The present global situation of COVID-19 pandemic has broke through regional, revolutionary, philosophical, spiritual, social, and educational barriers. By utilizing an interconnection of network, an Internet of Thing...
The present global situation of COVID-19 pandemic has broke through regional, revolutionary, philosophical, spiritual, social, and educational barriers. By utilizing an interconnection of network, an Internet of Things enabled medical system is useful for regular monitoring of COVID-19 patients. It also aids in improving patient experience and reducing re-hospitalizations. The arrival of the IoT has effect on reducing healthcare costs and enhancing the treatment outcome of affected patients. As a result, the goal of this study is to analyze and emphasize the overall applicability of the well-established IoT concept by providing a conceptual outlay to combat the COVID-19 pandemic.
For large tourist attraction Louvre, it is essential to establish an emergency evacuation plan for personnel in critical situations. We propose an effective graph-based path planning model, which is suitable for limit...
For large tourist attraction Louvre, it is essential to establish an emergency evacuation plan for personnel in critical situations. We propose an effective graph-based path planning model, which is suitable for limited space with dense visitors flow. This paper analyze individual's movement pattern with CA model, regional crowd flow, and overall suboptimal path planning. We establish a limited space fast flow model (LSFFM). Then we apply this evacuation model to the Louvre compared with other out of order evacuation condition. It shows a great performance for evacuation efficiency and visitors' safety.
Distributed transaction model has gradually replaced the traditional centralized transaction model and has become the leading direction of development in energy trading. As the underlying support, blockchain technolog...
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ISBN:
(数字)9781728109626
ISBN:
(纸本)9781728109633
Distributed transaction model has gradually replaced the traditional centralized transaction model and has become the leading direction of development in energy trading. As the underlying support, blockchain technology is attracting more and more attention due to its advantages, i.e., integrity and non-repudiation. However, most blockchain-based trading models face the problem of privacy protection. In this paper, to solve this problem, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is introduced as the core algorithm to reconstruct the transaction model. Specifically, we build a general model for distributed transaction called PP-BCTS (Privacy-Preserving Blockchain Trading Scheme). It can achieve fine-grained access control through transaction arbitration in ciphertext form. This design can maximize the protection of private information and can greatly improve the security and reliability of the transaction model. Additionally, a credibility-based equity proof consensus mechanism is proposed in PP-BCTS, which can greatly improve the operational efficiency. Security analysis and experimental evaluations are conducted to prove the validity and practicability of our proposed scheme.
A novel and facile oxidation-induced self-doping process of graphene-silicon Schottky junction by nitric acid（HNO3） vapor is reported. The HNO3 oxidation process makes graphene p-type self-doped, and leads to a high...
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A novel and facile oxidation-induced self-doping process of graphene-silicon Schottky junction by nitric acid（HNO3） vapor is reported. The HNO3 oxidation process makes graphene p-type self-doped, and leads to a higher built-in potential and conductivity to enhance charge transfer and to suppress charge carrier recombination at the graphene-silicon Schottky junction. After the HNO3 oxidation process, the open-circuit voltage is increased from the initial value of 0.36 V to the maximum value of 0.47 V, the short-circuit current is greatly increased from 0.80μA to 7.71μA, and the ideality factor is optimized from 4.4 to 1.0. The enhancement of the performance of graphene-Si solar cells may be due to oxidation-induced p-type self-doping of graphene-Si junctions.
With the development of cloud computing, serverless computing, that is, function as a service (FaaS) is considered to be the next stage of cloud computing development. Serverless computing can be seen as a logical ext...
With the development of cloud computing, serverless computing, that is, function as a service (FaaS) is considered to be the next stage of cloud computing development. Serverless computing can be seen as a logical extension of cloud computing, a disruptive approach to application development. It is based on the code written by the developer for accurate resource allocation, and the resources on the platform are started when a predefined event is triggered. By comparing with the traditional architecture, the advantages of serverless architecture are highlighted. Although serverless is a relatively new concept in the field of software architecture, it is also a technological innovation with great influence.
