On the multi-dimensional dataprocessing and analysis,data with missing or suspicious values is *** to use potential structure of the known data to reconstruct the missing data is a pressing problem to be ***,the miss...
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ISBN:
(纸本)9781509036202
On the multi-dimensional dataprocessing and analysis,data with missing or suspicious values is *** to use potential structure of the known data to reconstruct the missing data is a pressing problem to be ***,the missing data dealing normally aims at lowdimensional data in vector or matrix format,while research on high-dimensional data above 3-dimensional is very *** solve this problem,a multi-dimensional data filling algorithm based on tensor decomposition has been *** approach adequately using tensor decomposition's structure and uniqueness of CP model,to realize the multi-dimensional data filling *** and comparison with CPWOPT algorithm proves that this algorithm is not only accurate but also rapid.
Batch manufacturing processes (BMP) play an important role in many production industries, such as in semiconductor, electronic and pharmaceutical industries. They generally exhibit some batch-to-batch or unit-to-unit ...
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Batch manufacturing processes (BMP) play an important role in many production industries, such as in semiconductor, electronic and pharmaceutical industries. They generally exhibit some batch-to-batch or unit-to-unit variations due to many reasons such as variations in impurities and deviations of the process variables from their trajectories. The process monitoring for these systems has been considered as rather fault diagnosis than as fault prognosis, this latter has received scarce attention in the literature. This paper presents a data-driven prognostic method for BMP organized in three steps. The first step allows to reduce the data size and to extract a raw health index which represents the operating state of the system. In the second step, variations in the health index are processed by the percentile measure which is use in a way that gives rise to monotonic profiles. In the third step, these profiles are modelled by gamma process as it is the most appropriate for the stochastic modelling of monotonic and gradual deterioration. The remaining useful life (RUL) is then estimated using an aggregate probability density function (pdf) with a confidence interval (CI) that ensures the safety margins in industry. Finally, the proposed method is applied on semiconductor manufacturing equipment with two industrial datasets provided by STMicroelectronics. (C) 2016 Elsevier Ltd. All rights reserved.
Diverse areas of science and engineering are increasingly driven by high-throughput automated data capture and analysis. Modern acquisition technologies, used in many scientific applications (e.g., astronomy, physics,...
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ISBN:
(纸本)9781450347556
Diverse areas of science and engineering are increasingly driven by high-throughput automated data capture and analysis. Modern acquisition technologies, used in many scientific applications (e.g., astronomy, physics, materials science, geology, biology, and engineering) and often running at gigabyte per second data rates, quickly generate terabyte to petabyte datasets that must be stored, shared, processed and analyzed at similar rates. The largest datasets are often multidimensional, such as volumetric and time series data derived from various types of image capture. Costeffective and timely processing of these data require system and software architectures that incorporate on-the-fly processing to minimize I/O traffic and avoid latency limitations. In this paper we present the Virtual Volume File System, a new approach to on-demand processing with file system semantics, combining these principles into a versatile and powerful data pipeline for dealing with some of the largest 3D volumetric datasets. We give an example of how we have started to use this approach in our work with massive electron microscopy image stacks. We end with a short discussion of current and future challenges.
In this study, the changes in Spatially Resolved Diffuse Reflectance and AutoFluorescence spectra acquired on ev vivo human skin during optical clearing process (topical application) are investigated and their impact ...
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ISBN:
(纸本)9781728152332
In this study, the changes in Spatially Resolved Diffuse Reflectance and AutoFluorescence spectra acquired on ev vivo human skin during optical clearing process (topical application) are investigated and their impact on skin optical properties (absorption, scattering and fluorescence) analyzed using inverse problem solving and multidimensional data processing approaches.
With the development of GPS-positioning technology and mobile Internet, the intelligent transportation system plays an important role in our daily lives. Thus, despite many existing works focusing on improving the eff...
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ISBN:
(纸本)9789819755516;9789819755523
With the development of GPS-positioning technology and mobile Internet, the intelligent transportation system plays an important role in our daily lives. Thus, despite many existing works focusing on improving the efficiency and accuracy of the transportation system, however, few of them can handle multidimensional features on road networks. In this paper, we focus on a famous problem of the intelligent transportation system named estimated time of arrival (ETA). Specifically, we propose a novel multidimensional information perception model (MIPM) to address the ETA problem in a real-life environment. MIPM consists of the extract and recurrent modules. In the dataprocessing phase, we generate the sparse and select features of the multidimensional features. Then, in the extract module, we compute Raw-ETA by regression method based on the select features and then extract the Refine-ETA from Raw-ETA. After that, we design a SPETAformer block to ensure that the extract module can capture the multidimensional correlation of the above features. The recurrent module further enhances the learning ability of the MIPM in the temporal domain by week, daily, recent, and weather four different periodic slices. By combining the extract and recurrent module, our MIPM has the capability to obtain an accurate ETA. Evaluation experiments on a large-scale real-world dataset are conducted to show the superiority of our proposed model.
Higher-order singular value decomposition (HOSVD) is known as an effective technique to reduce the dimension of multidimensionaldata. We have proposed a method to perform third-order tensor product expansion (3OTPE) ...
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ISBN:
(纸本)9783037859063
Higher-order singular value decomposition (HOSVD) is known as an effective technique to reduce the dimension of multidimensionaldata. We have proposed a method to perform third-order tensor product expansion (3OTPE) by using the power method for the same purpose as HOSVD, and showed that our method had a better accuracy property than HOSVD, and furthermore, required fewer computation time than that. Since our method could not be applied to the tensors of fourth-order (or more) in spite of having those useful properties, we extend our algorithm of 3OTPE calculation to forth-order tensors in this paper. The results of newly developed method are compared to those obtained by HOSVD. We show that the results follow the same trend as the case of 3OTPE.
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