Search Results (12,529)

When the near-field and far-field information of a target is uncertain, it is necessary to choose a suitable localization method. The modified polar representation (MPR) method integrates the two scenarios and achieves a unified localization with direction of arrival (DOA) estimation in the far field and position estimation in the near field. Previous studies have only proposed solutions for stationary environments and have not considered the motion factor. Therefore, this paper proposes a new unified positioning algorithm using multi-sensor time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements without prior target source information. The method represents the position of the target source using MPR and describes the localization problem as a weighted least squares (WLS) problem with two constraints. We first obtain the initial estimates by WLS without considering the constraints and then investigate a two-step error correction method based on the constraints. The first step corrects the initial estimate using the Taylor series expansion technique, and the second step corrects the DOA estimate in the previous step using the direct error compensation technique based on the properties of the second constraint. Simulation experiments show that the method is effective for the unified positioning of moving targets and can achieve the Cramer–Rao lower bound (CRLB). Full article

Industrial anomaly detection is constrained by the scarcity of anomaly samples, limiting the applicability of supervised learning methods. Many studies have focused on anomaly detection by generating anomaly images and adopting self-supervised learning approaches. Leveraging pre-trained networks on ImageNet has been explored to assist in this training process. However, achieving accurate anomaly detection remains time-consuming due to the network’s depth and parameter count not being reduced. In this paper, we propose a self-supervised learning method based on Feature Enhancement Patch Distribution Modeling (FEPDM), which generates simulated anomalies. Unlike direct training on the original feature extraction network, our approach utilizes a pre-trained network to extract multi-scale features. By aggregating these multi-scale features, we are able to train at the feature level, thereby adapting more efficiently to various network structures and reducing domain bias with respect to natural image classification. Additionally, it significantly reduces the number of parameters in the training process. Introducing this approach not only enhances the model’s generalization ability but also significantly improves the efficiency of anomaly detection. The method was evaluated on MVTec AD and BTAD datasets, and (image-level, pixel-level) AUROC scores of (95.7%, 96.2%), (93.4%, 97.6%) were obtained, respectively. The experimental results have convincingly demonstrated the efficacy of our method in tackling the scarcity of abnormal samples in industrial scenarios, while simultaneously highlighting its broad generalizability. Full article

Perennial groundcover (PGC) has promise as a scalable approach to generating natural resource benefits and sustainable biofuel feedstock while preserving the high yields of annual row crop production. Partnering row crops with temporally and spatially complementary low-growing, shallow-rooted perennials, such as Kentucky bluegrass (KBG) (Poa pratensis L.), is one example of an emerging PGC system. PGC’s ecosystem benefits can only be fully realized if commercial-scale adoption occurs, which hinges on its economic feasibility. This paper utilizes an enterprise budget framework to detail and compare the expected cost and revenue of establishing and maintaining PGC in row crop systems with standard continuous corn (SCC) (Zea mays L.) production, including stover harvest, but excluding economic incentives for ecosystem services. Optimistic and pessimistic assumptions were used, along with Monte Carlo simulation, to characterize the uncertainty in results. In the optimistic stover market scenario, Year 1 net returns for PGC averaged USD 84/ac less than for SCC; Year 2+ net returns averaged USD 83/ac more, meaning that cost parity with SCC occurs by the second PGC system year. Without stover revenue, parity is achieved after five years. These results affirm that PGC’s economic viability is critically impacted by a groundcover’s lifespan, the yield parity with SCC, and the availability of a stover market. Full article

