Search Results (2,875)

With the growing penetration of renewable energy, automatic generation control (AGC) faces challenges like frequent frequency fluctuations and tie-line power deviations. Traditional proportional (PROP) allocation algorithms, limited by fixed weights, struggle to adapt to dynamic system changes. To address this, this study proposes a coordinated AGC allocation framework fusing a dual-flow Gate Recurrent Unit (GRU) with residual Multilayer Perceptron (MLP) based on PROP, preserving physical prior knowledge while learning adaptive correction terms. Validated on a provincial power grid, the proposed method reduces the cumulative absolute ACE (Sum) by about 0.3–0.9% compared with PROP under 10–100 MW step disturbances. Under random disturbances, it achieves larger reductions of about 3.2% (vs. PROP) and 4.8% (vs. MLP), while maintaining interpretability and deployment feasibility, improving the relevant performance indicators of AGC unit allocation while maintaining interpretability and deployment feasibility, providing an effective solution for AGC under high renewable energy penetration. Full article

This work presents the development of a differential-drive wheeled mobile robot educational prototype, manufactured using 3D additive techniques. The robot is powered by an embedded ARM-based computing system and uses open-source software. To validate the prototype, a trajectory-tracking task was successfully implemented. The aim of this contribution is to provide an easily replicable prototype for teaching automatic control and related engineering topics in academic settings. Full article

This article presents a gamified digital twin in Minecraft designed to support practical exercises in digital logic in the Computer Engineering I course at the University of Salamanca. Implemented as a Spigot/Paper server plugin based on the Platform for Automatic coNstruction of orGanizations of intElligent Agents (PANGEA) multi-agent architecture, the system orchestrates four virtual organizations and employs a world cloning strategy (via Multiverse and WorldGuard) to ensure individual and isolated workspaces, while also enabling collaborative work. The central contribution is a multi-agent system with an integrated ‘black box’ verification engine that mitigates redstone asynchrony and latency through controlled signal injection and software clock synchronization, enabling real-time deterministic validation of both basic logic gates and more complex sequential circuits. Additionally, the ecosystem includes a specialized suite of logic scenarios and a web-based dashboard for real-time teacher monitoring. In a pilot study ($N=30$), the system achieved an average task completion rate of 89.1%, and an adapted Unified Theory of Acceptance and Use of Technology (UTAUT) analysis indicated that technical stability is positively associated with student performance. Full article

Civil defence shelters constitute an essential component of safety systems in emergency situations. The aim of this article is to assess the modernization potential of existing civil defence shelters in Poland, using a representative facility located in Olsztyn (Poland) as a case study. The analysis is based on a review of the current legal framework and identification of legislative gaps that affect the implementation of effective protective solutions. Within the case study, a detailed technical assessment of the protective structure was carried out, focusing on construction, protection against radiation and contamination, fire safety, and user ergonomics. Based on this assessment, a comparative analysis was performed between the surveyed facility and current as well as proposed technical requirements. The results revealed partial compliance with regulations while identifying significant deficiencies. To address these issues, measures such as replacing ventilation units with modern systems featuring automatic control, installing EI120-certified doors, improving emergency exit dimensions, and adding emergency lighting were proposed. Subsequently, risks associated with modernization were identified in accordance with ISO 31000. The findings highlight the need for systematic modernization of existing protective structures, clarification of legal regulations, and increased investment, which are key conditions for improving civilian safety. Full article

The study of muscle signal classification has been widely explored for the control of myoelectric prostheses. Traditional approaches rely on manually designed features extracted from time- or frequency-domain representations, which may limit the generalization and adaptability of EMG-based systems. In this work, an autoencoder-based framework is proposed for automatic feature extraction, enabling the learning of compact latent representations directly from raw EMG signals and reducing dependence on handcrafted features. A custom instrumentation system with three surface EMG sensors was developed and placed on selected forearm muscles to acquire signals associated with five hand movements from 20 healthy participants aged 18 to 40 years. The signals were segmented into 200 ms windows with 75% overlap. The proposed method employs a recurrent autoencoder with a symmetric encoder–decoder architecture, trained independently for each sensor to achieve accurate signal reconstruction, with a minimum reconstruction loss of $3.3\times {10}^{-4}{V}^2$. The encoder’s latent representations were then used to train a dense neural network for gesture classification. An overall efficiency of 93.84% was achieved, demonstrating that the proposed reconstruction-based approach provides high classification performance and represents a promising solution for future EMG-based assistive and control applications. Full article

