Software, hardware, data, and computing power can be abstracted and encapsulated as services authorised to users in a paid or free manner for on demand deployment. Service composition combines multiple existing servic...
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Amid the global shift of smart manufacturing towards greener and more intelligent paradigms, the spatiotemporal coupling characteristics of dynamic heat conduction networks pose significant challenges for optimizing t...
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Object detection is an important task in drone vision. Since the number of objects and their scales always vary greatly in the drone-captured video, small object-oriented feature becomes the bottleneck of model perfor...
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Object detection is an important task in drone vision. Since the number of objects and their scales always vary greatly in the drone-captured video, small object-oriented feature becomes the bottleneck of model performance, and most existing object detectors tend to underperform in drone-vision scenes. To solve these problems, we propose a novel detector named YOLO-Drone. In the proposed detector, the backbone of YOLO is firstly replaced with ConvNeXt, which is the state-of-the-art one to extract more discriminative features. Then, a novel scale-aware attention(SAA) module is designed in detection head to solve the large disparity scale problem. A scale-sensitive loss(SSL) is also introduced to put more emphasis on object scale to enhance the discriminative ability of the proposed detector. Experimental results on the latest VisDrone 2022 test-challenge dataset(detection track) show that our detector can achieve average precision(AP) of 39.43%, which is tied with the previous state-of-the-art, meanwhile,reducing 39.8% of the computational cost.
The multimodal object detection technology based on visible-thermal vision sensors has drawn significant attention as it is capable of achieving reliable object detection in complex scenes with challenging lighting co...
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Accurate monitoring of urban waterlogging contributes to the city’s normal operation and the safety of residents’daily ***,due to feedback delays or high costs,existing methods make large-scale,fine-grained waterlog...
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Accurate monitoring of urban waterlogging contributes to the city’s normal operation and the safety of residents’daily ***,due to feedback delays or high costs,existing methods make large-scale,fine-grained waterlogging monitoring impossible.A common method is to forecast the city’s global waterlogging status using its partial waterlogging *** method has two challenges:first,existing predictive algorithms are either driven by knowledge or data alone;and second,the partial waterlogging data is not collected selectively,resulting in poor *** overcome the aforementioned challenges,this paper proposes a framework for large-scale and fine-grained spatiotemporal waterlogging monitoring based on the opportunistic sensing of limited bus *** framework follows the Sparse Crowdsensing and mainly comprises a pair of iterative predictor and *** predictor uses the collected waterlogging status and the predicted status of the uncollected area to train the graph convolutional neural *** combines both knowledge-driven and data-driven approaches and can be used to forecast waterlogging status in all regions for the upcoming *** selector consists of a two-stage selection procedure that can select valuable bus routes while satisfying budget *** experimental results on real waterlogging and bus routes in Shenzhen show that the proposed framework could easily perform urban waterlogging monitoring with low cost,high accuracy,wide coverage,and fine granularity.
Due to the fact that most ceramic products formed by drawing embryos can be geometrically viewed as rotating bodies generated by the rotation of their profile curves, the design of these ceramic products can focus on ...
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In recent years,the demand for optical imaging and detection in hypersonic aircraft has been on the *** hightemperature and high-pressure compressed flow field near airborne optoelectronic devices creates significant ...
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In recent years,the demand for optical imaging and detection in hypersonic aircraft has been on the *** hightemperature and high-pressure compressed flow field near airborne optoelectronic devices creates significant interference with light transmission,known as hypersonic aero-optical *** effect has emerged as a key technological challenge,limiting hypersonic optical imaging and detection *** article focuses on introducing the thermal effects and optical transmission effects of hypersonic aero-optical effects,as along with corresponding suppression *** addition,this article critically reviews and succinctly summarizes the advancements made in hypersonic aero-optical effects testing technology,while also delineating avenues for future research needs in this *** conclusion,there is an urgent call for further exploration into the study of aero-optical effects under conditions characterized by high Mach,high enthalpy,and high Reynolds number in the future.
Deploying models on resource-constrained edge devices remains always a critical challenge for the application of neural network. Quantization is one of the most popular methods to compress the model for meeting the pe...
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With the recent advances in the field of deep learning, an increasing number of deep neural networks have been applied to business process prediction tasks, remaining time prediction, to obtain more accurate predictiv...
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With the recent advances in the field of deep learning, an increasing number of deep neural networks have been applied to business process prediction tasks, remaining time prediction, to obtain more accurate predictive results. However, existing time prediction methods based on deep learning have poor interpretability, an explainable business process remaining time prediction method is proposed using reachability graph,which consists of prediction model construction and visualization. For prediction models, a Petri net is mined and the reachability graph is constructed to obtain the transition occurrence vector. Then, prefixes and corresponding suffixes are generated to cluster into different transition partitions according to transition occurrence vector. Next,the bidirectional recurrent neural network with attention is applied to each transition partition to encode the prefixes, and the deep transfer learning between different transition partitions is performed. For the visualization of prediction models, the evaluation values are added to the sub-processes of a Petri net to realize the visualization of the prediction models. Finally, the proposed method is validated by publicly available event logs.
The cubic cardinal spline is an important tool for constructing interpolation curves and surfaces. Global shape adjustment of the cubic cardinal spline can be achieved by modifying the value of the free parameter in t...
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