Search Results (121)

The large-scale integration of wind energy introduces increased uncertainty and variability in modern power systems, with direct implications for both system design and operation. In addressing similar aspects, energy storage plays a pivotal role as a key source of system flexibility. However, the design and sizing of storage systems remain challenging, especially under conditions of increased uncertainty. In this context, the present study proposes an alternative methodological framework, based on an inverse sizing pathway, i.e., from operations to design. More specifically, the uncertainty embedded in day-ahead forecasting of residual errors, associated with wind power generation and load demand, is currently exploited as a design-relevant signal, while energy storage is treated explicitly as a risk-hedging mechanism. Forecasting residuals spanning a year of operation are incorporated in the problem through probabilistic modeling, leading to the generation of trajectories that correspond to different risk levels and are managed as design scenarios. Regarding the modeling of uncertainties, the study examines two different strategies, namely a global modeling approach and a k-means clustering strategy. Accordingly, by mapping the interplay between storage capacity, uncertainty levels (or risk tolerance), achieved RES shares and system-level costs, we highlight the role of energy storage as a risk-hedging entity rather than merely a means of energy balancing. Our results to that end demonstrate that the achieved shares of RES exhibit increased sensitivity, even within constrained regions of wind power variation, while storage capacity features distinct zones of hedging value and hedging saturation effects emerging beyond certain storage levels. Moreover, evaluation of the two modeling strategies reflects on their complementary character, with the global modeling approach ensuring continuity and the clustering strategy capturing local asymmetries within different operational regimes. In conclusion, the methodology presented in this study bridges the gap between operational forecasting and long-term system design, offering a risk-aware framework for storage sizing, grounded in actual operational signals rather than relying on stationary historical data and relevant scenarios. Full article

To address the challenges of complex wiring, limited external power supply, and difficult maintenance in temperature monitoring during the construction of mass concrete, this study proposes a formwork-integrated self-powered temperature monitoring system based on hydration heat recovery. The system incorporates temperature sensing, thermal energy harvesting, energy storage and management, and wireless data transmission. Its heat-transfer performance, power-generation capability, and operational reliability are evaluated through experimental testing and seasonal condition analysis. The results show that interface optimization can substantially improve heat-transfer efficiency, enabling stable power generation and system operation even under low temperature-gradient conditions. The system exhibits a considerable energy surplus in summer and autumn, satisfies monitoring demands in spring, and is capable of achieving energy-neutral operation even in winter. Without requiring embedment within the concrete or reliance on an external power supply, the proposed system offers a convenient and efficient new solution for temperature monitoring during construction. Full article

Electrified railways are major industrial electricity consumers. The Grid-Source-Storage-Vehicle (GSSV) system supports a more sustainable railway power supply by improving local renewable energy utilization, strengthening multi-source energy coordination, and promoting low-carbon development. However, existing rule-based energy management strategies (EMS) remain limited in their ability to support the efficient coordinated operation of the GSSV system. Moreover, under strong source-load fluctuations, conventional optimization-based EMS often fail to provide sufficiently reliable and responsive decision-making for real-time operation of GSSV systems. To address these issues, this paper proposes a real-time EMS based on a rule-guided enhanced non-dominated sorting genetic algorithm (RG-NSGA-II). First, based on the GSSV architecture, the operating modes of the system under different working conditions are systematically analyzed, and a corresponding rule-based EMS is designed. Then, a multi-objective optimization model considering system economic performance and grid power-intake fluctuation is formulated. Furthermore, a coordination mechanism between the rule-based EMS and the optimization EMS is developed. By embedding power commands generated by the rule-based EMS into the optimization EMS and regulating their activation through a time threshold, the proposed method improves the reliability, economic efficiency, and real-time performance of the EMS. Finally, the proposed method is validated, and the results show that the proposed real-time EMS ensures effective utilization of RE, improves power coordination efficiency and operational adaptability under fluctuating operating conditions, and delivers tangible environmental and economic sustainability benefits for electrified railway power supply systems. Full article

