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AUGMENTED REALITY ENABLED KNOWLEDGE MESH

UPB Scientific Bulletin, Series D: Mechanical engineering 2025年第1期87卷 193-204页

作者： Kleiman, Igor Ionescu, Nicolae Blăjină, Ovidiu Doctoral School of Industrial Engineering and Robotics POLITEHNICA Bucharest Romania OKO Suisse Kleiman Switzerland Manufacturing Engineering Dept Doctoral School of Industrial Engineering and Robotics POLITEHNICA Bucharest Romania Manufacturing Engineering Dept. Faculty of Industrial Engineering and Robotics POLITEHNICA Bucharest Romania

In response to the evolving demands of knowledge management in complex environments, current systems face challenges in delivering real-time, context-aware knowledge. This work extends the principles of Data Mesh into the knowledge domain, introducing the concept of a Knowledge Mesh that fosters decentralization, knowledge democratization, and self-service infrastructure. The contribution centers on integrating Augmented Reality (AR) with Knowledge Product Contracts, utilizing Augmented Reality Markup Language as metadata to facilitate dynamic knowledge discovery and interaction. Leveraging GraphDB for ontology-driven management, the proposed framework supports AR-enhanced 3D knowledge visualizations while enriching ontologies with AR capabilities. These advancements significantly improve knowledge interoperability across industrial processes, enabling real-time knowledge integration and visualization. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Mesh generation

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METHODOLOGY OF EVALUATING FINISHED GOODS WAREHOUSE PERFORMANCE THROUGH LEAN METHODS

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Archives of Transport 2024年第2期70卷 43-64页

作者： Michlowicz, Edward AGH University of Science and Technology Faculty of Mechanical Engineering and Robotics Krakow Poland

This paper considers the problem of evaluating the effectiveness of the finished goods warehouse of a manufacturing company, in which a modified TPM method - Total Productive Management (TPM2) - was applied to improve productivity. A multi-stage methodology was proposed, including a decision to modify the system, determination of the scope of changes, monitoring the results obtained and a multi-criteria evaluation of the changes made. The decision to make modifications to the existing system was motivated by the lower-than-expected quality of customer service (frequent delivery delays). With regard to the transport department, lean flow pillar activities were focused on analysing losses (muda) in warehouse processes (product loading and package unloading). The purpose of these activities was to minimise interruptions in warehouse processes (product loading and package unloading). "The steps for solving the problem" methodology based on Deming's PDCA cycle was used to solve the problem. The analysis covered, among other things, the information flow processes between production planning and the customer service department, the planning processes of the dispatcher, the efficiency of the loading processes, and the causes of interruptions in warehouse operations. The analyses employed the chronometry of selected works, the 5W + 1H method and the Pareto method. By using the 5S method and some characteristics of the SMED method, the organisation of loading work was decisively changed (shunting yard changes, appropriate buffers for transport equipment). The changes introduced in the system were monitored for several months. Appropriately defined OEE indicators were used to assess the behaviour of the system after the changes. The indicators consider the use of available warehouse time, the efficiency of the loading process and the quality of the tasks performed. The results that can be achieved are presented using the specific example of the finished goods warehouse of a ma

关键词： Warehouses

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Effects of Viscous Dissipation and Periodic Heat Flux on MHD Free Convection Channel Flow with Heat Generation

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Frontiers in Heat and Mass Transfer 2024年第1期22卷 141-156页

作者： Mustafa Abdullah Robotics and Artificial Intelligence Engineering Department Faculty of EngineeringAl-Ahliyya Amman UniversityAmman19328Jordan

This study investigates the influence of periodic heat flux and viscous dissipation on magnetohydrodynamic(MHD)flow through a vertical channel with heat generation.A theoretical approach is *** channel is exposed to a perpendicular magnetic field,while one side experiences a periodic heat flow,and the other side undergoes a periodic temperature *** solutions for the governing partial differential equations are obtained using a finite difference approach,complemented by an eigenfunction expansion method for analytical *** and discussions illustrate how different variables affect the flow velocity and temperature *** offers comprehensive insights into MHD flow behavior and its interactions with the magnetic field,heat flux,viscous dissipation,and heat *** findings hold significance for engineering applications concerning fluid dynamics and heat transfer,offering valuable knowledge in this *** study concludes that the transient velocity and temperature profiles exhibit periodic patterns under periodic heat flow conditions.A temperature reduction is observed with an increase in the wall temperature phase *** contrast,an increase in the heat flux phase angle values raises the temperature values.

