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Generating Synthetic Data to Reduce prediction error of Energy Consumption

Computers, Materials & Continua 2022年第2期70卷 3151-3167页

作者： Debapriya Hazra Wafa Shafqat Yung-Cheol Byun Department of Computer Engineering Jeju National UniversityJeju-siKorea

Renewable and nonrenewable energy sources are widely incorporated for solar and wind energy that produces electricity without increasing carbon dioxide *** industries worldwide are trying hard to predict future energy consumption that could eliminate over or under contracting energy resources and unnecessary *** learning techniques for predicting energy are the trending solution to overcome the challenges faced by energy *** basic need for machine learning algorithms to be trained for accurate prediction requires a considerable amount of *** critical factor is balancing the data for enhanced *** Augmentation is a technique used for increasing the data available for *** data are the generation of new data which can be trained to improve the accuracy of prediction *** this paper,we propose a model that takes time series energy consumption data as input,pre-processes the data,and then uses multiple augmentation techniques and generative adversarial networks to generate synthetic data which when combined with the original data,reduces energy consumption prediction *** propose TGAN-skip-Improved-WGAN-GP to generate synthetic energy consumption time series tabular *** modify TGANwith skip connections,then improveWGANGPby defining a consistency term,and finally use the architecture of improved WGAN-GP for training *** used various evaluation metrics and visual representation to compare the performance of our proposed *** also measured prediction accuracy along with mean and maximum error generated while predicting with different variations of augmented and synthetic data with original *** mode collapse problemcould be handled by TGAN-skip-Improved-WGAN-GP model and it also converged faster than existing GAN models for synthetic data *** experiment result shows that our proposed technique of combining synthetic data with original data could significantly red

关键词： Energy consumption generative adversarial networks synthetic data time series data TGAN WGAN-GP TGAN-skip prediction error augmentation

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Investigate Targeted Factors to Achieve prediction Goal in Stroke Convalescence in Terms of Causal Relationships of prediction error

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Open Journal of Therapy and Rehabilitation 2022年第4期10卷 244-256页

作者： Takashi Kimura Department of Physical Therapy ASO Rehabilitation College Fukuoka Japan Department of Rehabilitation Medicine Saga University Hospital Saga Japan

Background and Purpose: To investigate target functional independence measure (FIM) items to achieve the prediction goal in terms of the causal relationships between prognostic prediction error and FIM among stroke patients in the convalescent phase using the structural equation modeling (SEM) analysis. Methods: A total of 2992 stroke patients registered in the Japanese Rehabilitation Database were analyzed retrospectively. The prediction error was calculated based on a prognostic prediction formula proposed in a previous study. An exploratory factor analysis (EFA) then the factor was determined using confirmatory factorial analysis (CFA). Finally, multivariate analyses were performed using SEM analysis. Results: The fitted indices of the hypothesized model estimated based on EFA were confirmed by CFA. The factors estimated by EFA were applied, and interpreted as follows: “Transferring (T-factor),” “Dressing (D-factor),” and “Cognitive function (C-factor).” The fit of the structural model based on the three factors and prediction errors was supported by the SEM analysis. The effects of the D- and C-factors yielded similar causal relationships on prediction error. Meanwhile, the effects between the prediction error and the T-factor were low. Observed FIM items were related to their domains in the structural model, except for the dressing of the upper body and memory (p prediction formula, was found in causal relationships with prediction error. It is suggested to intervene to transfer together with positive factors to recovery for achieving the prediction goal.

关键词： prediction error Functional Independence Measure Stroke Convalescent Phase Structural Equation Modeling

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A Meaningful Image Encryption Algorithm Based on prediction error and Wavelet Transform

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Journal on Big Data 2019年第3期1卷 151-158页

作者： Mengling Zou Zhengxuan Liu Xianyi Chen School of Computer and Software Nanjing University of Information Science&Technology NanjingChina

Image encryption(IE)is a very useful and popular technology to protect the privacy of *** algorithms usually encrypt the original image into an image similar to texture or noise,but texture and noise are an obvious visual indication that the image has been encrypted,which is more likely to cause the attacks of *** overcome this shortcoming,many image encryption systems,which convert the original image into a carrier image with visual significance have been ***,the generated cryptographic image still has texture *** line with the idea of improving the visual quality of the final password images,we proposed a meaningful image hiding algorithm based on prediction error and discrete wavelet *** of experimental results and safety analysis show that the proposed algorithm can achieve high visual quality and ensure the security at the same time.

