Search Results (52)

This paper presents a visual active SLAM method considering measurement and state uncertainty for space exploration in urban search and rescue environments. An uncertainty evaluation method based on the Fisher Information Matrix (FIM) is studied from the perspective of evaluating the localization uncertainty of SLAM systems. With the aid of the Fisher Information Matrix, the Cramér–Rao Lower Bound (CRLB) of the pose uncertainty in the stereo visual SLAM system is derived to describe the boundary of the pose uncertainty. Optimality criteria are introduced to quantitatively evaluate the localization uncertainty. The odometry information selection method and the local bundle adjustment information selection method based on Fisher Information are proposed to find out the measurements with low uncertainty for localization and mapping in the search and rescue process. By adopting the method above, the computing efficiency of the system is improved while the localization accuracy is equivalent to the classical ORB-SLAM2. Moreover, by the quantified uncertainty of local poses and map points, the generalized unary node and generalized unary edge are defined to improve the computational efficiency in computing local state uncertainty. In addition, an active loop closing planner considering local state uncertainty is proposed to make use of uncertainty in assisting the space exploration and decision-making of MAV, which is beneficial to the improvement of MAV localization performance in search and rescue environments. Simulations and field tests in different challenging scenarios are conducted to verify the effectiveness of the proposed method. Full article

Icing on transmission lines in cold regions can cause asymmetry in the conductor cross-section. This asymmetry can lead to low-frequency, large-amplitude oscillations, posing a serious threat to the stability and safety of power transmission systems. In this study, the aerodynamic characteristics of crescent-shaped and sector-shaped iced eight-bundled conductors were systematically investigated over an angle of attack range from 0° to 180°. A combined approach involving wind tunnel tests and high-precision computational fluid dynamics (CFD) simulations was adopted. In the wind tunnel tests, static aerodynamic coefficients and dynamic time series data were obtained using a high-precision aerodynamic balance and a turbulence grid. In the CFD simulations, transient flow structures and vortex shedding mechanisms were analyzed based on the Reynolds-averaged Navier–Stokes (RANS) equations with the SST k-ω turbulence model. A comprehensive comparison between the two ice accretion geometries was conducted. The results revealed distinct aerodynamic instability mechanisms and frequency-domain characteristics. The analysis was supported by Fourier’s fourth-order harmonic decomposition and energy spectrum analysis. It was found that crescent-shaped ice, due to its streamlined leading edge, induced a dominant single vortex shedding. In this case, the first-order harmonic accounted for 67.7% of the total energy. In contrast, the prismatic shape of sector-shaped ice caused migration of the separation point and introduced broadband energy input. Stability thresholds were determined using the Den Hartog criterion. Sector-shaped iced conductors exhibited significant negative aerodynamic damping under ten distinct operating conditions. Compared to the crescent-shaped case, the instability risk range increased by 60%. The strong agreement between simulation and experimental results validated the reliability of the numerical approach. This study establishes a multiscale analytical framework for understanding galloping mechanisms of iced conductors. It also identifies early warning indicators in the frequency domain and provides essential guidance for the design of more effective anti-galloping control strategies in resilient power transmission systems. Full article

Integrating cyberspace and geographic space through map visualization is a valuable approach for revealing distribution patterns and relational dynamics in cyberspace. The interdisciplinary integration of network science and geographic science has gained increasing attention in recent years. However, current geographic information data models are not suitable for representing cyberspace features and their relations, and there is a lack of general and systematic cyberspace map visualization methods. To address these problems, this paper introduces an integrated data model that aligns spatial and cyberspace features based on a “proxy mode”. This model is designed to support both the visualization of data maps and the analysis of complex networks and graph layouts. In addition, a framework for cyberspace map visualization is introduced, comprising three main stages: “cyberspace data processing”, “cyberspace data rendering”, and “base map processing and map layout”. Using the Routers, BrightKite, and Cables datasets, we developed a web-based CMV system and generated a statistical map, a node-link map, an edge bundling map, a flow map, and a feature distribution map. The experimental results showed that the proposed data model and method framework can be effectively applied to represent the distribution and relations of cyberspace features and help reveal the interaction patterns between cyberspace and geographic space. Full article

