Search Results (2,902)

Small hydropower plants (HPPs) equipped with water storage play an important role in managing fluctuating energy demand. This article presents a real-world case study in which model predictive control (MPC), driven by energy-demand and water-inflow forecasts produced using the Light Gradient Boosting Machine (LGBM), is applied to optimize the operation of a small hydropower plant for peak shaving. A comparative analysis is conducted between the current non-predictive control strategy, which relies on operator decisions for peak shaving, and a fully automatic controller that optimally schedules the utilization of available water resources based on ML predictions. Results show that the MPC can outperform the operator-based scheduling and that this has the potential to improve the peak shaving capabilities of small HPPs. Unlike previous studies that predominantly focus on large and complex hydropower systems or introduce new control formulations evaluated under idealized assumptions, this work offers a pragmatic solution to the underexplored context of peak shaving for small HPPs operated with limited data and resources, that small utilities can adopt with minimal effort using their own data. We show that even these small-scale hydropower operations have room for improvement through optimal scheduling. Full article

This paper presents a systematic comparative study of optical character recognition (OCR) architectures for Korean license plate recognition under identical detection conditions. Although recent automatic license plate recognition (ALPR) systems increasingly adopt Transformer-based decoders, it remains unclear whether performance differences arise primarily from sequence modeling strategies or from backbone feature representations. To address this issue, we employ a unified YOLOv12-based license plate detector and evaluate multiple OCR configurations, including a CNN with an Attention-LSTM decoder and a MobileNetV3 with a Transformer decoder. To ensure a fair comparison, a controlled ablation study is conducted in which the CNN backbone is fixed to ResNet-18 while varying only the sequence decoder. Experiments are performed on both static image datasets and tracking-based sequential datasets, assessing recognition accuracy, error characteristics, and processing speed across GPU and embedded platforms. The results demonstrate that the effectiveness of sequence decoders is highly dataset-dependent and strongly influenced by feature quality and region-of-interest (ROI) stability. Quantitative analysis further shows that tracking-induced error accumulation dominates OCR performance in sequential recognition scenarios. Moreover, Korean license plate–specific error patterns reveal failure modes not captured by generic OCR benchmarks. Finally, experiments on embedded platforms indicate that Transformer-based OCR models introduce significant computational and memory overhead, limiting their suitability for real-time deployment. These findings suggest that robust license plate recognition requires joint consideration of detection, tracking, and recognition rather than isolated optimization of OCR architectures. Full article

Giving consideration to cooking activity is important for sustainable housing. In contexts of limited ventilation, imposed by energy saving concerns, cooking causes deterioration of indoor air quality (IAQ) and occupants’ discomfort. This study presents a cooking event detection system that may support IAQ control to minimize the impact of cooking. The system consists of a multi-sensor device and a deep-learning neural network (DNN). The device monitors temperature (T), relative humidity (RH), suspended particulate matter (PM), CO₂, the responses of sensors to volatile organic compounds (VOCs), and other gases (NO₂, CO, CH₂O) in the kitchen zone. The collected data are processed by the DNN. The detection system generates a response every 7 s, indicating either ’COOKING’ or ’NO COOKING’. Feature vector selection was based on classification performance and cost considerations. Cooking event misdetections generate unjustified IAQ control costs: economic ones (UEC), when the system detects a non-existent event, and environmental ones (UEN), when the system fails to detect an actual event. In this study, several well-performing detection systems were developed, with miss rates ranging from 5.1% to 20.5% and false detection rates ranging from 7.7% to 11.7%. The results show that gas sensor responses—particularly to VOCs—had greater utility for cooking event detection compared with T, RH, CO₂, and PM. The cost analysis demonstrated that IAQ control supported by the developed cooking event detection systems could generate higher total unjustified environmental costs when the unit cost ratio UEN/UEC exceeded 1.25, or higher total unjustified economic costs when the unit cost ratio UEN/UEC was below 1.43. We believe this work will contribute to the development of novel automatic IAQ control systems supported by event detection. Full article

Modern buildings in subtropical and humid regions face growing challenges regarding energy consumption and indoor climate comfort. Traditional air conditioning and dehumidification systems are often inefficient, energy-intensive, and difficult to automate for real-time adaptation to fluctuating environments. The water curtain wall (WCW) leverages passive evaporative cooling and potential condensation dehumidification to deliver high energy efficiency and robust indoor microclimate regulation. Yet, its large-scale adoption depends on reliable automation, multi-point environmental sensing, and modular engineering that ensure stability, adaptability, and easy maintenance. The results of this study demonstrate a next-generation WCW system integrating multi-sensor feedback and dynamic control and a full cycle of engineering verification, operational analysis, and optimization for real-world deployment. Full article

