Different Levels Of Detail Research Articles

Synergistic Use of Sentinel-2 and UAV Multispectral Data to Improve and Optimize Viticulture Management

The increasing use of geospatial information from satellites and unmanned aerial vehicles (UAVs) has been contributing to significant growth in the availability of instruments and methodologies for data acquisition and analysis. For better management of vineyards (and most crops), it is crucial to access the spatial-temporal variability. This knowledge throughout the vegetative cycle of any crop is crucial for more efficient management, but in the specific case of viticulture, this knowledge is even more relevant. Some research studies have been carried out in recent years, exploiting the advantage of satellite and UAV data, used individually or in combination, for crop management purposes. However, only a few studies explore the multi-temporal use of these two types of data, isolated or synergistically. This research aims to clearly identify the most suitable data and strategies to be adopted in specific stages of the vineyard phenological cycle. Sentinel-2 data from two vineyard plots, located in the Douro Demarcated Region (Portugal), are compared with UAV multispectral data under three distinct conditions: considering the whole vineyard plot; considering only the grapevine canopy; and considering inter-row areas (excluding all grapevine vegetation). The results show that data from both platforms are able to describe the vineyards’ variability throughout the vegetative growth but at different levels of detail. Sentinel-2 data can be used to map vineyard soil variability, whilst the higher spatial resolution of UAV-based data allows diverse types of applications. In conclusion, it should be noted that, depending on the intended use, each type of data, individually, is capable of providing important information for vineyard management.

Exploring the Design of a Mixed-Reality 3D Minimap to Enhance Pedestrian Satisfaction in Urban Exploratory Navigation

The development of ubiquitous computing technology and the emergence of XR could provide pedestrian navigation with more options for user interfaces and interactions. In this work, we aim investigate the role of a mixed-reality map interface in urban exploration to enhance pedestrians’ mental satisfaction. We propose a mixed-reality 3D minimap as a part of the navigation interface which pedestrians could refer to and interact during urban exploration. To further explore the different levels of detail of the map interface, we conducted a user study (n = 28, two groups with two tasks). We designed two exploratory activities as experimental tasks with two map modes (a normal one and a simplified one) to discuss the detailed design of the minimap interface. The results indicated that participants showed a positive attitude toward our method. The simplified map mode could result in a lower perceived workload in both tasks while enhancing performance in specific navigation, such as wayfinding. However, we also found that pedestrians’ preference for the level of detail of the minimap interface is dynamic in navigation. Thus, we suggest discussing the different levels of detail further in specific scenarios. Finally, we also summarize some findings observed during user study for inspiring the study of virtual map interface of future mixed-reality navigation for urban exploration in various scenarios.

Model‐Based Design and Experimental Evaluation of a High‐Throughput Electrode Feeding and Stacking Process

To provide storage capacities for the emerging markets of electromobility and stationary applications, the increase of productivity within the production of lithium‐ion batteries is crucial. A special focus lies on the assembly of the electrode–separator–compound as a bottleneck process within battery cell production. Consequently, novel process technologies arise for high‐throughput assembly technologies. However, complex process–product interactions drive complexity within the design of new processes. Process models, experiments, and simulations support the process design but must depict nonlinear material behavior. This increases the expanse in time and resources for the process design. Herein, an approach to reduce the effort of process design using the example of the assembly of the electrode–separator–compound is shown. The approach aims to identify and select solutions within the process design. Analytical, simulative, and experimental methods and tools are applied within the approach to investigate the design solutions at different levels of detail. The practical application of the approach is demonstrated in two case studies within the assembly of the electrode–separator–compound. The results of the case studies show a profound choice of the process design and a gain in knowledge on process–product–interactions of the novel processes.

