In this paper,a reasoning enhancement method based on RGCN(Relational Graph Convolutional Network)is proposed to improve the detection capability of UAV(Unmanned Aerial Vehicle)on fast-moving military targets in urban...
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In this paper,a reasoning enhancement method based on RGCN(Relational Graph Convolutional Network)is proposed to improve the detection capability of UAV(Unmanned Aerial Vehicle)on fast-moving military targets in urban battlefield *** combining military images with the publicly available VisDrone2019 dataset,a new dataset called VisMilitary was built and multiple YOLO(You Only Look Once)models were tested on *** to the low confidence problem caused by fuzzy targets,the performance of traditional YOLO models on real battlefield images decreases ***,we propose an improved RGCN inference model,which improves the performance of the model in complex environments by optimizing the data processing and graph network *** results show that the proposed method achieves an improvement of 0.4%to 1.7%on mAP@0.50,which proves the effectiveness of the model in military target *** research of this paper provides a new technical path for UAV target detection in urban battlefield,and provides important enlightenment for the application of deep learning in military field.
Superconducting critical temperature is the most attractive material property due to its impact on the applications of electricity transmission,railway transportation,strong magnetic fields for nuclear fusion and medi...
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Superconducting critical temperature is the most attractive material property due to its impact on the applications of electricity transmission,railway transportation,strong magnetic fields for nuclear fusion and medical imaging,quantum computing,*** ability to predict its value is a constant pursuit for condensed matter *** developed a new hierarchical neural network(HNN)AI algorithm to resolve the contradiction between the large number of descriptors and the small number of datasets always faced by neural network AI approaches to materials *** this new HNN-based AI model,a much-increased number of 909 universal descriptors for inorganic compounds,and a dramatically cleaned database for conventional superconductors,we achieved high prediction accuracy with a test R^(2)score of 95.6%.The newly developed HNN model accurately predicted T_(c)of 45 new high-entropy alloy superconductors with a mean absolute percent error below 6%compared to the experimental *** demonstrated a significant potential for predicting other properties of inorganic materials.
With the advent of the Web 3.0 era, the amount and types of data in the network have sharply increased, and the application scenarios of recommendation algorithms are continuously expanding. Location recommendation ha...
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Wide field of view and light weight optics are critical for advanced eyewear,with applications in augmented/virtual reality and night *** refractive lenses are often stacked to correct aberrations at a wide field of v...
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Wide field of view and light weight optics are critical for advanced eyewear,with applications in augmented/virtual reality and night *** refractive lenses are often stacked to correct aberrations at a wide field of view,leading to limited performance and increased size and *** particular,simultaneously achieving a wide field of view and large aperture for light collection is desirable but challenging to realize in a compact ***,we demonstrate a wide field of view(greater than 60°)meta-optic doublet eyepiece with an entrance aperture of 2.1 *** the design wavelength of 633 nm,the meta-optic doublet achieves comparable performance to a refractive lens-based eyepiece *** meta-doublet eyepiece illustrates the potential for meta-optics to play an important role in the development of high-quality monochrome near-eye displays and night vision systems.
This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots (Bi-HFSP_CS). The objectives are to minimize the makespan and total energy consumption. First, the Bi-HFSP_CS is for...
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Continuous search problems(CSPs), which involve finding solutions within a continuous domain, frequently arise in fields such as optimization, physics, and engineering. Unlike discrete search problems, CSPs require na...
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Continuous search problems(CSPs), which involve finding solutions within a continuous domain, frequently arise in fields such as optimization, physics, and engineering. Unlike discrete search problems, CSPs require navigating an uncountably infinite space, presenting unique computational challenges. In this work, we propose a fixed-point quantum search algorithm that leverages continuous variables to address these challenges, achieving a quadratic speedup. Inspired by the discrete search results, we manage to establish a lower bound on the query complexity of arbitrary quantum search for CSPs, demonstrating the optimality of our approach. In addition, we demonstrate how to design the internal structure of the quantum search oracle for specific problems. Furthermore, we develop a general framework to apply this algorithm to a range of problem types, including optimization and eigenvalue problems involving continuous variables.
Partially-degradable biomaterials refers to smart implants where biodegradable metals can gradually be replaced by newly growing bone or living tissues, and leave behind a porous inert metal skeleton that stably binds...
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Partially-degradable biomaterials refers to smart implants where biodegradable metals can gradually be replaced by newly growing bone or living tissues, and leave behind a porous inert metal skeleton that stably binds with the new bone tissue. In this research, a partially degradable composite was designed by integrating Ti-Zr-Cu-Pd-Sn metallic glass (MG) with designed Fe-Mg alloy using spark plasma sintering (SPS). The mechanical alloying technique successfully enabled the fusion of immiscible Fe and Mg, addressing the issues of Fe's slow degradation and Mg's rapid breakdown, while also minimizing potential fractures in the metal framework due to hydrogen gas evolution. The controlled degradation of Mg(Fe) promotes the formation of Ca-P compounds, enhancing the bioactivity of the Fe-Mg composite. This design endows the composite with plastic and ductile deformation under compression, providing a viable solution to the brittle fracture behaviour commonly associated with conventional bulk metallic glasses (BMGs). This advancement holds promise for aligning with the natural growth rate of human bone, further augmenting the bioactive properties and practical applications of the MG/Fe-Mg composite material. Statement of significance: In this research, a partially degradable composite was designed by integrating Ti-Zr-Cu-Pd-Sn metallic glass (MG) with designed Fe-Mg alloy using SPS. The Fe-Mg alloy act as temporary space holders can gradually being replaced by newly formed bone, thus establishing a dynamic equilibrium between the biodegradation of the bio-metals and the inward growth of new bone. The degradation of Mg(Fe) promotes the formation of Ca-P compounds, enhancing the bioactivity of the composite. This design endows the composite with plastic deformation under compression, providing a viable solution to the brittle fracture behavior of conventional MGs. This advancement holds promise for aligning with the natural growth rate of human bone, further augmenting the
Low-Rank Adaptation (LoRA) is currently the most commonly used Parameter-efficient fine-tuning (PEFT) method. However, it still faces high computational and storage costs to models with billions of parameters. Most pr...
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Remote driving, an emergent technology enabling remote operations of vehicles, presents a significant challenge in transmitting large volumes of image data to a central server. This requirement outpaces the capacity o...
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Remote driving, an emergent technology enabling remote operations of vehicles, presents a significant challenge in transmitting large volumes of image data to a central server. This requirement outpaces the capacity of traditional communication methods. To tackle this, we propose a novel framework using semantic communications, through a region of interest semantic segmentation method, to reduce the communication costs by transmitting meaningful semantic information rather than bit-wise data. To solve the knowledge base inconsistencies inherent in semantic communications, we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge bases. This system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient management. Additionally, the implementation of blockchain sharding handles differentiated knowledge bases for various tasks, thus boosting overall blockchain efficiency. Experimental results show a great reduction in latency by sharding and an increase in model accuracy, confirming our framework's effectiveness.
In response to inquiries posed in natural languages, question-answering systems (QASs) produce responses. The capabilities of early QASs are limited because they were designed for certain domains. The current generati...
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