The existence of friction, flow–pressure coupling, load variations, internal leakage, and other fluidic nonlinearities makes it challenging to design classical model-based controllers for servo-valve-driven electro-hydraulic actuators (EHAs). To address these issues and achieve high-precision output tracking, this paper proposes a learning-based control framework that integrates Long Short-Term Memory with Deep Q-Learning and Bayesian Optimization (BO–LSTM–DQN) for high-precision position regulation of servo-valve-driven EHAs. In this framework, the LSTM augments Q-learning with temporal memory to first establish and infer hidden dynamics from measured sequences. Meanwhile, Bayesian Optimization is used to automatically optimize key hyperparameters to improve convergence and policy stability, without requiring manual trial-and-error. Additionally, a constraint-aware reward function is formulated to encode realistic servo-valve operational limits and satisfy motion stability requirements. The effectiveness of the proposed control strategy is verified through comparative simulations with PID– and BO–DQN-based controllers under different operating scenarios, subject to load disturbance and internal leakage. Furthermore, to evaluate the robustness of the proposed controller against parametric uncertainties, extensive Monte Carlo simulations are conducted with simultaneous variations of up to $50\%$ in five key system parameters. The results demonstrate that the proposed BO–LSTM–DQN framework achieves a significant reduction in Root Mean Square Error (RMSE) by up to $51.79\%$ compared with the conventional PID and maintains superior stability over the optimized DQN baselines, confirming its effectiveness for real-world EHA applications under extreme operating conditions. Full article

Chain breakage in dual-chain scraper conveyors poses significant risks to the safe and efficient operation of coal mines. To address the challenges of harsh underground environments and the lack of effective synchronization monitoring, this paper presents the design and implementation of an intelligent monitoring system for conveyor integrity. The system integrates non-contact Hall-effect sensors with a custom-designed intrinsically safe data acquisition unit. A systematic algorithmic framework is designed, comprising an adaptive threshold and plateau seeking (ATPS) module and an adaptive clustering-based identification (ACCI) module, to enable high-accuracy automatic identification of chain elements. Furthermore, a novel synchronization evaluation design based on event correlation and statistical features is introduced to quantify inter-chain timing deviations. This leads to the construction of a Chain Synchronization Index (CSI) for desynchronization anomaly detection. Field experiments conducted under representative operating conditions, including normal operation and controlled single-chain disconnection scenarios, demonstrate that the proposed design achieves a chain element recognition accuracy of 98.2%. Under normal conditions, the CSI remains consistently high, while breakage faults are sensitively detected. The proposed system provides a practical engineering solution for synchronization-aware condition monitoring and anomaly warning of scraper conveyor chains in underground coal mines. Full article

The emergence of Building Information Modelling, Internet of Things, and Cave Automatic Virtual Environments (CAVEs) has created new opportunities for remote monitoring and decision-making in the operational built environment, yet empirical evidence supporting their use as alternatives to on-site observation remains limited. This study evaluates task and human performance in a controlled experiment comparing a CAVE with a real-world setting (n = 26). Situation awareness, workload, anxiety, presence, usability, and user experience were measured across conditions. Participants in the CAVE demonstrated substantially higher situation awareness (M = 92.1%) than those in the real-world condition (M = 56.8%), alongside significantly lower overall workload (NASA-TLX weighted workload = 38.3 vs. 53.8). Anxiety remained consistently low in the CAVE (ΔSTAI = –1.0), whereas participants in the real-world condition exhibited higher baseline anxiety followed by a large reduction during task execution (ΔSTAI = –13.2). The CAVE also elicited high levels of spatial presence, involvement, and realism relative to comparable projection-based systems, while usability ratings (SUS) were above industry benchmarks (M = 74.2). Together, these findings indicate that controlled immersive representations of built environments can support sensemaking and reduce extraneous cognitive load relative to live, uncontrolled on-site observation, with important implications for remote facilities management and operational decision-making. Full article

The rapid integration of renewable energy into power systems has made voltage oscillations caused by the intermittency of wind and solar power a critical operational challenge. To mitigate these issues, this paper proposes a multi-mode coordinated reactive power control strategy to enhance voltage stability in renewable energy clusters. The approach integrates two key indicators: voltage sensitivity for steady-state regulation and an improved multi-renewable energy station short circuit ratio (MRSCR) that accounts for dynamic power interactions. Validation is conducted using a hardware-in-the-loop (HIL) platform combining real-time RMS-based simulation with physical controllers. Case studies on an offshore wind cluster demonstrate that the proposed method reduces voltage fluctuation amplitude more effectively than conventional automatic voltage control (AVC), successfully suppressing oscillations. The results confirm that the strategy exhibits stronger adaptability to varying grid conditions and offers a scalable solution for oscillation mitigation in large-scale renewable energy integration. Full article

