In semiconductor etching processes, fault detection monitors the quality of wafers. However, the detailed dynamics in batch data are ignored in many traditional methods. In this paper, sequential image-based data visu...
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In semiconductor etching processes, fault detection monitors the quality of wafers. However, the detailed dynamics in batch data are ignored in many traditional methods. In this paper, sequential image-based data visualization and fault detection, using bi-kernel t-distributed stochastic neighbor embedding (t-SNE), is proposed for semiconductor etching processes. In the proposed method, multi-modals, multi-phases, and abnormal samples in batches are visualized in two-dimensional maps. First, the batch data are restructured into sequential images and input to a convolutional autoencoder (CAE) to learn the abstract representation. Then, bi-kernel t-SNE is applied to visualize the CAE codes in two-dimensional maps. To reduce the computational burden and overcome the out-of-sample projection diffusion in bi-kernel t-SNE, data subsampling is used in the training procedure. Finally, a one-class support vector machine is employed to calculate the visualization control boundary, and a batch-wise index is presented for fault wafer detection. To demonstrate the feasibility and effectiveness of the proposed method, it was applied to two wafer etching datasets. The results indicate that the proposed method outperforms state-of-the-art methods in data visualization and fault detection.
With the ever-increasing energy demand, triggered by the continued population growth and accelerated industrial revolution, renewable energy has emerged as the world's fastest-growing energy source. Renewable ener...
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With the ever-increasing energy demand, triggered by the continued population growth and accelerated industrial revolution, renewable energy has emerged as the world's fastest-growing energy source. Renewable energy's popularity has grown because it is environmentally friendly and abundant in natural environments. Despite its enormous potential as a viable alternative to traditional (fossil fuel-based) energy sources, renewable energy has rarely been commercialized and utilized. Its lack of commercialization has something to do with a lack of evidence proving its eco- and cost-efficiency. With this in mind, this paper aims to assess the eco- and cost-efficiency of renewable energy such as algae-based biofuels using data visualization. It also intends to help increase public awareness and facilitate the commercialization of renewable energy such as biofuels. Through experiments, this paper found that the success of biofuel commercialization hinged on temperature, light intensity, and algae strain. Another important finding is that the low carbon footprint resulting from biofuel consumption may not directly contribute to the immediate revenue growth of a biofuel producing company, but it can foster a long-term positive image that will help attract more customers in the future with increased brand recognition. Furthermore, this paper evaluates the effectiveness, the level of user involvement, and the usability of two data visualization tools built upon the dashboard and the balanced scorecard. Based on the case study, this paper demonstrates how effective and useful the tools are in communicating the firm's strategic goals toward sustainability and thus provides easier practical guidelines for renewable energy development decisions. (C) 2018 Elsevier Ltd. All rights reserved.
visualization techniques have been front-and-center in the efforts to communicate the science around COVID-19 to the very broad audience of policymakers, scientists, healthcare providers, and the general public. In th...
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visualization techniques have been front-and-center in the efforts to communicate the science around COVID-19 to the very broad audience of policymakers, scientists, healthcare providers, and the general public. In this article, I summarize and illustrate with examples how visualization can help understand different aspects of the pandemic.
Interactive tabletops and surfaces (ITSs) provide rich opportunities for data visualization and analysis and consequently are used increasingly in such settings. A research agenda of some of the most pressing challeng...
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Interactive tabletops and surfaces (ITSs) provide rich opportunities for data visualization and analysis and consequently are used increasingly in such settings. A research agenda of some of the most pressing challenges related to visualization on ITSs emerged from discussions with researchers and practitioners in human-computer interaction, computer-supported collaborative work, and a variety of visualization fields at the 2011 Workshop on data Exploration for Interactive Surfaces (Dexis 2011). [ABSTRACT FROM AUTHOR]
data visualization in high-dimensional space is a significant problem in machine learning. In many data sets, the data apparently lie on a high dimensional ambient space due to redundant features, while the intrinsic ...
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data visualization in high-dimensional space is a significant problem in machine learning. In many data sets, the data apparently lie on a high dimensional ambient space due to redundant features, while the intrinsic dimension is very low. This work proposes an analytical approach to use Feature Based Ricci Flow Embedding (FBRFE) as a nonlinear dimensionality reduction technique. For visualization purposes, we have considered nonlinear data with an intrinsic dimension of 2D but lie on an ambient space 3D and reduced the dimensionality accordingly. FBRFE uses conformal mapping that preserves the angle between the points in the higher dimensional manifold. At first, a surface triangulation mesh is formed using all the data points, and then circle packing is done in order to compute the respective angles between the data points. Then, conformal mapping is performed through the surface Ricci flow algorithm. After that, the 3D surface triangulation mesh is flattened into 2D using a seed face flattening algorithm to reduce the dimensionality of the data. Comparison results show that FBRFE visualizes the data in a lower dimension with a much better mean correlation up to 120.17% and less overlapping than the existing conventional algorithms.
