Search Results (2,901)

A convolutional neural network (CNN) has been developed to forecast the occurrence of low-visibility events or hazes in the Paris area. It has been trained and validated using multi-decadal daily regional maps of many meteorological and hydrological variables alongside surface visibility observations. The strategy is to make the machine learn from available historical data to recognize various regional weather and hydrological regimes associated with low-visibility events. To better preserve the characteristic spatial information of input features in training, two branched architectures have recently been developed. These architectures process input features firstly through several branched CNNs with different kernel sizes to better preserve patterns with certain characteristic spatial scales. The outputs from the first part of the network are then processed by the second part, a deep non-branched CNN, to further deliver predictions. The CNNs with new architectures have been trained using data from 1975 to 2019 in a two-class (haze versus non-haze) classification mode as well as a regression mode that directly predicts the value of surface visibility. The predictions of regression have also been used to perform the two-class classification forecast using the same definition in the classification mode. This latter procedure is found to deliver a much better performance in making class-based forecasts than the direct classification machine does, primarily by reducing false alarm predictions. The branched architectures have improved the performance of the networks in the validation and also in an evaluation using the data from 2021 to 2023 that have not been used in the training and validation. Specifically, in the latter evaluation, branched machines captured 70% of the observed low-visibility events during the three-year period at Charles de Gaulle Airport. Among those predicted low-visibility events by the machines, 74% of them are true cases based on observation. Full article

Finite-time consensus problem of hybrid-order heterogeneous multi-agent systems under a signed digraph topology is investigated in this paper. For heterogeneous multi-agent systems composed of first-order and second-order agents, a novel pinning event-triggered control protocol is devised to facilitate the attainment of the desired consensus state within a finite time. This control method overcomes communication barriers between first-order and second-order multi-agent systems, achieving effective control performance while reducing controller update frequency and communication costs. Based on graph theory and the Lyapunov stability method, several novel matrices are defined to address the finite-time consensus problem in hybrid-order multi-agent systems, and these matrices also facilitate the theoretical derivation process. Furthermore, it is demonstrated that the control protocol designed for hybrid-order systems is devoid of Zeno behavior. Finally, a detailed numerical example is supplied to illustrate the validity of the theoretical analysis. Full article

The objective of this research is to evaluate four distinct models for multi-population mortality projection in order to ascertain the most effective approach for forecasting the impact of the COVID-19 pandemic on mortality. Utilizing data from the Human Mortality Database for five countries—Finland, Germany, Italy, the Netherlands, and the United States—the study identifies the generalized additive model (GAM) within the age–period–cohort (APC) analytical framework as the most promising for precise mortality forecasts. Consequently, this model serves as the basis for projecting the impact of the COVID-19 pandemic on future mortality rates. By examining various pandemic scenarios, ranging from mild to severe, the study concludes that projections assuming a diminishing impact of the pandemic over time are most consistent, especially for middle-aged and elderly populations. Projections derived from the superior GAM-APC model offer guidance for strategic planning and decision-making within sectors facing the challenges posed by extreme historical mortality events and uncertain future mortality trajectories. Full article

The intensity of typhoons affecting the Korean Peninsula has been rapidly increasing, resulting in significant damage. Notably, this intensification correlates with the rise in Sea Surface Temperature (SST) in the western Pacific Ocean and surrounding sea areas, where typhoons that impact the Korean Peninsula originate and develop. The SST in these regions is increasing at a faster rate than the global average. Typhoon-related meteorological disasters are not isolated events, such as strong winds, heavy rains, or storm surges, but rather multi-hazard occurrences that can affect different areas simultaneously. As a result, preparation and evaluation must address multi-hazard disasters, rather than focusing on individual weather phenomena. This study develops the Typhoon Ready System (TRS) to improve impact-based forecasting in Korea, in response to the growing threat of multi-hazard weather disasters. By providing region-specific pre-disaster information, the TRS enables local governments and individuals to better prepare for and mitigate the impacts of typhoons. The system will be continuously refined in collaboration with the U.S. Weather-Ready Nation (WRN), which possesses advanced impact forecasting capabilities. The findings of this study offer a crucial framework for enhancing Korea’s ability to forecast and respond to the escalating threats posed by typhoons. By utilizing the TRS, it will be possible to assess the risks of various multi-hazard weather disasters specific to each region during the typhoon forecast period, and the relevant data can be efficiently applied at both the individual and local government levels for typhoon prevention efforts. The system will be continuously improved through cooperation with the U.S. WRN, leveraging their advanced impact forecasting systems. It is expected that the TRS will enhance the accuracy of typhoon impact forecasts, which have been responsible for significant damage in Korea. Full article