Optical Wireless Power Transmission (OWPT) holds a significant position for enabling cable-free energy delivery in long-distance, high-energy, and mobile scenarios. However, ensuring human and equipment safety under high-power laser exposure remains a critical challenge. This study reports a vision-based OWPT safety system that implements the principle of automatic emission control (AEC)—dynamically modulating laser emission in real time to prevent hazardous exposure. While camera-based OWPT safety systems have been proposed in the concept, there are extremely limited working implementations to date. Moreover, existing systems struggle with response speed and single-object assumptions. To address these gaps, this research presents a low-latency safety architecture based on a customized deep learning-based object detection framework, a dedicated OWPT dataset, and a multi-threaded control stack. The research also introduces a real-time risk factor (RF) metric that evaluates proximity and velocity for each detected intrusion object (IO), enabling dynamic prioritization among multiple threats. The system achieves a minimum response latency of 14 ms (average 29 ms) and maintains reliable performance in complex multi-object scenarios. This work establishes a new benchmark for OWPT safety system and contributes a scalable reference for future development. Full article

Global Navigation Satellite Systems (GNSSs) are exploited in a wide range of applications, and their reliability and accuracy are more critical than ever. Weak GNSS signals are extremely susceptible to intentional or unintentional interference. The Joint Research Centre has explored the potential of leveraging the ubiquitous presence of Android smartphones for interference monitoring. Automatic Gain Control (AGC) measurements provided by the Android GNSS API are used for this purpose. A proof-of-concept, including an App to collect data and a back-end server for processing, has been developed and tested. The proposed approach demonstrates the potential to detect both intentional and unintentional interference. However, the approach has limitations, such as small AGC variations that cannot always be linked to GNSS interference and significant differences among smartphone models, which need to be considered for effective crowdsourcing. Full article

We developed an AI-assisted zero-trust control system at low capital expenditure to retrofit brownfield Ethernet environments without disruptive hardware upgrades or costly software-defined networking migration. Legacy network infrastructures in small and medium-sized enterprises (SMEs) lack the flexibility and programmability required by modern zero-trust architectures, creating a persistent security gap between static Layer-1 deployments and dynamic cyber threats. The developed system addresses this gap through a modular architecture that integrates genetic-algorithm-based virtual local area network (VLAN) optimization, large language model-guided firewall rule synthesis, threat-intelligence-driven policy automation, and telemetry-triggered adaptive isolation. Network assets are enumerated and evaluated through a risk-aware clustering model to enable micro-segmentation that aligns with the principle of least privilege. Optimized segmentation outputs are translated into pfSense firewall policies through structured prompt engineering and dual-stage validation, ensuring syntactic correctness and semantic consistency. A retrieval-augmented generation pipeline connects live telemetry with historical vulnerability intelligence, enabling rapid policy adjustments and automated containment responses. The system operates as an overlay on existing managed switches, orchestrating configuration changes through standards-compliant interfaces such as simple network management protocol and network configuration protocol. Experimental evaluation in a representative SME testbed demonstrates substantial improvements in segmentation granularity, refining seven flat subnets into thirty-four purpose-specific VLANs. Compliance scores improved significantly, with the International Organization for Standardization/International Electrotechnical Commission 27001 rising from 62.3 to 94.7% and the National Institute of Standards and Technology Cybersecurity Framework alignment increasing from 58.9 to 91.2%. All 851 automatically generated firewall rules passed dual-agent validation, ensuring reliable enforcement and enhanced auditability. The results indicate that the system developed provides an operationally feasible pathway for legacy networks to achieve zero-trust segmentation with minimal cost and disruption. Future extensions will explore adaptive learning mechanisms and hybrid cloud support to further enhance scalability and contextual responsiveness. Full article