This current work provides a comprehensive Computational Fluid Dynamics (CFD) investigation of three-dimensional magnetohydrodynamic (MHD) mixed convection of carbon nanotube (CNT)-water nanofluid within a vented rectangular cavity featuring an internal vertical rod bundle with circular, square, and triangular cross-sections. The finite element method (FEM) was used to investigate the effects of key parameters, including the Richardson number (0.01 ≤ Ri ≤ 10), Hartmann number (0 ≤ Ha ≤ 100), and CNT nanoparticle concentration (0 ≤ ϕ ≤ 0.045), in relation to fluid flow and heat transfer performance. The CNT nanoparticle incorporation increases the nanofluid’s heat transfer capacity by up to 22%, with the highest average Nusselt number (Nu_av) achieved with circular rods at ϕ = 0.045, which corresponds to the higher convective heat transfer efficiency. The magnetic field further stabilizes the flow by reducing thermal convection irregularities, with a 15% improvement in temperature distribution uniformity when Ha = 100. The investigation’s outcomes reveal that due to their smoother geometries, the circular rods exhibit better thermal exchange rates compared to square and triangular rods. Moreover, a polynomial regression model is used to correlate the governing parameters and heat transfer rates, and it achieves a high R² of 0.964. These findings highlight the potential of CNT-water nanofluid and magnetic field applications for thermal management optimization in various engineering systems. Full article

Composites with continuous fiber reinforcement offer excellent fatigue properties but are tedious to characterize due to anisotropy and the interplay of fatigue properties, processing conditions, and the constituents. The global fiber volume content can affect both monotonic and fatigue strength. This dependence can increase the necessary testing effort even when processing conditions and constituents remain identical. This work presents an in situ edge observation method, enabling light microscopy during loading. As a result, digital image correlation can be employed to study local strains at cracking sites on the scale of fiber bundles. The geometric influence on fatigue damage is examined in non-crimp fabrics of glass and carbon fibers. Two epoxy resins (one modified by irradiation) are investigated to verify the geometric influence under changed polymer properties. The microscopy-based image correlation revealed that damage forms at very low global strains of only 0.2–0.3% in glass fiber-reinforced epoxy laminates. For carbon fiber-reinforced epoxy, laminate cracking was found to emanate mainly from regions containing stitching fibers. Across both reinforcements, irradiation treatment led to delayed cracks, emanating from interfaces. This detailed analysis of the damage formation is used as a basis for proposed applications of the in situ strain information. Full article

As a cutting-edge technology, animal robots based on living organisms are being extensively studied, with potential for diverse applications in the fields of neuroscience, national security, and civil rescue. However, it remains a significant challenge to reliably control the animal robots with the objective of protecting their long-term survival, and this has seriously hindered their practical implementation. To address this issue, this work explored the use of a bio-friendly neurostimulation system that includes integrated stimulation electrodes together with a remote wireless stimulation circuit to control the moving behavior of rat robots. The integrated electrodes were implanted simultaneously in four stimulation sites, including the medial forebrain bundle (MFB) and primary somatosensory cortex, barrel field (S1BF). The control system was able to provide flexibility in adjusting the following four stimulation parameters: waveform, amplitude, frequency, and duration time. The optimized parameters facilitated the successful control of the rat’s locomotion, including forward movement and left and right turns. After training for a few cycles, the rat robots could be guided along a designated route to complete the given mission in a maze. Moreover, it was found that the rat robots could survive for more than 20 days with the control system implanted. These findings will ensure the sustained and reliable operation of the rat robots, laying a robust foundation for advances in animal robot regulation technology. Full article

Electrical steels are widely used in the electrical industry in the construction of many devices, e.g., power transformer cores and distribution transformers. An important parameter of electrical components that determines the efficiency of devices is energy loss during remagnetization. These losses are influenced, among other factors, by steel cutting processes. The common techniques for cutting electrical materials on industrial lines are mechanical cutting and laser cutting. High-pressure abrasive water jet (AWJ) cutting, unlike the technologies mentioned above, can ensure higher quality of the cut edge and limit the negative impact of the cutting process on the magnetic properties of sheet metal. However, the correct control of the process and the conditions of its implementation comprise a complex issue and require extensive scientific research. This work presents a new approach to cutting electric sheets, involving bundle cutting, which significantly increases the processing efficiency and the dimensional and shape accuracy of the cut details. The tests were carried out for bundles composed of a maximum of 30 sheets, ready to be joined in a stator and rotor in a motor. The influence of processing conditions on the quality of the cut edges of sheet metal, the width of the deformation zone, and the burr height were analyzed. The detailed analysis of the quality of the cut edges of electrical bundled sheets creates new possibilities for controlling the AWJ cutting process in order to obtain a product with the desired functional and operational properties. Full article

