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Regularized Autoencoder for The Analysis of multivariate Metrology data 36

Regularized Autoencoder for The Analysis of Multivariate Met...

Conference on Metrology, Inspection, and Process Control XXXVI Part of SPIE Advanced Lithography and Patterning Conference

作者： Saib, Mohamed Lorusso, Gian Francesco Charley, Anne-Laure Leraya, Philippe Kondo, Tsuyoshi Shindo, Hiroyuki Ebizuka, Yasushi Ban, Naoma Ikota, Masami IMEC Celestijnenlaan 1 B-3001 Leuven Belgium Hitachi High Tech Corp Hitachinaka Ibaraki 3128504 Japan

ISBN: (纸本)9781510649828;9781510649811

A new generation of metrology tools has been recently released to the market, allowing extensive characterization of semiconductor samples thanks to massive measurements. The resulting substantial growth of measurement sets in size and number of descriptors made the data analysis with the traditional statistical techniques laborious, being unable to consider all data simultaneously. In this work, we propose an analysis method applicable to the massive measurement sets based on a machine learning technique, called Autoencoder (AE). The performance of the original AE model has been boosted in this work thanks to its regularization, by imposing orthogonality of its internal representation and by training only half of the model weights while the second half is deduced. Practically, once the model is trained on the data set of interest, it is used to study relationships between input variables through the chart of the circle of correlations. In this chart, input variables are projected as vectors into the compression plane where the angular distance between them will express their degree of correlation. The representation of massive measurements data through a condensed simple chart that still shows complex interactions between variables is in fact very efficient and facilitating the interpretation of measurements themselves. The experimental validation of the proposed method has been done with measurements of Contact Hole (CH) patterns acquired on samples manufactured with 3 different exposure conditions (underexposure dose, nominal exposure dose, and overexposure dose). The proposed data analysis technique allowed us to clearly identify the impact of the process conditions on patterns characteristics, such as their eccentricity, their geometry, and more. Our results indicated that in case of overexposure, an anisotropic distortion of the CH geometry is present, where gamma-axis is the major axis, with larger impact on the pattern surface as compared to the X-axis. This

关键词： Regularized autoencoder dimensionality reduction multi-dimensional data visualization Massive metrology SEM inspection Machine Learning

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CREATING 3D GRAMIAN ANGULAR FIELD REPRESENTATIONS FOR HIGHER PERFORMANCE ENERGY data CLASSIFICATION 29

CREATING 3D GRAMIAN ANGULAR FIELD REPRESENTATIONS FOR HIGHER...

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IEEE International Conference on Image Processing (ICIP)

作者： Alsalemi, Abdullah Amira, Abbes Malekmohamadi, Hossein Diao, Kegong De Montfort Univ Leicester England Univ Sharjah Sharjah U Arab Emirates

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

The industrial revolution has elevated science and engineering to foster the development of Image Processing and Artificial Intelligence (AI) and put the visualization of information on an even higher pedestal. Yet, the demands of the industrial age have contributed to an ever-growing wildfire of climate change, sparking a revolution in energy efficiency research. With the aim to advance energy efficiency research from an AI standpoint, a novel transformation of raw-formatted data repositories, known as data lakes, into multi-dimensional visualizations data coupled with computationally lightweight, edge-based AI implementations are proposed as means to understand the energy consumption patterns in buildings. As a novel method of understanding energy data visually, current results comprise a multi-dimensional Gramian Angular Field (GAF) representation of energy data as both 2D and 3D interactive forms. Moreover, a case study on deep learning classification employed on ODROID-XU4 yields similar to 90% accuracy and a classification rate of 17.5 msec/image.

