisfactory results in certain areas and components, but there has also been progress in other areas, suggesting that maintaining quality is a realistic objective. This research aims to present an overview of the Government of India's operations, assess the situation on the ground, pinpoint the primary hurdles to ECCE quality development and offer workable solutions to overcome those hurdles.

The value attitude of a person to their psychological health: approaches to the definition

The article is devoted to identification of the main characteristics that constitute the concept “value attitude of a person to their psychological health” that was conducted on the basis of the analysis of domestic and foreign literature. Firstly, the characteristic “health” was highlighted which is a state of the body characterised by internal comfort providing various functions of vital activity, creating mental, social and physical well-being that forms the basis of life quality. Secondly, the authors defined the characteristic “psychological health” as a psychological state of internal well-being of a person which depends on the individual-personal characteristics of a human, on their views and attitudes, on the degree of satisfaction of their needs and implementation of their own life plans. It is also seen as the ability of a person to withstand various stresses, changes in life and the ability to recover after that. Thirdly, the characteristic “value” was outlined. It is ideological orientation of the individual which is formed by their ideas of ideals, moral standards of behaviour in everyday life and which determines the personal meaning. Fourthly, the characteristic “attitude” was also studied. It is a conscious, selective, experience-based psychological connection of a person with reality which is expressed in actions, reactions and experiences and is formed in the process of activity. The final characteristic of the considered concept was the “value attitude”. It is defined as a one-sided relationship between a subject and an object when the subject assess the objects of reality according to their certain moral criteria. Thus, on the basis of the analysis of domestic and foreign psychological and pedagogical approaches, the authors have formulated the definition of the concept “value attitude of a person to their psychological health”.

Can Generative AI improve social science?

Generative AI that can produce realistic text, images, and other human-like outputs is currently transforming many different industries. Yet it is not yet known how such tools might influence social science research. I argue Generative AI has the potential to improve survey research, online experiments, automated content analyses, agent-based models, and other techniques commonly used to study human behavior. In the second section of this article, I discuss the many limitations of Generative AI. I examine how bias in the data used to train these tools can negatively impact social science research—as well as a range of other challenges related to ethics, replication, environmental impact, and the proliferation of low-quality research. I conclude by arguing that social scientists can address many of these limitations by creating open-source infrastructure for research on human behavior. Such infrastructure is not only necessary to ensure broad access to high-quality research tools, I argue, but also because the progress of AI will require deeper understanding of the social forces that guide human behavior.

STABLE DIFFUSION TEXT TO IMAGE USING AI

The Stable Diffusion Text-to-Image Generation Project is an innovative endeavor in the field of generative adversarial networks (GANs) and natural language processing (NLP). This project aims to bridge the semantic gap between textual descriptions and visual content by utilizing the Stable Diffusion training framework to generate highly realistic and coherent images from text prompts. The project leverages recent advancements in deep learning techniques to tackle the challenging task of text-to image synthesis. The project introduces an innovative approach at the crossroads of generative adversarial networks (GANs) and natural language processing (NLP). It aims to bridge the semantic gap between textual descriptions and visual content by utilizing the Stable Diffusion training framework. The key goal is to generate highly realistic and coherent images from text prompts, leveraging recent deep learning Stable Diffusion. The Stable Diffusion training framework plays a central role in this project. It’s a sophisticated training methodology for GANs, designed to stabilize the training process. GANs have exhibited great potential in generating images but often suffer from issues like mode collapse and training instability. Keywords: Stable Diffusion, Text-to-Image Generation, Image Synthesis, Natural Language Processing(NLP), Generative Adversarial Networks (GANs)

Arm Robot using Machine Learning

As human civilization is advancing the usage of robots becoming very common. Robots plays a important role in industrialization and human advancement. Arm robot are one of the most popular robots which are used in industries. These robots are most efficient for manufacturing process. In our robot we have integrated with Arduino micro controller and a esp32 cam module which results in less capital investment for the robot. The esp32 cam enables the robot with real time object detection and the Arduino micro controller is used for robot moment manipulation. The body the robot is made using 3D printed parts. Key Words: Arm robot, Arduino micro controller, ESP32Cam module, Object detection, 3D printing.

Constructing immune and prognostic features associated with ADCP in hepatocellular carcinoma and pan-cancer based on scRNA-seq and bulk RNA-seq.

Despite the significant therapeutic outcomes achieved in systemic treatments for liver hepatocellular carcinoma (LIHC), it is an objective reality that only a low proportion of patients exhibit an improved objective response rate (ORR) to current immunotherapies. Antibody-dependent cellular phagocytosis (ADCP) immunotherapy is considered the new engine for precision immunotherapy. Based on this, we aim to develop an ADCP-based LIHC risk stratification system and screen for relevant targets. Utilizing a combination of single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data, we screened for ADCP modulating factors in LIHC and identified differentially expressed genes along with their involved functional pathways. A risk scoring model was established by identifying ADCP-related genes with prognostic value through LASSO Cox regression analysis. The risk scoring model was then subjected to evaluations of immune infiltration and immunotherapy relevance, with pan-cancer analysis and in vitro experimental studies conducted on key targets. Building on the research by Kamber RA etal., we identified GYPA, CLDN18, and IRX5 as potential key target genes regulating ADCP in LIHC. These genes demonstrated significant correlations with immune infiltration cells, such as M1-type macrophages, and the effectiveness of immunotherapy in LIHC, as well as a close association with clinical pathological staging and patient prognosis. Pan-cancer analysis revealed that CLDN18 was prognostically and immunologically relevant across multiple types of cancer. Validation through tissue and cell samples confirmed that GYPA and CLDN18 were upregulated in liver cancer tissues and cells. Furthermore, in vitro knockdown of CLDN18 inhibited the malignancy capabilities of liver cancer cells. We have identified an ADCP signature in LIHC comprising three genes. Analysis based on a risk scoring model derived from these three genes, coupled with subsequent experimental validation, confirmed the pivotal role of M1-type macrophages in ADCP within LIHC, establishing CLDN18 as a critical ADCP regulatory target in LIHC.