Carbon/Carbon (C/C) composites exhibit excellent mechanical properties at high temperatures, making them widely used in aerospace, such as the leading edges of spaceplane wings and the nose cones of hypersonic aircraft. However, damage caused by rain erosion to C/C composites affects their mechanical properties and poses significant challenges during operational service periods. A jet impingement test platform was employed to conduct single and multiple water-jet erosion tests on three-dimensional orthogonal C/C composite materials and to investigate the residual mechanical properties of the specimens after jet impact. The damage was characterized using optical microscopy, scanning electron microscopy, and X-ray computed tomography. The results showed that the damage types of the C/C composite materials under water-jet impingement included fiber bundle fracturing, delamination, and debonding. The extent of erosion damage was positively correlated with the jet velocity and diameter. The changes in the multi-jet damage indicated a cumulative expansion process, and z-directional fiber bundles exhibited superior resistance to jet impact damage propagation. The results of the three-point bending tests showed that the greater the initial impact damage, the lower the residual mechanical properties of the materials, and the residual strength of the specimen suddenly decreased when damage occurred at the back of the specimen. Full article

Electrical steels are magnetically soft materials and are widely used in the electrical industry for the construction of power transformer cores, distribution transformers, current transformers, and voltage transformers. An important parameter of electrical components, which determines the efficiency of devices, is energy loss during remagnetization. Energy losses are caused by eddy currents, hysteresis, and magnetic delay associated with the low quality of the cut edge after the cutting of steels, and material deformations and excessive stress concentration in the surrounding cutting zones. Common techniques for cutting electrical materials in industrial lines include mechanical cutting and laser cutting. Work has shown that mechanical cutting of electrical steel single layers results in the occurrence of large deformation zones, and in cutting processes with a high-pressure abrasive water jet (AWJ), significant uplifts of material and burrs at the bottom edges of sheets occur. The problem of increasing the cutting quality was solved through selecting the stream parameters for bundle cutting of electrical steels. It has been shown that in the process of cutting electrical sheet bundles, the height of burrs on the cut surface and the zone of plastic deformation are reduced. The work also presents comparison and analysis of characteristic features of the cut edge of electrical sheets obtained through high-pressure abrasive water jet and mechanical cutting processes. The influence of the type and processing parameters on the characteristic features of the material hysteresis loop was determined. Full article

Bundle recommendations provide personalized suggestions to users by combining related items into bundles, aiming to enhance users’ shopping experiences and boost merchants’ sales revenue. Existing solutions based on graph neural networks (GNN) face several significant challenges: (1) it is demanding to explicitly model multiple complex associations using standard graph neural networks, (2) numerous additional nodes and edges are introduced to approximate higher-order associations, and (3) the user–bundle historical interaction data are highly sparse. In this work, we propose a global structural hypergraph convolutional model for bundle recommendation (SHCBR) to address the above problems. Specifically, we jointly incorporate multiple complex interactions between users, items, and bundles into a relational hypergraph without introducing additional nodes and edges. The hypergraph structure inherently incorporates higher-order associations, thereby alleviating the training burden on neural networks and the dilemma of scarce data effectively. In addition, we design a special matrix propagation rule that captures non-pairwise complex relationships between entities. Using item nodes as links, structural hypergraph convolutional networks learn representations of users and bundles on a relational hypergraph. Experiments conducted on two real-world datasets demonstrate that the SHCBR outperforms the state-of-the-art baselines by 11.07–25.66% on Recall and 16.81–33.53% on NDCG. Experimental results further indicate that the approach based on hypergraphs can offer new insights for addressing bundle recommendation challenges. The codes and datasets have been publicly released on GitHub. Full article

This paper proposes a method to reduce seismogram variability as a determining factor in its interpretation, processing, and clustering. By introducing the concept of single fluctuations (SFs), the seismogram can be parsed into a sequence of random impulses with subsequent ordering. This rearrangement of SFs, if they are assigned by positive integers, represents the formal mapping of a regular string of integers into a random one, which can be represented with a bipartite random graph (bigraph). Due to its specific randomness, such a bigraph is considered a new type of random balanced bigraph. The R-envelope and R_Q-envelope, its equidistant version, are defined by tracing the peak envelope over ordered SFs. The equivalence and complementariness of the R_Q and bigraph are considered and discussed, forming a combined characteristic of the seismogram. The R/R_Q provided a considerable reduction in seismogram variability, which was confirmed by creating and analyzing an ensemble of R_Q from several seismograms. In the R_Q domain, distance is defined as a possible basis for metrics and clustering, but the ensemble variability was quite narrow and not as suitable for this purpose. Otherwise, the ensemble shows high redundancy hidden in the seismogram population. As for the bigraph, the mesh of its edges is structuralized in bundles, forming a skeleton, which reflects the internal structural content of the seismogram. The distance over the domain of bigraphs is proposed to show the possibility of clustering. This means that only a combined R_Q and bigraph provides a suitable frame for seismogram representation with reduced variability and, thus, the potential for more effectively organizing seismic databases and a deeper interpretation of seismograms; therefore, R_Qs and bigraphs can be considered as a transform of a seismogram. Many aspects of these concepts are thoroughly discussed. The similarity between concepts of SF and wavelets is briefly considered. This very complex theme is new and promises broad further research. All issues considered in the paper are abundantly illustrated. Full article