关键词： Magnetohydrodynamics(MHD) heat generation viscous dissipation periodic heat flux vertical channel

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How Sure is the Driver? Modelling Drivers’ Confidence in Left-Turn Gap Acceptance Decisions

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Computational Brain and Behavior 2024年第3期7卷 437-456页

作者： Bontje, Floor Zgonnikov, Arkady Department of Cognitive Robotics Faculty of Mechanical Engineering Delft University of Technology

When a person makes a decision, it is automatically accompanied by a subjective probability judgment of the decision being correct, in other words, a confidence judgment. A better understanding of the mechanisms responsible for these confidence judgments could provide novel insights into human behavior. However, so far confidence judgments have been mostly studied in simplistic laboratory tasks while little is known about confidence in naturalistic dynamic tasks such as driving. In this study, we made a first attempt of connecting fundamental research on confidence with naturalistic driver behavior. We investigated the confidence of drivers in left-turn gap acceptance decisions in a driver simulator experiment (N = 17). We found that confidence in these decisions depends on the size of the gap to the oncoming vehicle. Specifically, confidence increased with the gap size for trials in which the gap was accepted, and decreased with the gap size for rejected gaps. Similarly to more basic tasks, confidence was negatively related to the response times and correlated with action dynamics during decision execution. Finally, we found that confidence judgments can be captured with an extended dynamic drift–diffusion model. In the model, the drift rate of the evidence accumulator as well as the decision boundaries are functions of the gap size. Furthermore, we demonstrated that allowing for post-decision evidence accumulation in the model increases its ability to describe confidence judgments in rejected gap decisions. Overall, our study confirmed that principles known from fundamental confidence research extend to confidence judgments in dynamic decisions during a naturalistic task. © The Author(s) 2024.

关键词： Confidence Decision making Driver behavior Evidence accumulation Metacognition

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Active Robot Vision for Distant Object Change Detection: A Lightweight Training Simulator Inspired by Multi-Armed Bandits

Active Robot Vision for Distant Object Change Detection: A L...

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Kouki, Terashima Kanji, Tanaka Ryogo, Yamamoto Liang Jonathan, Tay Yu University of Fukui Robotics Course Faculty of Engineering Japan

ISBN: (纸本)9798350312072

In ground-view object change detection, the recently emerging mapless navigation has great potential to navigate a robot to objects distantly detected (e.g., books, cups, clothes) and acquire high-resolution object images, to identify their change states (no-change/appear/disappear). However, naively performing full journeys for every distant object requires huge sense/plan/action costs, proportional to the number of objects and the robot-to-object distance. To address this issue, we explore a new map-based active vision problem in this work: 'Which journey should the robot select next?' However, the feasibility of the active vision framework remains unclear;Since distant objects are only uncertainly recognized, it is unclear whether they can provide sufficient cues for action planning. This work presents an efficient simulator for feasibility testing, to accelerate the early-stage R&D cycles (e.g., prototyping, training, testing, and evaluation). The proposed simulator is designed to identify the degree of difficulty that a robot vision system (sensors/recognizers/planners/actuators) would face when applied to a given environment (workspace/objects). Notably, it requires only one real-world journey experience per distant object to function, making it suitable for an efficient R&D cycle. Another contribution of this work is to present a new lightweight planner inspired by the traditional multi-armed bandit problem. Specifically, we build a lightweight map-based planner on top of the mapless planner, which constitutes a hierarchical action planner. We verified the effectiveness of the proposed framework using a semantically non-trivial scenario 'sofa as bookshelf'. © 2024 IEEE.

关键词： Object detection

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Neural algorithm for optimization of multidimensional object controller parameters

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Neural Computing and Applications 2024年第25期36卷 15907-15924页

作者： Balazy, Patryk Lalik, Krzysztof Knap, Pawel Faculty of Mechanical Engineering and Robotics AGH University of Krakow Mickiewicza 30 Malopolska Kraków30-059 Poland

Optimal control of multivariable systems is a complex dynamic process that minimizes the cost function to obtain the optimal control strategy. Unfortunately, for nonlinear systems, it is not possible to use the traditional linear quadratic regulator (LQR), which would be optimal over the entire range of parameter variation. The problem of nonlinear multivariable systems and their optimal control is very momentous. The solution presented in this paper is based on the application of Reinforcement Learning (RL) networks in controlling a five-degree-of-freedom overhead crane system. Additionally, unlike the classical approach, the algorithm is adapted to directly analyze tabular data of inputs and outputs of the controlled model instead of analyzing its state as feedback (model-free). Implementing the new control structure for the multivariable system improved control quality compared to the classical LQR controller with linearization at the operating point. In addition to quality, the resource indicators, which in the LQR controller are represented by the matrix R, have been significantly improved. The architecture of the neural control system is presented, ensuring that over the entire range of nonlinearity, the quality of control is preserved while reducing the cost of its resource intensity. Obtaining optimal control with reduced resources for its implementation induces a wide range of applications of such neural control in engineering systems. The effectiveness of the proposed control system has been demonstrated in simulation studies. The simulation results present the system’s excellent control performance and adaptability over the entire range of object nonlinearity. The neural algorithm resulted in significantly shorter adjustment time and better control quality with significantly less system resource consumption and increased system dynamics. © The Author(s) 2024.

