Journal of Automation, Mobile Robotics and Intelligent Systems
Journal of Automation, Mobile Robotics and Intelligent Systems (JAMRIS) is an international scientific quarterly published in Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP located in Warsaw, Poland. JAMRIS publishes since 2007 high-quality technical, position and state-of-the-art papers in the fields of theory, design and applications of broadly perceived intelligent technologies, notably based on artificial intelligence and computational intelligence, particularly for analysing and solving scientific and engineering problems and systems. The articles are published in English, and they are peer-reviewed and open-access. ISSN: 1897-8649 (Print) | ISSN: 2080-2145 (Online) | DOI: 10.14313/JAMRIS/
Łukasiewicz – Industrial Research Institute for Automation and Measurements PIAP
Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
Evaluating Dijkstra and A* Pathfinding Algorithms for Mobile Robots in Warehouse Environments Using CoppeliaSim
In modern warehouse automation, mobile robots are essential for enhancing operational efficiency by autonomously navigating to pick and transport items. Effective path planning is crucial for these robots to move through complex environments, avoid obstacles, and minimize travel time. This study evaluates two prominent path planning algorithms Dijkstra and A*—implemented on a mobile robot within a simulated 3D warehouse environment using CoppeliaSim. Three distinct rack locations were analyzed to assess the performance of both algorithms concerning path optimality, computational efficiency, and real-time applicability. Simulation results indicate that while both algorithms successfully generated safe and accurate paths, A* outperformed Dijkstra in terms of speed and path efficiency. A*'s heuristic-driven approach resulted in lower computational load and faster execution time, making it more suitable for real-time warehouse operations where responsiveness is critical. The insights gained provide valuable guidance for robotics engineers and developers in selecting appropriate path planning strategies for autonomous navigation in industrial settings.
Copyright (c) 2026 Prabin Kumar Jha, Shambo Roy Chowdhury https://creativecommons.org/licenses/by-nc-nd/4.0
Towards Explainable Graph Spectral Clustering for BERT Embeddings
Artificial Intelligence algorithms are increasingly applied to tasks in Natural Language Processing, including document clustering. As these algorithms become increasingly complex (such as transformer-based embeddings, like BERT) and/or are of a ``black-box'' nature, such as Graph Spectral Clustering (GSC) algorithms, the demand for explaining the results of such algorithms is becoming increasingly urgent. In this paper, we propose a model-aware method to explain the results of GSC in the context of BERT-based embeddings. We present a novel theoretical methodology for explanation, based on the premise that document similarity in GSC is computed as cosine similarity of BERT embeddings of documents. We demonstrate the validity of this methodology by presenting strong GSC clustering results, restoring the human-made assignment of hashtags to tweets. We show that GSC based on BERT embeddings outperforms approaches using Term Vector Space and GloVe embeddings. Therefore, the resulting explanations are also expected to be of higher quality.
Copyright (c) 2026 Mieczysław Kłopotek, Sławomir T. Wierzchoń, Bartłomiej Starosta, Piotr Borkowski, Dariusz Czerski https://creativecommons.org/licenses/by-nc-nd/4.0
Energy-Aware Cluster-Based Routing with Federated Learning Integration for Scalable IoT Environments
As a result of the Internet of Things' (IoT) explosive growth secure routing, energy optimization, and privacy preservation in resource-constrained environments have become major challenges. High overhead, static decision-making and susceptibility to malevolent attacks are common problems with traditional routing protocols. Federated Learning-Assisted Encrypted Routing based on Cost Function (FL-ERCF), an improved routing protocol that combines encrypted transmission with intelligent, privacy-preserving cluster head selection, is proposed in this paper to address these issues. Three main steps make up the proposed protocol: (i) link quality evaluation using metrics like RSSI, SNR, and variance; (ii) trust-based cluster formation directed by a federated learning model trained across dispersed IoT nodes to dynamically choose the best CHs; and (iii) secure data transmission using a lightweight symmetric encryption mechanism enhanced with digital certificates. Federated learning protects node-level privacy while improving flexibility and resilience to changing network conditions. The NetSim simulator is used to implement and assess the suggested FL-ERCF protocol, and it is compared to more established protocols like Energy Efficient Secure Routing (EESR) and Hybrid Secure Routing (HSR). Even in the face of an adversarial attack, experimental results show increased packet delivery ratios, decreased routing overhead, and enhanced throughput. A scalable and secure IoT routing framework that is appropriate for mission-critical applications, smart cities, and healthcare is established by this work.