Social networks have become important objects of study in recent years. Social media marketing has, for example, greatly benefited from the vast literature developed in the past two decades. The study of social networks has taken advantage of recent advances in machine learning to process these immense amounts of data. Automatic emotional labeling of content on social media has, for example, been made possible by the recent progress in natural language processing. In this work, we are interested in the influence maximization problem, which consists of finding the most influential nodes in the social network. The problem is classically carried out using classical performance metrics such as accuracy or recall, which is not the end goal of the influence maximization problem. Our work presents an end-to-end learning model, SGREEDYNN, for the selection of the most influential nodes in a social network, given a history of information diffusion. In addition, this work proposes data visualization techniques to interpret the augmenting performances of our method compared to classical training. The results of this method are confirmed by visualizing the final influence of the selected nodes on network instances with edge bundling techniques. Edge bundling is a visual aggregation technique that makes patterns emerge. It has been shown to be an interesting asset for decision-making. By using edge bundling, we observe that our method chooses more diverse and high-degree nodes compared to the classical training. Full article

As metastasis is responsible for most cancer-related deaths, understanding the cellular and molecular events that lead to cancer cell migration and invasion will certainly provide insights into novel anti-metastatic therapeutic targets. Fascin-1 is an actin-bundling protein fundamental to all physiological or pathological processes that require cell migration. It is responsible for cross-linking actin microfilaments during the formation of actin-rich cellular structures at the leading edge of migrating cells such as filopodia, lamellipodia and invadopodia. While most epithelial tissues express low levels of Fascin-1, it is dramatically elevated in the majority of cancers and its expression has been associated with more aggressive disease and decreased overall survival. Hence, it has been proposed as a potential anti-cancer target. In the present review, we studied recent literature with regard to Fascin-1 expression in different cancers, its role in altering the mechanical properties of cancer cells, promoting cancer cell migration, invasion and metastasis and the effect of its inhibition, via various pharmacological inhibitors, in eliminating metastasis in vitro and/or in vivo. Recent studies corroborate the notion that Fascin-1 is critically involved in metastasis and prove that it is a valuable anti-metastatic target that is worth investigating further. Full article

Modern distributed systems that operate concurrently generate interleaved logs. Identifiers (ID) are always associated with active instances or entities in order to track them in logs. Consequently, log messages with similar IDs can be categorized to aid in the localization and detection of anomalies. Current methods for achieving this are insufficient for overcoming the following obstacles: (1) Log processing is performed in a separate component apart from log mining. (2) In modern software systems, log format evolution is ongoing. It is hard to detect latent technical issues using simple monitoring techniques in a non-intrusive manner. Within the scope of this paper, we present a reliable and consistent method for the detection and localization of anomalies in interleaved unstructured logs in order to address the aforementioned drawbacks. This research examines Log Sequential Anomalies (LSA) for potential performance issues. In this study, IDs are used to group log messages, and ID relation graphs are constructed between distributed components. In addition to that, we offer a data-driven online log parser that does not require any parameters. By utilizing a novel log parser, the bundled log messages undergo a transformation process involving both semantic and temporal embedding. In order to identify instance–granularity anomalies, this study makes use of a heuristic searching technique and an attention-based Bi-LSTM model. The effectiveness, efficiency, and robustness of the paper are supported by the research that was performed on real-world datasets as well as on synthetic datasets. The neural network improves the F1 score by five percent, which is greater than other cutting-edge models. Full article

We introduce resource allocation techniques for problems where (i) the agents express requests for obtaining item bundles as compact edge-weighted directed acyclic graphs (each path in such a graph is a bundle whose valuation is the sum of the weights of the traversed edges), and (ii) the agents do not bid on the exact same items but may bid on conflicting items that cannot be both assigned or that require accessing a specific resource with limited capacity. This setting is motivated by real applications such as Earth observation slot allocation, virtual network functions, or multi-agent path finding. We model several directed path allocation problems (vertex-constrained and resource-constrained), investigate several solution methods (qualified as exact or approximate, and utilitarian or fair), and analyze their performances on an orbit slot ownership problem, for realistic requests and constellation configurations. Full article