The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations. Clustering-based communication helps reduce a node’s energy decay during data transmission and enhances network lifetime. Many hybrid combination algorithms have been proposed for clustering and routing protocols to improve network lifetime in disaster scenarios. However, the performance of these protocols varies widely based on the underlying network configuration and the optimisation parameters considered. In this research, we used the clustering parameters most relevant to disaster scenarios, such as the node’s residual energy, distance to sink, and network coverage. We then proposed the bio-inspired hybrid BOA-PSO algorithm, where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm was compared with that of various benchmark protocols: LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime were considered performance metrics. The simulation results demonstrate that the proposed algorithm effectively conserves residual energy, achieving more than a 17% improvement for short-range scenarios and a 10% improvement for long-range scenarios. In terms of throughput, the proposed method delivers a 60% performance enhancement compared to LEACH, a 53% enhancement compared to DEEC, and a 37% enhancement compared to PSO. Additionally, the proposed method results in a 60% reduction in packet drops compared to LEACH and DEEC, and a 30% reduction compared to PSO. It increases network lifetime by 10–20% compared to the benchmark algorithms. Full article

This study presents a numerical investigation into the adhesion strength of micro fibrillar structures, incorporating statistical analysis and the effects of excessive pre–load leading to fibril buckling. Fibrils are modeled as soft cylinders using the Euler–Bernoulli beam theory, with buckling conditions described across three distinct states, each affecting the adhesive properties of the fibrils. Iterative simulations analyze how adhesion strength varies with pre–load, roughness, number of fibrils, and the work of adhesion. Roughness is modeled both in fibril heights and in the texture of a rigid counter surface, following a normal distribution with a single variance parameter. Results indicate that roughness and pre–load significantly influence adhesion strength, with excessive pre–load causing substantial buckling and a dramatic reduction in adhesion. This study also finds that adhesion strength decreases exponentially with increasing roughness, in line with theoretical expectations. The findings highlight the importance of buckling and roughness parameters in determining adhesion strength. This study offers valuable insights into the complex adhesive interactions of fibrillar structures, offering a scalable solution for rapid assessment of adhesion in various rough surface and loading scenarios. Full article

This article offers a comprehensive analysis of the impact of MSPF on concrete’s mechanical properties and fracture behavior. Combining findings from numerical simulations and laboratory experiments, our study validates numerical models against diverse fiber percentages and aggregate distributions, affirming their reliability. Key findings reveal that mixed-mode fracture scenarios in fiber-reinforced concrete are significantly influenced by the mode mixity parameter (M^e), quantifying the balance between mode I and mode II fracture components, ranging from 1 (pure mode I) to 0 (pure mode II). The introduction of the effective stress intensity factor (K_eff) provides a profound understanding of the material’s response to mixed-mode fracture. Our research demonstrates that as M^e approaches zero, indicating shear deformation dominance, the concrete’s resistance to mixed-mode fracture decreases. Crucially, the addition of MSPF considerably enhances mixed-mode fracture toughness, especially when Me ranges between 0.5 and 0.9, resulting in an approximately 400% increase in fracture toughness. However, beyond a specific threshold (approximately 4% FVF), diminishing returns occur due to reduced fiber–cement mortar bonding forces. We recommend an optimal fiber content of around 4% by weight of the total concrete mixture to avoid material distribution disruption and strength reduction. The practical implications of these findings suggest improved design strategies for more resilient infrastructure, particularly in earthquake-resistant constructions and sustainable urban development. These insights provide a valuable framework for future research and development in concrete technology. Full article

The rudder surfaces and lifting surfaces of a missile are utilized to acquire aerodynamic forces and moments, adjust the missile’s attitude, and achieve precise strike missions. However, the harsh flying conditions of missiles make the rudder surfaces and lifting surfaces susceptible to faults. In practical scenarios, there is often a scarcity of fault data, and sometimes, it is even difficult to obtain such data. Currently, data-driven fault detection and localization methods heavily rely on fault data, posing challenges for their applicability. To address this issue, this paper proposes an HBOS (Histogram-Based Outlier Score) online fault-detection method based on statistical distribution. This method generates a fault-detection model by fitting the probability distribution of normal data and incorporates an adaptive threshold to achieve real-time fault detection. Furthermore, this paper abstracts the interrelationships between the missile’s flight states and the propagation mechanism of faults into a hierarchical directed graph model. By utilizing bilateral adaptive thresholds, it captures the first fault features of each sub-node and determines the fault propagation effectiveness of each layer node based on the compatibility path principle, thus establishing a fault inference and localization model. The results of semi-physical simulation experiments demonstrate that the proposed algorithm is independent of fault data and exhibits high real-time performance. In multiple sets of simulated tests with randomly parameterized deviations, the fault-detection accuracy exceeds 98% with a false-alarm rate of no more than 0.31%. The fault-localization algorithm achieves an accuracy rate of no less than 97.91%. Full article

