Search Results (4,832)

The quality of water supplied to consumers through drinking water distribution networks is a matter of growing concern and is subject to increasingly stringent new regulations. The utilization of simulation models, which encompass the movement of water through pipes and storage tanks, has been demonstrated to provide valuable information with regard to the improvement of the system operation. However, once a calibrated quality model is available, justifying the evolution of the quality provided by the model at any junction in the network is not direct; however, this is sometimes necessary to carry out the appropriate interventions to improve quality parameters. A methodology to help the comprehension of the quality results provided by simulation models has been developed in this paper. This methodology is based on the principles of event-based transport methods, whereby the quality of a particle is tracked as it moves downstream from a starting point or upstream from an arrival point. Upon reaching a junction, an event occurs that determines the subsequent trajectory of the particle. The details of the method and its potential are demonstrated through an illustrative example, reinforced by its application in a more realistic case. Consequently, by monitoring the particles, it becomes feasible to interpret the quality values obtained at any junction in the network and at any designated moment. If the quality value were the result of a measurement, the method would also allow us to track the origin of that value; in this way, it could be used in the future to locate the possible source of a detected contaminant. Full article

Reliable detection of unknown signals is essential for ensuring the robustness of underwater acoustic sensing systems, particularly in maritime security and autonomous navigation. However, Conventional deep learning models often exhibit overconfidence when encountering unknown signals and are unable to quantify predictive uncertainty due to their deterministic inference process. To address these limitations, this study proposes a novelty detection framework that integrates an out-of-distribution detector for neural networks (ODIN) with Monte Carlo (MC) dropout. ODIN mitigates model overconfidence and enhances the separability between known and unknown signals through softmax probability calibration, while MC dropout introduces stochasticity via multiple forward passes to estimate predictive uncertainty—an element critical for stable sensing in real-world underwater environments. The resulting probabilistic outputs are modeled using Gaussian mixture models fitted to ODIN-calibrated softmax distributions of known classes. The Kullback–Leibler divergence is then employed to quantify deviations of test samples from known class behavior. Experimental evaluations on the DeepShip dataset demonstrate that the proposed method achieves, on average, a 9.5% and 5.39% increase in area under the receiver operating characteristic curve, and a 7.82% and 2.63% reduction in false positive rate at 95% true positive rate, compared to the MC dropout and ODIN baseline, respectively. These results confirm that integrating stochastic inference with ODIN significantly enhances the stability and reliability of novelty detection in underwater acoustic environments. Full article

The high penetration of distributed photovoltaics (PV) into distribution networks alters the system’s short-circuit current characteristics, posing risks of maloperation and failure-to-operate to conventional inverse-time overcurrent protection. Based on an equivalent model of distributed PV during faults, this paper analyzes its impact on the protection characteristics of traditional distribution networks. With protection selectivity and the physical constraints of protection devices as conditions, an optimization model for inverse-time overcurrent protection is established, aiming to minimize the total operation time. To enhance the solution capability for this complex optimization problem, the standard Osprey Optimization Algorithm (OOA) is improved through the incorporation of three strategies: arccosine chaotic mapping for population initialization, a nonlinear convergence factor to balance global and local search, and a dynamic spiral search strategy combining mechanisms from the Whale and Marine Predators algorithms. Based on this improved algorithm, an optimized protection scheme for distribution networks with distributed PV is proposed. Simulations conducted in PSCAD/EMTDC (V4.6.2) and MATLAB (R2023b) verify that the proposed method effectively prevents protection maloperation and failure-to-operate under both fault current contribution and extraction scenarios of PV, while also reducing the overall relay operation time. Full article