A Shape-Preserving Simplification Method for Urban Building Models

With the expansion of model scale and the improvement of model accuracy, the real-time rendering and displaying of 3D mesh models remain infeasible. To relieve such pressure, mesh simplification methods have been proposed to reduce the structural complexity while preserving the appearance. This work introduces a shape-preserving simplification method for urban building models. Compared to traditional simplification methods that only consider preserving local geometric features, we also consider the overall shapes of building models to avoid collapse with an increased simplification rate. The proposed method works in four steps. First, we filter mesh models to yield planar structures while preserving sharp features. Second, we detect the shapes of planar regions. Third, we simplify the planar and non-planar regions constrained by the overall shapes and local geometric features. Finally, we remap the texture. Experiments show the effectiveness of this method by evaluating it on various building models and comparing its performance with the original QEM algorithms. Furthermore, we maintain better spatial consistency in building models with different levels of detail (LOD) than traditional methods.

Ship routing optimisation based on forecasted weather data and considering safety criteria

AbstractA weather routing tool is presented based on forecasted weather data along the route and considering safety aspects. The tool aims to determine the optimal path for the minimisation of the fuel oil consumption, ensuring a safe passage. It is developed in MATLAB and considers detailed ship characteristics. Specifically, ship's motions and fuel oil consumption of the main engine during a potential path are estimated. For the latter, a physics-based model for a specific vessel is developed where tools of different level of detail are utilised to calculate the various resistance components. A speed management strategy along the route is specified as well as safety criteria representing acceptable limits of ship's responses. When the set criteria and constraints have been set, a genetic algorithm is used to find the optimal route by means of ship's heading or by considering both heading and ship's power settings as variables to minimise the fuel oil consumption. The search space of the algorithm lies within a predefined envelop, but still the evolutionary optimisation approach used has no pre-assigned values to any possible candidate waypoint.

Incorporating social knowledge structures into computational models

To navigate social interactions successfully, humans need to continuously learn about the personality traits of other people (e.g., how helpful or aggressive is the other person?). However, formal models that capture the complexities of social learning processes are currently lacking. In this study, we specify and test potential strategies that humans can employ for learning about others. Standard Rescorla-Wagner (RW) learning models only capture parts of the learning process because they neglect inherent knowledge structures and omit previously acquired knowledge. We therefore formalize two social knowledge structures and implement them in hybrid RW models to test their usefulness across multiple social learning tasks. We name these concepts granularity (knowledge structures about personality traits that can be utilized at different levels of detail during learning) and reference points (previous knowledge formalized into representations of average people within a social group). In five behavioural experiments, results from model comparisons and statistical analyses indicate that participants efficiently combine the concepts of granularity and reference points—with the specific combinations in models depending on the people and traits that participants learned about. Overall, our experiments demonstrate that variants of RW algorithms, which incorporate social knowledge structures, describe crucial aspects of the dynamics at play when people interact with each other.

Semi-supervised Remote Sensing Image Scene Classification Based on Generative Adversarial Networks

With the availability of numerous high-resolution remote sensing images, remote sensing image scene classification has been widely used in various fields. Compared with the field of natural images, the insufficient number of labeled remote sensing images limits the performance of supervised scene classification, while unsupervised methods are difficult to meet the practical applications. Therefore, this paper proposes a semi-supervised remote sensing image scene classification method using generative adversarial networks. The proposed method introduces dense residual block, pre-trained Inception V3 networks, gating unit, pyramidal convolution, and spectral normalization into GANs to promote the semi-supervised classification performance. To be specific, the pre-trained Inception V3 network is introduced to extract semantic features to enhance the feature discriminant capability. The gating unit is utilized to capture the relationships among features. The pyramidal convolution is integrated into dense residual block to capture different levels of details to strengthen the feature representation capability. The spectral normalization is introduced to stabilize the GANs training to improve semi-supervised classification accuracy. Extensive experimental results on publicly available EuroSAT and UC Merced datasets show that the proposed method gains the highest overall accuracy, especially when only a few labeled samples are available.