Copyright (c) 2026 Ankur Sisodia, Swati Vishnoi, Shivshanker Singh, Nandini Sharma, Ajay Kumar Yadav https://creativecommons.org/licenses/by-nc-nd/4.0
Semantic-Aware Trajectory Planning for UAV in Dynamic Environments
Generating trajectories that leverage semantic information to guide a UAV safely and accurately to its destination in a dynamic environment remains an open problem. In the existing literature, semantics have been used to prioritize certain areas - either to guide the UAV through or to avoid them - for specific objectives, such as reducing errors in visual-inertial SLAM (VI-SLAM). However, prior work typically assumes a static environment when performing collision checking, even in cluttered and dynamic settings. We propose a two-stage workflow: The first stage performs semantic-aware pathfinding. The second stage optimizes the resulting path, incorporating kinematic constraints and performing collision checking that accounts for obstacle motion, while still ensuring real-time performance. To the best of our knowledge, this is the first approach that generates UAV trajectories by simultaneously leveraging semantic information and accounting for cluttered, dynamic environments. A summary video is available at https://youtu.be/I5w6AP7HThU
Copyright (c) 2026 Van Hung Nguyen, The Tien Nguyen, Tran Thang Le, Viet Hong Le https://creativecommons.org/licenses/by-nc-nd/4.0
Finding the Sweet Spot: A Study of Data Augmentation Intensity for Small-Scale Image Classification
Finding the optimal level of data augmentation intensity remains one of the most challenging aspects of training deep learning models on small-scale datasets. While data augmentation is universally recognized as essential for preventing overfitting and improving generalization, excessive augmentation can paradoxically harm model performance by introducing too much variability in the training data. This research investigates the "sweet spot" of augmentation intensity through a comprehensive study of six distinct augmentation strategies on CIFAR-10, a representative small-scale image classification benchmark. We designed a controlled experiment comparing: No Augmentation (baseline), Basic torchvision transforms, Light Advanced albumentations, Moderate Advanced geometric-photometric combinations, Strong Advanced with noise injection and dropout, and AutoAugment Style with complex transformations. Our findings reveal a clear quadratic relationship between augmentation intensity and model performance (R² = 0.85), with peak performance achieved at moderate intensity levels (0.21 on our normalized scale). The Basic augmentation strategy wins with 79.04% validation accuracy, significantly outperforming both minimal augmentation (77.15%) and excessive augmentation (72.17%). Through statistical analysis including effect size calculations, confidence intervals, and correlation studies, we demonstrate that the "sweet spot" lies in balanced augmentation that provides regularization benefits without overwhelming the learning process. These findings offer evidence-based guidelines for practitioners working with small-scale image datasets.
Design of a Fuzzy PID Controller to Improve Electric Vehicles Performance Based on Regenerative Braking System
This study aims to improve energy storage in electric vehicle applications using a regenerative braking system. A brushless DC motor (BLDC) was chosen to power the vehicle due to its inherent advantages and suitability for electric vehicles. These vehicles require a low rate of error and stable and transient responses to make wheel acceleration smoother. The main objective of this study is to improve response time and stability under different operating conditions and overcome the drawbacks of traditional control techniques. Traditional PID controllers suffer from several problems, including transient overshoots, load fluctuations, and non-linear response, which lead to poor performance in electric vehicle propulsion systems. In this study, a fuzzy PID controller and a hysteresis current control loop are designed for the BLDC motor to address the above issues. The motor speed and battery charge state are verified using the MATLAB/Simulink environment in different situations to measure the reliability of the proposed controller. The results show that this proposed controller improves the dynamic performance of the regenerative braking system and enhances other operating characteristics such as maximum overshoot and settling time.
A Simple Approach to Designing and Implementing a Fault Detection Device for Large Quantities of LED Boards
Light-emitting diodes (LEDs) play a crucial role in various practical applications. This paper presents a simple and cost-effective approach for detecting faults in LED boards with a large number. The types of faults detected include open circuits and short circuits. The proposed solution is based on a voltage threshold and a statistical algorithm to filter out noise. The paper first outlines the design concept, followed by the development and fabrication of a fault classification module. Subsequently, a control center is established to collect signals from all fault classification modules connected to LED boards in pairs. The received signals are processed and stored on a memory card. Finally, the resulting data can be accessed either from the memory card or directly via an external computer through a USB port. The experimental results demonstrate that the proposed design and fabricated device can accurately detect and classify faults on the tested boards. The data obtained can be used for statistical analysis and root cause investigation to enhance product quality.