We propose a novel structured discriminative blockdiagonal dictionary learning method, referred to as scalable Locality-Constrained Projective Dictionary Learning (LC-PDL), for efficient representation and classificat...
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Connected vehicle technologies can track the vehicular level information as well as give the road driver the access to participate in the traffic management. In this paper, we formulate the isolated intersection contr...
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Laboratory experiments next to a variety of observations, especially in subduction zones, have explored the existence of a premonitory stable slow slip growth phase preceding large earthquakes. These phe- nomena play ...
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Laboratory experiments next to a variety of observations, especially in subduction zones, have explored the existence of a premonitory stable slow slip growth phase preceding large earthquakes. These phe- nomena play an important role in the earthquake cycle and thus precise imaging and monitoring of these events are of great significance. In the literature, ENIF （extended network inversion filter） has been proposed as a rigorous algorithm capable of isolating signal from different types of noise and thereby provides us with deep insight into spatio-temporal evolution of slow slip events. Despite its considerable advantages, ENIF still suffers from some limitations. ENIF applies Tikhonov method of regularization with a quadratic form of cost function. While anomalous slip regions have clear contrast with the background slip in reality, Tikhonov regularization tends to over smooth （globally smooth） the slipping portion on the estimated images. In order to avoid over smoothing phenomenon, we have incorporated into ENIF an image segmentation step which tries to preserve edges of slow-slip event. As a second limitation, due to the nonlinearity imposed by such constraint as non-negativity of slip rate, uncertainty propagation through model is not simple. As the core of ENIF, EKF （extended Kalman filter）, performs uncertainty propagation by linearization of nonlinear model using Jacobian and Hessian matrices. As an alternative for EKE we have also investigated the application of UKF （unscented Kalman filter） which uses UT （unscented transform） for uncertainty propagation. Finally, we tested our proposed algorithm using a low signal to noise ratio synthetic data set. The results show a significant improvement in the perfor- mance of ENIF when the segmentation step is incorporated into the algorithm.
Hydraulic systems is a class of nonlinear complex *** are many typical characteristics with the systems:multiple functional components,multiple operation modes,space-time coupling work,and monitoring signals for fault...
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Hydraulic systems is a class of nonlinear complex *** are many typical characteristics with the systems:multiple functional components,multiple operation modes,space-time coupling work,and monitoring signals for faults are multivariate time series data,*** of the characteristics,fault diagnosis for Hydraulic systems is not *** fault diagnosis methods mostly ignore the multivariable timing characteristics of monitoring signals,it has made many detection and diagnosis(especially for multiple fault)can not keep high accuracy,and some of the methods are not even be able to multiple fault *** at the problem,a multivariate time series classification based diagnosis method is ***,extracting timing characteristics(transformed features)from the time series data collected via sensors by 1-NN ***,training the transformed features by multi-class OVO-SVM to classify multivariate time *** of the method contains single fault and multiple faults conditions,the results show that the method has high accuracy,it can complete multiple-faults classification.
Pollutants emissions is strictly controlled in modern power plants, and Nitrogen Oxides(NOx), which is the main contaminants is the exhaust gas. The Selective Catalytic Reduction process(SCR) is commonly used for deni...
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Pollutants emissions is strictly controlled in modern power plants, and Nitrogen Oxides(NOx), which is the main contaminants is the exhaust gas. The Selective Catalytic Reduction process(SCR) is commonly used for denitration. For achieving an effective the SCR outlet NOx concentration control, an accurate outlet NOx concentration model is necessary. A model using historical data is proposed, and long-short term memory(LSTM) algorithm is applied, which could describe relevance in time series. The accuracy performances for proposed data-driven model are verified, and root mean square error( RMSE) and mean absolute error(MAPE) for training set are, 0.706 mg/m3 and 1.99%, respectively, which for test set are 1.44 mg/m3 and 2.90%, respectively, The verification reveals that the accuracy for data-driven model is acceptable for control system design.
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