Academic literature on the topic 'Prediction error expansion'

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Journal articles on the topic "Prediction error expansion"

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Bensebia, Bensaber, Fatma-Zohra Chaouche, Ouahida Bensebia, and Soumia Moustefaï. "Bed expansion in turbulent bed contactor: Experiments and prediction." Chemical Industry and Chemical Engineering Quarterly, no. 00 (2023): 10. http://dx.doi.org/10.2298/ciceq230304010b.

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In this work, the hydrodynamics of the turbulent bed contractor (TBC) have been studied in terms of bed expansion (Hd/Hst) using a particular approach for the prediction of this important property for the design of such equipment. The study is based on 1604 sets of experimental data of the bed expansion, obtained by varying the operating variables (gas velocity, liquid spray, packing characteristics, static bed height and free opening of the supporting grid. The prediction of the bed expansion necessitates the estimation of gas and liquid holdups. To achieve this, we employed a variety of corr
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Ni, Chengcai, and Lianjun Zhang. "An Estimator of Prediction Error Variance for Projection Equations." Forest Science 54, no. 6 (2008): 569–78. http://dx.doi.org/10.1093/forestscience/54.6.569.

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Abstract An estimator of prediction error variance for projection equations was derived using the first-order Taylor expansion in this study. The estimator, a modified estimator of the prediction error variance for a population mean regression model, was adapted for situations in which projection equations are applied to unsampled individuals. The estimator accounted for the errors associated with the response variable on the right side of a projection equation, as well as the errors associated with parameter estimation and serial correlations in data. The application of the estimator was demo
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Kong, Xiaoxi, Wenguang He, and Zhanchuan Cai. "A Novel High-Fidelity Reversible Data Hiding Method Based on Adaptive Multi-pass Embedding." Mathematics 13, no. 11 (2025): 1881. https://doi.org/10.3390/math13111881.

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In reversible data hiding, prediction error generation plays a crucial role, with pixel value ordering (PVO) standing out as a prediction method that achieves high fidelity. However, conventional PVO approaches select predicted pixels and their predictions independently, failing to fully exploit the inherent redundancy in ordered pixel sequences. This paper proposes a novel PVO-based prediction method that leverages the continuity and spatial correlation of ordering pixels. We first introduce a new prediction technique that exploits the redundancy of consecutive pixels. Our approach selects th
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Caciula, Ion, Henri George Coanda, and Dinu Coltuc. "Multiple moduli prediction error expansion reversible data hiding." Signal Processing: Image Communication 71 (February 2019): 120–27. http://dx.doi.org/10.1016/j.image.2018.11.005.

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Harati, Ehsan, and Hossein Ahmadi Noubari. "Long Time Prediction of Uncertain Systems Using Singular Perturbation." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, no. 05 (2017): 707–21. http://dx.doi.org/10.1142/s0218488517500301.

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This paper considers the problem of long time prediction of uncertain dynamic systems. Spectral methods such as polynomial chaos expansion (PCE) provides a suitable alternative for classical Monte Carlo method with lower computational load. However, polynomial chaos expansion has a major drawback of long time integration error. In this paper, we will apply singular perturbation (SP) method for reducing long time integration error. Using SP the accuracy of long time predictions are improved with comparable computational load. We will apply SP to illustrative exemplify problems to show effective
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Zeng, Xiao, Zhenyong Chen, Ming Chen, and Zhang Xiong. "Reversible Video Watermarking Based on Inter-prediction Error Expansion." Journal of Computer-Aided Design & Computer Graphics 22, no. 5 (2010): 871–78. http://dx.doi.org/10.3724/sp.j.1089.2010.10822.

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Ou, Bo, Xiaolong Li, Yao Zhao, Rongrong Ni, and Yun-Qing Shi. "Pairwise Prediction-Error Expansion for Efficient Reversible Data Hiding." IEEE Transactions on Image Processing 22, no. 12 (2013): 5010–21. http://dx.doi.org/10.1109/tip.2013.2281422.

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Roy, Aniket, and Rajat Subhra Chakraborty. "Toward Optimal Prediction Error Expansion-Based Reversible Image Watermarking." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 8 (2020): 2377–90. http://dx.doi.org/10.1109/tcsvt.2019.2911042.

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Nguyen, Thai-Son, and Phuoc-Hung Vo. "Reversible image authentication scheme based on prediction error expansion." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (2021): 253. http://dx.doi.org/10.11591/ijeecs.v21.i1.pp253-262.

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<span>Reversible image authentication scheme is a technique that detects tampered areas in images and allows them to be reconstructed to their original version without any distortion. In this article, a new, reversible, image authentication scheme based on prediction error expansion is proposed for digital images. The proposed scheme classifies the host image into smooth blocks and complex blocks. Then, an authentication code that is created randomly with a seed is embedded adaptively into each image block. Experimental results showed that our proposed scheme achieves the high accuracy o
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Yu, Xiang, Xiang Wang, and Qingqi Pei. "Reversible watermarking based on multi-dimensional prediction-error expansion." Multimedia Tools and Applications 77, no. 14 (2018): 18085–104. http://dx.doi.org/10.1007/s11042-018-5794-y.