Copyright (c) 2026 Van Anh Pham, Minh Tien Do https://creativecommons.org/licenses/by-nc-nd/4.0
A Custom Robotic Vehicle Prototype for Disinfecting Workplaces
The SARS‐CoV‐2 pandemic has heightened the need for advanced and automated disinfection methods to ensure workplace safety and hygiene. This study presents the de‐ sign and implementation of a robotic vehicle capable of autonomously disinfecting high‐risk areas in diverse work environments based on human activity levels. The sys‐ tem integrates a machine vision module using YOLOv5 for real‐time human detection, a Decision Support System to assess contamination risks, an autonomous navigation module for path planning, and a user‐friendly interface for operator control. By leveraging real‐time data, the robot precisely applies disinfectant to identified high‐risk zones, dynamically adjusting the spray volume based on the level of contamination. The system was validated in a real‐world workplace setting, demonstrating its ability to autonomously perform targeted disinfection, offering a scalable solution to support workplace hygiene.
Integration of Trailer and Tractor Mobile Robot Considering Towing Dynamics
The industrial robots are used in different areas, one of these areas is towing trailer’s. This research proposes the use of an omniwheel mobile robot, designed specif‐ ically for towing purposes, to enhance the towing ca‐ pabilities. The study focuses on analyzing, controlling, and testing the dynamics of a trailer under the influence of external towing forces applied by an omniwheel mo‐ bile robot. The study includes dynamical analysis, path planning, tracking control, prototype design, tuning of selected actuators, and simulation results of the pro‐ posed omniwheel tractor robot. The dynamic equations, the path planning algorithm, the path tracking methods, and the PID tuning of actuators are tested in a MATLAB Simulink Simscape environment using the designed 3D models. The simulation results are used to fine‐tune the system parameters and minimize errors. Improved tuning of these parameters leads to better simulation outcomes. The final simulation results show that positional accuracy reaches 4.0 cm RMS precision (peak deviations: 7.6 cm) over a 6.63‐meter course, and speed deviations remain below %4.3. Angular control maintains stability by cap‐ ping errors at 0.117 rad (average: 0.008 rad) and limiting transient overshoot to %17.17 for aggressive maneuvers.
Copyright (c) 2026 Ünal Dana, Levent Çetin https://creativecommons.org/licenses/by-nc-nd/4.0
A Kinematic Prototype of a Compliant Artificial Prosthetic Knee Joint
This study presents the development and kinematic evaluation of a compliant artificial knee joint prototype fabricated using multi-material 3D printing. The design integrates a rolling-contact compliant mechanism—derived from the CORE and D-CORE concepts—into a prosthetic knee construction, aiming to replicate biological joint behavior while reducing weight, mechanical complexity, and friction. The prototype underwent iterative refinement, including geometric modifications, material selection for flexure bands, and structural asymmetry to mitigate overextension and increase durability. The resulting joint exhibited hybrid kinematic characteristics, blending features of both single-axis and polycentric knee mechanisms, and achieved a functional flexion range of approximately 142 degrees. Initial cyclic tests confirmed satisfactory stiffness and shape recovery of flexure bands with optimized dimensions, although long-term fatigue performance remains a challenge. A basic spring-damper system was also integrated, potentially aligning the prototype with K-1/K-2 prosthetic classification. However, full validation requires further mechanical testing in accordance with ISO 10328 standards, as well as optimization of the damping system for commercial viability. This research demonstrates the feasibility and potential of compliant mechanisms in lower-limb prosthetics while identifying critical areas for future development.
Innovation Capacities and Business Model in Colombian Farming Organizations
The main objective of the paper is to analyze the interaction between innovation capabilities and elements of the business model of farming organizations in Boyacá, Colombia. The study used a quantitative methodology, employing the Partial Least Squares Path Modeling (PLS-PM) technique. The theoretical model includes seven hypotheses that outline the relationships between innovation capabilities (sensing, seizing, and transformation) and business model (creation, delivery, and capture), The study revealed a significant correlation between the ability to detect and retain innovation and the development and implementation of business models. This underscores the significance of continuous learning and efficient alliance management in acquiring and utilizing environmental knowledge. The implications of this study are directed towards decision-makers, stakeholders, and policy makers within Colombia's National Agricultural Innovation System (NAIS). The study emphasizes the crucial role of a well-integrated system among its various actors in influencing the success of business models. It examines innovation as a dynamic capability and its interplay with vital components of the business model, thereby enhancing comprehension of business dynamics within this sector.