3D radiative transfer modeling of structurally complex forest canopies through a lightweight boundary-based description of leaf clusters

Three-dimensional (3D) radiative transfer simulations are critical for studying the radiometric properties of canopies. Efficient and easy-to-use 3D radiative transfer models are required by remote sensing inversion and many validation applications. Extensive efforts have been made to improve the computational efficiency, accuracy, and useability of 3D radiative transfer models. This study focuses on the abstraction of canopies for 3D radiative transfer simulations by proposing a lightweight boundary-based description of leaf clusters (B-cluster) to ease the creation of 3D scenes while keeping the simulation as accurate as possible. B-cluster partitions a tree crown into sub-crown leaf clusters and abstracts each of them into a turbid medium enclosed by a complex and tight boundary, while terrain and branches are described with precise mesh surfaces. The radiative transfer simulation within B-cluster has been developed based on an efficient Monte Carlo path-tracing algorithm and implemented in the LargE-Scale remote sensing data and image Simulation framework (LESS) model by considering the presence of both turbid medium and surface scattering. The performance of the model was assessed by comparing with original LESS version, which describes all landscape elements with mesh surfaces (here called M-surface approach), and with a uniform voxel-based approach (U-voxel) in terms of the multiangle bidirectional reflectance factor (BRF) as well as with pixel-wise images. Results show that B-cluster is highly consistent with M-surface in abstract canopies (mean normalized absolute BRF differences δ¯ < 2%) and in realistic forest stand (δ¯ < 5% at 5-m resolution) with considerably reduced requirements for computational memory. Compared with U-voxel, B-cluster is also more robust and better at describing canopy structures with different levels of detail. B-cluster enables to quickly construct accurate 3D scenes with reduced requirements of computational resources. It is also a unified and scale-adaptive approach that can describe crowns as simple as geometric primitives and as complex as explicitly described meshes. The newly proposed approach has been released in new LESS versions at http://lessrt.org/.

Front Cover: A Theoretical Investigation of Cu+, Ni2+ and Co2+‐Exchanged Zeolites for Hydrogen Storage (ChemPhysChem 20/2022)

The Front Cover illustrates the application of ion-exchanged zeolites for hydrogen storage in the fuel cell vehicles. The different zoom levels show hydrogen molecule adsorbed on the extra-framework cations (Cu+, Co2+, or Ni2+) in CHA and AEI frameworks. Different levels of detail also represent our atomic level understanding and solution to a macro level problem. Cover design by Olus Ozbek. More information can be found in the Research Article by Olus Ozbek and Bahar Ipek.

3D DATA INTEGRATION FOR WEB BASED OPEN SOURCE WebGL INTERACTIVE VISUALISATION

Abstract. Recent advances in open-source geospatial technologies in WebGIS allowed the visualization of a 3D complex environment on the web, exploiting realistic Globe reproduction of the real territorial asset. At the same time, in the field of gaming technologies, the new possibilities offered by open-source WebGL JavaScript libraries allowed the creation of Virtual Reality navigation models on the web. The integration between 3D GIS globe navigation models and VR environment navigation is a solution that offers a further level of detail in web navigation, exploiting the capabilities of web browsers in the best way. This research further contributes to this field, showing a workflow to integrate different 3D data in a VR and 3D WebGIS navigation model. The case study for this research is the new building of the University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC) of Enschede (The Netherlands). This work tests the online integration of variety of 3D input data that can lead to different Levels of Details (LoD) of the buildings inside the Globe-based WebGIS platform. The developed solution works on desktop and mobile devices using the capabilities of the most common web browsers, avoiding any software installation. The result of this work is based on completely open-source solutions that offers the possibility to navigate within a 3D model, which is useful for citizens, governmental or private institutions in decision-making processes. This work represents a first step towards the ambition to generate a web Digital Twin platform to combine datasets from different sources in a unique open-source solution.

Towards the Simulation of a Realistic Large-Scale Spiking Network on a Desktop Multi-GPU System.

The reproduction of the brain ’sactivity and its functionality is the main goal of modern neuroscience. To this aim, several models have been proposed to describe the activity of single neurons at different levels of detail. Then, single neurons are linked together to build a network, in order to reproduce complex behaviors. In the literature, different network-building rules and models have been described, targeting realistic distributions and connections of the neurons. In particular, the Granular layEr Simulator (GES) performs the granular layer network reconstruction considering biologically realistic rules to connect the neurons. Moreover, it simulates the network considering the Hodgkin–Huxley model. The work proposed in this paper adopts the network reconstruction model of GES and proposes a simulation module based on Leaky Integrate and Fire (LIF) model. This simulator targets the reproduction of the activity of large scale networks, exploiting the GPU technology to reduce the processing times. Experimental results show that a multi-GPU system reduces the simulation of a network with more than 1.8 million neurons from approximately 54 to 13 h.