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Dissertations / Theses on the topic "Prediction error expansion"

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LAI, CHIAO-CHIH, and 賴喬志. "Greedy Pairing Prediction-Error Expansion for Reversible Data Hiding." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/49250067995165595680.

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碩士<br>國立臺北大學<br>資訊工程學系<br>105<br>Reversible data hiding (RDH) is a method to embed messages in an image that human eyes are difficult to recognize the differences between the original image and the embedded image. But the method has to make sure that the original image and the embedded information can be exactly recovered. Then, prediction-error expansion (PEE) is a successful way to realize RDH. However, it is fixed when pairing the conventional 2D PEH. So, the capacities and embedding distortion is not ideal. In this thesis, we propose a method called greedy pairing prediction-error expansio
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NI, CHENG-HUAI, and 倪正懷. "4-wise Prediction-Error Expansion for Reversible Data Hiding." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/94709265788575026472.

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碩士<br>國立臺北大學<br>資訊工程學系<br>104<br>Reversible data hiding (RDH) techniques applied on military, medical, and legal fields have been broadly presented in the recent years. Some prediction-error expansion (PEE) based methods based on the modification of two-dimensional prediction-error histogram (2D PEH) exploit image redundancy to obtain correlation among prediction-errors. In this work, a novel RDH method called 4-wise PEE over 2D PEH framework is presented here. The 4-wise PEE can make use of image redundancy to get better correlation. First, regarding every four adjacent prediction-errors as a
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Yao, Ya-Shin, and 姚雅心. "Study on Reversible Data Hiding Schemes based on Prediction-Error-Expansion Techniques." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/58698703936788391666.

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碩士<br>國立臺北大學<br>資訊工程學系<br>102<br>We revisit reversible data hiding schemes based on prediction-error-expansion techniques. Recently, in Signal Processing, Li, Li, Li, and Yang proposed a new prediction-error-expansion technique and used it to construct a high-fidelity reversible data embedding scheme. However, the embedding capacity of their proposed scheme is not high. Directly applying multi-pass embedding increases the embedding capacity but results in a large of image degradation. In this paper, we generalize the prediction-error-expansion technique proposed by Li, Li, Li, and Yang. The
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Books on the topic "Prediction error expansion"

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Fox, Raymond. The Use of Self. Oxford University Press, 2011. http://dx.doi.org/10.1093/oso/9780190616144.001.0001.

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This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic
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Raff, Lionel, Ranga Komanduri, Martin Hagan, and Satish Bukkapatnam. Neural Networks in Chemical Reaction Dynamics. Oxford University Press, 2012. http://dx.doi.org/10.1093/oso/9780199765652.001.0001.

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This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic
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Book chapters on the topic "Prediction error expansion"

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Li, Qi, Bin Yan, Hui Li, and Jeng-Shyang Pan. "Reversible Watermarking Based on Adaptive Prediction Error Expansion." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5841-8_26.

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Qu, Xiaochao, Suah Kim, Run Cui, Fangjun Huang, and Hyoung Joong Kim. "Reversible Data Hiding Based on Combined Predictor and Prediction Error Expansion." In Digital-Forensics and Watermarking. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19321-2_19.

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Roy, Aniket, and Rajat Subhra Chakraborty. "Optimal Distortion Estimation for Prediction Error Expansion Based Reversible Watermarking." In Digital Forensics and Watermarking. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53465-7_20.

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Duevedi, Manisha, Sushila Madan, and Sunil Kumar Muttoo. "An Efficient Reversible Data Hiding Based on Prediction Error Expansion." In Soft Computing and Signal Processing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8669-7_7.

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Geng, Mingqin, Yuqing Zhang, Puyi Yu, and Yifu Gao. "Reversible Watermarking Based on Prediction-Error Expansion for 2D Vector Maps." In Emerging Technologies for Information Systems, Computing, and Management. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7010-6_50.

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Deepthi, Jangam, and T. Venu Gopal. "Analysis of Block-Level Prediction-Error Expansion Approach in Data Encryption." In Intelligent Communication Technologies and Virtual Mobile Networks. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1767-9_21.

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Hoa, Le Quang, Cao Thi Luyen, Nguyen Kim Sao, and Pham Van At. "An Improved Reversible Watermarking Based on Pixel Value Ordering and Prediction Error Expansion." In Intelligent Information and Database Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42058-1_49.

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Kelkar, Vishakha, Jinal H. Mehta, and Kushal Tuckley. "A Novel Robust Reversible Watermarking Technique Based on Prediction Error Expansion for Medical Images." In Proceedings of 2nd International Conference on Computer Vision & Image Processing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7895-8_11.