关键词： Gramian angular field energy efficiency artificial intelligence edge artificial intelligence multi-dimensional data visualization data lakes image processing

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Visual Analysis of Merchandise Sales Trend Based on Online Transaction Log

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2020年第11期34卷

作者： Yu, Shidong Yang, Dongsheng Hao, Ying Lian, Mengjia Zang, Ying Chinese Acad Sci Shenyang Inst Comp Technol Shenyang 110168 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Yingkou Inst Technol Dept Elect Engn Yingkou 115014 Peoples R China Dalian Maritime Univ Dept Informat Sci Dalian 116026 Peoples R China

Online transaction log records the relevant information of the users, commodities and transactions, as well as changes over time, which can help analysts understand commodities' sales. The existing visualization methods mainly analyze the purchase behavior from the perspective of users, while analyzing the sales trend of commodities can better help merchants to make business decisions. Based on the transaction log, this paper puts forward the visual analysis framework of commodity sales trend and the corresponding data processing algorithm. The concepts of volatility and dynamic performance of sales trend are proposed, through which the multi-dimensional sales data of time-oriented are displayed in two-dimensional space. The "Feature Ring" is designed to display the detailed sales information of the products. Based on the above methods, a visual analysis system is designed and implemented. The usability and validity of the visualization methods are verified by using JD online transaction data. The visualization methods enable manufacturers to formulate production plans and carry out product research and develop better.

关键词： Visual analysis analysis of sales trend multi-dimensional data visualization online transaction log

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Painting image browser applying an associate-rule-aware multidimensional data visualization technique

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Visual Computing for Industry,Biomedicine,and Art 2020年第1期3卷 18-30页

作者： Ayaka Kaneko Akiko Komatsu Takayuki Itoh Florence Ying Wang Ochanomizu University 2-1-1 OtsukaBunkyo-ku1128610 TokyoJapan CSIRO Julius Ave.2113 North RydeNSWAustralia

Exploration of artworks is enjoyable but often time *** example,it is not always easy to discover the favorite types of unknown painting *** is not also always easy to explore unpopular painting works which looks similar to painting works created by famous *** paper presents a painting image browser which assists the explorative discovery of user-interested painting *** presented browser applies a new multidimensional data visualization technique that highlights particular ranges of particular numeric values based on association rules to suggest cues to find favorite painting *** study assumes a large number of painting images are provided where categorical information(e.g.,names of artists,created year)is assigned to the *** presented system firstly calculates the feature values of the images as a preprocessing *** the browser visualizes the multidimensional feature values as a heatmap and highlights association rules discovered from the relationships between the feature values and categorical *** mechanism enables users to explore favorite painting images or painting images that look similar to famous painting *** case study and user evaluation demonstrates the effectiveness of the presented image browser.

关键词： Painting image multi-dimensional data visualization Association rule

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Stroke data Analysis through a HVN Visual Mining Platform 23

Stroke Data Analysis through a HVN Visual Mining Platform

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23rd International Conference in Information visualization (IV) / 16th International Conference Computer Graphics, Imaging and visualization (CGiV)

作者： Huang, Mao Lin Yue, Zhixiong Liang, Jie Quang Vinh Nguyen Luo, Zongwei Univ Technol Sydney NSW Australia Southern Univ Sci & Technol Shenzhen Peoples R China Univ Western Sydney Sydney NSW Australia

ISBN: (纸本)9781728128504

Today there are abounding collected data in cases of various diseases in medical sciences. Physicians can access new findings about diseases and procedures in dealing with them by probing these data. Clinical data is a collection of large and complex datasets that commonly appear in multidimensional data formats. It has been recognized as a big challenge in modern data analysis tasks. Therefore, there is an urgent need to find new and effective techniques to deal with such huge datasets. This paper presents an application of a new visual data mining platform for visual analysis of the stroke data for predicting the levels of risk to those people who have the similar characteristics of the stroke patients. The visualization platform uses a hierarchical clustering algorithm to aggregate the data and map coherent groups of data-points to the same visual elements - curved 'super-polylines' that significantly reduces the visual complexity of the visualization. On the other hand, to enable users to interactively manipulate data items (super-polylines) in the parallel coordinates geometry through the mouse rollover and clicking, we created many 'virtual nodes' along the multi-axis of the visualization based on the hierarchical structure of the value range of selected data attributes. The experimental result shows that we can easily verily research hypothesis and reach to the conclusion of research questions through human-data & human-algorithm interactions by using this visual platform with a fully transparency manner of data processing.