In this paper, we investigate the formation isolation regulation issue regarding multiple Autonomous Underwater Vehicles (AUVs) characterized by a “leader–follower” framework. Considering the cooperative–competitive relationship among the follower AUVs and the impact of unknown external disturbances, an extended state observer is designed based on backstepping to mitigate these disturbances, and an event-triggered control scheme is designed to realize the two-part consensus control within the multi-AUV system. Through rigorous theoretical analysis, it is shown that the system achieves asymptotic steadiness and is free from Zeno behavior under the proposed event-triggered control scheme. Finally, numerical simulations confirm the efficiency of the regulation strategy in achieving formation separation within the multi-AUV, where the trajectory tracking errors of individual AUVs gather in a compact vicinity close to the source, and the structure convergence is achieved, with the absence of Zeno behavior also demonstrated. Full article

Coastal land use represents the combination of various land cover forms in a coastal area, which helps us understand the historical events, current conditions, and future progress of a coastal area. Currently, the emergence of high-resolution optical satellite images significantly extends the scope of coastal land cover recognition, and deep learning models provide a significant possibility of extracting high-level abstract features from an optical satellite image to characterize complicated coastal land covers. However, recognition systems for labeling are always defined differently for specific departments, organizations, and institutes. Moreover, considering the complexity of coastal land uses, it is impossible to create a benchmark dataset that fully covers all types of coastal land uses. To improve the transferability of high-level features generated by deep learning to reduce the burden of creating a massive amount of labeled data, this paper proposes an integrated framework to support semantically enriched coastal land use recognition, including foundation model-powered multi-label coastal land cover classification and conversion from coastal land cover mapping into coastal land use semantics with a vector space model (VSM). The experimental results prove that the proposed method outperformed the state-of-the-art deep learning approaches in complex coastal land use recognition. Full article

In the context of constructing new power systems, distribution networks are increasingly incorporating distributed resources such as distributed photovoltaic (PV) systems, decentralized wind turbines (WTs), and new types of energy storage system (ESS), which may lead to prominent issues such as voltage overruns and reverse heavy overloads in the distribution network. While distributed resources are valuable for voltage regulation, their regulation characteristics vary with their operation means, and the randomness and volatility of renewable power generation will also influence the optimization and regulation of voltage in the distribution network. This paper proposes a multi-timescale reactive power optimization and regulation method for distribution networks in a multi-source interactive environment. Firstly, the voltage regulation characteristics of distributed PV systems, decentralized ESSs, and distributed WTs are analyzed. Based on this analysis, a multi-timescale voltage optimization scheme for distribution networks using the MPC method is proposed, which optimizes the voltage regulation strategies for each distributed resource in a rolling manner. Furthermore, an event-triggered real-time voltage zoning control strategy based on voltage sensitivity is proposed to address the real-time sudden voltage overlimit problems. The modified IEEE 33-node system is used to verify the performance of the proposed method. Simulation results indicate that the issue of voltage overruns at distribution network nodes has been improved, and the intraday rolling optimization yields results are more realistic compared with the day-ahead optimization method. Full article