With the rapid growth of residential electric vehicles, synchronized charging during peak periods can induce severe load ramping and exceed distribution network capacity limits. To mitigate these issues, governments have promoted a unified build-and-operate community model that enables centralized coordination of community charging and ensures real-time responsiveness to grid dispatch signals. Targeting this emerging operational paradigm, a dual-dimensional compensation mechanism for real-time electric vehicle (EV) demand response is proposed. The mechanism integrates two types of compensation: power regulation compensation, which rewards users for providing controllable power flexibility, and state-of-charge (SoC) loss compensation, which offsets energy deficits resulting from demand response actions. This dual-layer design enhances user willingness and long-term engagement in community-level coordination. Based on the proposed mechanism, a bi-level optimization framework is developed to realize efficient real-time regulation: the upper level maximizes the active response capacity under budget constraints, while the lower level minimizes the aggregator’s total compensation cost subject to user response behavior. Simulation results demonstrate that, compared with conventional fair-share curtailment and single-compensation approaches, the proposed mechanism effectively increases active user participation and reduces incentive expenditures. The study highlights the mechanism’s potential for practical deployment in unified build-and-operate communities and discusses limitations and future research directions. Full article

This study proposes a novel artificial neural network-based methodology for classifying the incident wave direction during ship navigation using the heave–roll–pitch motion response spectra as input. The proposed model demonstrated a balanced performance with an overall accuracy of approximately 0.888, effectively classifying the wave direction into three major categories: head-sea, beam-sea, and following-sea. The methodology utilizes Response Amplitude Operators derived from linear potential flow theory to generate motion response spectra, which are then used to classify the incident wave direction. The model effectively learns the frequency-distribution characteristics of the response spectrum, enabling wave direction classification without the need for complex inverse analysis procedures. This approach is significant in that it allows wave direction recognition solely based on measurable ship motion responses, without the need for additional external sensors or mathematical modeling. This data-driven approach has strong potential for integration into autonomous ship situational awareness modules and real-time wave monitoring technologies. However, the study simplified the directional domain into three representative groups, and the model was validated primarily using a numerically generated dataset, indicating the need for future improvements. Future research will expand the dataset to include a broader range of sea states, improve directional resolution, and explore continuous wave direction prediction. Additionally, further validation using field-measured data will be conducted to assess the real-time applicability of the proposed model. Full article

Contemporary nurseries of fruit trees and ornamental plants constitute a key component in the production of high-quality planting material. At present, conventional technology dominates in nurseries in Poland and throughout the European Union. It is based on universal agricultural tractors working with numerous specialized machines—typically underutilized—including sprayers, inter-row cultivation equipment, fertilizer spreaders, and tree lifters. This concept entails several limitations and high investment costs. Because of the considerable size and turning radius of such machinery, a dense network of service roads (every 15–18 m) and wide headlands must be maintained. These areas, which constitute approximately 20% of the total surface, are effectively wasted yet require continuous agronomic maintenance. An alternative concept employs a set of implements mounted on a high-clearance portal tractor (1.6–1.8 m), forming a specialized unit capable of moving above the rows of nursery crops. The study objective of the research was to evaluate the air distribution generated by an air-jet system installed on a crop-spray boom mounted on a portal sprayer, and to assess spray deposition during treatments in nursery trees. Such a configuration enables the mechanization of a broader range of nursery operations than currently possible, while reducing investment costs compared with conventional technology. One still underutilized technology consists of sprayers with an auxiliary airflow (AA) generated by air sleeves. Mean air velocity was measured in three vertical planes, and they showed lower air velocity between 1.0 m and 5.5 m. Spray deposition on apple nursery trees was assessed using a fluorescent tracer. The experimental design consists of a comparative field experiment with and without air flow support, spraying at two standard working rates (200 and 400 L·ha⁻¹) and determining the application of the liquid to plants in the nursery. The results demonstrated a positive effect of the AA system on deposition. At a travel speed of 6.0 km·h⁻¹ and an application rate of 200 L·ha⁻¹, deposition on the upper leaf surface was 68% higher with the fan engaged. For a 400 L·ha⁻¹ rate, deposition increased by 47%, with both differences statistically significant. The study showed that the nursery sprayer mounted on a high-clearance portal tractor and equipped with an AA system achieved an increase of 58% in spray deposition on the upper leaf surface when the fan was operating at 200 L·ha⁻¹ and 28% at 400 L·ha⁻¹. Substantial differences were found between deposition on the upper and lower leaf surfaces, with the former being 20–30 times greater. Given the complexity of nursery production technology, sprayers that ensure the highest possible biological efficacy and high quality of nursery material will play a pivotal role in its development. At the current stage, AA technology fulfils these requirements. Full article

