다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1315&context=electricalengineeringfacpub

A Survey on Smart Grid CommunicationInfrastructures: Motivations, Requirements andChallenges

In recent years, the Internet of Things (IoT) has been one of the most popular technologies that facilitate new interactions among things and humans to enhance the quality of life. With the rapid development of IoT, the fog computing paradigm is emerging as an attractive solution for processing the data of IoT applications. In the fog environment, IoT applications are executed by the intermediate computing nodes in the fog, as well as the physical servers in cloud data centers. On the other hand, due to the resource limitations, resource heterogeneity, dynamic nature, and unpredictability of fog environment, it necessitates the resource management issues as one of the challenging problems to be considered in the fog landscape. Despite the importance of resource management issues, to the best of our knowledge, there is not any systematic, comprehensive and detailed survey on the field of resource management approaches in the fog computing context. In this paper, we provide a systematic literature review (SLR) on the resource management approaches in fog environment in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and providing open issues as well. The presented taxonomy are classified into six main fields: application placement, resource scheduling, task offloading, load balancing, resource allocation, and resource provisioning. The resource management approaches are compared with each other according to the important factors such as the performance metrics, case studies, utilized techniques, and evaluation tools as well as their advantages and disadvantages are discussed.

Resource Management Approaches in Fog Computing: a Comprehensive Review

Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. It is envisaged that this study will provide AI researchers and the wider community an overview of the current status of AI applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.

/pdf/artificial-intelligence-in-the-battle-against-coronavirus-3ayvkhps4h.pdf

Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions

COVID-19 infection is growing in a rapid rate. Due to unavailability of specific drugs, early detection of (COVID-19) patients is essential for disease cure and control. There is a vital need to detect the disease at early stage and instantly quarantine the infected people. Many research have been going on, however, none of them introduces satisfactory results yet. In spite of its simplicity, K-Nearest Neighbor (KNN) classifier has proven high flexibility in complex classification problems. However, it can be easily trapped. In this paper, a new COVID-19 diagnose strategy is introduced, which is called COVID-19 Patients Detection Strategy (CPDS). The novelty of CPDS is concentrated in two contributions. The first is a new hybrid feature selection Methodology (HFSM), which elects the most informative features from those extracted from chest Computed Tomography (CT) images for COVID-19 patients and non COVID-19 peoples. HFSM is a hybrid methodology as it combines evidence from both wrapper and filter feature selection methods. It consists of two stages, namely; Fast Selection Stage (FS2) and Accurate Selection Stage (AS2). FS2relies on filter, while AS2 uses Genetic Algorithm (GA) as a wrapper method. As a hybrid methodology, HFSM elects the significant features for the next detection phase. The second contribution is an enhanced K-Nearest Neighbor (EKNN) classifier, which avoids the trapping problem of the traditional KNN by adding solid heuristics in choosing the neighbors of the tested item. EKNN depends on measuring the degree of both closeness and strength of each neighbor of the tested item, then elects only the qualified neighbors for classification. Accordingly, EKNN can accurately detect infected patients with the minimum time penalty based on those significant features selected by HFSM technique. Extensive experiments have been done considering the proposed detection strategy as well as recent competitive techniques on the chest CT images. Experimental results have shown that the proposed detection strategy outperforms recent techniques as it introduces the maximum accuracy rate.

A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier

A data mining based load forecasting strategy for smart electrical grids

Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network.

Internet of things (IoT) enables the smart electrical grids (SEGs) to support a lot of tasks throughout the generation, transmission, distribution and consumption of energy Large amounts of data are generated through these smart devices, which can be sent to the cloud for further processing Hence, appropriate actions can be taken based on the cloud processing However, sending the complete captured data directly to the cloud would lead to resource wastage To overcome the cloud challenges, fog computing tier acts as a bridge in the middle between the IoT devices embedded in SEG and the cloud Thus, a 3-tiers architecture is proposed to replace 2-tiers one The added fog tier offers a place for collecting, computing, storing smart meter data before transmitting them to the cloud In this paper, a new electrical load forecasting (ELF) strategy has been proposed based on the pre-mentioned three tiers architecture The proposed strategy consists of two phases; which are; (i) data pre-processing phase (DP2) and (ii) load prediction phase (LP2) Both phases take place at a cloud data center (CDC) on the collected data, which is received from all fogs connected to the entire cloud The aim of the data preprocessing phase is to; (i) reject outliers from the collected data, and (ii) feature selection The main contribution of this paper lied on the feature selection, which elects only the effective features for the next load prediction phase Features selection is an essential pre-processing operation to reduce the dataset dimensions from all irrelevant features leading to enable the ELF from taking fast and correct decision for other electrical subsystems A new feature selection methodology called fuzzy based feature selection (FBFS) is presented, which involves two stages; feature ranking stage (FRS) and feature selection stage (FS2) FBFS is a hybrid method based on both features selection methods filter and wrapper providing fast and more accurate significant features Then, during LP2, the filtered data is used to give a fast and accurate load prediction Experimental results have shown that the proposed FBFS promotes the load forecasting efficiency in terms of precision, recall, accuracy and F-measure compared with recent features selection methodologies FBFS provides the highest precision, recall, and accuracy results On the other hand, it provides the lowest error value

Asmaa H. Rabie

Papers

A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier

A data mining based load forecasting strategy for smart electrical grids

Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network.

A fog based load forecasting strategy for smart grids using big electrical data

Accurate Detection of COVID-19 Patients Based on Distance Biased Naïve Bayes (DBNB) Classification Strategy.