This paper describes a dataset collected by infrared thermography, a non-contact, non-intrusive technique to acquire data and analyze the built environment in various aspects. While most studies focus on the city and ...
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Pyrolysis is a prominent technique to harvest energy while addressing the global waste problem. Product yield relies heavily on precise reactor temperature control in a pyrolysis process;thus, a representative mathema...
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In the Internet of Vehicles (IoV) era, connected vehicles are leveraging intelligent sensors to share sensitive and real-time information to enhance road safety, driving comfort, and traffic efficiency. Despite these ...
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Integrating social robotics into the construction industry, particularly in the context of Industry 5.0, faces several challenges in creating complex environments that seamlessly blend human and machine interactions. ...
Integrating social robotics into the construction industry, particularly in the context of Industry 5.0, faces several challenges in creating complex environments that seamlessly blend human and machine interactions. In this regard, the emergence of intelligent and expert systems holds promising technologies to enhance construction tasks focused on robots and workers in 3D printing applications. This work compares several methods of convolutional neural network-based object detectors designed to identify distinct construction assets and workers within the dynamic environment of 3D printing. To this end, different versions of the You Only Look Once v8 (YOLO v8) algorithm have been implemented, trained, and experimentally tested using several images captured within dynamic construction environments. Furthermore, we present an in-depth comparison between YOLO v8 and its preceding versions, namely YOLO v7 and YOLO v5. Experimental results disclosed the high performance of the proposed approach in effectively detecting three distinct entities (workers, robotic platforms, and building elements), achieving a precision rate of up to 98.8%.
In order to support advanced vehicular Internet-of-Things(IoT)applications,information exchanges among different vehicles are required to find efficient solutions for catering to different application requirements in ...
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In order to support advanced vehicular Internet-of-Things(IoT)applications,information exchanges among different vehicles are required to find efficient solutions for catering to different application requirements in complex and dynamic vehicular *** learning(FL),which is a type of distributed learning technology,has been attracting great interest in recent years as it performs knowledge exchange among different network entities without a violation of user ***,client selection and networking scheme for enabling FL in dynamic vehicular environments,which determines the communication delay between FL clients and the central server that aggregates the models received from the clients,is still *** this paper,we propose an edge computing-based joint client selection and networking scheme for vehicular *** proposed scheme assigns some vehicles as edge vehicles by employing a distributed approach,and uses the edge vehicles as FL clients to conduct the training of local models,which learns optimal behaviors based on the interaction with *** clients also work as forwarder nodes in information sharing among network *** client selection takes into account the vehicle velocity,vehicle distribution,and the wireless link connectivity between vehicles using a fuzzy logic algorithm,resulting in an efficient learning and networking *** use computer simulations to evaluate the proposed scheme in terms of the communication overhead and the information covered in learning.
Structural coloration generates colors by the interaction between incident light and micro-or nanoscale *** has received tremendous interest for decades,due to advantages including robustness against bleaching and env...
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Structural coloration generates colors by the interaction between incident light and micro-or nanoscale *** has received tremendous interest for decades,due to advantages including robustness against bleaching and environmentally friendly properties(compared with conventional pigments and dyes).As a versatile coloration strategy,the tuning of structural colors based on micro-and nanoscale photonic structures has been extensively explored and can enable a broad range of applications including displays,anti-counterfeiting,and ***,scholarly research on structural colors has had limited impact on commercial products because of their disadvantages in cost,scalability,and *** this review,we analyze the key challenges and opportunities in the development of structural *** first summarize the fundamental mechanisms and design strategies for structural colors while reviewing the recent progress in realizing dynamic structural *** promising potential applications including optical information processing and displays are also discussed while elucidating the most prominent challenges that prevent them from translating into technologies on the ***,we address the new opportunities that are underexplored by the structural coloration community but can be achieved through multidisciplinary research within the emerging research areas.
Internet of Things enables devices to communicate, collect and exchange data with the network. As the number of IoT devices keeps growing, the volume of data they produce is also increasing exponentially. Given the fe...
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Internet of Things enables devices to communicate, collect and exchange data with the network. As the number of IoT devices keeps growing, the volume of data they produce is also increasing exponentially. Given the feature of limited computing and storage resources of IoT, it is inevitable to store data in the cloud for better services. However, for users to effectively and efficiently inspect those data over the cloud is a critical and open problem. Most public integrity auditing over the cloud schemes requires the user to do a sheer amount of preprocessing work on the local devices, which is unsuitable for IoT devices. With the development of edge computing extending cloud computing, it can provide computing capability for resource-constrained devices in close geographic proximity. In this paper, we design an auditing scheme based on secure computation outsourcing assisted by edge computing, in which the data preprocessing work can be offloaded to the edge server. The experiments show that it reduces the computing load on the devices and improves the efficiency of task processing.
Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and the available measurements between non-...
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As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or...
As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network’s dataflow graph and device cluster’s topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method.
Nature-based solutions (NBS) are actions to protect, sustainably manage, or restore natural ecosystems. They are increasingly recognized worldwide as a key strategy for sustainable development, integrating climate cha...
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Nature-based solutions (NBS) are actions to protect, sustainably manage, or restore natural ecosystems. They are increasingly recognized worldwide as a key strategy for sustainable development, integrating climate change adaptation and mitigation and biodiversity conservation. NBS is fundamental to the quality of life of residents, and urban planning often includes strategies that are directly or indirectly related to greening. Although numerous studies have been conducted to quantitatively evaluate the benefits of NBS, analysis of the potential effect of NBS remains insufficient. Therefore, we developed a spatial decision-making support model that optimizes the location of NBS on multiple effects (biomass density, urban heat stress mitigation, and landscape connectivity) using a Non-Sorting dominated Genetic Algorithm-II (NSGA-II). Potential was assessed by comparing the NBS plan considering co-benefits with NBS strategy scenarios focused on individual objectives connectivity, biomass density, heat stress based scenario(CBS, BBS, and HBS). The model was applied to Suwon City, South Korea, demonstrating the potential of an integrated approach to maximize co-benefits simultaneously. The analysis revealed a trade-off between NBS benefits and implementation costs, with performance improvement in one objective often leading to decreased performance in others. Correlation analysis revealed significant positive correlations among the three objectives, with heat stress showing the lowest correlation. Simulations under four distinct scenarios highlighted the diverse potential arrangements of NBS and their effects. An integrated strategy demonstrated a consistently high overall value (a 221% improvement), which proved to be the most efficient approach when considering all aspects collectively. This study underscores the potential benefits of integrating multiple objectives into urban green space planning, thereby providing valuable insights for sustainable urban development.
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