The container relocation problem (CRP) is a critical optimisation problem in maritime port operations, in which efficient container handling is essential for maximising terminal throughput. Relocation rules (RRs) are a widely adopted solution approach for the CRP, particularly in online and dynamic environments, as they enable fast, rule-based decision-making. However, the manual design of effective relocation rules is both time-consuming and highly dependent on problem-specific characteristics. To overcome this limitation, genetic programming (GP), a bio-inspired optimisation technique grounded in the principles of natural evolution, has been employed to automatically generate RRs. By emulating evolutionary processes such as selection, recombination, and mutation, GP can explore large heuristic search spaces and often produces rules that outperform manually designed alternatives. Despite these advantages and their inherently white-box nature, GP-generated relocation rules frequently exhibit excessive complexity, which hinders their interpretability and limits insight into the underlying decision logic. Motivated by the biomimetic observation that evolutionary systems tend to favour compact and efficient structures, this study investigates two mechanisms for controlling rule complexity, parsimony pressure, and solution pruning, and it analyses their effects on both the quality and size of relocation rules evolved by GP. The results demonstrate that substantial reductions in rule size can be achieved with only minor degradation in performance, measured as the number of relocated containers, highlighting a favourable trade-off between heuristic simplicity and solution quality. This enables the derivation of simpler and more interpretable heuristics while maintaining competitive performance, which is particularly valuable in operational settings where human planners must understand, trust, and potentially adjust automated decision rules. Full article

The Longmaxi from the Anchang Syncline in northern Guizhou exhibits a high degree of thermal evolution of organic matter and significant variation in gas content. Because the synclinal is narrow, steep, and internally faulted, the mechanisms controlling shale gas preservation and escape remain poorly understood, complicating development planning and engineering design. Research on oil and gas migration and accumulation mechanisms in synclinal structures is therefore essential. To address this issue, three proportionally scaled strata—pure shale, gray shale, and sandy shale—were fabricated, and faults and artificial fractures with different displacements and inclinations were introduced. The simulation system consisted of two glass tanks (No. 1 and No. 2). Each tank had three rows of eight transmitting electrodes on one side, and a row of eight receiving electrodes on the opposite side. Tank 1 remained fixed, while Tank 2 could be hydraulically tilted up to 65° to simulate air and water migration under varying formation inclinations. A gas-water injection device was connected at the base. Gas was first injected slowly into the model. After injecting a measured volume (recorded via the flowmeter), the system was allowed to rest for 24–48 h to ensure uniform gas distribution. Water was then injected to displace the gas. During displacement, Tank 1 remained horizontal, and Tank 2 was inclined at a preset angle. An embedded monitoring program automatically recorded resistivity data from the 48 electrodes, and water-driven gas migration was analyzed through resistivity changes. A gas escape rate parameter (G_d), based on differences in gas saturation, was developed to quantify escape velocity. The simulation results show that gas escape increased with formation inclination. Beyond a critical angle, the escape rate slowed and approached a maximum. Faults and fractures significantly enhanced gas escape. Full article

Concrete arch dams represent a predominant dam type in water conservancy and hydropower projects in China. The control of concrete placement progress during construction directly impacts project quality and construction efficiency. Traditional manual monitoring methods, characterized by delayed response and strong subjectivity, struggle to meet the demands of modern intelligent construction management. This study introduces machine vision technology to monitor the concrete placement process and establishes an intelligent analysis system for construction scenes based on deep learning. By comparing the performance of U-Net and DeepLabV3+ semantic segmentation models in complex construction environments, the U-Net model, achieving an IoU of 89%, was selected to identify vibrated and non-vibrated concrete areas, thereby optimizing the concrete image segmentation algorithm. A comprehensive real-time analysis method for placement progress was developed, enabling automatic ternary classification and progress calculation for key construction stages, including concrete unloading, spreading, and vibration. In a continuous placement case study of Monolith No. 3 at a project site, the model’s segmentation results showed only an 8.2% error compared with manual annotations, confirming the method’s real-time capability and reliability. The research outcomes provide robust data support for intelligent construction management and hold significant practical value for enhancing the quality and efficiency of hydraulic engineering construction. Full article