Microcontroller-based embedded systems integrating multiple sensors are increasingly required to support continuous data acquisition, on-board processing, and long-term storage within tightly coupled hardware–software architectures. In such platforms, overall performance is often constrained not by computational capability but by storage I/O behavior, particularly under real-time constraints and concurrent workloads. This study presents a comprehensive empirical evaluation of eMMC storage performance on an STM32U5 microcontroller running the ThreadX RTOS. The proposed methodology combines multi-dimensional stress testing, controlled task concurrency (0–4 tasks), and long-duration aging analysis (90 h), together with timing variability assessment under electrical stress and interrupt-driven preemption. Both synthetic workloads and realistic sensor-node scenarios with heterogeneous and asynchronous access patterns are considered. The results highlight significant performance limitations, including up to 98% throughput degradation under four concurrent tasks and a nonlinear increase in metadata latency as free space decreases below 40% (from 10 ms to over 200 ms for file creation). Additionally, timing jitter increases by 2–5× under voltage variation and interrupt load. Based on these findings, practical firmware-level design guidelines are derived, including sector-aligned buffering, dedicated I/O task architectures, and proactive capacity management, enabling substantial improvements in throughput and latency. This study provides quantitative insights and reproducible methodologies for optimizing storage subsystems in multi-sensor embedded applications. Full article

Nowadays, MLOps approaches are gaining popularity thanks to their ability to apply DevOps best practices to machine learning models. They enable the automation and optimization of model training, deployment, and monitoring in various environments, while ensuring effective Continuous Integration/Continuous Deployment (CI/CD). These approaches thus promote real-time applications that can react quickly and improve continuously. This paper examines the feasibility of implementing MLOps practices in embedded systems, specifically on the Zynq-7000 FPGA board. We present a comprehensive MLOps architecture that enables the automated deployment and monitoring of a convolutional neural network model for face recognition on an embedded hardware platform for datacenter physical access control scenarios. This architecture integrates GitLab CI/CD for version control and pipeline automation, MLflow for experiment tracking and model lifecycles management, Prometheus and Grafana for monitoring, and data storage in an S3 Bucket cloud connected to DVC for dataset versioning. The results demonstrate that the proposed pipeline can be effectively deployed on a Zynq-7000 FPGA board enabling automated model retraining, redeployment, and performance monitoring. This approach reduces operational complexity and supports faster adaptation to dataset changes. Full article

The integration of Artificial Intelligence (AI) into the design, manufacturing, and lifecycle management of Composite Overwrapped Pressure Vessels (COPVs) is transforming the pathway toward autonomous and adaptive composite systems. This paper presents a comprehensive review and roadmap for AI-enabled COPVs development, bridging materials design, process optimisation, and predictive maintenance. The study synthesises over a decade of research on data-driven composite manufacturing, combining technology intelligence, PESTEL-SWOT environmental assessment, and cross-sectoral analysis of industrial and academic advances. A unified workflow is proposed to illustrate AI integration across the COPVs lifecycle, highlighting data feedback loops for continuous optimisation through digital twins and intelligent process control. Structural Health Monitoring (SHM) plays a central role in this ecosystem by providing real-time high-fidelity data on damage evolution and environmental interactions in COPVs. Through embedded sensing technologies such as fibre optic sensors and acoustic emission systems, SHM enhances digital twin fidelity, supports AI-based anomaly detection, and strengthens model validation in safety-critical hydrogen storage applications. Critical challenges are identified, including limited hydrogen-exposure datasets, lack of real-time adaptability, explainability in safety-critical design, and sustainability of AI-intensive workflows. These challenges highlight the need for tighter SHM-AI integration to enable reliable condition assessment and prognostics under multi-physics loading conditions. Based on these findings, the paper outlines actionable research directions to enable reliable, transparent, and sustainable AI adoption in composite manufacturing under the Industry 4.0 and hydrogen-economy paradigms. Full article