Unmanned aerial vehicles (UAVs) need high data rate connectivity, which is achievable through mm-waves and sub-THz bands. The proposed two-port leaky wave MIMO antenna, employing a coplanar proximity technique that combines capacitive and inductive loading, addresses this need. Featuring mesh-like slots and a vertical slot to mitigate open-stopband (OSB) issues, the antenna radiates broadside and bidirectionally. H-shaped slots on a strip enhance port isolation, and a coffee bean metasurface (MTS) boosts radiation efficiency and gain. Simulations and experiments considering various realistic scenarios, each at varying vertical and horizontal distances, show steered beam patterns, circular polarization (CP), and high-gain properties, with a maximum gain of 13.8 dBi, an axial ratio (AR) <2.9, a diversity gain (DG) >9.98 dB, and an envelope correlation coefficient (ECC) <0.003. This design supports drones-to-ground (D2G), drone-to-drone (D2D), and drone-to-satellite (D2S) communications. Full article

The production of Moringa oleifera capsules has emerged as a promising agricultural activity in Mexico, driven by the plant’s well-documented health and nutritional benefits. In response, farmers have begun producing Moringa oleifera as a dietary supplement, using artisanal methods with minimal production controls. This study aimed to estimate the processing times of the Moringa oleifera production system using simulation modeling with Arena^® software and to evaluate the economic feasibility of capsule production. Methodologically, it was approached as a case study in the state of Veracruz, and processing times were estimated through systematic sampling and modeled with Arena simulation software. Three simulation scenarios were considered to select a technically and economically viable alternative for agricultural producers, as follows: one with a 200-capsule encapsulator (1E200C), another with two 200-capsule encapsulators (2E200C), and a third with an 800-capsule encapsulator (1E800C). For the initial diagnosis, a research stay was conducted for 180 days, and the production capacity was set at 24,000 capsules due to raw material limitations. Results indicated that the 1E800C scenario reduced processing time by 60% compared to the 1E200C scenario and by 35% compared to the 2E200C scenario. Additionally, the 1E800C model required only Arena simulation software version 162 days (16 h) of work, while the 2E200C required 4 days (32 h) and the 1E200C required 7 days (56 h). In terms of production costs per unit of 90 capsules, the 1E200C was USD 3.93, the 2E200C was USD 3.64, and the 1E800C was USD 3.45. This study concluded that due to raw material limitations, the company can produce 12,800 units of 90 capsules per year. It is recommended to adopt the 1E800C encapsulator, which would lower the overall production costs by 12.23%, representing USD 0.48 per unit produced and resulting in an additional profit of USD 6150.50. Over a five-year evaluation period, the benefit–cost ratio was 5.03, the NPV was 922,370.11, and the IRR was 42.09%, indicating that this type of agribusiness in Mexico is both technically and economically feasible. Full article