This work presents a hierarchical deep reinforcement learning (DRL) framework based on Soft Actor–Critic (SAC) for the autonomy of rock-breaking machinery in surface mining comminution processes. The proposed approach explicitly integrates mobile navigation and hydraulic manipulation as coupled subprocesses within a unified decision-making architecture, designed to operate under the unstructured and highly uncertain conditions characteristic of open-pit mining operations. The system employs a hysteresis-based switching mechanism between specialized SAC subagents, incorporating automatic entropy tuning to balance exploration and exploitation, twin critics to mitigate value overestimation, and curriculum learning to manage the progressive complexity of the task. Two coupled subsystems are considered, namely: (i) a tracked mobile machine with a differential drive, whose continuous control enables safe navigation, and (ii) a hydraulic manipulator equipped with an impact hammer, responsible for the fragmentation and dismantling of rock piles through continuous joint torque actuation. Environmental perception is modeled using processed perceptual variables obtained from point clouds generated by an overhead depth camera, complemented with state variables of the machinery. System performance is evaluated in unstructured and uncertain simulated environments using process-oriented metrics, including operational safety, task effectiveness, control smoothness, and energy consumption. The results show that the proposed framework yields robust, stable policies that achieve superior overall process performance compared to equivalent hierarchical configurations and ablation variants, thereby supporting its potential applicability to DRL-based mining automation systems. Full article

The exponential growth of data generated by modern digital applications, including systems inspired by Industrial Internet of Things (IIoT) requirements, has accelerated the adoption of NoSQL databases due to their scalability, flexibility, and performance advantages over traditional relational systems. Among document-oriented solutions, MongoDB and RavenDB stand out due to their architectural features and their ability to manage dynamic, large-scale datasets. This paper presents a comparative analysis of MongoDB and RavenDB, focusing on the performance of fundamental CRUD (Create, Read, Update, Delete) operations. To ensure a controlled performance evaluation, a mobile and web application for managing product orders was implemented as a case study inspired by IIoT data characteristics, such as high data volume and frequent transactional operations, with experiments conducted on datasets ranging from 1000 to 1,000,000 records. Beyond the core CRUD evaluation, the study also investigates advanced operational scenarios, including joint processing strategies (lookup versus document inclusion), bulk data ingestion techniques, aggregation performance, and full-text search capabilities. These complementary tests provide deeper insight into the systems’ architectural strengths and their behavior under more complex and data-intensive workloads. The experimental results highlight MongoDB’s consistent performance advantage in terms of response time, particularly with large data volumes, while RavenDB demonstrates competitive behavior and offers additional benefits such as built-in ACID compliance, automatic indexing, and optimized mechanisms for relational retrieval and bulk ingestion. The analysis does not propose a new benchmarking methodology but provides practical insights for selecting an appropriate document-oriented database for data intensive mobile and web application contexts, including IIoT-inspired data characteristics, based on a controlled single-node experimental setting, while acknowledging the limitations of a single-host experimental environment. Full article

This paper presents the development of an autonomous grasping mechanism for drone-based parcel delivery systems towards developing capabilities for in-flight package transfer. The approach integrates a mechanical gripper fitted with sensors and a pose estimation method for parcels, all coordinated through a hybrid Model Predictive Control (MPC) architecture. The gripper’s mechanical structure and prototype are developed using 3D printing technology for both the main framework and gear components. A hybrid dynamical model is formulated that integrates the gripper mechanics with simplified drone dynamics, capturing distinct operational phases including package acquisition, transport, and release. The hybrid MPC framework computes reference trajectories for both the gripper arm configuration and the drone’s spatial path toward designated target positions. Experimental validation is conducted using the operational gripper prototype and pose estimation system, while drone behavior is represented through simulation. Full article