关键词： Image encryption meaningful prediction error wavelet transform

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Robust Reactive Power Scheduling of Distribution Networks Based on Modified Bootstrap Technique

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 154-166页

作者： Wenlong Liao Shouxiang Wang Birgitte Bak-Jensen Jayakrishnan Radhakrishna Pillai Zhe Yang the AAU Energy Aalborg UniversityAalborg 9220Denmark the Key Laboratory of Smart Grid Ministry of EducationSchool of Electrical and Information EngineeringTianjin UniversityTianjin 300072China

The uncertainties of the power load,wind power,and photovoltaic power lead to errors between point prediction values and real values,which challenges the safe operation of distribution *** this paper,a robust reactive power scheduling(RRPS)model based on a modified bootstrap technique is proposed to consider the uncertainties of power loads and renewable energy ***,a deterministic reactive power scheduling(DRPS)model and an RRPS model are ***,a modified bootstrap technique is proposed to estimate prediction errors of power loads and renewable energy sources without artificially assuming the probability density function of prediction *** represent all possible scenarios,point prediction values and prediction errors are combined to construct two worst-case scenarios in the RRPS ***,the RRPS model is solved to find a scheduling scheme,which ensures the security of distribution networks for all possible scenarios in *** results show that the worst-case scenarios constructed by the modified bootstrap technique outperform popular ***,the RRPS model based on the modified bootstrap technique balances economics and security well.

关键词： Distribution network worst-case scenario robust programming prediction error bootstrap technique

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Predicting bathymetry based on vertical gravity gradient anomaly and analyses for various influential factors

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Geodesy and Geodynamics 2024年第4期15卷 386-396页

作者： Huan Xu Jinhai Yu Yanyan Zeng Qiuyu Wang Yuwei Tian Zhongmiao Sun College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijingChina Beijing Institute of Surveying and Mapping BeijingChina National Key Laboratory of Geoinformation Engineering Xi'an 710054China

The prediction of bathymetry has advanced significantly with the development of satellite ***,the majority of its data originate from marine gravity *** this study,based on the expression of vertical gravity gradient(VGG)of a rectangular prism,the governing equations for determining sea depths to invert *** governing equation is solved by linearization through an iterative process,and numerical simulations verify its algorithm and its *** also study the processing methods of different interference *** regularization method improves the stability of the inversion process for errors.A piecewise bilinear interpolation function roughly replaces the low-frequency error,and numerical simulations show that the accuracy can be improved by 41.2%after this *** variable ocean crust density,simulation simulations verify that the root-mean-square(RMS)error of prediction is approximately 5 m for the sea depth of 6 km if density is chosen as the average ***,two test regions in the South China Sea are predicted and compared with ship soundings data,RMS errors of predictions are 71.1 m and 91.4 m,respectively.

关键词： Rectangular prism Vertical gravity gradient Bathymetry Numerical simulation prediction error

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The role of constant optimal forcing in correcting forecast models

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Science China Earth Sciences 2013年第2期56卷 434-443页

作者： FENG Fan DUAN WanSuo LASG Institute of Atmospheric PhysicsChinese Academy of Sciences University of Chinese Academy of Sciences

In this paper,the role of constant optimal forcing(COF) in correcting forecast models was numerically studied using the well-known Lorenz 63 *** results show that when we only consider model error caused by parameter error,which also changes with the development of state variables in a numerical model,the impact of such model error on forecast uncertainties can be offset by superimposing COF on the tendency equations in the numerical *** COF can also offset the impact of model error caused by stochastic *** reality,the forecast results of numerical models are simultaneously influenced by parameter uncertainty and stochastic process as well as their *** results indicate that COF is also able to significantly offset the impact of such hybrid model error on forecast *** summary,although the variation in the model error due to physical process is time-dependent,the superimposition of COF on the numerical model is an effective approach to reducing the influence of model error on forecast ***,the COF method may be an effective approach to correcting numerical models and thus improving the forecast capability of models.

关键词： predictability prediction error model error optimal forcing

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Reversible Data Hiding in Encrypted Images Based on prediction and Adaptive Classification Scrambling

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Computers, Materials & Continua 2020年第12期65卷 2623-2638页

作者： Lingfeng Qu Hongjie He Shanjun Zhang Fan Chen School of Information Science and Technology Southwest Jiaotong UniversityChengdu611756China Department of Information Science The Faculty of ScienceKanagawa UniversityKanagawa259129 Japan

Reversible data hiding in encrypted images(RDH-EI)technology is widely used in cloud storage for image privacy *** order to improve the embedding capacity of the RDH-EI algorithm and the security of the encrypted images,we proposed a reversible data hiding algorithm for encrypted images based on prediction and adaptive classification ***,the prediction error image is obtained by a novel prediction method before ***,the image pixel values are divided into two categories by the threshold range,which is selected adaptively according to the image *** high-significant bits of pixels within the threshold range are used for embedding data and pixel values outside the threshold range remain *** optimal threshold selected adaptively ensures the maximum embedding capacity of the ***,the security of encrypted images can be improved by the combination of XOR encryption and classification scrambling encryption since the embedded data is independent of the pixel *** results demonstrate that the proposed method has higher embedding capacity compared with the current state-of-the-art methods for images with different texture complexity.

