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Research on Cloud Platform Security Protection System for Power Plant
In the electric power industry, the information construction of IT systems is in an important period evolving to cloud computing technology and implementing digital transformation. The power cloud is a critical infrastructure for the digital construction in electric power companies. Cloud computing technology fundamentally introduces a flexible and dynamically allocated shared resource pool, and provides a way to share cloud resources different from traditional information systems, but it also brings new security risks and challenges. This paper analyses the security risks of the power cloud from the aspects of network security, host security, application security, data security, and management risks. The focus of research is on the construction of a security protection system for private cloud infrastructure in electric power companies. According to the business characteristics and important levels of business modules, the partition and domain protection design recommendations of the power cloud is proposed. An overall security protection architecture including three aspects of cloud platform self-security, cloud platform security services and cloud security operation management is presented. And specific key protection measures in the above three aspects are given, which has important guiding significance to electric power companies.
Journal Article
Isolated Forest-Based Prediction of Container Resource Load Extremes
Given the wide application of container technology, the accurate prediction of container CPU usage has become a core aspect of optimizing resource allocation and improving system performance. The high volatility of container CPU utilization, especially the uncertainty of extreme values of CPU utilization, is challenging to accurately predict, which affects the accuracy of the overall prediction model. To address this problem, a container CPU utilization prediction model, called ExtremoNet, which integrates the isolated forest algorithm, and classification sub-models are proposed. To ensure that the prediction model adequately takes into account critical information on the CPU utilization’s extreme values, the isolated forest algorithm is introduced to compute these anomalous extreme values and integrate them as features into the training data. In order to improve the recognition accuracy of normal and extreme CPU utilization values, a classification sub-model is used. The experimental results show that, on the AliCloud dataset, the model has an R2 of 96.51% and an MSE of 7.79. Compared with the single prediction models TCN, LSTM, and GRU, as well as the existing combination models CNN-BiGRU-Attention and CNN-LSTM, the model achieves average reductions in the MSE and MAE of about 38.26% and 23.12%, proving the effectiveness of the model at predicting container CPU utilization, and provides a more accurate basis for resource allocation decisions.
Journal Article
A New Decentralized Control Strategy of Microgrids in the Internet of Energy Paradigm
The Energy Internet paradigm is the evolution of the Internet of Things concept in the power system. Microgrids (MGs), as the essential element in an Energy Internet, are expected to be controlled in a corporative and flexible manner. This paper proposes a novel decentralized robust control strategy for multi-agent systems (MASs) governed MGs in future Energy Internet. The proposed controller is based on a consensus algorithm applied with the connected distributed generators (DGs) in the MGs in the energy internet paradigm. The proposed controller’s objectives are the frequency/voltage regulation and proportional reactive/active power-sharing for the hybrid DGs connected MGs. A proposed two-level communication system is implemented to explain the data exchange between the MG system and the cloud server. The local communication level utilizes the transmission control protocol (TCP)/ internet protocol (IP) and the message queuing telemetry transport (MQTT) is used as the protocol for the global communication level. The proposed control strategy has been verified using a hypothetical hybrid DGs connected MG such as photovoltaic or wind turbines in MATLAB Simulink environment. Several scenarios based on the system load types are implemented using residential buildings and small commercial outlets. The simulation results have verified the feasibility and effectiveness of the introduced strategy for the MGs’ various operating conditions.
Journal Article
Practical Microsoft Azure IaaS : Migrating and Building Scalable and Secure Cloud Solutions
\"Adopt Azure IaaS and migrate your on-premise infrastructure partially or fully to Azure. This book provides practical solutions by following Microsoft's design and best practice guidelines for building highly available, scalable, and secure solution stacks using Microsoft Azure IaaS.\"--Back cover.
Book
Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation
The sustainable management of natural heritage is presently considered a global strategic issue. Owing to the ever-growing availability of free data and software, remote sensing (RS) techniques have been primarily used to map, analyse, and monitor natural resources for conservation purposes. The need to adopt multi-scale and multi-temporal approaches to detect different phenological aspects of different vegetation types and species has also emerged. The time-series composite image approach allows for capturing much of the spectral variability, but presents some criticalities (e.g., time-consuming research, downloading data, and the required storage space). To overcome these issues, the Google Earth engine (GEE) has been proposed, a free cloud-based computational platform that allows users to access and process remotely sensed data at petabyte scales. The application was tested in a natural protected area in Calabria (South Italy), which is particularly representative of the Mediterranean mountain forest environment. In the research, random forest (RF), support vector machine (SVM), and classification and regression tree (CART) algorithms were used to perform supervised pixel-based classification based on the use of Sentinel-2 images. A process to select the best input image (seasonal composition strategies, statistical operators, band composition, and derived vegetation indices (VIs) information) for classification was implemented. A set of accuracy indicators, including overall accuracy (OA) and multi-class F-score (Fm), were computed to assess the results of the different classifications. GEE proved to be a reliable and powerful tool for the classification process. The best results (OA = 0.88 and Fm = 0.88) were achieved using RF with the summer image composite, adding three VIs (NDVI, EVI, and NBR) to the Sentinel-2 bands. SVM and RF produced OAs of 0.83 and 0.80, respectively.
Journal Article
Digital Twin of Experimental Smart Manufacturing Assembly System for Industry 4.0 Concept
This article deals with the creation of a digital twin for an experimental assembly system based on a belt conveyor system and an automatized line for quality production check. The point of interest is a Bowden holder assembly from a 3D printer, which consists of a stepper motor, plastic components, and some fastener parts. The assembly was positioned in a fixture with ultra high frequency (UHF) tags and internet of things (IoT) devices for identification of status and position. The main task was parts identification and inspection, with the synchronization of all data to a digital twin model. The inspection system consisted of an industrial vision system for dimension, part presence, and errors check before and after assembly operation. A digital twin is realized as a 3D model, created in CAD design software (CDS) and imported to a Tecnomatix platform to simulate all processes. Data from the assembly system were collected by a programmable logic controller (PLC) system and were synchronized by an open platform communications (OPC) server to a digital twin model and a cloud platform (CP). Digital twins can visualize the real status of a manufacturing system as 3D simulation with real time actualization. Cloud platforms are used for data mining and knowledge representation in timeline graphs, with some alarms and automatized protocol generation. Virtual digital twins can be used for online optimization of an assembly process without the necessity to stop that is involved in a production line.
Journal Article