Evolutionary Search for Complete Neural Network Architectures With Partial Weight Sharing

Neural architecture search (NAS) provides an automatic solution in designing network architectures. Unfortunately, the direct search for complete task-dependent network architectures is laborious since training and evaluating complete neural architectures over a large search space are computationally prohibitive. Recently, one-shot NAS (OSNAS) has attracted great attention in the NAS community because it significantly speeds up the candidate architecture evaluation procedure through weight sharing. However, the full weight sharing training paradigm in OSNAS may result in strong interference across candidate architectures and mislead the architecture search. To alleviate the problem, we propose a partial weight sharing OSNAS framework that directly evolves complete neural network architectures. In particular, we suggest a novel node representation scheme that randomly activates a subset of nodes of the one-shot model in each generation to reduce the weight coupling in the one-shot model. During the evolutionary search, a tailored crossover operator randomly samples the nodes from two parent individuals or a single parent to construct new candidate architectures, thus effectively constraining the degree of weight sharing. Furthermore, we introduce a new mutation operator that replaces the chosen nodes of the one-shot model with randomly generated nodes to enhance the exploratory capability. Finally, we encode a set of pyramidal convolution operations in the search space, enabling the evolved neural networks to capture different levels of details in the images. The proposed method is examined and compared with 26 state-of-the-art algorithms on ten image classification tasks, including CIFAR series, CINIC10, ImageNet, and MedMNIST series. The experimental results demonstrate that the proposed method can computationally much more efficiently find neural architectures that achieve comparable classification accuracy to the state-of-the-art designs.

Abordagem formal e multimodal da notícia como gênero, estrutura e metalinguagem em contextos de mídia social e digital. O exemplo do Twitter

ABSTRACT The goal of this paper is to improve heuristics for hard news discourse by proposing a cognitive model of abstraction, regarding social media contexts. To this end, hard news is discussed as a genre, structure and metalanguage, under the formal definition of a semiotic mode. Annotation is a successful technology to control the effects of genre operations, reveal relations, and to inquire about data and documents. It proceeds to characterize Twitter’s interface, in terms of formal and material regularities, employed recursively. It further demonstrates the formalization of a semantics for hard news discourse in the so-called logical forms, which has been adapted from analytical tools developed by the Segmented Theory of Discourse Representation (SDRT) in order to examine coherence in different levels of detail. Finally, the implication of this approach as a discipline is discussed, regarding the production of transversal knowledge aiming at digital literacy, which urges in present times.1

Impact of a mechanical attachment on the preheating temperatures of a central receiver tube

The receiver tubes of Solar Power Tower plants are typically attached through clips and sliding rods to the reradiating wall of the receiver in order to reduce the thermal deflection of the tubes. However, as a side effect, these mechanical attachments increase heat dissipation from the tube to the exterior and can reduce the local temperature of the tube during the receiver start-up, an operation performed every morning or after a severe drop of solar irradiation due to passing clouds. In the start-up operation, the empty tubes of molten salt receivers must be preheated with a careful aiming sequence of the heliostat field until their temperature is high enough to avoid the freezing of the incoming flow of the molten salt. This work numerically addresses for the first time the influence of the mechanical attachment on the preheating temperature of a receiver tube. To achieve this goal, a set of three-dimensional thermal fluid simulations of the temperature distribution in the tube and the mechanical attachment, described with different levels of detail, has been performed for the receiver of the Gemasolar Solar Power Tower plant. The results of this work show that the mechanical attachment locally affects the tube temperature, leading to a temperature overestimation above 25.1% when the tube is modeled without a mechanical attachment. In addition, the temperature measured separated from the attachment at the rear face of the tube to control the preheating sequence may not be very representative of the minimum tube temperature, which occurs in the internal face of tube just beneath the mechanical attachment. The results indicate that accumulation of heat not only by the clip but also by the guide and the rod of the mechanical attachment plays a significant role in the dissipation of heat from the tube to the attachment. This reveals the need of considering all the elements of the mechanical attachment if an accurate modeling of the preheat temperatures of the tube is needed. Besides, the use of a simplification in the modeling of the air and radiative heat transfer outside the tube reduces by a factor of 4.9 the computational cost involved in a full simulation of these transfer processes, with a temperature discrepancy between models below 14.3%. Finally, the effect of the size of the attachment clip on the minimum tube temperature is investigated, revealing that the minimum tube temperature of the preheated tube can decrease below the freezing temperature of the molten salt for clip heights larger than 170 mm . • Preheat temperatures in a receiver tube with a mechanical attachment are simulated. • The attachment clip, guide and rod need to be modeled to ensure accuracy. • The heat absorbed by the tube is partially transferred to the attachment. • The attachment creates a cold spot in the tube close to the clip during the preheating. • The control thermocouple temperature is not representative of minimum temperature.