A Flexible Mobile Manipulator Architecture: A Case Study on Plasterboard Wall Preparation
Skilled labor shortage and a competitive market are challenges for many businesses. Hence, automation and robotization are well-established in the large-scale, well-structured industrial environments of many factories. Moreover, there is a push for safer and more easily programmable robots, so-called cobots, that would be more suitable for smaller-scale applications. However, the implementation and customization of an overall robot system with different sensors, tools, external systems, etc. for the use cases of in particular small and medium-sized enterprises (SMEs) are still often infeasible. Therefore, this paper describes the design, implementation, and testing of a flexible mobile manipulator architecture with the following three abstraction layers. A high-level semantic layer models and utilizes OWL ontologies of abstract manufacturing processes, specific workcell environments, linked context knowledge, and the current semantic world state. An intermediate translation layer collects and exchanges data from multiple system components via a unified internal database, which is used, e.g., in combination with behavior trees to convert symbolic, high-level actions into multiple numeric, low-level commands, including their parameter values. A low-level control layer is implemented locally on a mobile robot to provide a unified interface of its potentially customized subsystems to the higher layers. This architectural approach facilitates the implementation of new use cases, e.g., by a system integrator, in that, e.g., the ontologies and behavior trees can be adapted on a flexible model level without changing source code. The implemented system was adapted to and deployed in five different real-world use cases of industrial partners during the VOJEXT project. This paper focuses on the plasterboard wall preparation use case from the construction domain as a guiding example, which includes taping, spraying, and sanding operations.
Copyright (c) 2026 Łukasz Granat, Michał Bryła, Kuba Kamiński, Filip Jędrzejczyk, Sławomir Puchalski, Ingmar Kessler, Alexander Perzylo, Ángel Soriano https://creativecommons.org/licenses/by-nc-nd/4.0
Self-Tuning Controller for Scalar Systems: a Preselected-Time Control Approach
A Single-Input Single-Output (SISO) linear system can represent various physical systems. One of the challenges in controlling practical systems is that the settling point and settling time cannot be prescribed together. In this paper, a novel Self-Tuning Control (STC) algorithm is presented, which ensures system stability while allowing the simultaneous determination of the settling time and settling point. Four possible scenarios will be defined to investigate the effectiveness of the proposed control method. Three different practices will be described in the simulation section for each scenario. Linear Quadratic Regulation (LQR) will be employed for comparison purposes. MATLAB software will be used to test and simulate these practices. The simulations will demonstrate that the STC is an optimal solution for SISO linear systems, comparable to LQR, with the significant advantage of guaranteeing the attainment of the settling point at the predefined settling time.
Comparison of Computer Vision and Convolutional Neural Networks for Vehicle Parking Control
This study compares two artificial intelligence approaches for parking occupancy detection: computer vision and convolutional neural networks (CNN). A dataset of 1,000 parking images was captured and labeled, using OpenCV in Python for computer vision processing and the YOLO V5 model for CNN. Results showed that the YOLO V5 model achieved 88% precision and 82% sensitivity, outperforming the computer vision method, which achieved 80% precision and 79% sensitivity. The research suggests that while CNNs offer superior performance, computer vision is a more economical option in contexts with limited resources. Future research will focus on evaluating the YOLOv7 version to reduce false positives and combining techniques to achieve a balance between accuracy and efficiency under variable conditions.
Grey Wolf Optimization Algorithm for a Concurrent Real-Time Optimization Problem in Game Theory
In this paper we present a grey wolf algorithm for a concurrent real time optimization problem in searching for optimal game solving solution. There are many solutions to solve the game. Each solution can demand different optimal values of different parameters. However some ways in which the players try to solve the game do not lead to success. The optimization problem consists of two phases. Each phase Impacts the second one in real time. The first phase is responsible for the optimization parameters choice. The second phase validates the choice and makes the optimization of the parameters. As a optimizing method we chose grey wolf optimization. At the beginning the algorithm generates some number of solutions. The solution which has the value of the parameters the closest to maximum is a position of an alfa wolf. The rest of solutions are, according to the values of the parameters, split to be the positions of beta, delta and omega wolfs.