关键词： Reversible data hiding classification scrambling prediction error multi-bits embedding

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Reversible Data Hiding Based on Varying Radix Numeral System

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Computers, Materials & Continua 2021年第10期69卷 283-300页

作者： J.Hemalatha S.Geetha R.Geetha C.Balasubramanian Daniela Elena Popescu D.Jude Hemanth AAA College of Engineering and Technology SivakasiIndia SCOPE VIT UniversityChennaiIndia SENSE VIT UniversityChennaiIndia Department of CSE Kalasalingam Academy of Research and EducationIndia Faculty of Electrical Engineering and Information Technology University of OradeaOradeaRomania Department of ECE Karunya Institute of Technology and SciencesCoimbatoreIndia

A novel image reversible data-hiding scheme based on primitive and varying radix numerical model is presented in this *** varying radix,variable sum of data may be embedded in various pixels of *** scheme is made adaptive using the correlation of the neighboring *** are embedded as blocks of non-uniform length in the high-frequency regions of the rhombus mean interpolated image.A higher amount of data is embedded in the high-frequency regions and lesser data in the low-frequency regions of the *** size of the embedded data depends on the statistics of the pixel distribution in the cover *** of the major issues in reversible data embedding,the location map,is minimized because of the interpolation *** technique,which is actually LSB matching,embeds only the residuals of modulo radix into the LSBs of each *** attacks on this RDH technique will be able to decode the hidden content in the marked *** proposed scheme delivers a prominent visual quality despite high embedding *** tests carried out on over 100 natural image data sets and medical images show an improvement in results compared to the existing *** the algorithm is based on the variable radix number system,it is more resistant to most of the steganographic *** results were compared with a higher embedding capacity of up to 1.5 bpp reversible schemes for parameters like Peak Signal-to-Noise Ratio(PSNR),Embedding Capacity(EC)and Structural Similarity Index Metric(SSIM).

关键词： Data hiding spatial correlation radix system prediction error embedding capacity

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What Kind of Initial errors Cause the Severest prediction Uncertainty of E1 Nino in Zebiak-Cane Model

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Advances in Atmospheric Sciences 2008年第4期25卷 577-584页

作者：徐辉段晚锁 State Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG) Institute of Atmospheric PhysicsChinese Academy of Sciences

With the Zebiak-Cane （ZC） model, the initial error that has the largest effect on ENSO prediction is explored by conditional nonlinear optimal perturbation （CNOP）. The results demonstrate that CNOP-type errors cause the largest prediction error of ENSO in the ZC model. By analyzing the behavior of CNOPtype errors, we find that for the normal states and the relatively weak E1 Nifio events in the ZC model, the predictions tend to yield false alarms due to the uncertainties caused by CNOP. For the relatively strong E1 Nino events, the ZC model largely underestimates their intensities. Also, our results suggest that the error growth of E1 Nifio in the ZC model depends on the phases of both the annual cycle and ENSO. The condition during northern spring and summer is most favorable for the error growth. The ENSO prediction bestriding these two seasons may be the most difficult. A linear singular vector （LSV） approach is also used to estimate the error growth of ENSO, but it underestimates the prediction uncertainties of ENSO in the ZC model. This result indicates that the different initial errors cause different amplitudes of prediction errors though they have same magnitudes. CNOP yields the severest prediction uncertainty. That is to say, the prediction skill of ENSO is closely related to the types of initial error. This finding illustrates a theoretical basis of data assimilation. It is expected that a data assimilation method can filter the initial errors related to CNOP and improve the ENSO forecast skill.

关键词： ENSO predictability prediction error optimal perturbation

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Character analysis of large area-to-mass ratio LEO debris orbit prediction

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Journal of Beijing Institute of Technology 2016年第S2期25卷 124-129页

作者：刘卫王荣兰刘四清龚建村 National Space Science Center Chinese Academy of Sciences

Based on rapid science orbits(RSO) for CHAMP satellite,the orbit of low earth orbit(LEO) space debris with large area to mass ratio and unstable attitude were *** is found that the forecast error of unstable mode is larger than the stable one in any space environment and the prediction error grow *** TLE orbital data,the US-Russian satellite collision debris forecast error are analyzed,so more breakup event debris orbit prediction error character were *** study has some significance for refined grasp large area to mass ratio space debris orbit prediction error character and enhance collision warning confidence.

关键词： large area-to-mass ratio space debris prediction error error character

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