Defining Recommendations to Guide User Interface Design: Multimethod Approach.

BackgroundFor the development of digital solutions, different aspects of user interface design must be taken into consideration. Different technologies, interaction paradigms, user characteristics and needs, and interface design components are some of the aspects that designers and developers should pay attention to when designing a solution. Many user interface design recommendations for different digital solutions and user profiles are found in the literature, but these recommendations have numerous similarities, contradictions, and different levels of detail. A detailed critical analysis is needed that compares, evaluates, and validates existing recommendations and allows the definition of a practical set of recommendations.ObjectiveThis study aimed to analyze and synthesize existing user interface design recommendations and propose a practical set of recommendations that guide the development of different technologies.MethodsBased on previous studies, a set of recommendations on user interface design was generated following 4 steps: (1) interview with user interface design experts; (2) analysis of the experts’ feedback and drafting of a set of recommendations; (3) reanalysis of the shorter list of recommendations by a group of experts; and (4) refining and finalizing the list.ResultsThe findings allowed us to define a set of 174 recommendations divided into 12 categories, according to usability principles, and organized into 2 levels of hierarchy: generic (69 recommendations) and specific (105 recommendations).ConclusionsThis study shows that user interface design recommendations can be divided according to usability principles and organized into levels of detail. Moreover, this study reveals that some recommendations, as they address different technologies and interaction paradigms, need further work.

A demand side management approach to increase self-consumption in buildings

There is a growing interest in increasing the presence of renewable energy in the electric network. Photovoltaic production from grid-connected systems is leading this growth in terms of households. Alongside this development, concern about network security has emerged, because excesses of intermittent renewable energy on the grid could exceed voltage limits. Self-consumption, understood as the capacity of the producer to consume his or her own production, can partially solve these problems. Thermostatic controllable loads, such as heating and cooling, represent 50% of the total amount of energy consumed by buildings; the proper allocation of these loads could be a driving force for self-consumption. In this study, a demand side management strategy is proposed based on a building energy model equipped with an inverter heat pump coupled with a photovoltaic plant. The goal is to maximize the use of local energy from the photovoltaic plant (self-consumption), reducing the export and import of energy to and from the grid. This goal is achieved by optimizing the set-points in each room. An array of optimal set-points over six years is presented. The results show the capacity of the methodology to match similar values of self-consumption (70% in winter and 50% in summer) obtained by strategies based on chemical batteries. The findings are shown in an energy matching chart at different levels of detail (yearly and monthly). Color bubbles are added to the matching chart to help visualize the unmatched energy of the system graphically. In comparison with actual model predictive control technologies, this study’s strategy offers great simplicity and a large saving in computational time.

An Algorithmic Approach to Reducing Taxonomic Detail from Actual Datasets to their Metadata Representation to Increase Findabilty

An Algorithmic Approach to Reducing Taxonomic Detail from Actual Datasets to their Metadata Representation to Increase Findabilty

Rapid risk assessment tool (RRAT) to prioritize emerging and re-emerging livestock diseases for risk management.

