Integrated computer digital decision for Offshore production automated management

Abstract Rising requirements for the hydrocarbons production management system state are based on the industrial situation control necessity according to the environmental conditions. Development of the system construction and complexity indicates the integrative approach implementation reasonability for the producing capacity main indexes estimation and regularity of pace parameters evaluation as well as the layer productiveness level identification. Data processing and management tasks are the waymarks for the industrial multilevel structures models creation. Computer integrated model includes the estimated analytical tool for the extended operational functional support for the production parameters evaluation and technological process state diagnostics in changeable conditions and productive region origin and features traceability. Based on the well observations it is possible to apply the systematic approach for the trends occurrence and dynamics evaluation of the reservoir development time series data. And integrated decision includes the information value degree identification tool for the hydrocarbons production digital model updating.

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Time series data interpretation for ‘wheel-flat’ identification including uncertainties

Structural Health Monitoring ◽

10.1177/1475921719887117 ◽

2019 ◽

pp. 147592171988711

Author(s):

Wen-Jun Cao ◽

Shanli Zhang ◽

Numa J Bertola ◽

I F C Smith ◽

C G Koh

Keyword(s):

Time Series ◽

Time Series Data ◽

Model Updating ◽

Information Gain ◽

Data Interpretation ◽

Detection Methods ◽

Series Data ◽

Point Selection ◽

Modelling Uncertainties ◽

Wheel Flat

Train wheel flats are formed when wheels slip on rails. Crucial for passenger comfort and the safe operation of train systems, early detection and quantification of wheel-flat severity without interrupting railway operations is a desirable and challenging goal. Our method involves identifying the wheel-flat size by using a model updating strategy based on dynamic measurements. Although measurement and modelling uncertainties influence the identification results, they are rarely taken into account in most wheel-flat detection methods. Another challenge is the interpretation of time series data from multiple sensors. In this article, the size of the wheel flat is identified using a model-falsification approach that explicitly includes uncertainties in both measurement and modelling. A two-step important point selection method is proposed to interpret high-dimensional time series in the context of inverse identification. Perceptually important points, which are consistent with the human visual identification process, are extracted and further selected using joint entropy as an information gain metric. The proposed model-based methodology is applied to a field train track test in Singapore. The results show that the wheel-flat size identified using the proposed methodology is within the range of true observations. In addition, it is also shown that the inclusion of measurement and modelling uncertainties is essential to accurately evaluate the wheel-flat size because identification without uncertainties may lead to an underestimation of the wheel-flat size.

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Systems thinking theory in the safety production management system construction

Progress in Mine Safety Science and Engineering II ◽

10.1201/b16606-122 ◽

2014 ◽

pp. 641-646

Author(s):

Hao Liu ◽

Xin-Ling Wang

Keyword(s):

Systems Thinking ◽

Management System ◽

Production Management ◽

System Construction ◽

Production Management System ◽

Safety Production

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A Higher Order Fuzzy Logic Model with Genetic Algorithm Used to Predict the Rice Production in India

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l3191.1081219 ◽

2019 ◽

Vol 8 (12) ◽

pp. 771-775

Keyword(s):

Time Series ◽

Production Management ◽

Time Series Data ◽

Critical Time ◽

Higher Order ◽

Rice Production ◽

Series Data ◽

Percentage Error ◽

Data Set ◽

Computing Technique

Forecasting paddy production is considered as a difficult problem in the real world due to in deterministic behavior of the nature. Specifically, rice production is forecasted for a leading year for overall planning of the crop, utilization of the agricultural resources and the rice production management. Likewise, the key challenge of the forecasting rice production is to create a realistic model that can able to handle the critical time series data and forecast with minor error. Prognostication of the Future data is highly correlated with the time series data set. If the accuracy of your prediction is more appropriate, then the value of the forecast will improve as well. This paper represents a new technique depends on Higher Order Fuzzy Logical Relationship. Here, Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) are used to estimate the errors of predicted data. historical data relating to the rice production of 1981 to 2003 is used as secondary data and the error of the predicted data is further reduced using different soft computing technique.

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Support System for Multimedia Information Data Acquisition Based on Fuzzy Inference with a Fuzzy Shift

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2000.p0387 ◽

2000 ◽

Vol 4 (5) ◽

pp. 387-394 ◽

Cited By ~ 1

Author(s):

Kabsuk Oh ◽

◽

Kaoru Hirota ◽

Keyword(s):

Data Acquisition ◽

Multimedia Information ◽

Support System ◽

Time Series Data ◽

Fuzzy Inference ◽

Series Data ◽

Rule Base ◽

Time Interval ◽

System Construction ◽

Boundary Subset

Support system construction for multimedia information data acquisition based on fuzzy inference with a concept of fuzzy shift is proposed, where the multimedia means the five senses. Observed information from the outside world is characterized by VAGOT (visual, acoustic, gustatory, olfactory, and tactile) time series data. Here, multimedia information centers on image and sound are represented by membership functions. Fuzzy rules based on visual and acoustic information are used to identify the appropriate time interval on multimedia input data. The proposed system is constructed by rule construction, data acquisition, a rule base, and a support system. When an instruction of the user has temporal vagueness, rule base is updated by the support system based on fuzzy shift. The method is applied to an experiment related to the decision of a temporal boundary subset of vehicles and results verify the feasibility of the proposed method.