关键词： Reinforcement learning

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VISION-GUIDED DRY LEAF COLLECTING ROBOT

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International Journal of Electrical engineering 2023年第1期30卷 9-17页

作者： Sawangbunrod, Burid Chonwitthayasittikul, Thaksin Ruangpayoongsak, Niramon Producion and Robotics Engineering Department Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand

Dry leaves fallen on ground in yards need to be cleaned up. Leaf collecting is time consuming and is repetitive task, therefore the task can be done by a robot. This paper presents hardware design and software implementation of a dry leaf collecting autonomous robot to replace human worker. The robot is a low-cost robot guided by vision using a web camera. The robot is controlled by algorithm developed on ROS running on Jetson Nano and Arduino Mega. The leaves are detected and identified by using YOLO. Real time leaves detection by using YOLOv5 and YOLOv8 are compared. The experimental results show that the YOLOv8 has impressive higher performance in leaf identification than YOLOv5 and the robot can successfully collect dry leaves on ground. © 2023, Chinese Institute of Electrical engineering. All rights reserved.

关键词： Robot vision

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A Comparison of CNN-based 2D Static Facial Emotion Recognition Techniques

A Comparison of CNN-based 2D Static Facial Emotion Recogniti...

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2024 International Conference on Intelligent Systems and robotics, CISR 2024

作者： Dong, Zhixin Wu, Chengdong Zhang, Xiangyue Yu, Xiaosheng Li, Junlin Faculty of Robotics Science and Engineering Northeastern University Shenyang110167 China

In the field of computer vision, recognizing expressions in 2D static facial images is a crucial aspect of facial emotion recognition (FER), with broad applications in real-world scenarios such as mental health diagnosis and security monitoring. Despite the various convolutional neural network (CNN) architectures proposed for FER, there is still a lack of systematic comparative studies on the classification capabilities of different CNN architectures. In this paper, we systematically compare the classification performance of four commonly used CNN architectures in FER research for 2D static facial expression recognition on the classic FER2013 dataset. Through experiments, we evaluate the classification accuracy of these architectures and analyze the recognition results for different emotion categories. © Published under licence by IOP Publishing Ltd.

关键词： Convolutional neural networks

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Prediction of directivity characteristics of piezoelectric macro-fiber composite interdigital transducers: a semi-analytical approach 11

Prediction of directivity characteristics of piezoelectric m...

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11th European Workshop on Structural Health Monitoring, EWSHM 2024

作者： Raorane, Siddhesh Ambrozinski, Lukasz Stepinski, Tadeusz Packo, Pawel Department of Robotics and Mechatronics Faculty of Mechanical Engineering and Robotics AGH University of Science and Technology Krakow30-059 Poland

The employment of guided waves for structural health monitoring applications has attracted substantial attention in recent years. Piezoelectric transducers are frequently employed in these systems for elastic wave generation and acquisition. Their dynamic properties, i.e., direction-dependent frequency characteristics such as directivity patterns, are an important feature for designing a reliable monitoring strategy. Although analysis of the transducer directivity pattern can be performed using finite element models, these usually tend to be computationally very expensive, especially for parametric and optimization studies. In this paper, a semi-analytical framework is proposed that can predict the far-field directivity patterns of complex transducers of arbitrary shape and structure mounted on a substrate. The framework described in this paper integrates analytical far-field predictions with a local finite element model evaluating the traction field produced by the transducer. Consequently, the FE model accurately captures near-field responses, whereas the analytical component predicts the far-field responses. The implementation of this semi-analytical framework requires the development of analytical and numerical approaches. The former includes computation of dispersion curves of the inspected plate, the stress and displacement profiles of wave modes, and ultimately, the excitability relations, while the latter includes estimation of the traction field between the transducer and the plate using the FE model. Owing to the popularity of piezoelectric macro-fiber composite (MFC) transducers, in this paper, we apply the proposed method to predict the directivity patterns of MFC M2807-P2 transducer. To validate the reliability of the proposed method, the predicted patterns are compared with experimental results obtained using Laser Doppler Vibrometer. © 2024 11th European Workshop on Structural Health Monitoring, EWSHM 2024. All rights reserved.

关键词： Piezoelectric transducers

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Skeleton-Based Action Analysis for Improving Jump Height in Volleyball 6

Skeleton-Based Action Analysis for Improving Jump Height in ...

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6th International Symposium on Advanced Technologies and Applications in the Internet of Things, ATAIT 2024

作者： Yoshioka, Shuma Kong, Xiangbo Department of Intelligent Robotics Faculty of Information Engineering Toyama Prefectural University Japan

This study aims to improve the jumping height for spiking in volleyball through skeleton-based action analysis using the Azure Kinect DK. Building on previous studies, this work focuses on the stride length, arm swing height, and spine angle during the jumping motion. Experiments are conducted to compare the effects of relatively better and relatively worse executions of these movements on jumping height. The results indicate that a relatively better stride length resulted in a significant increase of 187 mm in jumping height, while a relatively better arm swing height led to a 34 mm increase. However, variations in spine angle do not produce a significant difference in jumping height. These results suggest that optimizing stride length and arm swing height can effectively enhance the jumping performance of volleyball players. © 2024 Copyright for this paper by its authors.

关键词： Musculoskeletal system

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