Combat effectiveness of unmanned aerial vehicle(UAV)formations can be severely affected by the mission execution *** the practical execution phase,there are inevitable risks where UAVs being destroyed or targets faile...
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Combat effectiveness of unmanned aerial vehicle(UAV)formations can be severely affected by the mission execution *** the practical execution phase,there are inevitable risks where UAVs being destroyed or targets failed to be *** improve the mission reliability,a resilient mission planning framework integrates task pre-and re-assignment modules is developed in this *** the task pre-assignment phase,to guarantee the mission reliability,probability constraints regarding the minimum mission success rate are imposed to establish a multi-objective optimization *** an improved genetic algorithm with the multi-population mechanism and specifically designed evolutionary operators is used for efficient *** in the task-reassignment phase,possible trigger events are first analyzed.A real-time contract net protocol-based algorithm is then proposed to address the corresponding emergency *** the dual objective used in the former phase is adapted into a single objective to keep a consistent combat *** cases of different scales demonstrate that the two modules cooperate well with each *** the one hand,the pre-assignment module can generate high-reliability mission schedules as an elaborate mathematical model is *** the other hand,the re-assignment module can efficiently respond to various emergencies and adjust the original schedule within a *** corresponding animation is accessible at ***/video/BV12t421w7EE for better illustration.
作者：
Ma, HaoYang, JingyuanHuang, HuiShenzhen University
Visual Computing Research Center College of Computer Science and Software Engineering Shenzhen China (GRID:grid.263488.3) (ISNI:0000 0001 0472 9649)
Exemplar-based image translation involves converting semantic masks into photorealistic images that adopt the style of a given ***,most existing GAN-based translation methods fail to produce photorealistic *** this st...
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Exemplar-based image translation involves converting semantic masks into photorealistic images that adopt the style of a given ***,most existing GAN-based translation methods fail to produce photorealistic *** this study,we propose a new diffusion model-based approach for generating high-quality images that are semantically aligned with the input mask and resemble an exemplar in *** proposed method trains a conditional denoising diffusion probabilistic model(DDPM)with a SPADE module to integrate the semantic *** then used a novel contextual loss and auxiliary color loss to guide the optimization process,resulting in images that were visually pleasing and semantically *** demonstrate that our method outperforms state-of-the-art approaches in terms of both visual quality and quantitative metrics.
Detection of epileptic seizures is important for early diagnosis and treatment. It is known that the behavioral patterns of the brain in electroencephalogram (EEG) signals have huge and complex fluctuations. Diagnosin...
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Detection of epileptic seizures is important for early diagnosis and treatment. It is known that the behavioral patterns of the brain in electroencephalogram (EEG) signals have huge and complex fluctuations. Diagnosing epilepsy by analyzing signals are costly process. Various methods are used to classify epileptic seizures. However, the inadequacy of these approaches in classifying signals makes it difficult to diagnose epilepsy. Complex network science produces effective solutions for analyzing interrelated structures. Using methods based on complex network analysis, it is possible to EEG signals analyze the relationship between signals and perform a classification process. In this study proposes a novel approach for classifying epileptic seizures by utilizing complex network science. In addition, unlike the studies in the literature, classification processes were carried out with lower dimensional signals by using 1-s EEG signals instead of 23.6-s fullsize EEG signals. Using the topological properties of the EEG signal converted into a complex network, the classification process has been performed with the Jaccard Index method. The success of the classification process with the Jaccard Index was evaluated using Accuracy, F1 Score, Recall, and K-Fold metrics. In the results obtained, the signals of individuals with epileptic seizures were separated with an accuracy rate of 98.15%.
Perovskite solar cells（PSCs） are promising in the field of photovoltaics but are hindered by surface defects and ***,the energetic losses occurring at the interfaces between the perovskite and the charge transport l...
Perovskite solar cells（PSCs） are promising in the field of photovoltaics but are hindered by surface defects and ***,the energetic losses occurring at the interfaces between the perovskite and the charge transport layers often lead to reduced power conversion efficiency（PCE）.Surface treatment is an effective strategy but the passivating ligands usually bind with a single active *** resulted dense packing of resistive passivators perpendicular to the surface is detrimental to charge ***,we present a passivator that can bind to two neighboring lead（Ⅱ） ion(Pb2+) defect sites simultaneously with an aligned parallel mode to the perovskite surface,effectively suppressing the surface trap density and preventing the *** target device fulfills a PCE of 25.1% and maintains over 85% of the initial efficiency after 800 h of exposure to a relative humidity（RH） of 65% ± 5%.
Infrastructure-as-a-Service(IaaS)cloud platforms offer resources with diverse buying *** can run an instance on the on-demand market which is stable but expensive or on the spot market with a significant ***,users hav...