In recent years, advanced controllers, including Model Predictive Control (MPC), have emerged as promising solutions to improve the efficiency of building energy systems. This paper explores the capabilities of MPC in handling multiple control objectives and constraints. A first MPC controller focuses on the task of ensuring thermal comfort in a residential house served by a heat pump while minimizing the operating costs when subject to different pricing schedules. A second MPC controller working on the same system tests the ability of MPC to deal with demand response events by enforcing a time-varying maximum power usage limitation signal from the electric grid. Furthermore, multiple combinations of the control parameters are tested in order to assess their influence on the controller performance. The controllers are tested on the BOPTEST framework, which offers standardized test cases in high-fidelity emulation models, and pre-defined baseline control strategies to allow fair comparisons also across different studies. Results show that MPC is able to handle multi-objective optimal control problems, reducing thermal comfort violations by between 66.9% and 82% and operational costs between 15.8% up to 20.1%, depending on the specific scenario analyzed. Moreover, MPC proves its capability to exploit the building thermal mass to shift heating power consumption, allowing the latter to adapt its time profile to time-varying constraints. The proposed methodology is based on technologically feasible steps that are intended to be easily transferred to large scale, in-field applications. Full article

A joint study of multi-particle pseudo-rapidity correlations and event-by-event fluctuations in the distributions of secondary particles and fragments of the target nucleus and the projectile nucleus was carried out in order to search for correlated clusters of secondary particles. An analysis of the collisions of the sulfur nucleus with photoemulsion nuclei at an energy of 200 A·GeV is presented based on experimental data obtained at the SPS at CERN. The analysis of multi-particle correlations was performed using the Hurst method. A detailed analysis of each individual event showed that in events of complete destruction of a projectile nucleus with a high multiplicity of secondary particles, long-distance multi-particle pseudo-rapidity correlations are observed. The distribution of average pseudo-rapidity in such events differs significantly from others, as it is much narrower, and its average value is noticeably shifted towards lower values <η>. Full article

The Energy Performance of Buildings Directive (EPBD) has set a target to achieve carbon-neutral building stock and generate 80% of its electricity from renewable sources by 2050. While Model Predictive Control (MPC) can contribute significantly to energy flexibility in buildings, its remote implementation remains relatively unexplored, especially in the residential sector. The purpose of this research is to demonstrate the reliability, robustness, and computational efficiency of a cloud-based application of an MPC called Smart Energy Management (SEM) on a multi-family residential building. The SEM was tested on a virtual building model in TRNSYS using an open-source distributed event streaming platform for data exchange and synchronization. Simplified models for thermal behavior prediction, including an R3C3 model of the building, were developed in C++. The SEM was evaluated in eight scenarios with varying weather conditions, optimization criteria, and runtime periods. The results demonstrate that the SEM maintains stability and robustness over a 2-week period with a 15-minute planning resolution while ensuring thermal comfort. The C++ implementation of the optimization algorithm enables SEM deployment on low-spec servers, supporting cost-effective applications in real buildings with minimal intervention. Full article

The degradation of products is an integral part of their life-cycle, often following predictable trajectories. However, sudden, unexpected events, termed ’shocks’, can substantially alter these degradation paths. Shocks can significantly influence the pace of degradation, leading to accelerated system failure. Moreover, they may initiate changes in degradation patterns, transitioning from linear to non-linear or random trajectories. To address this challenge, we present a novel multi-state reliability model for competing failure processes that account for degradation-shock dependencies by considering the state-varying degradation pattern. The degradation process is divided into s-states, with each state treated according to its pattern based on the time-transform Wiener process. The reliability function is derived based on soft failure caused by continuous degradation involving the s-states, the sudden increase in degradation caused by random shocks, and hard failure due to some shock processes. Additionally, we performed a sensitivity analysis to determine which parameters have the most significant impact on product reliability. Due to the complexity of the likelihood function, we adopted the ABC method to estimate the model parameters. A simulation study and a practical application with micro-electro-mechanical systems (MEMS) degradation results are used to demonstrate the efficiency and effectiveness of the proposed approach. Full article

