Risk Assessment Model and Experimental Analysis of Electric Power Production Based on Big Data

2019 ◽  
Author(s):  
Wang Zeyong ◽  
Hong Yutian ◽  
Tong Zhongzheng
Keyword(s):  
Risk Assessment ◽  
Big Data ◽  
Electric Power ◽  
Experimental Analysis ◽  
Power Production ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Electric Power Production

Using big data to retrospectively validate the COMPASS-CAT risk assessment model: considerations on methodology

2020 ◽  
Author(s):  
Ilias Nikolakopoulos ◽  
Soheila Nourabadi ◽  
Joanna B. Eldredge ◽  
Lalitha Anand ◽  
Meng Zhang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Big Data ◽  
Assessment Model ◽  
Risk Assessment Model

scholarly journals A Privacy Risk Assessment Model for Medical Big Data Based on Adaptive Neuro-Fuzzy Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Mingyue Shi ◽  
Rong Jiang ◽  
Wei Zhou ◽  
Sen Liu ◽  
Savio Sciancalepore
Keyword(s):  
Risk Assessment ◽  
Big Data ◽  
Privacy Protection ◽  
Internet Security ◽  
Assessment Model ◽  
Security And Privacy ◽  
Risk Assessment Model ◽  
Experimental Process ◽  
Data Environment ◽  
Medical Big Data

Information leakage in the medical industry has become an urgent problem to be solved in the field of Internet security. However, due to the need for automated or semiautomated authorization management for privacy protection in the big data environment, the traditional privacy protection model cannot adapt to this complex open environment. Although some scholars have studied the risk assessment model of privacy disclosure in the medical big data environment, it is still in the initial stage of exploration. This paper analyzes the key indicators that affect medical big data security and privacy leakage, including user access behavior and trust, from the perspective of users through literature review and expert consultation. Also, based on the user’s historical access information and interaction records, the user’s access behavior and trust are quantified with the help of information entropy and probability, and a definition expression is given explicitly. Finally, the entire experimental process and specific operations are introduced in three aspects: the experimental environment, the experimental data, and the experimental process, and then, the predicted results of the model are compared with the actual output through the 10-fold cross verification with Matlab. The results prove that the model in this paper is feasible. In addition, the method in this paper is compared with the current more classical medical big data risk assessment model, and the results show that when the proportion of illegal users is less than 15%, the model in this paper is more superior in terms of accuracy and recall.

scholarly journals Accurate Demand Forecasting: A Flexible and Balanced Electric Power Production Big Data Virtualization Based on Photovoltaic Power Plant

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6915
Author(s):  
Seung-Mo Je ◽  
Hyeyoung Ko ◽  
Jun-Ho Huh
Keyword(s):  
Big Data ◽  
Renewable Energy ◽  
Power Plant ◽  
Electric Power ◽  
Demand Forecasting ◽  
Power Production ◽  
Supply System ◽  
Power Demand ◽  
Photovoltaic Power ◽  
Electric Power Production

This paper has tried to execute accurate demand forecasting by utilizing big data visualization and proposes a flexible and balanced electric power production big data virtualization based on a photovoltaic power plant. First of all, this paper has tried to align electricity demand and supply as much as possible using big data. Second, by using big data to predict the supply of new renewable energy, an attempt was made to incorporate new and renewable energy into the current power supply system and to recommend an efficient energy distribution method. The first presented problem that had to be solved was the improvement in the accuracy of the existing electricity demand for forecasting models. This was explained through the relationship between the power demand and the number of specific words in the paper that use crawling by utilizing big data. The next problem arose because the current electricity production and supply system stores the amount of new renewable energy by changing the form of energy that is produced through ESS or that is pumped through water power generation without taking the amount of new renewable energy that is generated from sources such as thermal power, nuclear power, and hydropower into consideration. This occurs due to the difficulty of predicting power production using new renewable energy and the absence of a prediction system, which is a problem due to the inefficiency of changing energy types. Therefore, using game theory, the theoretical foundation of a power demand forecasting model based on big data-based renewable energy production forecasting was prepared.

