After COVID-19, the healthcare system’s ineffectiveness in managing pandemics or public health emergencies is significantly highlighted. Based on the increase in the frequency of pandemics, our objective in this rese...
After COVID-19, the healthcare system’s ineffectiveness in managing pandemics or public health emergencies is significantly highlighted. Based on the increase in the frequency of pandemics, our objective in this research is to define and propose an integrated health system to support healthcare preparedness for future public health emergencies. This system can support managers and authorities in healthcare and disaster management, through data collection, sharing, and analysis, which would ultimately enhance the effectiveness of managing an outbreak before becoming a pandemic.
Nowadays, smartphones became an integral part of human life due to the great necessity for their daily activities. Most smartphone users are downloading and installing mobile apps without worrying about security. Ther...
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In the indoor design process, architects make crucial decisions regarding architectural layout and the selection of non-structural elements. However, there is a lack of comprehensive consideration for human evacuation...
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There are some problems in the electric vehicle (EV) charging pile industry, such as the unreasonable location of charging station construction, low utilization rate of charging piles, and imprecise marketing strategi...
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ISBN:
(纸本)9781450397889
There are some problems in the electric vehicle (EV) charging pile industry, such as the unreasonable location of charging station construction, low utilization rate of charging piles, and imprecise marketing strategies, which may elicit a negative response from EV users, cause colossal waste of resources and hinder the development of EVs. Based on the relevant charging pile data to be analyzed, this paper combines big data-related technologies to propose a big data framework for analyzing the charging pile data to solve the common charging service problems from a systemic perspective. The purpose of the framework is to provide decision-making reference information for charging pile operators, charging pile application operators and charging service marketers to improve the charging pile business more effectively. The framework is demonstrated by functional structure and technical structure.
Multi-step ahead time series forecasting is essential in Internet of Things (IoT) applications in smart cities and smart homes to make accurate future predictions and precise decision-making. Thus, this study introduc...
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The integration of the Internet of Vehicles(IoV)in future smart cities could help solve many traffic-related challenges,such as reducing traffic congestion and traffic *** congestion pricing and electric vehicle charg...
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The integration of the Internet of Vehicles(IoV)in future smart cities could help solve many traffic-related challenges,such as reducing traffic congestion and traffic *** congestion pricing and electric vehicle charging policies have been introduced in recent ***,the majority of these schemes emphasize penalizing the vehicles that opt to take the congested roads or charge in the crowded charging station and do not reward the vehicles that cooperate with the traffic management *** this paper,we propose a novel dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in an urban smart city *** proposed system rewards the drivers that opt to take alternative congested-free ways and congested-free charging *** propose a token management system that serves as a virtual currency,where the vehicles earn these tokens if they take alternative non-congested ways and charging stations and use the tokens to pay for the charging *** proposed system is designed for Vehicular Ad-hoc Networks(VANETs)in the context of a smart city environment without the need to set up any expensive toll collection *** large-scale traffic simulation in different smart city scenarios,it is proved that the system can reduce the traffic congestion and the total charging time at the charging stations.
Early skin cancer detection and its treatment are crucial for reducing death rates worldwide. Deep learning techniques have been used successfully to develop an automatic lesion detection system. This study explores t...
Early skin cancer detection and its treatment are crucial for reducing death rates worldwide. Deep learning techniques have been used successfully to develop an automatic lesion detection system. This study explores the impact of pre-processing steps such as data augmentation, contrast enhancement, and segmentation on improving the convolutional neural network (CNN) performance for lesion classification. The classification network was designed from scratch by uniquely organizing its layers and using a different number of kernels, depth of the network, size, and hyperparameters. In addition, the network’s performance was improved by pre-processing and segmentation steps. The proposed network was compared with the current state-of-the-art to demonstrate its best performance on the benchmark HAM10000 lesion dataset. The experimental study revealed that the classification network using denoised+segmented data achieved an accuracy (ACC), precision (PRE), recall (REC), specificity (SPE), and F-score of 93.40%, 93.45%, 94.51%, 92.08%, and 93.98%, respectively. To conclude, classification performance can be improved by incorporating pre-processing and segmentation steps.
Due to the difffcult topography and bad weather conditions across large distances, dc line breakdown is a serious danger to the safety of high-voltage direct-current systems (HVDC). Thus, there is a need of an accurat...
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We construct vector differentially-private (DP) mechanisms that are asymptotically optimal in the limit of the number of compositions growing without bound. First, we derive via the central limit theorem a reduction f...
We construct vector differentially-private (DP) mechanisms that are asymptotically optimal in the limit of the number of compositions growing without bound. First, we derive via the central limit theorem a reduction from DP to a KL-divergence minimization problem. Second, we formulate the general theory of spherically-symmetric DP mechanisms in the large-composition regime. Specifically, we show that additive, continuous, spherically-symmetric DP mechanisms are optimal if one considers a spherically-symmetric cost (e.g., bounded noise variance) and an ℓ 2 sensitivity metric. We then formulate a finite-dimensional problem that produces noise distributions that can get arbitrarily close to optimal among monotone mechanisms. Finally, we demonstrate numerically that our proposed mechanism achieves better DP parameters than the vector Gaussian mechanism for the same variance constraint.
For the economic success and extensive application of Wireless Power Transfer (WPT) systems, the interoperability of high-power inductive charging systems is crucial. As the standard is only defined up to 11 kVA, inte...
For the economic success and extensive application of Wireless Power Transfer (WPT) systems, the interoperability of high-power inductive charging systems is crucial. As the standard is only defined up to 11 kVA, interoperability studies of highpower systems (e.g. 50 kW) are rare. For this reason, this paper provides an interoperability assessment of two independently designed 50 kW high-power WPT systems. The study is done by using an impedance plane method which allows system limitations to be considered, e.g. of the inverter or the magnetic coils, along with system characteristics such as leakage *** results show that good interoperability is generally achieved. However, this paper also discusses different limitations and possible consequences such as degradation of the transfered power under certain operating conditions, in systems without secondary impedance control.
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