The advent of 5G/6G technologies and the pervasive deployment of IoT devices are driving the emergence of demanding applications that necessitate ultra-low latency, high bandwidth, and significant computational power. Traditional cloud computing models fall short in meeting these stringent requirements. To address this, Software-Defined Edge Networks (SDENs) have emerged as a promising architecture, yet efficiently managing their heterogeneous and geographically distributed resources poses substantial challenges for optimal application provisioning. In response, this paper proposes a novel framework for intelligent task offloading, which reframes the intricate multi-component application task offloading problem as a Virtual Network Embedding (VNE) challenge within a SDEN environment. We introduce a comprehensive model where complex applications are represented as Virtual Network Requests (VNRs). In this model, each VNR consists of virtual nodes that demand specific computing and storage resources, as well as virtual links that demand specific bandwidth and must adhere to maximum tolerable delay constraints. To dynamically solve this NP-hard VNE problem in the face of stochastic VNR arrivals and dynamic network conditions, we leverage Deep Reinforcement Learning (DRL). Specifically, a Soft Actor-Critic (SAC) agent is employed at the SDN controller. This agent learns a sequential decision-making policy for mapping virtual nodes to physical edge servers and virtual links to network paths. To guide the agent towards efficient resource utilization, we define the reward for each successful embedding as the long-term revenue-to-cost ratio. By learning to maximize this reward, the agent is naturally driven to find economically viable allocation strategies. Comprehensive simulation experiments demonstrate that our SAC-based VNE approach significantly outperforms other baselines across key metrics, affirming its efficacy in dynamic SDEN environments. Full article

Metal hydride-based hydrogen storage reactors combine high volumetric hydrogen density with intrinsic safety, yet their performance is fundamentally limited by inefficient thermal management arising from the strong coupling among heat transfer, thermodynamics, and reaction kinetics. The highly exothermic and endothermic nature of hydrogen absorption and desorption requires rapid and spatially uniform heat removal or supply, which is difficult to achieve due to the low thermal conductivity and complex internal structure of hydride beds. This review presents a mechanistic and architectural overview of thermal management in metal hydride hydrogen storage reactors. Key heat transfer limitations within hydride beds are first analyzed, followed by a systematic classification and critical comparison of major thermal management architectures, including bed-level modifications, structural reactor designs, and heat-exchanger intensification strategies such as embedded tubes, fins, and phase-change materials. The advantages and limitations of these approaches are discussed in terms of heat transfer efficiency, hydrogen storage capacity, structural complexity, and scalability. Finally, the review highlights the central design trade-offs governing compactness, efficiency, and manufacturability, and outlines future directions toward application-oriented and scalable reactor design through integrated thermal and structural optimization. Full article

The escalating global rates of traffic accidents in urban areas and the growing demands of smart cities underscore the urgent need for advanced real-time monitoring solutions. This paper presents an EdgeML-based system for vehicle tracking that performs real-time speed and distance analysis and traffic violation detection. This is achieved by deploying a YOLOv8 object detection model on a Raspberry Pi 5 with a Coral USB Edge TPU accelerator. The system integrates computer vision and IoT technologies to enable real-time processing. It utilizes the Message Queuing Telemetry Transport (MQTT) protocol to allow scalable communication between distributed edge devices and a central MongoDB database, facilitating real-time storage and analysis of traffic data. A synthetic dataset generated via the Blender 3D modeling tool validates the system’s accuracy, demonstrating average speed and distance measurement errors of ±2.11 km/h and ±0.58 m, respectively. These findings are further supported by preliminary practical experiments in a real-world environment, where speed estimation errors remained within 0–2 km/h and distance errors stayed below 0.11 m. Key innovations of this work include license plate recognition, speeding and collision detection, and context analysis using Google’s Gemini-2.5-Flash API. A Streamlit dashboard provides real-time visualization of traffic metrics, violations, and aggregated data. A comparative evaluation of YOLOv5n, YOLOv8n, YOLOv11n, and YOLOv12n identifies YOLOv8n as the most suitable model for embedded deployment, achieving 91.07 ± 0.61% mAP@0.5 without quantization, 88.77 ± 3.31% mAP@0.5 with quantization, while maintaining real-time performance of 30–43 frames per second (FPS) on the Edge TPU. The system’s modular architecture, low latency, and robust performance highlight its suitability for smart city applications, enhancing traffic safety and enabling data-driven urban mobility management. Full article