Most 2D visualization methods based on multidimensional scaling (MDS) and self-organizing maps (SOMs) use a symmetric distance matrix to represent and visualize object relationships in a data set. In many real-world a...
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Most 2D visualization methods based on multidimensional scaling (MDS) and self-organizing maps (SOMs) use a symmetric distance matrix to represent and visualize object relationships in a data set. In many real-world applications, however, raw data such as a world-trade data are best captured as an asymmetric proximity matrix. Such asymmetric matrices cannot be perfectly represented by most previous methods. To handle such an intrinsic limitation, in this paper, we propose a dynamic learning for metric representations of asymmetric proximity data to better understand the data. The proposed learning generates two representations (maps) with the row vectors (sending or exporting) and column vectors (receiving or importing) of the matrix, respectively. To better present the patterns, we supplement the maps with two analysis tools: cluster analysis and distance analysis, which connect and compare the different patterns from the different maps. Experiment results using three real world data sets confirm that the proposed learning method is useful to understand asymmetric proximity data. (C) 2013 Elsevier B.V. All rights reserved.
data analytics literature has not adequately addressed the empirical investigation of dashboard evaluations. Instruments that measure the dashboard dimensions and usefulness are yet to be proposed. This research is ge...
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data analytics literature has not adequately addressed the empirical investigation of dashboard evaluations. Instruments that measure the dashboard dimensions and usefulness are yet to be proposed. This research is geared toward this end. The objective is to examine the theoretical underpinnings of data visualization using dashboards, to develop and validate an instrument for the measuring of dashboard usefulness in the public sector. data collected from 160 dashboards of public or government institutions were used to validate a measurement model using structural equation modeling. PLS (Partial Least Squares) techniques and the SPSS software were used to assess the model and measures. Theoretical and practical implications are offered.
A rainbow colormap is often supplied as a default in visualizationsoftware. In this kind of colormap, red is mapped to the highest datavalue, blue to the lowest, and the other data values are interpolatedalong the ful...
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A rainbow colormap is often supplied as a default in visualizationsoftware. In this kind of colormap, red is mapped to the highest datavalue, blue to the lowest, and the other data values are interpolatedalong the full extent of the spectrum. But there is more to color thanmeets the eye. Color is a perceptual as well as physical *** is commonly called color-hue-is only one of three *** is the brightness of the signal-intensity. The third is theadmixture of white -saturation. Change any one parameter enough, and thecolor looks different. (The hue-intensity-saturation model of color isone of a several explored through the years, and captures some of thebasic characteristics of basic color perception.) To make matters worse,the parameters' relationship to what is perceived is nonlinear. At thesame intensity, for example, yellow appears brighter than blue. Some ofthe perceptual principles involved have been implemented in softwaredeveloped at IBM Corp.'s Thomas J. Watson Research Center, YorktownHeights, NY. The module runs with IBM's visualization package dataExplorer and is called Pravda (for perceptual rule based architecturefor visualizing data accurately)
Although bar graphs are designed for categorical data, they are routinely used to present continuous data in studies that have small sample sizes. This presentation is problematic, as many data distributions can lead ...
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Although bar graphs are designed for categorical data, they are routinely used to present continuous data in studies that have small sample sizes. This presentation is problematic, as many data distributions can lead to the same bar graph, and the actual data may suggest different conclusions from the summary statistics. To address this problem, many journals have implemented new policies that require authors to show the data distribution. This paper introduces a free, web-based tool for creating an interactive alternative to the bar graph (http://***/interactive-dotplot/). This tool allows authors with no programming expertise to create customized interactive graphics, including univariate scatterplots, box plots, and violin plots, for comparing values of a continuous variable across different study groups. Individual data points may be overlaid on the graphs. Additional features facilitate visualization of subgroups or clusters of non-independent data. A second tool enables authors to create interactive graphics from data obtained with repeated independent experiments (http://***/interactive-repeated-experiments-dotplot/). These tools are designed to encourage exploration and critical evaluation of the data behind the summary statistics and may be valuable for promoting transparency, reproducibility, and open science in basic biomedical research.
visualization techniques deal with multidimensional multivariable data sets. We introduce visualization methods for multidimensional data sets, including an effective dimension reduction method for the multivariate ge...
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visualization techniques deal with multidimensional multivariable data sets. We introduce visualization methods for multidimensional data sets, including an effective dimension reduction method for the multivariate genetic algorithm data set
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