scholarly journals Online monitoring and error detection of real-time tumor displacement prediction accuracy using control limits on respiratory surrogate statistics

2012 ◽  
Vol 39 (4) ◽  
pp. 2042-2048 ◽  
Author(s):  
Kathleen Malinowski ◽  
Thomas J. McAvoy ◽  
Rohini George ◽  
Sonja Dieterich ◽  
Warren D. D'Souza
Keyword(s):  
Real Time ◽  
Error Detection ◽  
Prediction Accuracy ◽  
Online Monitoring ◽  
Displacement Prediction ◽  
Control Limits

Real-time error detection techniques based on FPGA

2013 ◽  
Vol 33 (5) ◽  
pp. 1459-1462
Author(s):  
Xiaoming JU ◽  
Jiehao ZHANG ◽  
Yizhong ZHANG
Keyword(s):  
Real Time ◽  
Error Detection ◽  
Time Error ◽  
Detection Techniques

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

Energies ◽  
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.

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

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.

Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

2021 ◽  
pp. 1-10
Author(s):  
Lipeng Si ◽  
Baolong Liu ◽  
Yanfang Fu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Image Recognition ◽  
Error Detection ◽  
Convolution Neural Network ◽  
Data Sets ◽  
Small Target ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Real Time Tracking

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.

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

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
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.

Development and operation of a fiber Bragg grating based online monitoring strategy for slope deformation

Sensor Review ◽  
2015 ◽  
Vol 35 (4) ◽  
pp. 348-356 ◽  
Author(s):  
Yongxing Guo ◽  
Dongsheng Zhang ◽  
Jianjun Fu ◽  
Shaobo Liu ◽  
Shengzhuo Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Online Monitoring ◽  
Deformation Monitoring ◽  
Slope Deformation ◽  
Remote Access ◽  
Monitoring Strategy ◽  
Content Type ◽  
Flexible Pipes ◽  
Sensor Structure ◽  
Distributed Strain

Purpose – The purpose of this paper is to investigate an online monitoring strategy that incorporates fiber Bragg gratings (FBGs) for deformation displacement detection, with the background that slope deformation monitoring is crucial to engineering safety supervision and disaster prevention. Design/methodology/approach – A “beam element” method has been proposed, introduced and experimentally verified in detail. The deformation displacement along a flexible bar can be obtained based on this method, using the distributed strain detected by the FBGs embedded in the bar. A novel sensor structure containing inclinometer casings and a series of connected flexible pipes with FBGs embedded has been proposed. Based on the features of this structure, two FBG deformation sensors have been manufactured and installed into a slope. A matched monitoring station which permits real-time supervision, warning and remote access across the Internet was established and operated. Findings – Displacement data from September 2013 to August 2014 are obtained, which is basically consistent with the practical situation. Originality/value – The FBG deformation sensors demonstrated a robust and reliable measurement performance, which is promising for real-time disaster warning in slope engineering.

Online Monitoring System for Stand-Alone Photovoltaic Applications—Analysis of System Performance From Monitored Data

2012 ◽  
Vol 134 (3) ◽  
Author(s):  
M. Torres ◽  
F. J. Muñoz ◽  
J. V. Muñoz ◽  
C. Rus
Keyword(s):  
Performance Analysis ◽  
Real Time ◽  
Monitoring System ◽  
Virtual Instrument ◽  
System Performance ◽  
Online Monitoring ◽  
Research Centre ◽  
Photovoltaic Systems ◽  
System Monitoring ◽  
Online Monitoring System

The Guidelines for the Assessment of Photovoltaic Plants provided by the Joint Research Centre (JRC) and the International Standard IEC 61724 recommend procedures for the analysis of monitored data to asses the overall performance of photovoltaic (PV) systems. However, the latter do not provide a well adapted method for the analysis of stand-alone photovoltaic systems (SAPV) with charge regulators without maximum power point tracker (MPPT). In this way, the IDEA Research Group has developed a new method that improves the analysis performance of these kinds of systems. Moreover, it has been validated an expression that compromises simplicity and accuracy when estimating the array potential in this kind of systems. SAPV system monitoring and performance analysis from monitored data are of great interest to engineers both for detecting a system malfunction and for optimizing the design of future SAPV system. In this way, this paper introduces an online monitoring system in real time for SAPV applications where the monitored data are processed in order to provide an analysis of system performance. The latter, together with the monitored data, are displayed on a graphical user interface using a virtual instrument (VI) developed in LABVIEW®. Furthermore, the collected and monitored data can be shown in a website where an external user can see the daily evolution of all monitored and derived parameters. At present, three different SAPV systems, installed in the Polytechnic School of University of Jaén, are being monitorized and the collected data are being published online in real time. Moreover, a performance analysis of these stand-alone photovoltaic systems considering both IEC 61724 and the IDEA Method is also offered. These three systems use the charge regulators more widespread in the market. Systems #1 and #2 use pulse width modulation (PWM) charge regulators, (a series and a shunt regulator, respectively), meanwhile System #3 has a charge regulator with MPPT. This website provides a tool that can be used not only for educational purposes in order to illustrate the operation of this kind of systems but it can also show the scientific and engineering community the main features of the system performance analysis methods mentioned above. Furthermore, it allows an external user to download the monitored and analysis data to make its own offline analysis. These files comply with the format proposed in the standard IEC 61724. The SAPV system monitoring website is now available for public viewing on the University of Jaén. (http://voltio.ujaen.es/sfa/index.html).

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

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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