Design, Implementation, and Performance Optimization of a ROS Based Autonomous Mobile Robot for Intralogistics in Manufacturing Facilities
This article presents the development and optimization of a ROS-based Autonomous Mobile Robot (AMR) designed for intralogistics in manufacturing environments. By integrating robust mechanical design, advanced sensor fusion, and the Hybrid A* path-planning algorithm, the AMR offers significant improvements in flexibility, safety, and efficiency over traditional AGVs. The proposed AMR system enables dynamic, autonomous navigation in complex industrial layouts, responding adaptively to obstacles and varying payloads. Validation in simulated and real-world settings demonstrates the system’s performance in navigation accuracy, energy efficiency, and load-handling capability, highlighting its potential to streamline logistics and reduce operational costs in Industry 4.0 settings.
Copyright (c) 2026 Neslihan Demir, Pinar Demircioglu, Ismail Bogrekci https://creativecommons.org/licenses/by-nc-nd/4.0
Research on a Robust Adaptive Controller with Disturbance Observer for Wheeled Mobile Robot
This paper presents a robust adaptive controller for wheeled mobile robots (WMRs) designed to effectively compensate for disturbances and system uncertainties. The proposed control scheme includes an inner loop with a PID controller for wheels speed control and an outer loop with a disturbance observer to track the trajectory and minimize position errors. The outer loop control signal is derived based on the WMR's dynamic and kinematic models, while the disturbance observer adapts to uncertainties and external disturbances. The system's stability is proven in sense of Lyapunov theory. The performance of the proposed controller is validated through simulations, which show significant improvements in trajectory tracking compared to existing method. Furthermore, experimental results confirm that the controller maintains stability and robustness under varying load conditions and different trajectory paths, demonstrating its effectiveness in real-world applications
Copyright (c) 2026 Trong Tai Nguyen, Doan Phuc An Nguyen, Dai Nghia Tran, Phuc Bao Nguyen Nguyen, Thanh Dat Mai https://creativecommons.org/licenses/by-nc-nd/4.0
Autonomous Goal Following for Quadruped Robot using Fuzzy Proportional Control
In this paper a fuzzy Proportional (PD) Controller was designed and implemented for dynamically adapting the velocity and motion parameters in a quadruped robot for autonomously following a goal. The FNK0050 Freenove quadruped robot was utilized for the experiments, which has 12 degrees of freedom and this is why it has higher complexity. Experimental results show that the proposed fuzzy PD controller surpasses the standard PID controller provided as default by the manufacturer of the robot.
Mutual Inductance Model of the Mechanical Stress Sensitivity of the Power Transformer Functional Parameters
The mutual inductance model enables testing the behavior of power transformers under different operation conditions, especially under the environmental influences on the transformer’s core. This paper presents the investigation results of the stress dependence of the magnetic relative permeability of the power transformer. It was observed that under tensile mechanical stresses up to 98 MPa applied to the core, the transformer input current amplitude increases by almost 67%, whereas the transformer's reactive power increases by 53%. In industrial systems, such changes can potentially lead to unwanted power system shutdowns due to overloading. This effect should be considered during the development of critical power systems.
Copyright (c) 2024 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
Patterns of Acoustic Emission Changes with Alterations in the Damage Area of a Composite Material According to the Mises Criterion
In this paper, the use of a highly sensitive acoustic emission method for studying the deformation and failure processes of composite materials is considered, which provides a substantial amount of information about phenomena occurring at the sub-micro, micro, and macro levels. However, the additional influence of various factors leads to the problem of interpreting and identifying the recorded information. Addressing this problem involves determining the influence of different factors on acoustic emission signal parameters and their sensitivity to the influencing factor. In this study, during the failure of a composite material under transverse force according to the Mises criterion, an analysis is conducted on the impact of changes in the number of composite material elements (damage area) on the amplitude-time parameters of the acoustic emission signal based on a developed signal model. The results of the simulation allowed for the identification and description of patterns in the changes of amplitude-time parameters of acoustic emission signals (maximum amplitude, area under the signal curve, and signal duration) with variations in the number of composite material elements. These patterns enable the determination of the sensitivity of acoustic emission signal parameters to the influencing factor. The findings of this study may be of interest in the development of methods for monitoring, diagnosing, and predicting the failure of composite materials and products through the registration and analysis of acoustic emission signals.