Temperature and humidity monitoring in grain-carton warehousing is essential for quality assurance, yet fixed wiring is difficult under frequent stacking and battery-powered tags require routine maintenance. This study proposes a distributed passive monitoring sensing system that combines high-efficiency light energy harvesting with low-power long-range wide-area network (LoRa) communication. The key novelty is a carton-oriented separated architecture: an external photovoltaic harvester is wired to internal sensing/communication modules, mitigating stack-induced shading and enabling reliable operation for sensors embedded inside densely stacked cartons; an occlusion-tolerant multi-tag reporting strategy is further adopted. The tag integrates (i) an energy management module based on the bq25570 with a monocrystalline light cell and energy storage for low-light/intermittent illumination, (ii) a LoRa transceiver optimized for long-range and occlusion-tolerant data delivery, and (iii) a temperature–humidity sensing module for reliable microenvironment measurements. A hardware layout with an external photovoltaic panel and internal core modules mitigates carton-induced shading, while low-power scheduling and a lightweight protocol ensure robust sensing and transmission. Experiments show that the energy management module achieves > 60% charging efficiency at a 1.3 V input. After penetrating three layers of grain cartons, the LoRa link maintains a stable range of 500–800 m with ≤1% packet loss under concurrent multi-tag transmission. The measurement errors are within ±1 °C and ±3% relative humidity (RH) in the experimental setup. The proposed system eliminates fixed bus wiring and routine battery replacement, offering a scalable solution that enables maintenance-free monitoring in densely stacked warehousing environments. Full article

In this study, we present the design and development of a mobile, multi-modal electroencephalography and functional near-infrared spectroscopy (EEG/fNIRS) device for wireless neurophysiological monitoring. The system was engineered to achieve high signal fidelity, low power consumption, and a fully untethered operation suitable for ambulatory brain research. The device integrates four Texas Instruments ADS1299 24-bit biopotential amplifiers, providing up to 32 simultaneous acquisition channels. Signal control, processing, and local storage via an SD card are managed by an STM32H7 microcontroller, while an ESP32-S2 module handles Wi-Fi communication. Dual-wavelength light-emitting diodes and OPT101 photodiodes form the optical front-end, driven by digitally controlled constant-current sources for stable illumination. The design employs galvanic isolation, multi-rail power management, and a four-layer PCB layout to minimise interference between analogue, power, and digital domains. Data are captured by a deterministic, clock-driven STM32 acquisition loop and forwarded to the ESP32, which operates under an RTOS and streams packets over Wi-Fi for collection on a mobile phone or PC using the Lab Streaming Layer (LSL) framework. The STM32H7 architecture was chosen for its capability to support future embedded edge-machine-learning functions, enabling on-device signal quality assessment and artefact rejection. Validation demonstrations include 32-channel synchronised acquisition using the ADS1299 internal test signal, eyes-open/eyes-closed alpha modulation visualised in EEGLAB, a forehead fNIRS breath-hold response with physiological spectral content, and real-time ECG/optical pulse streaming via LSL. The resulting system provides a compact platform with explicitly defined acquisition and data interfaces for synchronised EEG/fNIRS acquisition, enabling scalable, low-cost mobile neuroimaging research. Full article

Background: Salmonella enterica remains a leading cause of foodborne illness worldwide despite decades of advances in surveillance and control. Traditional interventions have targeted specific points in the food chain, yet recurrent outbreaks show that Salmonella exploits system-wide gaps and inconsistencies. Methods: This review synthesises recent evidence from epidemiology, experimental microbiology, and regulatory practice to evaluate how management decisions, from farm through processing, influence Salmonella risk in livestock-derived foods. Results: Poultry, pig, and cattle farms employ targeted measures, including rodent control, litter management, batch rearing, and secure feed storage, to reduce contamination. The greatest reductions in Salmonella prevalence occur when these measures are embedded in coherent farm-to-fork programmes. Future gains are likely to come less from novel interventions and more from rigorous implementation, integration, and the validation of existing tools, supported by high-resolution surveillance (including whole-genome sequencing) and prevention-focused management systems. Artificial intelligence can enhance control through real-time surveillance, predictive risk modelling, and targeted interventions informed by diverse farm data. Conclusions: Sustained progress in Salmonella control will depend on rigorously applying existing interventions, supported by high-resolution surveillance and prevention-focused management. Carefully governed AI can enhance real-time monitoring and risk prediction, but its value hinges on addressing data, cost, and regulatory challenges. Full article