In semi-arid areas, water loss from small agricultural water storage facilities is significant, owing to evaporation. A longitudinal study was conducted between 2019 and 2022 at the Agricultural Research Station, Ananthapuramu, located in the semi-arid climate of Peninsular India, which compared 12 distinct treatments designed to reduce evaporation. These treatments included bamboo sheets, agricultural residues, Azolla (Azolla pinnata), monomolecular alcohol films, and oil-based films, along with an untreated control. Evaporation rates and meteorological data were measured using the depth loss method and automatic weather station. Results indicated substantial treatment effects, such as bamboo sheets decreasing evaporation by 88%, reducing daily loss from 5.2 mm to 0.8 mm, while Azolla achieved a 62% reduction (2.8 mm). Organic residues decreased evaporation by 37–47%, and chemical monolayers and oils by 21–42%. Ridge regression models demonstrated strong performance (R² = 0.789–0.808), with bamboo sheets exhibiting the lowest Root Mean Square Error (0.127 mm day⁻¹). Economic analysis revealed annual water savings of 4700–4800 m³ ha⁻¹ for bamboo sheets and 2300–2500 m³ ha⁻¹ for less effective covers. Assuming a baseline water value of 0.20 US$ m⁻³, annual net benefits ranged from 250 to 900 US$ ha⁻¹, with Net Present Values spanning from 7000 to 160,000 US$ ha⁻¹ across various scenarios. Overall, bamboo sheets and Azolla were identified as the most effective and economically viable options for mitigating evaporation in semi-arid smallholder water systems. Maximum air temperature (T_max) was a key meteorological variable used to model daily evaporation, together with wind speed, followed by relative humidity and sunshine duration. Full article

Developmental dyslexia (DD) often involves difficulties in phonological processing of speech. Objectives: While underlying neural changes have been identified in terms of stimulus- and task-related responses within specific brain regions and their neural connectivity, there is still limited understanding of how these changes affect the overall organization of brain networks. Methods: This study used EEG and functional network analysis, focusing on small-world propensity across various frequency bands (from δ to γ), to explore the global brain organization during the auditory discrimination of words and pseudowords in children with DD. Results: The main finding revealed a systemic inefficiency in the functional network of individuals with DD, which did not achieve the optimal small-world propensity. This inefficiency arises from a fundamental trade-off between localized specialization and global communication. During word listening, the δ-/γ1-networks (related to impaired syllabic and phonemic processing of words) and the θ-/β-networks (related to pseudoword listening) in the DD group showed lower local clustering and connectivity compared to the control group, resulting in reduced functional segregation. In particular, the θ-/β-networks for words in the DD group exhibited a less optimal balance between specialized local processing and effective global communication. Centralized midline hubs, such as the postcentral gyrus (PstCG) and inferior frontal gyrus (IFG), which are crucial for global coordination, attention, and executive control, were either absent or inconsistent in individuals with DD. Consequently, the DD network adopted a constrained, motor-compensatory, and left-lateralized strategy. This led to the redirection of information flow and processing effort toward the left PstCG/IFG loop, interpreted as a compensatory effort to counteract automatic processing failures. Additionally, the γ1-network, which is involved in phonetic feature binding, lacked engagement from posterior sensory hubs, forcing this critical process into a slow and effortful motor loop. The γ2-network exhibited unusual activation of right-hemisphere posterior areas during word processing, while it employed a simpler, less mature routing strategy for pseudoword listening, which further diminished global communication. Conclusions: This functionality highlights the core phonological and temporal processing deficits characteristic of dyslexia. Full article

We propose a semi-automatic artificial lips control device that allows a human performer to produce sound on a brass instrument without the need to vibrate their own lips. The device integrates position control that presses artificial lips toward the mouthpiece and aperture control via wire traction, together with a pre-calibrated motor table and acoustic feedback for pitch stabilization. In evaluations using a euphonium, we verified timbre, pitch range, and pitch stabilization, including harmonic modes. The results showed that the harmonic structure of tones produced by a human using the device can be comparable to those produced by a human player in the conventional manner. Pitch-range and pitch-stabilization tests confirmed that the system can generate practical musical intervals and achieve reliable harmonic mode changes. Furthermore, real-time acoustic feedback improved pitch stability during performance. These findings demonstrate that, rather than fully automating human performance, the proposed system provides a compact and reproducible framework for controllable brass sound generation and pitch stabilization using only three actuators. Full article

To address the requirements for multi-channel decoupling and high-precision control in quadrotor UAV systems, this paper proposes a novel intelligent controller (TD3-ADRC) which integrates Active Disturbance Rejection Control (ADRC) with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Firstly, the dynamic model of the quadrotor is established. Secondly, a parameterized tanh function is introduced and applied to design the tracking differentiator, extended state observer, and nonlinear feedback control law. Then, the TD3 learning mechanism is incorporated to automatically learn and optimize controller parameters, thereby significantly enhancing the system’s disturbance rejection capability. Finally, simulation studies comparing conventional PID, ADRC, DDPG and the proposed TD3-ADRC algorithms are conducted in Simulink. In addition, a bench test system is developed using the PX4 flight controller. Experimental results show that, under complex environmental conditions, the proposed TD3-ADRC controller outperforms both conventional PID and linear ADRC methods in terms of reliability and adaptability, validating the effectiveness of the proposed control approach. Full article