Contemporary post-industrial urban areas face opposing transformation trends: on one hand, abandonment or underutilization, and its replacement by new constructions and uses, on the other hand, the revaluation of the historical fabric and the implementation of initiatives to rehabilitate this legacy as industrial heritage. This study aimed to understand the factors that influence trends, and simulate land-use scenarios. A methodology based on three phases is proposed: digitization, exploratory spatial data analysis and simulation. Using the former textile district of Bellavista in Tomé (Chile), this study created and used historical land-use maps from 1970, 1992 and 2019. Meanwhile the main change observed from 1970 to 1992 was a 59.4% reduction in Historical Informal Open Spaces. The major change from 1992 to 2019 was the Historical Informal Open Space loss trend continuing; 65% of the land dedicated to this use changed to new usages. Consequently, the influence of two morphological factors and three urban management instruments on land-use changes between 1992 and 2019 was studied. The projection to 2030 showed a continued trend of expansion of new housing uses over historic urban green spaces and industrial areas on the waterfront, although restrained by the preservation of the central areas of historic housing and the textile factory. Full article

The modern energy landscape is undergoing a significant transformation towards cleaner, decentralized energy sources. This change is driven by environmental and sustainability needs, causing traditional centralized electric grids, which rely heavily on fossil fuels, to be replaced by a diverse range of decentralized distributed energy resources. Virtual power plants (VPPs) have surfaced as a flexible solution in this transition. A VPP’s primary role is to optimize energy production, storage, and distribution by coordinating output from various connected sources. Relying on advanced communication and control systems, a VPP can balance supply and demand in real time, offer ancillary services, and support grid stability. However, aligning VPPs’ economic and operational practices with broader environmental goals and policies is a challenging yet crucial aspect. This article introduces a new VPP management and optimization algorithm designed for quick and intelligent decision-making, aiming for the lowest levelized cost of energy (LCOE), minimum grid technical losses, and greenhouse gas (GHG) emissions. The algorithm’s effectiveness is confirmed using the IEEE 33-bus grid with 10 different distributed power generators. Simulation results show the algorithm’s responsiveness to complex variables found in practical scenarios, finding the optimal combination of available energy resources. This minimizes the LCOE, technical losses, and GHG emissions in less than 0.08 s, achieving a total LCOE reduction of 16% from the baseline. This work contributes to the development of intelligent energy management systems, aiding the transition towards a more resilient and sustainable energy infrastructure. Full article

Due to the swift development of the Internet of Things (IoT), massive advanced terminals such as sensor nodes have been deployed across diverse applications to sense and acquire surrounding data. Given their limited onboard capabilities, these terminals tend to offload data to servers for further processing. However, terminals cannot transmit data directly in regions with restricted communication infrastructure. With the increasing proliferation of unmanned aerial vehicles (UAVs), they have become instrumental in collecting and transmitting data from the region to servers. Nevertheless, because of the energy constraints and time-consuming nature of data processing by UAVs, it becomes imperative not only to utilize multiple UAVs to traverse a large-scale region and collect data, but also to overcome the substantial challenge posed by the time sensitivity of data information. Therefore, this paper introduces the important indicator Age of Information (AoI) that measures data freshness, and develops an intelligent AoI optimization data processing approach named AODP in a hierarchical cloud–edge architecture. In the proposed AODP, we design a management mechanism through the formation of clusters by terminals and the service associations between terminals and hovering positions (HPs). To further improve collection efficiency of UAVs, an HP clustering strategy is developed to construct the UAV-HP association. Finally, under the consideration of energy supply, time tolerance, and flexible computing modes, a gray wolf optimization algorithm-based multi-objective path planning scheme is proposed, achieving both average and peak AoI minimization. Simulation results demonstrate that the AODP can converge well, guarantee reliable AoI, and exhibit superior performance compared to existing solutions in multiple scenarios. Full article

