The Experts below are selected from a list of 305913 Experts worldwide ranked by ideXlab platform
Mingsheng Shang - One of the best experts on this subject based on the ideXlab platform.
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non negativity constrained missing data estimation for high dimensional and sparse matrices from Industrial Applications
IEEE Transactions on Systems Man and Cybernetics, 2020Co-Authors: Xin Luo, Mengchu Zhou, Mingsheng ShangAbstract:High-dimensional and sparse (HiDS) matrices are commonly seen in big-data-related Industrial Applications like recommender systems. Latent factor (LF) models have proven to be accurate and efficient in extracting hidden knowledge from them. However, they mostly fail to fulfill the non-negativity constraints that describe the non-negative nature of many Industrial data. Moreover, existing models suffer from slow convergence rate. An alternating-direction-method of multipliers-based non-negative LF (AMNLF) model decomposes the task of non-negative LF analysis on an HiDS matrix into small subtasks, where each task is solved based on the latest solutions to the previously solved ones, thereby achieving fast convergence and high prediction accuracy for its missing data. This paper theoretically analyzes the characteristics of an AMNLF model, and presents detailed empirical studies regarding its performance on nine HiDS matrices from Industrial Applications currently in use. Therefore, its capability of addressing HiDS matrices is justified in both theory and practice.
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randomized latent factor model for high dimensional and sparse matrices from Industrial Applications
IEEE CAA Journal of Automatica Sinica, 2019Co-Authors: Mingsheng Shang, Zhigang Liu, Xin Luo, Ye Yuan, Jia Chen, Mengchu ZhouAbstract:Latent factor ( LF ) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse ( HiDS ) matrices which are commonly seen in various Industrial Applications. An LF model usually adopts iterative optimizers, which may consume many iterations to achieve a local optima, resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor ( RLF ) model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly. Experimental results on three HiDS matrices from Industrial Applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data. I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for Industrial Applications demanding highly efficient models.
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an inherently nonnegative latent factor model for high dimensional and sparse matrices from Industrial Applications
IEEE Transactions on Industrial Informatics, 2018Co-Authors: Xin Luo, Mengchu Zhou, Mingsheng ShangAbstract:High-dimensional and sparse (HiDS) matrices are commonly encountered in many big-data-related and Industrial Applications like recommender systems. When acquiring useful patterns from them, nonnegative matrix factorization (NMF) models have proven to be highly effective owing to their fine representativeness of the nonnegative data. However, current NMF techniques suffer from: 1) inefficiency in addressing HiDS matrices; and 2) constraints in their training schemes. To address these issues, this paper proposes to extract nonnegative latent factors (NLFs) from HiDS matrices via a novel inherently NLF (INLF) model. It bridges the output factors and decision variables via a single-element-dependent mapping function, thereby making the parameter training unconstrained and compatible with general training schemes on the premise of maintaining the nonnegativity constraints. Experimental results on six HiDS matrices arising from Industrial Applications indicate that INLF is able to acquire NLFs from them more efficiently than any existing method does.
V.a. Tarbokov - One of the best experts on this subject based on the ideXlab platform.
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High intensity pulsed ion beam sources and their Industrial Applications
Surface and Coatings Technology, 1999Co-Authors: Gennady E. Remnev, V. M. Matvienko, M.s. Opekounov, I.i. Grushin, A.n. Zakoutayev, A.v. Potyomkin, V.k. Struts, I.f. Isakov, Vasilevich Aleksandr Ryzhkov, V.a. TarbokovAbstract:This paper presents research on practical Applications of high intensity pulsed ion beams (HIPIBs) investigated at the Nuclear Physics Institute of the Tomsk Polytechnic University (NPI TPU) and the Scientific Industrial Enterprise ‘Linetron’, N. Novgorod. The most interesting scientific results have been obtained in the following fields: $ HIPIB surface modification for the increase of wear resistance of tools; $ deposition of thin metal, composite and diamond-like carbon (DLC) films; $ short-pulse ion implantation in semiconductors. It was shown that ion beams with relatively low power density (106–109W/cm2) are very promising for Industrial Applications. The paper presents a brief description of the HIPIB–solids interaction and main HIPIB parameters used in the research, as well as modification of properties of treated samples.
Mengchu Zhou - One of the best experts on this subject based on the ideXlab platform.