Automated optical inspection (AOI) technologies are widely used in PCB and semiconductor manufacturing to improve accuracy and reduce human error during quality inspection. While existing AOI systems can perform defect detection, they often rely on pre-defined camera positions and lack flexibility for interactive inspection, especially when the operator needs to visually verify solder pad conditions or examine specific layout regions. This study focuses on the front-end optical positioning and inspection stage of the AOI workflow, providing an automated mechanism to link digitally generated layout reports from EDA layout tools with real PCB inspection tasks. The proposed system operates on component-placement reports exported by EDA layout environments and uses them to automatically guide the camera to the corresponding PCB coordinates. Since PCB design reports may vary in format and structure across EDA tools, this study proposes a vision-based extraction approach that employs Hough transform-based region detection and a CNN-based digit recognizer to recover component coordinates from visually rendered design data. A dual-axis sliding platform is driven through a hierarchical control architecture, where coarse positioning is performed via TB6600 stepper control and Bluetooth-based communication, while fine alignment is achieved through a non-contact, gesture-based interface designed for clean-room operation. A high-resolution autofocus camera subsequently displays the magnified solder pads on a large screen for operator verification. Experimental results show that the proposed platform provides accurate, repeatable, and intuitive optical positioning, improving inspection efficiency while maintaining operator ergonomics and system modularity. Rather than replacing defect-classification AOI systems, this work complements them by serving as a positioning-assisted inspection module for interactive and semi-automated PCB quality evaluation. Full article

The current study presents results from experimental tests of productivity, fuel consumption, and fuel efficiency of a wheeled excavator Hyundai HW160A that were performed at a construction site in Sofia, Bulgaria. The experiments happened during the span of two consecutive days, and the feedback of the excavator’s operator was also taken into consideration. During the first day, the excavator operated as a conventional excavator only equipped with a bucket, while during the second day it also made use of a 3D semi-automatic digging system and a tiltrotator control system. The productivity was increased by 21.2%, the fuel consumption was decreased by 13.8%, and the fuel efficiency was increased by 32%, respectively, when applying the mentioned systems. The systems are briefly reviewed in this paper. Full article

Mobile edge-AI plant monitoring systems enable automated disease control in greenhouses and open fields, reducing dependence on manual inspection and the variability of visual diagnostics. This paper proposes a lightweight two-stage edge-AI system for strawberries, in which a YOLOv10n detector on board a mobile agricultural robot locates leaves affected by seven common diseases (including Leaf Spot) with real-time capability on an embedded platform. Patches are then automatically extracted for leaves classified as Leaf Spot and transmitted to the second module—a compact MobileViT-S-based classifier with ordinal output that assesses the severity of Leaf Spot on three levels (S1—mild, S2—moderate, S3—severe) on a specialised set of 373 manually labelled leaf patches. In a comparative experiment with lightweight architectures ResNet-18, EfficientNet-B0, MobileNetV3-Small and Swin-Tiny, the proposed Ordinal MobileViT-S demonstrated the highest accuracy in assessing the severity of Leaf Spot (accuracy ≈ 0.97 with 4.9 million parameters), surpassing both the baseline models and the standard MobileViT-S with a cross-entropy loss function. On the original image set, the YOLOv10n detector achieves an mAP@0.5 of 0.960, an F1 score of 0.93 and a recall of 0.917, ensuring reliable detection of affected leaves for subsequent Leaf Spot severity assessment. The results show that the “YOLOv10n + Ordinal MobileViT-S” cascade provides practical severity-aware Leaf Spot diagnosis on a mobile agricultural robot and can serve as the basis for real-time strawberry crop health monitoring systems. Full article

Meteorological data from automatic weather stations (AWS) in tea plantations is critical for agricultural management, but is often compromised by sensor errors and physical implausibilities that traditional quality control (QC) methods fail to detect. This study proposes a novel, meteorologically informed QC framework that mines association rules from long-term ERA5 reanalysis data (2012–2023) using the Apriori algorithm to establish a knowledge base of normal multivariate atmospheric patterns. A comprehensive feature engineering process generated temporal, physical, and statistical features, which were discretized using meteorological thresholds. The mined rules were filtered, prioritized, and integrated with hard physical constraints. The system employs a fuzzy logic mechanism for violation assessment and a weighted anomaly scoring system for classification. When validated on a synthetic dataset with injected anomalies, the method significantly outperformed traditional QC techniques, achieving an F1-score of 0.878 and demonstrating a superior ability to identify complex physical inconsistencies. Application to an independent historical dataset from a Zhenjiang tea plantation (2008–2016) successfully identified 14.6% anomalous records, confirming the temporal transferability and robustness of the approach. This framework provides an accurate, interpretable, and scalable solution for enhancing the quality of meteorological data, with direct implications for improving the reliability of frost prediction and pest management in precision agriculture. Full article