With the rapid development of the general aviation industry in China, the influence of high-impact aeronautical weather events, such as aircraft icing, on flight safety has become more and more prominent. On 1 March 2021, an aircraft conducting weather modification operations crashed over Ji’an City, due to severe icing. Using multi-source meteorological observations and atmospheric numerical simulations, we analyzed the meteorological causes of this icing accident. The results indicate that a cold front formed in northwestern China and then moved southward, which is the main weather system in the icing area. Based on the icing index, we conducted an analysis of the temperature, relative humidity, cloud liquid water path, effective particle radius, and vertical flow field, it was found that aircraft icing occurred behind the ground front, where warm-moist airflows rose along the front to result in a rapid increase of water vapor in 600–500 hPa. The increase of water vapor, in conjunction with low temperature, led to the formation of a cold stratiform cloud system. In this cloud system, there were a large number of large cloud droplets. In addition, the frontal inversion increased the atmospheric stability, allowing cloud droplets to accumulate in the low-temperature region and forming meteorological conditions conducive to icing. The Weather Research and Forecasting model was employed to provide a detailed description of the formation process of the atmospheric conditions conducive to icing, such as the uplifting motion along the front and supercooled water. Based on a real case, we investigated the formation process of icing-inducing meteorological conditions under the influence of a front in detail in this study and verified the capability of a numerical model to simulate the meteorological environment of frontal icing, in order to provide a valuable reference for meteorological early warnings and forecasts for general aviation. Full article

The Lofoten Vortex (LV), which is identified as a quasi-permanent anticyclonic eddy, strengthens through continuous merging with external anticyclonic eddies. Our investigation used the Lagrangian method to monitor the LV on a daily basis. Utilizing satellite altimeter data, we conducted multi-year tracking and statistical analysis of merging events involving the LV. The results indicate a characteristic radius of approximately 42.72 km and a mean vorticity at the eddy center of approximately −2.23 × 10⁻⁵ s⁻¹. The eddy exhibits oscillatory motion within the sea basin depression, centered at 70°N, 3°E, characterized by counterclockwise trajectories between 0.5°E and 6°E and between 69°N and 70.5°N. There are two types of merging events: fusion events (55%), in which eddies of similar strengths interact within a closed flow line and then merge to form a new eddy; and absorption events (45%), in which the stronger LV absorbs the weaker anticyclonic eddies without destroying the structure of the LV itself. The nodes where strong vorticity advection occurs correspond to the nodes where merging occurs, suggesting that their effect on merging can be well characterized by the vorticity advection time series. We also observe occasional fluctuations and substitution events involving the LV and external anticyclonic eddies, suggesting a dynamic succession rather than a single vortex entity. Full article

In recent years, passive vehicle safety has become one of the major concerns for the automotive industry due to the considerable increase in the use of cars as a means of daily transport. Since real crash testing has a high financial cost, finite element simulations are generally used, which entail high computational cost and long simulation times. In this paper, we make use of the recent advances in the deep learning field to propose an affordable method to provide reliable approximations of the finite element simulator model that significantly reduce the computational load and time required. We compare the prediction performance in crash tests of different models, namely feed-forward neural networks and bayesian neural networks, as well as two multi-output regression methods. Our results show promising results, as deep learning models are able to drastically reduce the engineering costs while providing a feasible first approximation to the passenger’s injuries in a crash event, thus being a potential game changer in the vehicle safety design process. Full article

Flux transfer events (FTEs) are magnetic structures generally believed to originate from time-varying magnetic reconnection at the Earth’s magnetopause. Despite years of research, the mechanism of how FTEs are formed through reconnection remains controversial. In various models, FTEs exhibit different global configurations. Studying the FTE axial orientation can provide insights into their global shape, thereby helping to distinguish the generation mechanisms. In this paper, taking advantage of the orbital characteristics of the four Cluster spacecraft, we devised a multi-spacecraft timing method to determine the axes of a total of 57 FTEs observed sequentially by Cluster during a high-latitude duskside magnetopause crossing. During the nearly five-hour observation, the interplanetary magnetic field (IMF) experienced a large rotation, leading to a substantial rotation of the magnetosheath magnetic field. The analysis results show two new features of the FTE axis that have not been reported before: (1) the axes of the FTEs gradually rotate in response to the turning of the IMF and the magnetosheath magnetic field; (2) the axes of the FTEs vary between the direction of the magnetosheath magnetic field and the direction of the reconnection X-line. These features indicate that FTEs may have a more complex global configuration than depicted by traditional FTE models. Full article