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non negativity constrained missing data estimation for high dimensional and sparse matrices from Industrial Applications
IEEE Transactions on Systems Man and Cybernetics, 2020Co-Authors: Xin Luo, Mengchu Zhou, Mingsheng ShangAbstract:High-dimensional and sparse (HiDS) matrices are commonly seen in big-data-related Industrial Applications like recommender systems. Latent factor (LF) models have proven to be accurate and efficient in extracting hidden knowledge from them. However, they mostly fail to fulfill the non-negativity constraints that describe the non-negative nature of many Industrial data. Moreover, existing models suffer from slow convergence rate. An alternating-direction-method of multipliers-based non-negative LF (AMNLF) model decomposes the task of non-negative LF analysis on an HiDS matrix into small subtasks, where each task is solved based on the latest solutions to the previously solved ones, thereby achieving fast convergence and high prediction accuracy for its missing data. This paper theoretically analyzes the characteristics of an AMNLF model, and presents detailed empirical studies regarding its performance on nine HiDS matrices from Industrial Applications currently in use. Therefore, its capability of addressing HiDS matrices is justified in both theory and practice.
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randomized latent factor model for high dimensional and sparse matrices from Industrial Applications
IEEE CAA Journal of Automatica Sinica, 2019Co-Authors: Mingsheng Shang, Zhigang Liu, Xin Luo, Ye Yuan, Jia Chen, Mengchu ZhouAbstract:Latent factor ( LF ) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse ( HiDS ) matrices which are commonly seen in various Industrial Applications. An LF model usually adopts iterative optimizers, which may consume many iterations to achieve a local optima, resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor ( RLF ) model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly. Experimental results on three HiDS matrices from Industrial Applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data. I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for Industrial Applications demanding highly efficient models.
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an inherently nonnegative latent factor model for high dimensional and sparse matrices from Industrial Applications
IEEE Transactions on Industrial Informatics, 2018Co-Authors: Xin Luo, Mengchu Zhou, Mingsheng ShangAbstract:High-dimensional and sparse (HiDS) matrices are commonly encountered in many big-data-related and Industrial Applications like recommender systems. When acquiring useful patterns from them, nonnegative matrix factorization (NMF) models have proven to be highly effective owing to their fine representativeness of the nonnegative data. However, current NMF techniques suffer from: 1) inefficiency in addressing HiDS matrices; and 2) constraints in their training schemes. To address these issues, this paper proposes to extract nonnegative latent factors (NLFs) from HiDS matrices via a novel inherently NLF (INLF) model. It bridges the output factors and decision variables via a single-element-dependent mapping function, thereby making the parameter training unconstrained and compatible with general training schemes on the premise of maintaining the nonnegativity constraints. Experimental results on six HiDS matrices arising from Industrial Applications indicate that INLF is able to acquire NLFs from them more efficiently than any existing method does.
Mohsen Guizani - One of the best experts on this subject based on the ideXlab platform.
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Blockchain-Based Anonymous Authentication With Selective Revocation for Smart Industrial Applications
IEEE Transactions on Industrial Informatics, 2020Co-Authors: Yanqi Zhao, Lianhai Wang, Mohsen GuizaniAbstract:Personal privacy disclosure is one of the most serious challenges in smart Industrial Applications. Anonymous authentication is an effective solution to protect personal privacy. However, the existing anonymous credential protocols are not perfectly suitablefor smart Industrial environments such as smart vehicles in the sense that the credential revocation issue is not well-solved. In this article, we propose a Blockchain-based Anonymous authentication with Selective revocation for Smart Industrial Applications (BASS) for smart Industrial Applications supporting attribute privacy, selective revocation, credential soundness, and multishowing-unlinkability. Specifically, an efficient selective revocation mechanism is proposed based on dynamic accumulators and the signature algorithm due to Pointcheval and Sanders as the overlay of the BASS. According to the diverse demands of credential authorities, BASS can selectively provide revocation of credentials or revocation of users. We extend BASS from single-attribute privacy to multiattribute privacy as well. Finally, we implement a prototype to evaluate the cryptographic core primitives of BASS by deploying smart contracts in Ethereum to demonstrate the validity of BASS in smart Industrial Applications.
Abdul Hameed - One of the best experts on this subject based on the ideXlab platform.
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Industrial Applications of microbial lipases
Enzyme and Microbial Technology, 2006Co-Authors: Fariha Hasan, Aamer Ali Shah, Abdul HameedAbstract:Lipases are a class of enzymes which catalyse the hydrolysis of long chain triglycerides. Microbial lipases are currently receiving much attention with the rapid development of enzyme technology. Lipases constitute the most important group of biocatalysts for biotechnological Applications. This review describes various Industrial Applications of microbial lipases in the detergent, food, flavour industry, biocatalytic resolution of pharmaceuticals, esters and amino acid derivatives, making of fine chemicals, agrochemicals, use as biosensor, bioremediation and cosmetics and perfumery.
