Impact of seasonal changes in vegetation on the river model prediction accuracy and real‐time flood control performance

Summary Improved numerical efficiency in simulating wellbore gas-influx behaviors is essential for realizing real-time model-prediction-based gas-influx management in wells equipped with managed-pressure-drilling (MPD) systems. Currently, most solution algorithms for high-fidelitymultiphase-flow models are highly time consuming and are not suitable for real-time decision making and control. In the application of model-predictive controllers (MPCs), long calculation time can lead to large overshoots and low control efficiency. This paper presents a drift-flux-model (DFM)-based gas-influx simulator with a novel numerical scheme for improved computational efficiency. The solution algorithm to a Robertson problem as differential algebraic equations (DAEs) was used as the numerical scheme to solve the control equations of the DFM in this study. The numerical stability and computational efficiency of this numerical scheme and the widely used flux-splitting methods are compared and analyzed. Results show that the Robertson DAE problem approach significantly reduces the total number of arithmetic operations and the computational time compared with the hybrid advection-upstream-splitting method (AUSMV) while maintaining the same prediction accuracy. According to the “Big-O notation” analysis, the Robertson DAE approach shows a lower-order growth of computational complexity, proving its good potential in enhancing numerical efficiency, especially when handling simulations with larger scales. The validation of both the numerical schemes for the solution of the DFM was performed using measured data from a test well drilled with water-based mud (WBM). This study offers a novel numerical solution to the DFM that can significantly reduce the computational time required for gas-kick simulation while maintaining high prediction accuracy. This approach enables the application of high-fidelity two-phase-flow models in model-prediction-based decision making and automated influx management with MPD systems.

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Development and Evaluation of ICT Operation Support System for Urban Flood Control Facilities

Journal of Disaster Research ◽

10.20965/jdr.2021.p0395 ◽

2021 ◽

Vol 16 (3) ◽

pp. 395-402

Author(s):

Yusuke Sakae ◽

◽

Masaya Endo ◽

Yoshikazu Nakayama

Keyword(s):

Real Time ◽

Support System ◽

Prediction Accuracy ◽

Flood Control ◽

Facility Management ◽

Effective Control ◽

Flood Prediction ◽

Urban Flood ◽

Operation Support System ◽

Reduction Effect

This study was conducted to develop and evaluate the prediction accuracy and effectiveness of “ICT operation support system for urban flood control facilities,” which is installed in Eba catchment area in Hiroshima city, Japan. This system consists of real-time facilities monitoring technology, rainfall data from eXtended RAdar Information Network (XRAIN), and Real-time flood prediction technology. High prediction accuracy is crucial for effective control facility management using ICT operation support system. In this study, ICT operation support system was installed for effective operation of stormwater pump. The prediction accuracy and effectiveness of this system were evaluated based on the past rainfall record from XRAIN. The system takes only three minutes to distribute the urban flood prediction information, and it has been operated stably during the operation. Flood risk reduction effect can be better expected in case of central concentrated rainfall pattern.

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A Study of Developing a Prediction Equation of Electricity Energy Output via Photovoltaic Modules

Energies ◽

10.3390/en14051503 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1503

Author(s):

Minsu Kim ◽

Hongmyeong Kim ◽

Jae Hak Jung

Keyword(s):

Experimental Data ◽

Real Time ◽

Air Temperature ◽

Temperature Difference ◽

Prediction Accuracy ◽

Prediction Equation ◽

Energy Output ◽

Photovoltaic Modules ◽

Mean Error ◽

Pv Module

Various equations are being developed and applied to predict photovoltaic (PV) module generation. Currently, quite diverse methods for predicting module generation are available, with most equations showing accuracy with ≤5% error. However, the accuracy can be determined only when the module temperature and the value of irradiation that reaches the module surface are precisely known. The prediction accuracy of outdoor generation is actually extremely low, as the method for predicting outdoor module temperature has extremely low accuracy. The change in module temperature cannot be predicted accurately because of the real-time change of irradiation and air temperature outdoors. Calculations using conventional equations from other studies show a mean error of temperature difference of 4.23 °C. In this study, an equation was developed and verified that can predict the precise module temperature up to 1.64 °C, based on the experimental data obtained after installing an actual outdoor module.

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Multi-step real-time wind speed prediction based on convolution memory network

Wind Engineering ◽

10.1177/0309524x21998266 ◽

2021 ◽

pp. 0309524X2199826

Author(s):

Guowei Cai ◽

Yuqing Yang ◽

Chao Pan ◽

Dian Wang ◽

Fengjiao Yu ◽

...

Keyword(s):

Feature Extraction ◽

Wind Speed ◽

Real Time ◽

Wind Power ◽

Spatial Correlation ◽

Prediction Accuracy ◽

Spatial Information ◽

Spatial Association ◽

Spatial Feature ◽

Time Prediction

Multi-step real-time prediction based on the spatial correlation of wind speed is a research hotspot for large-scale wind power grid integration, and this paper proposes a multi-location multi-step wind speed combination prediction method based on the spatial correlation of wind speed. The correlation coefficients were determined by gray relational analysis for each turbine in the wind farm. Based on this, timing-control spatial association optimization is used for optimization and scheduling, obtaining spatial information on the typical turbine and its neighborhood information. This spatial information is reconstructed to improve the efficiency of spatial feature extraction. The reconstructed spatio-temporal information is input into a convolutional neural network with memory cells. Spatial feature extraction and multi-step real-time prediction are carried out, avoiding the problem of missing information affecting prediction accuracy. The method is innovative in terms of both efficiency and accuracy, and the prediction accuracy and generalization ability of the proposed method is verified by predicting wind speed and wind power for different wind farms.