Increasing globalization and international trade contribute to rapid expansion of animal and human diseases. Hence, preparedness is warranted to prevent outbreaks of emerging and re-emerging diseases or detect outbreaks in an early stage. We developed a rapid risk assessment tool (RRAT) to inform risk managers on the incursion risk of multiple livestock diseases, about the main sources for incursion and the change of risk over time. RRAT was built as a relational database to link data on disease outbreaks worldwide, on introduction routes and on disease-specific parameters. The tool was parameterized to assess the incursion risk of 10 livestock diseases for the Netherlands by three introduction routes: legal trade in live animals, legal trade of animal products, and animal products illegally carried by air travelers. RRAT calculates a semi-quantitative risk score for the incursion risk of each disease, the results of which allow for prioritization. Results based on the years 2016-2018 indicated that the legal introduction routes had the highest incursion risk for bovine tuberculosis, whereas the illegal route posed the highest risk for classical swine fever. The overall incursion risk via the illegal route was lower than via the legal routes. The incursion risk of African swine fever increased over the period considered, whereas the risk of equine infectious anemia decreased. The variation in the incursion risk over time illustrates the need to update the risk estimates on a regular basis. RRAT has been designed such that the risk assessment can be automatically updated when new data becomes available. For diseases with high-risk scores, model results can be analyzed in more detail to see which countries and trade flows contribute most to the risk, the results of which can be used to design risk-based surveillance. RRAT thus provides a multitude of information to evaluate the incursion risk of livestock diseases at different levels of detail. To give risk managers access to all results of RRAT, an online visualization tool was built.

Multi‐scale group‐fusion convolutional neural network for high‐resolution range profile target recognition

Convolution neural networks (CNNs) represent one of the workhorses of artificial intelligence applications. As a typical artificial intelligence application, a high-resolution range profile (HRRP) target recognition method based on CNNs has aroused a lot of research interest. Most CNNs use a relatively small and single-scale convolution kernel size to control the number of parameters and computational complexity, but recent studies indicate that CNNs with a small kernel size cannot extract enough spatial information, which hurts the recognition performance. Aiming at this problem, this paper proposes a multi-scale group-fusion one-dimensional convolution neural network (MSGF-1D-CNN) for HRRP target recognition. MSGF-1D-CNN utilises multi-scale group one-dimensional convolution (MSG 1D-Conv) and point-wise convolution (PW-Conv) to replace the standard convolution. Multi-scale group one-dimensional convolution can significantly reduce complexity and capture the information of targets within HRRP in different levels of detail to enhance feature extraction, while PW-Conv can realise the fusion of multi-scale features to help boosting recognition performance. Experiments on five mid-course ballistic targets in the HRRP dataset show that MSGF-1D-CNN has superior recognition performance, and the parameter number of the model is reduced by more than 2.4 times than standard 1D-CNN. Furthermore, MSGF-1D-CNN shows better performance on fine-grained HRRP target recognition and anti-noise robustness in most cases.

SMS-I: Intelligent Security for Cyber–Physical Systems

Critical infrastructures are an attractive target for attackers, mainly due to the catastrophic impact of these attacks on society. In addition, the cyber–physical nature of these infrastructures makes them more vulnerable to cyber–physical threats and makes the detection, investigation, and remediation of security attacks more difficult. Therefore, improving cyber–physical correlations, forensics investigations, and Incident response tasks is of paramount importance. This work describes the SMS-I tool that allows the improvement of these security aspects in critical infrastructures. Data from heterogeneous systems, over different time frames, are received and correlated. Both physical and logical security are unified and additional security details are analysed to find attack evidence. Different Artificial Intelligence (AI) methodologies are used to process and analyse the multi-dimensional data exploring the temporal correlation between cyber and physical Alerts and going beyond traditional techniques to detect unusual Events, and then find evidence of attacks. SMS-I’s Intelligent Dashboard supports decision makers in a deep analysis of how the breaches and the assets were explored and compromised. It assists and facilitates the security analysts using graphical dashboards and Alert classification suggestions. Therefore, they can more easily identify anomalous situations that can be related to possible Incident occurrences. Users can also explore information, with different levels of detail, including logical information and technical specifications. SMS-I also integrates with a scalable and open Security Incident Response Platform (TheHive) that enables the sharing of information about security Incidents and helps different organizations better understand threats and proactively defend their systems and networks.