Application of AI Using UNet in Skin Lesion Segmentation
Skin lesion segmentation is a critical task in dermatology, essential for the accurate diagnosis and treatment of various skin conditions, including skin cancer. The precise identification of lesion boundaries in medical images significantly helps in early detection and effective management of these conditions. In this study, a U-Net model was employed to perform segmentation of skin lesions, using its advanced encoder-decoder architecture and skip connections to capture fine details and spatial hierarchies within the images. The model yielded an overall accuracy of 95.47%, a precision of 97.21%, a recall of 84.04%, and an F1 score of 90.15%. These results affirm the U-Net model's proficiency in accurately segmenting skin lesions. The precise boundary delineation provided by the model can help healthcare professionals detect malignant lesions, which often exhibit irregular boundaries, thereby improving diagnostic accuracy. The integration of this model into clinical practice can enhance diagnostic accuracy and efficiency, reduce the workload on healthcare professionals, and improve patient outcomes. The promising performance of the U-Net model emphasizes its potential to revolutionize dermatological diagnostics and support healthcare professionals in delivering timely and precise patient care.
Copyright (c) 2026 Serra Aksoy https://creativecommons.org/licenses/by-nc-nd/4.0
Formation Control of Multi-agent Nonlinear Systems using the State-Dependent Riccati Equation
This work investigates the formation control of a multi-agent robotic system via the state-dependent Riccati equation (SDRE). The system's agents interact with each other and follow a leader in point-to-point motion control (regulation). The number of agents is unlimited in conventional multi-agent formation control considering a complex dynamic for each agent, though the complexities of the algorithms usually result in small-scale simulations. Here a formalism is proposed that considers fully coupled nonlinear dynamics for robotic systems in multi-agent system formation control with a large number of agents. The interaction of the agents with each other and obstacle avoidance are embedded in the design through the weighting matrix of states in the SDRE. The input constraint also limits the actuators to create a more realistic scenario. Two dynamical systems have been modeled and simulated in this work: wheeled mobile robots (WMR) and multirotor unmanned aerial vehicles (UAVs). The simulation results show success in the implementation of a total of 1,089 agents in the desired square formation shape in the UAV case study, and a figure of 45 agents and 1,050 differential wheeled mobile robots in the circular desired shape, considering obstacle avoidance and also collision avoidance between the agents.
Positivity and Stability of Descriptor Discrete-Time Linear Systems with Interval State Matrices
The positivity and stability of descriptor discrete-time linear systems with interval state matrices are addressed. Necessary and sufficient conditions for the positivity of descriptor discrete-time linear systems are established. The stability of descriptor linear systems with interval state matrices is investigated.
Monocular 3D Object Localization using 2D Estimates for Industrial Robot Vision System
3D Object Localization has been emerging as one of the main challenges in Machine Vision tasks. In this paper, we proposed a novel 3D object localization method, leveraging a blend of deep learning techniques primarily rooted in object detection, post-image processing, and pose estimation algorithms. Our approach involves 3D calibration methods tailored for low-cost industrial robotics systems, requiring only a single 2D image input. Initially, object detection is performed using the You Only Look Once (YOLO) model, followed by an R-CNN model for segmenting the object into two distinct parts, i.e., the top face and the remainder. Subsequently, the center of the top face is served as an initialization position, and being refined with a novel calibration algorithm. Experimental results demonstrate a notable reduction in localization error by 87.65% when compared to existing methodologies.
Multi-Input Melanoma Classification using MobileNet-V3-Large Architecture
All over the globe there exists a serious problem with skin cancer, most especially melanoma; a malignancy which is known to behave aggressively and able to metastasize. Detecting this early is the key to saving life. This study introduces a new method of classifying melanoma using an advanced model known as MobileNet-V3-Large. This technique differs from others in that it considers both the images of the skin lesion and tabular data including factors consisting of patient’s approximate age, gender and the location of the lesion on the body of the patient is considered here. Such an approach empowers the predictions of whether skin lesion may be malignant or benign. Tested on huge collection consisting of skin images combined with the tabular data, it was established that the method outperforms others already existing. The results of this study showed that a high accuracy of 99.56% was achieved using the proposed model. The study indicates that utilizing multi-input method will substantially enhance diagnosis for melanoma hence reducing mortalities in the future.