With global climate change and irrational human activities, regional water resource conflicts are becoming more and more pronounced. The availability of water resource in watersheds can be indicated by the water yield. Exploring the factors that influence the water yield is crucial in responding to climate change and protecting water resource. Previous research on the factors influencing the water yield has frequently adopted a macro-level perspective, which has failed to reflect the influencing mechanisms of changes at the local scale adequately. Therefore, this study proposes a novel framework for integrally evaluating the impacts of climate change and human activities on water yield services from both local and global perspectives. Taking Ganzhou City, the source of the Ganjiang River, as an example, the results show the following: (1) Ganzhou City had the largest water yield of 1307.29 mm in 2016, and the lowest was only 375.32 mm in 2011. The spatial distribution pattern was mainly affected by the surface environment, and the high-value water yield regions in the study area were predominantly located in urban areas with flat terrain. (2) At the local scale, regions where human activities contribute more than 80% accounted for 25% of the area. In comparison, the impact of climate change accounted for 0.95%. The contribution rate of human activities to the water yield in Ganzhou City was significantly greater than that of climate change. (3) At the global scale, the simulation results of four scenarios show that climate change contributed (>98%) to the water yield, which is significantly higher than human activities (<2%). This study puts forward pioneering views on the research of water yield driving forces and provides a valuable theoretical basis for water resource protection and ecological environment construction. Full article

The growth of electric vehicles (EVs) and their integration into existing and future buildings bring new considerations for energy efficiency (EE) and balance when combined with renewable energy. However, for buildings with an energy efficiency label, such as Near Zero Energy Building (NZEB) or Positive Energy Building (PEB), the introduction of EVs may result in the declassification of the EE label due to the additional energy required for the charging infrastructure. This underscores the increasing relevance of demand-side management techniques to effectively manage and utilize energy consumption and generation in buildings. This paper evaluates the influence of electric vehicle (EV) charging on NZEB/PEB-labeled buildings of the Brazilian Building Labeling Program (PBE Edifica). Utilizing on-site surveys, computational modeling, and thermos-energetic analysis with software tools such as OpenStudio v. 1.1.0 and EnergyPlus v. 9.4.0, an energy classification was conducted in a building in the city of Belem, State of Para, Brazil. Subsequently, power flow simulations employing probabilistic models and Monte Carlo approaches were executed in the OpenDSS software v. 10.0.0.2 to examine the impact of EV integration, both with and without the implementation of demand-side management techniques. Analyses using the labeling methodology demonstrated that the building has EE level C and NZEB self-sufficiency classification. The assessment of the impact of EV integration on the building’s total energy consumption in the base (current) scenario was carried out in two scenarios, with (2) and without (1) supply management. Scenario 01 generated a 69.28% increase in energy consumption, reducing the EE level to D and resulting in the loss of the NZEB class. Scenario 02 resulted in a smaller increase in energy consumption of 40.50%, and guaranteed the return of the NZEB class lost in scenario 1, but it was not enough to return the EE level to class C. The results highlight the need for immediate and comprehensive energy management strategies, as the findings show that the two scenarios present a difference of 41.55% in energy consumption. Nonetheless, these strategies are not enough if other consumption restrictions or energy efficiency measures are not applied to other building systems. Full article

Accurate robot dynamics models are crucial for safe and stable control as well as for generalization to new conditions. Data-driven methods are increasingly used in robotics dynamics modeling for their superior approximation, with extrapolation performance being a critical efficacy indicator. While deep learning is widely used, it often overlooks essential physical principles, leading to weaker extrapolation capabilities. Recent innovations have introduced physics-inspired deep networks that integrate deep learning with physics, leading to improved extrapolation due to their informed structure, but potentially to underfitting in real-world scenarios due to the presence of unmodeled phenomena. This paper presents an experimental framework to assess the extrapolation capabilities of data-driven methods. Using this framework, physics-inspired deep networks are applied to learn the inverse dynamics models of a simulated robotic manipulator and two real physical systems. The results show that under ideal observation conditions physics-inspired models can learn the system’s underlying structure and demonstrate strong extrapolation capabilities, indicating a promising direction in robotics by offering more accurate and interpretable models. However, in real systems their extrapolation often falls short because the physical priors do not capture all dynamic phenomena, indicating room for improvement in practical applications. Full article