scholarly journals Study on drought events in China based on time-varying nested Archimedean-copula function

2021 ◽  
Author(s):  
Ziyang Zhao ◽  
Hongrui Wang ◽  
Qiuyang Shi ◽  
Cheng Wang
Keyword(s):  
Gibbs Sampling ◽  
Water Resource Management ◽  
Early Stage ◽  
Copula Function ◽  
Archimedean Copula ◽  
Optimal Time ◽  
Joint Probability Distribution ◽  
Time Varying ◽  
Drought Forecasting ◽  
Occurrence Time

Abstract Drought forecasting, which can enable contingency actions to be implemented in advance of a drought, plays a significant role in reducing the risks and impacts of drought. In this study, a simulation framework of the occurrence probability of drought events based on nested Copula function and Gibbs sampling is proposed to effectively compensate for the high-dimensional problems and lack of initial data in traditional methods. And the precipitation data of 718 meteorological stations from 1961 to 2018 in China was analyzed. The results showed that the occurrence location of drought events was mainly concentrated from 35° to 42° north latitude and 105° to 120° east longitude, with the occurrence time mainly concentrated from September to November. The Archimedean-copula function, constructed based on latitude, longitude, and occurrence time, could precisely determine the spatiotemporal joint probability distribution of drought events (RMSE:0.01). The optimal time-varying nested Archimedean-copula functions were obtained from February to May (Spring), June to September (Summer) and October to January (Autumn and Winter). Compared to the nested Archimedean-copula function, the accuracy of Gibbs sampling and simulation based on time-varying nested Archimedean-copula function was increased by 84.05% latitude and 69.76% longitude. The results provide an effective means for scientific drought forecasting, and water resource management departments can take preventive measures at an early stage.

scholarly journals Dynamic Optimization of a Subcritical Steam Power Plant under Time-Varying Power Load

2018 ◽  
Author(s):  
Chen Chen ◽  
George M. Bollas
Keyword(s):  
Power Plant ◽  
Dynamic Optimization ◽  
Electricity Market ◽  
Optimization Problems ◽  
Optimal Time ◽  
Time Varying ◽  
Power Demand ◽  
Test Bed ◽  
Simulation And Optimization ◽  
Power Load

The increasing variability in power plant load, in response to a wildly uncertain electricity market and the need to to mitigate CO2 emissions, lead power plant operators to explore advanced options for efficiency optimization. Model-based, system-scale dynamic simulation and optimization are useful tools in this effort, and the subject of the work presented here. In prior work, a dynamic model validated against steady-state data from a 605 MW subcritical power plant was presented. This power plant model is used as a test-bed for dynamic simulations, in which the coal load is regulated to satisfy a varying power demand. Plant-level control regulates plant load to match an anticipated trajectory of the power demand. The efficiency of the power plant operating at varying load is optimized through a supervisory control architecture that performs set point optimization on the regulatory controllers. Dynamic optimization problems are formulated to search for optimal time-varying input trajectories that satisfy operability and safety constraints during the transition between plant states. An improvement in time-averaged efficiency of up to 1.8% points is shown feasible with corresponding savings in coal consumption of 184.8 tons/day and carbon footprint decrease of 0.035 kg/kWh.

scholarly journals Online Topology Inference from Streaming Stationary Graph Signals with Partial Connectivity Information

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 228
Author(s):  
Rasoul Shafipour ◽  
Gonzalo Mateos
Keyword(s):  
Inverse Problem ◽  
Covariance Matrix ◽  
A Priori ◽  
Dynamic Network ◽  
Streaming Data ◽  
Optimal Time ◽  
Time Varying ◽  
Graph Learning ◽  
Topology Inference ◽  
Diffusion Dynamics

We develop online graph learning algorithms from streaming network data. Our goal is to track the (possibly) time-varying network topology, and affect memory and computational savings by processing the data on-the-fly as they are acquired. The setup entails observations modeled as stationary graph signals generated by local diffusion dynamics on the unknown network. Moreover, we may have a priori information on the presence or absence of a few edges as in the link prediction problem. The stationarity assumption implies that the observations’ covariance matrix and the so-called graph shift operator (GSO—a matrix encoding the graph topology) commute under mild requirements. This motivates formulating the topology inference task as an inverse problem, whereby one searches for a sparse GSO that is structurally admissible and approximately commutes with the observations’ empirical covariance matrix. For streaming data, said covariance can be updated recursively, and we show online proximal gradient iterations can be brought to bear to efficiently track the time-varying solution of the inverse problem with quantifiable guarantees. Specifically, we derive conditions under which the GSO recovery cost is strongly convex and use this property to prove that the online algorithm converges to within a neighborhood of the optimal time-varying batch solution. Numerical tests illustrate the effectiveness of the proposed graph learning approach in adapting to streaming information and tracking changes in the sought dynamic network.