Enhancing Efficiency and Security in Healthcare IoT: a Novel Approach for Fog Computing Resource Optimization Using TGA-RNN
Fog computing, a new computing paradigm that has gained popularity, brings calculations closer to data sources from healthcare facilities. The health care industry is the driving force behind the growth of Internet of Things (IoT) driven Fog computing that improves network performance and efficiency, particularly when it comes to the safe and effective aggregation and transmission of healthcare data. This requires optimizing resource allocation and addressing overflow issues. This study introduces a novel approach that combines Task Group Aggregation (TGA) with a Recurrent Neural Network (RNN) to assess Quality of Service (QoS) characteristics and detect overloaded servers. The TGA method is utilized to effectively manage data movement to Virtual Machines (VMs), thereby alleviating congestion and improving system stability. Furthermore, it uses Chaotic Fruit Fly Optimization Algorithm (CFOA), a neural computing system, to optimize service and user separation based on individual qualities in the context of secure healthcare data aggregation and transmission within IoT networks. The integration of TGA with CFF enhances the detection of overflow problems within the RNN framework, enabling proactive management of resource allocation. The proposed work is evaluated by using the Java programming language and the results demonstrate the effectiveness of the Fog computing overflow control model in mitigating congestion and optimizing resource scheduling, thereby facilitating the efficient and secure aggregation and transmission of healthcare data within IoT networks.
Adaptive Upper Limb Robot-Assisted Rehabilitation: Learn-from-Therapist Demonstrations
Robotic-assisted rehabilitation is a promising method for improving motor function in individuals with upper limb impairments. However, generation of personalized and adaptive assistance patterns remains a challenge. Our study introduces a Learn-from-Therapist Demonstration (LfTD) framework, which employs Dynamic Movement Primitives (DMP) to train a robot arm to learn from therapist skills. Initially, therapist movements were captured via visual tracking, and the DMP accurately learned and replicated these motions via a robotic arm to assist patients. These movements were then effectively generalized to new goals while maintaining the original motion patterns. Meanwhile, a Model Reference Adaptive Controller (MRAC) has been utilized to refine the robot's adaptive performance while assuring demonstration tracking. We assessed the LfTD's efficacy with a simulated two-link robot, which showed excellent learning, adaptation, and ability to perform complex rehabilitation tasks with precise trajectory tracking. Further tests evaluated the MRAC's robustness against introduced human deviations, demonstrating its resilience and adaptability. These findings highlight LfTD’s potential to improve upper limb robotic-assisted rehabilitation through precise, adaptable motion replication, setting the stage for clinical trials with actual robots.
Copyright (c) 2026 Ismail Auta, Ahmed Fares, Hiroyasu Iwata, Haitham El-Hussieny https://creativecommons.org/licenses/by-nc-nd/4.0
Partitioning of Complex Discrete Models for Highly Scalable Simulations
The need for more and more accurate simulations of groups of autonomous beings directs the attention of researchers towards the ways of parallelizing simulation algorithms. Parallel execution of discrete simulation models update methods requires their division between workers. Existing methods used for grid division aim at providing equal areas of fragments and minimizing the length of created borders. However, in real life simulations, other factors also have to be considered. In this paper we present a method for grid partitioning, which also allows defining indivisible areas, considers complex shapes of real-life environments and supports division suitable for defined architecture of nodes and cores. The method is evaluated using several scenarios, providing satisfactory results.
Optimization of the Nominal Capacity of the Energy Storage System for Ensuring Security of Electrical Energy Supply
This paper presents an optimization model for determining the nominal capacity of an energy storage system is presented, which transfers excess amounts of electrical energy from solar power plants as part of a group of distributed generation power plants, based on the criterion of the minimum cost of supplying electricity to end consumers. The use of the developed optimization model allows to determine the optimal parameters of the energy storage system to achieve an economic balance between the excess amounts of electric energy of solar power plants that are transferred and the amount of restrictions on inverters that are subject to payment. Based on the developed method, based on the forecast level of electric energy consumption and statistical data on the level of electric energy production at wind and solar power plants for the territory of Ukraine, the installed capacity of the wind, solar, reserve power plant and the nominal capacity of the two-hour energy storage system were determined, which will provide the minimum levelized cost of energy of supplying to consumers during the entire period of commercial operation of a group of distributed generation power plants.