Monitoring the health of power lines (PL) is essential for ensuring reliable power delivery, facilitating predictive maintenance, and maintaining a resilient grid infrastructure. Given the extensive length of PL networks, large numbers of wireless sensor nodes must be deployed, often in remote and harsh environments where battery replacement is costly and impractical. To address these limitations, this work proposes a hybrid energy-harvesting approach that combines piezoelectric and photovoltaic (PV) technologies to enable long-term, battery-free PL monitoring. The primary energy source is a compact, tunable, magnetically coupled piezoelectric vibrational energy harvester (VEH) that exploits local magnetic field distribution, inducing mechanical excitation of a cantilever and enabling the harvesting of vibrational energy near the PL at a frequency of 50 Hz. A complementary PV harvester is integrated to ensure operation during power outages or conditions where the piezoelectric excitation is reduced, thereby enhancing system robustness. Electromechanical characterization and a lumped-parameter model show good agreement with experimental results of the proposed VEH. The system is validated both on a PL test bench (5 A–10 A) and through inertial excitation using an electrodynamic shaker, demonstrating stable performance across a wide range of operating conditions. The combined hybrid architecture highlights a promising pathway toward self-sustaining, maintenance-free sensor nodes for next-generation power line monitoring. Finally, we demonstrate the feasibility of using such system for powering a WSN node by comparing the power produced by the proposed system with the power consumption of a potential application. Full article

The demand for real-time land cover mapping from high-resolution remote sensing (HR-RS) imagery motivates lightweight segmentation models running directly on satellites. By processing on-board and transmitting only fine-grained semantic products instead of massive raw imagery, these models provide timely support for disaster response, environmental monitoring, and precision agriculture. Many recent methods combine convolutional neural networks (CNNs) with Transformers to balance local and global feature modeling, with convolutions as explicit information aggregation modules. Such heterogeneous hybrids may be unnecessary for lightweight models if similar aggregation can be achieved homogeneously, and operator inconsistency complicates optimization and hinders deployment on resource-constrained satellites. Meanwhile, lightweight Transformer components in these architectures often adopt aggressive channel compression and shallow contextual interaction to meet compute budgets, impairing boundary delineation and recognition of small or rare classes. To address this, we propose SatViT-Seg, a lightweight semantic segmentation model with a pure Vision Transformer (ViT) backbone. Unlike CNN-Transformer hybrids, SatViT-Seg adopts a homogeneous two-module design: a Local-Global Aggregation and Distribution (LGAD) module that uses window self-attention for local modeling and dynamically pooled global tokens with linear attention for long-range interaction, and a Bi-dimensional Attentive Feed-Forward Network (FFN) that enhances representation learning by modulating channel and spatial attention. This unified design overcomes common lightweight ViT issues such as channel compression and weak spatial correlation modeling. SatViT-Seg is implemented and evaluated in LuoJiaNET and PyTorch; comparative experiments with existing methods are run in PyTorch with unified training and data preprocessing for fairness, while the LuoJiaNET implementation highlights deployment-oriented efficiency on a graph-compiled runtime. Compared with the strongest baseline, SatViT-Seg improves mIoU by up to 1.81% while maintaining the lowest FLOPs among all methods. These results indicate that homogeneous Transformers offer strong potential for resource-constrained, on-board real-time land cover mapping in satellite missions. Full article

