Mobile devices, such as Android and iOS devices, are often used for electronic/mobile commerce (e.g. payments using WeChat Pay and Bitcoin wallet). Hence, ensuring the security of a user’s private key stored on the d...
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Most clustering validity indexes (CVIs) for fuzzy clustering are based upon the fuzzy c-means (FCM) algorithm, and the effect of these CVIs is limited due to the "uniform effect" of FCM. Besides, main existi...
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This paper addresses a target-enclosing problem for multiple spacecraft systems by proposing a two-layer affine formation control strategy. Compared with the existing methods,the adopted two-layer network structure in...
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This paper addresses a target-enclosing problem for multiple spacecraft systems by proposing a two-layer affine formation control strategy. Compared with the existing methods,the adopted two-layer network structure in this paper is generally directed, which is suitable for practical space missions. Firstly, distributed finite-time sliding-mode estimators and formation controllers in both layers are designed separately to improve the flexibility of the formation control system. By introducing the properties of affine transformation into formation control protocol design,the controllers can be used to track different time-varying target formation patterns. Besides, multilayer time-varying encirclements can be achieved with particular shapes to surround the moving target. In the sequel, by integrating adaptive neural networks and specialized artificial potential functions into backstepping controllers, the problems of uncertain Euler-Lagrange models, collision avoidance as well as formation reconfiguration are solved simultaneously. The stability of the proposed controllers is verified by the Lyapunov direct method. Finally, two simulation examples of triangle formation and more complex hexagon formation are presented to illustrate the feasibility of the theoretical results.
Deep learning(DL)has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data.A...
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Deep learning(DL)has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data.A number of comprehensive reviews have been published on such applications,ranging from high-level reviews with future perspectives to those mainly serving as tutorials.
Mobile computing systems, service-based systems and some other systems with mobile interacting components have recently received much attention. However, because of their characteristics such as mobility and disconnec...
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This paper aims to present the development of an operational, monitoring, as well as high-resolution local-scale meteorological and air quality forecasting information system for West Macedonia region, Hellas, in a dy...
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For consensus on measurement-based distributed filtering (CMDF), through infinite consensus fusion operations during each sampling interval, each node in the sensor network can achieve optimal filtering performance wi...
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The proposed three-phase boost Current Source Inverter (CSI) is equipped with Reverse-Blocking IGBTs (RB-IGBT) and the Phasor Pulse Width Modulation (PPWM) switching pattern to provide system efficiency greater than 9...
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Background Despite global efforts to reduce and eventually interrupt malaria transmission,the disease remains a pressing public health problem,especially in sub-Saharan *** study presents a detailed spatio-temporal an...
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Background Despite global efforts to reduce and eventually interrupt malaria transmission,the disease remains a pressing public health problem,especially in sub-Saharan *** study presents a detailed spatio-temporal analysis of malaria transmission in Rwanda from 2012 to *** main objective was to gain insights into the evolving patterns of malaria and to inform and tailor effective public health *** The study used yearly aggregated data of malaria cases from the Rwanda health management information *** employed a multifaceted analytical approach,including descriptive statistics and spatio-temporal analysis across three demographic groups:children under the age of 5 years,and males and females above 5 *** spatially explicit models and spatio scan statistics were utilised to examine geographic and temporal patterns of relative risks and to identify clusters of malaria *** We observed a significant increase in malaria cases from 2014 to 2018,peaking in 2016 for males and females aged above 5 years with counts of 98,645 and 116,627,respectively and in 2018 for under 5-year-old children with 84,440 cases with notable geographic *** like Kamonyi(Southern Province),Ngoma,Kayonza and Bugesera(Eastern Province)exhibited high burdens,possibly influenced by factors such as climate,vector control practices,and cross-border *** spatially explicit modeling revealed elevated relative risks in numerous districts,underscoring the heterogeneity of malaria transmission in these districts,and thus contributing to an overall rising trend in malaria cases until 2018,followed by a subsequent *** findings emphasize that the heterogeneity of malaria transmission is potentially driven by ecologic,socioeconomic,and behavioural *** The study underscores the complexity of malaria transmission in Rwanda and calls for climate adaptive,gender-,age-and district-specific strategies in
We propose a weakly-supervised framework for the semantic segmentation of circular-scan synthetic-aperture-sonar (CSAS) imagery. The first part of our framework is trained in a supervised manner, on image-level labels...
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