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Infrastructure-as-a-Service(IaaS)cloud platforms offer resources with diverse buying *** can run an instance on the on-demand market which is stable but expensive or on the spot market with a significant ***,users have to carefully weigh the low cost of spot instances against their poor *** instances will be revoked when the revocation event ***,an important problem that an IaaS user faces now is how to use spot in-stances in a cost-effective and low-risk *** on the replication-based fault tolerance mechanism,we propose an on-line termination algorithm that optimizes the cost of using spot instances while ensuring operational *** prove that in most cases,the cost of our proposed online algorithm will not exceed twice the minimum cost of the optimal of-fline algorithm that knows the exact future a *** a large number of experiments,we verify that our algorithm in most cases has a competitive ratio of no more than 2,and in other cases it can also reach the guaranteed competitive ratio.
Federated Graph Neural Networks (FedGNNs) have achieved significant success in representation learning for graph data, enabling collaborative training among multiple parties without sharing their raw graph data and so...
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Federated Graph Neural Networks (FedGNNs) have achieved significant success in representation learning for graph data, enabling collaborative training among multiple parties without sharing their raw graph data and solving the data isolation problem faced by centralized GNNs in data-sensitive scenarios. Despite the plethora of prior work on inference attacks against centralized GNNs, the vulnerability of FedGNNs to inference attacks has not yet been widely explored. It is still unclear whether the privacy leakage risks of centralized GNNs will also be introduced in FedGNNs. To bridge this gap, we present PIAFGNN, the first property inference attack (PIA) against FedGNNs. Compared with prior works on centralized GNNs, in PIAFGNN, the attacker can only obtain the global embedding gradient distributed by the central server. The attacker converts the task of stealing the target user’s local embeddings into a regression problem, using a regression model to generate the target graph node embeddings. By training shadow models and property classifiers, the attacker can infer the basic property information within the target graph that is of interest. Experiments on three benchmark graph datasets demonstrate that PIAFGNN achieves attack accuracy of over 70% in most cases, even approaching the attack accuracy of inference attacks against centralized GNNs in some instances, which is much higher than the attack accuracy of the random guessing method. Furthermore, we observe that common defense mechanisms cannot mitigate our attack without affecting the model’s performance on mainly classification tasks.
Coronavirus disease 2019(Covid-19)is a life-threatening infectious disease caused by a newly discovered strain of the *** by the end of 2020,Covid-19 is still not fully understood,but like other similar viruses,the ma...
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Coronavirus disease 2019(Covid-19)is a life-threatening infectious disease caused by a newly discovered strain of the *** by the end of 2020,Covid-19 is still not fully understood,but like other similar viruses,the main mode of transmission or spread is believed to be through droplets from coughs and sneezes of infected *** accurate detection of Covid-19 cases poses some questions to scientists and *** two main kinds of tests available for Covid-19 are viral tests,which tells you whether you are currently infected and antibody test,which tells if you had been infected ***-tine Covid-19 test can take up to 2 days to complete;in reducing chances of false negative results,serial testing is *** image processing by means of using Chest X-ray images and Computed Tomography(CT)can help radiologists detect the *** imaging approach can detect certain characteristic changes in the lung associated with *** this paper,a deep learning model or tech-nique based on the Convolutional Neural Network is proposed to improve the accuracy and precisely detect Covid-19 from Chest Xray scans by identifying structural abnormalities in scans or X-ray *** entire model proposed is categorized into three stages:dataset,data pre-processing andfinal stage being training and classification.
FDTD method is a common approach for electromagnetic propagation, scattering and coupling simulation. Conventional FDTD with universal mesh requires numerous computations to represent solve problems with multi-scale s...
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Community detection is a fundamental task in complex network analysis, aiming to partition networks into tightly multiple dense subgraphs. While community detection has been widely studied, existing methods often lack...
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Community detection is a fundamental task in complex network analysis, aiming to partition networks into tightly multiple dense subgraphs. While community detection has been widely studied, existing methods often lack interpretability, making it challenging to explain key aspects such as node assignments, community boundaries, and inter-community relationships. In this paper, the explainable community detection issue is addressed, in which each community is characterized using a central node and its corresponding radius. The central node represents the most representative node in the community, while the radius defines its influence scope. Such an explainable community detection issue is formulated as an optimization problem in which the objective is to maximize central node's coverage and accuracy in explaining its associated community. To solve this problem, two algorithms are developed: a naive algorithm and a fast approximate algorithm that incorporate heuristic strategies to improve computational efficiency. Experimental results on 9 real-world networks demonstrate that the proposed methods can effectively interpret community structures with high accuracy and efficiency. More precisely, the objective function values achieved by the identified pairs of center and radius exceed 0.7 on most communities and the running time is generally no more than 10 s on a network with approximatively one thousand nodes. The source code of the proposed methods can be found at: https://***/xuannnn523/CCTS.
Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, t...
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