This work presents the development of a low-cost and high-performance multi-sensory gas detection instrument named the AIMNet Sensor, with the integration of a machine learning-based data processing method. The compact and low-power instrument (8.5 × 11.5 cm, 1.4 W) houses the core sensing hardware module, Senseair K96, that integrates both a non-dispersive infrared (NDIR)-based gas sensing unit and a BME280 environmental sensing unit. To address the outdoor operation challenges caused by environmental fluctuation due to the varying temperature, humidity, and pressure, from the software aspect, multiple machine learning-based regression models were trained in this work on 13,125 calibration data points collected under controlled laboratory conditions. Among ten tested algorithms, the Multilayer Perceptron (MLP) and Elastic Net models achieved the highest accuracy, with R-squared coefficient $R^2>0.8$ on both indoor and outdoor scenarios, and with inter-sensor root mean square error (RMSE) within 1.5 ppm across four identical instruments. Moreover, field mobile validation was performed near a wastewater management facility using this solution, confirming a strong correlation with LI-COR reference measurements and a reliable detection of CH${}_4$ leaks with concentrations up to 18 ppm at the test site. Overall, this machine learning-integrated NDIR sensing solution (i.e., AIMNet) offers a practical and scalable solution towards a more robust distributed CH${}_4$ monitoring network for real-world field-deployable applications. Full article

The strong uncertainty of renewable energy poses significant reliability and safety challenges for the coordinated operation of multi-integrated energy systems (MIES). To address this, a data-driven two-stage distributed robust collaborative optimization scheduling model for MIES is proposed, based on a spatiotemporal fusion conditional diffusion model (STF-CDM). First, to more accurately capture the uncertainty in renewable energy output, the model utilizes a scenario set generated by the STF-CDM model and reduced via the K-means clustering algorithm as the initial renewable energy scenarios for the distributed robust optimization set. The STF-CDM model employs a Temporal module component (TMC) unit composed of Transformer time-series modules and a Spatial module component (SMC) unit composed of CNN neural networks for feature extraction and fusion of time-series and spatial-series data. Second, a benefit allocation method based on multi-energy trading contribution rates is proposed to achieve equitable distribution of cooperative gains. Finally, to protect participant privacy and enhance computational efficiency, an alternating direction multiplier method (ADMM) coupled with parallelizable column and constraint generation (C&CG) is employed to solve the energy trading problem. The case analysis results demonstrate that the STF-CDM model proposed in this study exhibits superior performance in addressing the uncertainty of renewable energy output. Concurrently, the asymmetric Nash game mechanism and the ADMM-C&CG solution algorithm proposed in this study achieve a fair and reasonable distribution of benefits among all participants when handling energy transactions and cooperative gains. This is accomplished while ensuring system robustness, economic efficiency, and privacy. Full article

The rapid development of the Internet of Things (IoT), a network of interconnected devices and sensors, has improved operational efficiency, comfort, and sustainability in smart buildings. However, relying on interconnected systems also introduces cybersecurity vulnerabilities. For instance, attackers can exploit zero-day vulnerabilities (previously unknown security flaws), launch Distributed Denial of Service (DDoS) attacks (overwhelming network resources with traffic), or access sensitive Building Management Systems (BMS, centralized platforms for controlling building operations). By targeting critical assets such as Heating, Ventilation, and Air Conditioning (HVAC) systems, security cameras, and access control networks, they may compromise the safety and functionality of the entire building. To address these threats, this paper presents a cybersecure intelligent sensor framework to protect smart buildings from various IoT-related cyberattacks. The main component is an automated Intrusion Detection System (IDS, software that monitors network activity for suspicious actions), which uses machine learning algorithms to rapidly identify, classify, and respond to potential threats. Furthermore, the framework integrates intelligent sensor networks with AI-based analytics, enabling continuous monitoring of environmental and system data for behaviors that might indicate security breaches. By using predictive modeling (forecasting attacks based on prior data) and automated responses, the proposed system enhances resilience against attacks such as denial of service, unauthorized access, and data manipulation. Simulation and testing results show high detection rates, low false alarm frequencies, and fast response times, thereby supporting the cybersecurity of smart building infrastructures and minimizing downtime. Overall, the findings suggest that AI-enhanced cybersecurity systems offer promise for IoT-based smart building security. Full article