Financial risk assessment model based on big data

2019 ◽  
Vol 10 (04) ◽  
pp. 1950021 ◽  
Author(s):  
Qiong Kang
Keyword(s):  
Risk Assessment ◽  
Big Data ◽  
Regression Analysis ◽  
Data Analysis ◽  
Financial Risk ◽  
Big Data Analysis ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Analysis Method ◽  
Variable Model

Conventional financial risk assessment is not accurate and its adaptive assessment ability is low. In order to solve this problem, a financial risk assessment model based on big data is proposed. In this method, the quantitative analysis method is adopted to analyze the explanatory variable model and the control variable model of financial risk assessment. The market-to-book ratio, asset–liability ratio, cash flow ratio and financing structure model are adopted as constraint parameters to construct a big data analysis model for financial risk assessment. On this basis, the adaptive fuzzy weighted control method is adopted for information fusion of financial risk assessment data and big data classification, and the asset income control and innovative evaluation model are adopted for linear planning and square fitting during financial risk assessment. Based on the intervention factors of financial market participants, quantitative regression analysis is performed, and according to the economic game theory, big data analysis and prediction of financial risk assessment are performed through the regression analysis method. Then the big data fusion and clustering algorithms are adopted for financial risk assessment. The simulation results show that this method can provide a relatively high accuracy in financial risk assessment, and has relatively strong adaptive evaluation capability to the risk coefficient, so it has a good application value in the prevention and control of risk factors in financial systems.

Risk assessment model for venous thromboembolism in hospitalized surgical and non-surgical patients in the 4th Hungarian Antithrombotic Guidelines entitled “Diminution and Treatment of Venous Thromboembolism”

2010 ◽  
Vol 151 (34) ◽  
pp. 1365-1374 ◽  
Author(s):  
Marianna Dávid ◽  
Hajna Losonczy ◽  
Miklós Udvardy ◽  
Zoltán Boda ◽  
György Blaskó ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Venous Thromboembolism ◽  
Assessment Model ◽  
Surgical Patients ◽  
Risk Assessment Model

A kórházban kezelt sebészeti és belgyógyászati betegekben jelentős a vénásthromboembolia-rizikó. Profilaxis nélkül, a műtét típusától függően, a sebészeti beavatkozások kapcsán a betegek 15–60%-ában alakul ki mélyvénás trombózis vagy tüdőembólia, és az utóbbi ma is vezető kórházi halálok. Bár a vénás thromboemboliát leggyakrabban a közelmúltban végzett műtéttel vagy traumával hozzák kapcsolatba, a szimptómás thromboemboliás események 50–70%-a és a fatális tüdőembóliák 70–80%-a nem a sebészeti betegekben alakul ki. Nemzetközi és hazai felmérések alapján a nagy kockázattal rendelkező sebészeti betegek többsége megkapja a szükséges trombózisprofilaxist. Azonban profilaxis nélkül marad a rizikóval rendelkező belgyógyászati betegek jelentős része, a konszenzuson alapuló nemzetközi és hazai irányelvi ajánlások ellenére. A belgyógyászati betegek körében növelni kell a profilaxisban részesülők arányát és el kell érni, hogy trombózisrizikó esetén a betegek megkapják a hatásos megelőzést. A beteg trombóziskockázatának felmérése fontos eszköze a vénás thromboembolia által veszélyeztetett betegek felderítésének, megkönnyíti a döntést a profilaxis elrendeléséről és javítja az irányelvi ajánlások betartását. A trombózisveszély megállapításakor, ha nem ellenjavallt, profilaxist kell alkalmazni. „A thromboemboliák kockázatának csökkentése és kezelése” című, 4. magyar antithromboticus irányelv felhívja a figyelmet a vénástrombózis-rizikó felmérésének szükségességére, és elsőként tartalmazza a kórházban fekvő belgyógyászati és sebészeti betegek kockázati kérdőívét. Ismertetjük a kockázatbecslő kérdőíveket és áttekintjük a kérdőívekben szereplő rizikófaktorokra vonatkozó bizonyítékokon alapuló adatokat.

Signature Analysis: Statistical Models and Their Application to FA

1996 ◽  
Author(s):  
C.K. Lakshminarayan ◽  
S. Pabbisetty ◽  
O. Adams ◽  
F. Pires ◽  
M. Thomas ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Failure Analysis ◽  
Statistical Models ◽  
Essential Elements ◽  
Assessment Model ◽  
Signature Analysis ◽  
Risk Assessment Model ◽  
Sample Sizes ◽  
Reliability Engineering ◽  
Semiconductor Chips

Abstract This paper deals with the basic concepts of Signature Analysis and the application of statistical models for its implementation. It develops a scheme for computing sample sizes when the failures are random. It also introduces statistical models that comprehend correlations among failures that fail due to the same failure mechanism. The idea of correlation is important because semiconductor chips are processed in batches. Also any risk assessment model should comprehend correlations over time. The statistical models developed will provide the required sample sizes for the Failure Analysis lab to state "We are A% confident that B% of future parts will fail due to the same signature." The paper provides tables and graphs for the evaluation of such a risk assessment. The implementation of Signature Analysis will achieve the dual objective of improved customer satisfaction and reduced cycle time. This paper will also highlight it's applicability as well as the essential elements that need to be in place for it to be effective. Different examples have been illustrated of how the concept is being used by Failure Analysis Operations (FA) and Customer Quality and Reliability Engineering groups.

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