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RAMP2-AS1 Regulates Endothelial Homeostasis and Aging

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.635307 ◽

2021 ◽

Vol 9 ◽

Author(s):

Chih-Hung Lai ◽

Aleysha T. Chen ◽

Andrew B. Burns ◽

Kiran Sriram ◽

Yingjun Luo ◽

...

Keyword(s):

Vascular Function ◽

Cardiovascular Health ◽

Time Series Data ◽

Principal Component ◽

Series Data ◽

Integrative Approach ◽

Protein Coding ◽

Vascular Integrity ◽

Functional Changes ◽

Endothelial Homeostasis

The homeostasis of vascular endothelium is crucial for cardiovascular health and endothelial cell (EC) aging and dysfunction could negatively impact vascular function. Leveraging transcriptome profiles from ECs subjected to various stimuli, including time-series data obtained from ECs under physiological pulsatile flow vs. pathophysiological oscillatory flow, we performed principal component analysis (PCA) to identify key genes contributing to divergent transcriptional states of ECs. Through bioinformatics analysis, we identified that a long non-coding RNA (lncRNA) RAMP2-AS1 encoded on the antisense of RAMP2, a determinant of endothelial homeostasis and vascular integrity, is a novel regulator essential for EC homeostasis and function. Knockdown of RAMP2-AS1 suppressed RAMP2 expression and caused EC functional changes promoting aging, including impaired angiogenesis and increased senescence. Our study demonstrates an integrative approach to quantifying EC aging based on transcriptome changes, which also identified a number of novel regulators, including protein-coding genes and many lncRNAs involved EC functional modulation, exemplified by RAMP2-AS1.

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

2013 ◽

Author(s):

Stephen J. Tueller ◽

Richard A. Van Dorn ◽

Georgiy Bobashev ◽

Barry Eggleston

Keyword(s):

Time Series ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Time Series Data ◽

Series Data ◽

Exploratory Data

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Some statistical and CI models to predict chaotic high-frequency financial data

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189107 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6419-6430

Author(s):

Dusan Marcek

Keyword(s):

Time Series Data ◽

Moving Average ◽

Methodological Approach ◽

Back Propagation ◽

Large Data ◽

Series Data ◽

Data Set ◽

Training Time ◽

Optimal Population ◽

Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

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Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

2017 ◽

Vol 1 (2) ◽

pp. 177-191

Author(s):

Rizki Rahma Kusumadewi ◽

Wahyu Widayat

Keyword(s):

Time Series ◽

Exchange Rate ◽

Money Supply ◽

Time Series Data ◽

The United States ◽

Economic Conditions ◽

Series Data ◽

Arch Model ◽

United States Dollar ◽

The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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Economic Turmoil in Islamic Banking Investment

ETIKONOMI ◽

10.15408/etk.v19i2.17206 ◽

2020 ◽

Vol 19 (2) ◽

Author(s):

Budiandru Budiandru ◽

Sari Yuniarti

Keyword(s):

Interest Rates ◽

Stock Price ◽

Time Series Data ◽

Series Data ◽

Islamic Banking ◽

Vector Error Correction ◽

Investment Financing ◽

Vector Error ◽

Operational Activities

Investment financing is one of the operational activities of Islamic banking to encourage the real sector. This study aims to analyze the effect of economic turmoil on investment financing, analyze the response to investment financing, and analyze each variable's contribution in explaining the diversity of investment financing. This study uses monthly time series data from 2009 to 2020 using the Vector Error Correction Model (VECM) analysis. The results show that the exchange rate, inflation, and interest rates significantly affect Islamic banking investment financing in the long term. The response to investment financing is the fastest to achieve stability when it responds to shocks to the composite stock price index. Inflation is the most significant contribution in explaining diversity in investment financing. Islamic banking should increase the proportion of funding for investment. Customers can have a larger business scale to encourage economic growth, with investment financing increasing.JEL Classification: E22, G11, G24How to Cite:Budiandru., & Yuniarti, S. (2020). Economic Turmoil in Islamic Banking Investment. Etikonomi: Jurnal Ekonomi, 19(2), xx – xx. https://doi.org/10.15408/etk.v19i2.17206.

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