This paper presents Nexus, a proof-of-concept low-cost, modular, and reprogrammable multichannel data logger aimed at validating the architectural feasibility of an open and scalable acquisition platform for scientific instrumentation. The system was conceived to address common limitations of commercial data loggers, such as high cost, restricted configurability, and limited autonomy, by relying exclusively on widely available components and open hardware/software resources, thereby facilitating reproducibility and adoption in resource-constrained academic and industrial environments. The proposed architecture supports up to six interchangeable acquisition modules, enabling the integration of up to 20 analog channels with heterogeneous resolutions (24-bit, 12-bit, and 10-bit ADCs), as well as digital acquisition through multiple communication interfaces, including I²C (two independent buses), SPI (two buses), and UART (three interfaces). Quantitative validation was performed using representative acquisition configurations, including a 24-bit ADS1256 stage operating at sampling rates of up to 30 kSPS, 12-bit microcontroller-based stages operating at approximately 1 kSPS, and 10-bit operating at 100 SPS, consistent with stable real-time acquisition and visualization under proof-of-concept constraints. SPI communication was configured with an effective clock frequency of 2 MHz, ensuring deterministic data transfer across the tested acquisition modules. A hybrid data management strategy is implemented, combining high-capacity local storage via USB 3.0 solid-state drives, optional cloud synchronization, and a 7-inch touchscreen human–machine interface based on Raspberry Pi OS for system control and visualization. Power continuity is addressed through an integrated smart uninterruptible power supply, which provides telemetry, automatic source switching, and limited backup operation during power interruptions. As a proof of concept, the system was functionally validated through architectural and interface-level tests, demonstrating stable communication across all supported protocols and reliable acquisition of synthetic and biosignal-like waveforms. The results confirm the feasibility of the proposed modular architecture and its ability to integrate heterogeneous acquisition, storage, and interface subsystems within a unified open-source platform. While not intended as a finalized commercial product, Nexus establishes a validated foundation for future developments in modular data logging, embedded intelligence, and application-specific instrumentation. Full article

Cattle behavior is closely related to their health status, and monitoring cattle behavior using intelligent devices can assist herders in achieving precise and scientific livestock management. Current behavior recognition algorithms are typically executed on server platforms, resulting in increased power consumption due to data transmission from edge devices and hindering real-time computation. An edge-based cattle behavior recognition method via Graph Neural Network (GNN) compression is proposed in this paper. Firstly, this paper proposes a wearable device that integrates data acquisition and model inference. This device achieves low-power edge inference function through a high-performance embedded microcontroller. Secondly, a sequential residual model tailored for single-frame data based on Inertial Measurement Unit (IMU) and displacement information is proposed. The model incrementally extracts deep features through two Residual Blocks (Resblocks), enabling effective cattle behavior classification. Finally, a compression method based on GNNs is introduced to adapt edge devices’ limited storage and computational resources. The method adopts GNNs as the backbone of the Actor–Critic model to autonomously search for an optimal pruning strategy under Floating-Point Operations (FLOPs) constraints. The experimental results demonstrate the effectiveness of the proposed method in cattle behavior classification. Moreover, enabling real-time inference on edge devices significantly reduces computational latency and power consumption, thereby highlighting the proposed method’s advantages for low-power, long-term operation. Full article

The integration of a user-side energy storage system (ESS) faces notable economic challenges, including high upfront investment, uncertainty in quantifying battery degradation, and fragmented ancillary service revenue streams, which hinder large-scale deployment. Conventional configuration studies often handle capacity planning and operational scheduling at different stages, complicating consistent life-cycle valuation under degradation and multi-service participation. This paper proposes a life-cycle multi-service co-optimization model (LC-MSCOM) to jointly determine ESS power–energy ratings and operating strategies. A unified revenue framework quantifies stacked revenues from time-of-use arbitrage, demand charge management, demand response, and renewable energy accommodation, while depth of discharge (DoD)-related lifetime loss is converted into an equivalent degradation cost and embedded in the optimization. The model is validated on a modified IEEE benchmark system using real generation and load data. Results show that LC-MSCOM increases net present value (NPV) by 26.8% and reduces discounted payback period (DPP) by 12.7% relative to conventional benchmarks, and sensitivity analyses confirm robustness under discount-rate, inflation-rate, and tariff uncertainties. By coordinating ESS dispatch with distribution network operating limits (nodal power balance, voltage bounds, and branch ampacity constraints), the framework provides practical, investment-oriented decision support for user-side ESS deployment. Full article