Optimal time-varying potential profile for electro-hydro-dimerization reactions

2009 ◽  
Vol 19 (2) ◽  
pp. 241-246
Author(s):  
Vijayasekaran Boovaragavan ◽  
C. Ahmed Basha
Keyword(s):  
Potential Profile ◽  
Optimal Time ◽  
Time Varying

scholarly journals Panacea: Visual exploration system for analyzing trends in annual recruitment using time-varying graphs

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247587
Author(s):  
Toshiyuki T. Yokoyama ◽  
Masashi Okada ◽  
Tadahiro Taniguchi
Keyword(s):  
Early Stage ◽  
Visual Exploration ◽  
Recruitment Strategy ◽  
Time Varying ◽  
Graph Visualization ◽  
Dynamic Graph ◽  
Visualization System ◽  
New Graduates ◽  
Real World Datasets ◽  
The Relationship

Annual recruitment data of new graduates are manually analyzed by human resources (HR) specialists in industries, which signifies the need to evaluate the recruitment strategy of HR specialists. Different job seekers send applications to companies every year. The relationships between applicants’ attributes (e.g., English skill or academic credentials) can be used to analyze the changes in recruitment trends across multiple years. However, most attributes are unnormalized and thus require thorough preprocessing. Such unnormalized data hinder effective comparison of the relationship between applicants in the early stage of data analysis. Thus, a visual exploration system is highly needed to gain insight from the overview of the relationship among applicant qualifications across multiple years. In this study, we propose the Polarizing Attributes for Network Analysis of Correlation on Entities Association (Panacea) visualization system. The proposed system integrates a time-varying graph model and dynamic graph visualization for heterogeneous tabular data. Using this system, HR specialists can interactively inspect the relationships between two attributes of prospective employees across multiple years. Further, we demonstrate the usability of Panacea with representative examples for finding hidden trends in real-world datasets, and we discuss feedback from HR specialists obtained throughout Panacea’s development. The proposed Panacea system enables HR specialists to visually explore the annual recruitment of new graduates.

scholarly journals Modelling High Dimensional Paddy Production Data using Copulas

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Nuranisyha Mohd Roslan ◽  
Wendy Ling Shinyie ◽  
Sim Siew Ling
Keyword(s):  
Time Series ◽  
Marginal Distribution ◽  
Reference Group ◽  
Estimation Method ◽  
Information Criterion ◽  
Copula Function ◽  
Archimedean Copula ◽  
Copula Functions ◽  
Estimation Errors ◽  
Elliptical Copula

As the climate change is likely to be adversely affecting the yield of paddy production, thence it has brought a limelight of the probable challenges on human particularly regional food security issues. This paper aims to fit multivariate time series of paddy production variables using copula functions and predicts the next year event based on the data of five countries in southeast Asia. In particular, the most appropriate marginal distribution for each univariate time series was first identified using maximum likelihood parameter estimation method. Next, we performed multivariate copula fitting using two types of copula families, namely, elliptical copula family and Archimedean copula family. Elliptical copula family studied are normal and t copula, while Archimedean copula family considered are Joe, Clayton and Gumbel copulas. The performance of marginal distribution and copula fitting was examined using Akaike information criterion (AIC) values. Finally, we used the best fitted copula model to forecast the succeeding event. In order to assess the performance of copula function, we computed the forecast means and estimation errors of copula function with a generalized autoregressive conditional heteroskedasticity model as reference group. Based on the smallest AIC, the majority of the data favoured the Gumbel copula, which belongs to Archimedean copula family as well as extreme value copula family. Likewise, applying the historical data to forecast the future trends may assist all relevant stakeholders, for instance government, NGO agencies, and professional practitioners in making informed decisions without compromising the environmental as well as economical sustainability in the region.

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