In the conventional agricultural production process, the harvesting of mature fruits is frequently dependent on the observation and labor of workers, a process that is often time-consuming and labor-intensive. This study proposes an enhanced YOLOv7 detection and recognition model that incorporates a cross-spatial-channel 3D attention mechanism, a prediction head, and a weighted bidirectional feature pyramid neck optimization. The motivation for this study is to address the issues of uneven target distribution, mutual occlusion of fruits, and uneven light distribution that are prevalent in harvesting operations within orchards. The experimental findings demonstrate that the proposed model achieves an mAP@0.5–0.95 of 89.3%, representing an enhancement of 8.9% in comparison to the initial network. This method has resolved the issue of detecting and positioning the harvesting manipulator in complex orchard scenarios, thereby providing technical support for unmanned agricultural operations. Full article

In this paper, we propose a mechanical fault diagnosis technology for circuit breakers based on the NGO-VMD, aiming to improve the accuracy and efficiency of fault diagnosis. The circuit breaker is a key protection device in power systems, and its operational status is crucial to grid security. This paper introduces the NGO-VMD method to decompose its vibration signals, aiming to improve the accuracy and efficiency of fault diagnosis. Failure to detect and address mechanical faults in circuit breakers can lead to equipment damage, power outages, and even personal injury. Therefore, it is of great significance to develop efficient and accurate mechanical fault diagnosis technology for after converting the mechanical fault signal of the vacuum circuit breaker in the distribution network into the IMF form, the modal information of the vibration signal under different faults of the circuit breaker is effectively extracted, and the singular value decomposition of the IMF signal component is carried out to make the information characteristics contained more obvious, Finally, LSTM is used to achieve precise identification of circuit breaker faults. In this paper, the experimental test is carried out on the basis of the actual vacuum circuit breaker in the distribution network, and the feasibility of the design scheme is verified by comprehensive analysis. The comparison and analysis with other methods can be obtained, and the scheme has the advantages of higher efficiency, stronger stability and more accuracy. Full article

To achieve low-carbon collaborative operation of active distribution networks (ADNs) and microgrid clusters, this paper proposes a signal-guided collaborative optimization method. Firstly, a spatiotemporal carbon intensity equilibrium model (STCIEM) is constructed, overcoming the limitations of centralized carbon emission flow models in terms of data privacy and equitable distribution, and enabling distributed and precise carbon emission measurement. Secondly, a dual-market mechanism for carbon and electricity is designed to support peer-to-peer (P2P) carbon quota trading between microgrids and ADN-backed clearing, enhancing market liquidity and flexibility. In terms of scheduling strategy optimization, the multi-agent deep deterministic policy gradient (MADDPG) algorithm is incorporated into the carbon-electricity cooperative game framework, enabling differentiated energy scheduling under constraints. Simulation results demonstrate that the proposed method can effectively coordinate the operation of energy storage, gas turbines, and demand response, reduce system carbon intensity, improve market fairness, and enhance overall economic performance and robustness. The study shows that this framework provides theoretical support and practical reference for future distributed energy consumption and carbon neutrality paths. Full article

Given the pressing need to strengthen operational reliability in electrical distribution networks, this study proposes an optimization methodology based on the Teaching–Learning-Based Optimization (TLBO) algorithm for the strategic location of redundant lines. The model is validated on the “MV Distribution Network—Base Model” test system, considering the combination of the MTBF (Mean Time Between Failures) and MTTR (Mean Time To Repair) indicators as the objective function. After 500 independent runs, it is determined that the configuration with three redundant lines identified as LN_1011, LN_1058, and LN_0871 offers the most stable solution. Specifically, this topology increases the MTBF from 403.64 h to 409.42 h and reduces the MTTR from 2.351 h to 2.306 h. In addition, significant improvements are observed in the voltage profile and angle, along with a more balanced redistribution of active and reactive power, more efficient use of existing lines, and an overall reduction in energy losses. Full article