关键词： multi-dimensional data visualization visual analytics data mining risk prediction stroke

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Overcoming the Curse of dimensionality When Clustering multivariate Volume data 13

Overcoming the Curse of Dimensionality When Clustering Multi...

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13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) and International Conference on Information visualization Theory and Applications (IVAPP)

作者： Molchanov, Vladimir Linsen, Lars Jacobs Univ Bremen Germany Westfalische Wilhelms Univ Munster Munster Germany

ISBN: (纸本)9789897583063

Visual analytics of multidimensional data suffer from the curse of dimensionality, i.e., that even large numbers of data points will be scattered in a high-dimensional space. The curse of dimensionality prohibits the proper use of clustering algorithms in the high-dimensional space. Projecting the space before clustering imposes a loss of information and possible mixing of separated clusters. We present an approach where we overcome the curse of dimensionality for a particular type of multidimensional data, namely for attribute spaces of multivariate volume data. For multivariate volume data, it is possible to interpolate between the data points in the high-dimensional attribute space based on their spatial relationship in the volumetric domain (or physical space). We apply this idea to a histogram-based clustering algorithm. We create a uniform partition of the attribute space in multidimensional bins and compute a histogram indicating the number of data samples belonging to each bin. Only non-empty bins are stored for efficiency. Without interpolation, the analysis is highly sensitive to the cell sizes yielding inaccurate clustering for improper choices: Large cells result in no cluster separation, while clusters fall apart for small cells. Using tri-linear interpolation in physical space, we can refine the data by generating additional samples. The refinement scheme can adapt to the data point distribution in attribute space and the histogram's bin size. As a consequence, we can generate a density computation, where clusters stay connected even when using very small cell sizes. We exploit this result to create a robust hierarchical cluster tree. It can be visually explored using coordinated views to physical space visualizations and to parallel coordinates plots. We apply our technique to several datasets and compare the results against results without interpolation.

关键词： multi-dimensional data visualization multi-field data Clustering

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Interactive Machine Learning by visualization: A Small data Solution

Interactive Machine Learning by Visualization: A Small Data ...

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IEEE International Conference on Big data (Big data)

作者： Li, Huang Fang, Shiaofen Mukhopadhyay, Snehasis Saykin, Andrew J. Shen, Li Indiana Univ Purdue Univ Dept Comp & Informat Sci Indianapolis IN 46202 USA Indiana Univ Sch Med Indianapolis IN 46202 USA Univ Penn Perelman Sch Med Philadelphia PA 19104 USA

ISBN: (纸本)9781538650356

Machine learning algorithms and traditional data mining process usually require a large volume of data to train the algorithm-specific models, with little or no user feedback during the model building process. Such a "big data" based automatic learning strategy is sometimes unrealistic for applications where data collection or processing is very expensive or difficult, such as in clinical trials. Furthermore, expert knowledge can be very valuable in the model building process in some fields such as biomedical sciences. In this paper, we propose a new visual analytics approach to interactive machine learning and visual data mining. In this approach, multi-dimensional data visualization techniques are employed to facilitate user interactions with the machine learning and mining process. This allows dynamic user feedback in different forms, such as data selection, data labeling, and data correction, to enhance the efficiency of model building. In particular, this approach can significantly reduce the amount of data required for training an accurate model, and therefore can be highly impactful for applications where large amount of data is hard to obtain. The proposed approach is tested on two application problems: the handwriting recognition (classification) problem and the human cognitive score prediction (regression) problem. Both experiments show that visualization supported interactive machine learning and data mining can achieve the same accuracy as an automatic process can with much smaller training data sets.