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Advertisement Click-Through Rate Prediction Based on the Weighted-ELM and Adaboost Algorithm

Scientific Programming ◽

10.1155/2017/2938369 ◽

2017 ◽

Vol 2017 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Sen Zhang ◽

Qiang Fu ◽

Wendong Xiao

Keyword(s):

Real Time ◽

Prediction Accuracy ◽

Historical Data ◽

User Profile ◽

Imbalanced Learning ◽

Sample Distribution ◽

The Real ◽

Adaboost Algorithm ◽

Click Through Rate ◽

Prediction Approach

Accurate click-through rate (CTR) prediction can not only improve the advertisement company’s reputation and revenue, but also help the advertisers to optimize the advertising performance. There are two main unsolved problems of the CTR prediction: low prediction accuracy due to the imbalanced distribution of the advertising data and the lack of the real-time advertisement bidding implementation. In this paper, we will develop a novel online CTR prediction approach by incorporating the real-time bidding (RTB) advertising by the following strategies: user profile system is constructed from the historical data of the RTB advertising to describe the user features, the historical CTR features, the ID features, and the other numerical features. A novel CTR prediction approach is presented to address the imbalanced learning sample distribution by integrating the Weighted-ELM (WELM) and the Adaboost algorithm. Compared to the commonly used algorithms, the proposed approach can improve the CTR significantly.

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A new strategy based on feature filtering technique for improving the real-time control performance of myoelectric prostheses

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2021.102969 ◽

2021 ◽

Vol 70 ◽

pp. 102969

Author(s):

Xiangxin Li ◽

Lan Tian ◽

Yue Zheng ◽

Oluwarotimi Williams Samuel ◽

Peng Fang ◽

...

Keyword(s):

Real Time ◽

Control Performance ◽

Real Time Control ◽

The Real ◽

Myoelectric Prostheses ◽

Time Control ◽

New Strategy ◽

Feature Filtering ◽

Filtering Technique

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Using Stage-Dependent Temperature Parameters to Improve Phenological Model Prediction Accuracy in Rice Models

Crop Science ◽

10.2135/cropsci2016.01.0072 ◽

2016 ◽

Vol 57 (1) ◽

pp. 444-453 ◽

Cited By ~ 3

Author(s):

Hussain Sharifi ◽

Robert J. Hijmans ◽

James E. Hill ◽

Bruce A. Linquist

Keyword(s):

Model Prediction ◽

Prediction Accuracy ◽

Phenological Model

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Real-time control performance of a model-reference adaptive structural control system

World Forum on Smart Materials and Smart Structures Technology ◽

10.1201/9781439828441.ch211 ◽

2008 ◽

Author(s):

S Lo ◽

S Chu

Keyword(s):

Control System ◽

Real Time ◽

Structural Control ◽

Control Performance ◽

Real Time Control ◽

Model Reference ◽

Time Control ◽

Model Reference Adaptive

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Moving Time UCAV Maneuver Decision Based on the Dynamic Relational Weight Algorithm and Trajectory Prediction

Mathematical Problems in Engineering ◽

10.1155/2021/6641567 ◽

2021 ◽

Vol 2021 ◽

pp. 1-19

Author(s):

Xie Lei ◽

Ding Dali ◽

Wei Zhenglei ◽

Xi Zhifei ◽

Tang Andi

Keyword(s):

Decision Making ◽

Real Time ◽

Prediction Accuracy ◽

Control Variable ◽

Trajectory Prediction ◽

Constant Weight ◽

Adaptive Boosting ◽

Time Performance ◽

Correlation Degree ◽

Air Combat

To improve the accuracy and real-time performance of autonomous decision-making by the unmanned combat aerial vehicle (UCAV), a decision-making method combining the dynamic relational weight algorithm and moving time strategy is proposed, and trajectory prediction is added to maneuver decision-making. Considering the lack of continuity and diversity of air combat situation reflected by the constant weight in situation assessment, a dynamic relational weight algorithm is proposed to establish an air combat situation system and adjust the weight according to the current situation. Based on the dominance function, this method calculates the correlation degree of each subsituation and the total situation. According to the priority principle and information entropy theory, the hierarchical fitting function is proposed, the association expectation is calculated by using if-then rules, and the weight is dynamically adjusted. In trajectory prediction, the online sliding input module is introduced, and the long- and short-term memory (LSTM) network is used for real-time prediction. To further improve the prediction accuracy, the adaptive boosting (Ada) method is used to build the outer frame and compare with three traditional prediction networks. The results show that the prediction accuracy of Ada-LSTM is better. In the decision-making method, the moving time optimization strategy is adopted. To solve the problem of timeliness and optimization, each control variable is divided into 9 gradients, and there are 729 control schemes in the control sequence. Through contrast pursuit simulation experiments, it is verified that the maneuver decision method combining the dynamic relational weight algorithm and moving time strategy has a better accuracy and real-time performance. In the case of using prediction and not using prediction, the adaptive countermeasure simulation is carried out with the current more advanced Bayesian inference maneuvering decision-making scheme. The results show that the UCAV maneuvering decision-making ability combined with accurate prediction is better.

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