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Bandyopadhyay, Sakhi, Sunita Sarkar, Subhadip Mukherjee, and Somnath Mukhopadhyay. "Pixel Interpolation Followed by Prediction Error Expansion-Based Reversible Information Hiding Algorithm for Securing Healthcare Data." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5191-6_31.

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Garrett, Steven L. "Comfort for the Computationally Crippled." In Understanding Acoustics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44787-8_1.

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Abstract The difference between engineering and science, and all other human activity, is the fact that engineers and scientists make quantitative predictions about measurable outcomes and can specify their uncertainty in such predictions. Because those predictions are quantitative, they must employ mathematics. This chapter is intended as review of some of the more useful mathematical concepts, strategies, and techniques that are employed in the description of vibrational and acoustical systems and in the calculation of their behavior. Topics in this review include techniques such as Taylor s
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Conference papers on the topic "Prediction error expansion"

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Floros, Stylianos, Satyajeet S. Bhonsale, Sotiria Gaspari, Simen Akkermans, and Jan F. M. Van Impe. "Modelling the in vitro FooD Digestion SIMulator FooDSIM." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.162389.

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Understanding the complexity of human digestion is critical for designing models that serve as valuable research tools for process simulation and prediction. Due to the high cost of medical intervention &amp; recent advancements in in vitro digestion protocols, increased demand for inexpensive in silico solutions emerges. This study aims to develop a mathematical model that simulates the in vitro dynamic Food Digestion SIMulator (FooDSIM) functionalities via a digital twin approach. Ordinary Differential Equations (ODEs) simulate the system as a series of Continuously Stirred Tank Reactors (CS
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Chen, Ming, Zhenyong Chen, Xiao Zeng, and Zhang Xiong. "Reversible data hiding using additive prediction-error expansion." In the 11th ACM workshop. ACM Press, 2009. http://dx.doi.org/10.1145/1597817.1597822.

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Rongrong Ni, H. D. Cheng, Yu Hou, and Yao Zhao. "Error-free Authentication Watermarking Based on Prediction-Error-Expansion Reversible Technique." In 24th IET Irish Signals and Systems Conference (ISSC 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/ic.2013.0055.

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Fei, Wenbin, and Xianghong Tang. "Reversible Text Watermarking Algorithm Using Prediction-error Expansion Method." In The International Conference on Computer, Networks and Communication Engineering (ICCNCE 2013). Atlantis Press, 2013. http://dx.doi.org/10.2991/iccnce.2013.100.

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Mehta, Jinal H., and Vishakha Kelkar. "Comparison of reversible watermarking usingprediction error expansion and prediction error expansion considering region of interest for medical images." In 2017 2nd International Conference for Convergence in Technology (I2CT). IEEE, 2017. http://dx.doi.org/10.1109/i2ct.2017.8226231.

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Tang, Xin, Linna Zhou, Dan Liu, Boyu Liu, and Xinyi Lu. "Reversible Data Hiding Based on Improved Rhombus Predictor and Prediction Error Expansion." In 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2020. http://dx.doi.org/10.1109/trustcom50675.2020.00016.

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Zhu, Anping, Caiming Zhang, Xingqiang Yang, and Xifeng Gao. "Reversible watermarking of 3D mesh models using prediction-error expansion." In 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5646790.

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Nishimura, Akira. "Reversible Audio Data Hiding Using Linear Prediction and Error Expansion." In 2011 Seventh International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). IEEE, 2011. http://dx.doi.org/10.1109/iihmsp.2011.76.

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Wu, Hao-tian, and Jean-Luc Dugelay. "Reversible watermarking of 3D mesh models by prediction-error expansion." In 2008 IEEE 10th Workshop on Multimedia Signal Processing (MMSP). IEEE, 2008. http://dx.doi.org/10.1109/mmsp.2008.4665183.

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Yan, Diqun, and Rangding Wang. "Reversible Data Hiding for Audio Based on Prediction Error Expansion." In 2008 Fourth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). IEEE, 2008. http://dx.doi.org/10.1109/iih-msp.2008.27.

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Reports on the topic "Prediction error expansion"

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Bhurtyal, Sanjeev, Hieu Bui, Sarah Hernandez, et al. Prediction of waterborne freight activity with Automatic Identification System using machine learning. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49794.

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This paper addresses latency issues related to publicly available port-level commodity tonnage reports. Predicting commodity tonnage at the port-level, near real time vessel tracking data is used with historical WCS with a machine learning model. Commodity throughput is derived from WCS data which is released publicly approximately two years after collection. This latency presents a challenge for short-term planning and other operational uses. This study leverages near real time vessel tracking data from the AIS data set. LSTM, TCN, and TFT machine learning models are developed using the featu
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