关键词： interactive machine learning visual data mining multi-dimensional data visualization user interaction

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Upsampling for Improved multidimensional Attribute Space Clustering of multifield data

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INFORMATION 2018年第7期9卷 156-156页

作者： Molchanov, Vladimir Linsen, Lars Westfalische Wilhelms Univ Munster Dept Math & Informat D-48149 Munster Germany

Clustering algorithms in the high-dimensional space require many data to perform reliably and robustly. For multivariate volume data, it is possible to interpolate between the data points in the high-dimensional attribute space based on their spatial relationship in the volumetric domain (or physical space). Thus, sufficiently high number of data points can be generated, overcoming the curse of dimensionality for this particular type of multidimensional data. We applies this idea to a histogram-based clustering algorithm. We created a uniform partition of the attribute space in multidimensional bins and computed a histogram indicating the number of data samples belonging to each bin. Without interpolation, the analysis was highly sensitive to the histogram cell sizes, yielding inaccurate clustering for improper choices: Large histogram cells result in no cluster separation, while clusters fall apart for small cells. Using an interpolation in physical space, we could refine the data by generating additional samples. The depth of the refinement scheme was chosen according to the local data point distribution in attribute space and the histogram's bin size. In the case of field discontinuities representing sharp material boundaries in the volume data, the interpolation can be adapted to locally make use of a nearest-neighbor interpolation scheme that avoids averaging values across the sharp boundary. Consequently, we could generate a density computation, where clusters stay connected even when using very small bin sizes. We exploited this result to create a robust hierarchical cluster tree, apply our technique to several datasets, and compare the cluster trees before and after interpolation.

关键词： multi-dimensional data visualization multi-field data clustering

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A Space Optimized Scatter Plot Matrix visualization 13th

A Space Optimized Scatter Plot Matrix Visualization

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13th International Conference on Cooperative Design, visualization, and Engineering (CDVE)

作者： Wang, Wen Bo Huang, Mao Lin Nguyen, Quang Vinh Univ Technol Sydney Sch Software Sydney NSW Australia Univ Western Sydney Sch Comp Engn & Math Sydney NSW Australia

ISBN: (纸本)9783319467719;9783319467702

This paper proposed a Space-Optimized Scatter Plot Matrix that used for the presentation of multi-dimensional dataset. This technique achieves the display space utilization in a 2D geometrical space. Our strategy is to maximize the utilization of computer space by optimizing the distribution of the plots in a geometrical plane of a display screen;We also apply interact mechanism, user query and visual cues, to support users' communication with variables and the discovery of deeper contents.

关键词： multi-dimensional data visualization Space optimization Scatter plot matrix Visual cue User query

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A new concentric-circle visualization of multi-dimensional data and its application in network security

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JOURNAL OF VISUAL LANGUAGES AND COMPUTING 2010年第4期21卷 194-208页

作者： Lu, Liang Fu Zhang, Jia Wan Huang, Mao Lin Fu, Lei Tianjin Univ Sch Comp Sci & Technol Tianjin 300072 Peoples R China Tianjin Univ Dept Math Tianjin 300072 Peoples R China Univ Technol Sydney Fac Engn & IT Sydney NSW 2007 Australia

With the rapid growth of networked data communications in size and complexity, network administrators today are facing more challenges to protect their networked computers and devices from all kinds of attacks. This paper proposes a new concentric-circle visualization method for visualizing multi-dimensional network data. This method can be used to identify the main features of network attacks, such as DDoS attack, by displaying their recognizable visual patterns. To reduce the edge overlaps and crossings, we arrange multiple axes displayed as concentric circles rather than the traditional parallel lines. In our method, we use polycurves to link values (vertexes) rather than polylines used in parallel coordinate approach. Some heuristics are applied in our new method in order to improve the readability of views. We discuss the advantages as well as the limitations of our new method. In comparison with the parallel coordinate visualization, our approach can reduce more than 15% of the edge overlaps and crossings. In the second stage of the method, we have further enhanced the readability of views by increasing the edge crossing angle. Finally, we introduce our prototype system: a visual interactive network scan detection system called CCScanViewer. It is based on our new visualization approach and the experiments have showed that the new approach is effective in detecting attack features from a variety of networking patterns, such as the features of network scans and DDoS attacks. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Concentric-circle coordinate multi-dimensional data visualization Crossing reduction Network visualization Security visualization Network intrusion detection Polycurve

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