Visually enhanced situation awareness for complex manufacturing facility monitoring in smart factories

Zhou, Fangfang; Lin, Xiaoru; Luo, Xiaobo; Zhao, Ying; Chen, Yi; Chen, Ning; Gui, Weihua

doi:10.1016/j.jvlc.2017.11.004

Cited by 36 publications

(15 citation statements)

References 14 publications

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“…Monitoring and analyzing these event sequence data can help managers understand factory conditions, quickly respond to various sorts of events, and optimize the productivity of factories. A variety of visual techniques have been introduced to help users exploring anomalous events [8], [40], [115], [121], [132] and optimizing manufacturing plans [45], [97].…”

Section: Industry 40mentioning

confidence: 99%

Visual Progression Analysis of Event Sequence Data

Guo

Jin

Gotz

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Event sequence data is common to a broad range of application domains, from security to health care to scholarly communication. This form of data captures information about the progression of events for an individual entity (e.g., a computer network device; a patient; an author) in the form of a series of time-stamped observations. Moreover, each event is associated with an event type (e.g., a computer login attempt, or a hospital discharge). Analyses of event sequence data have been shown to help reveal important temporal patterns, such as clinical paths resulting in improved outcomes, or an understanding of common career trajectories for scholars. Moreover, recent research has demonstrated a variety of techniques designed to overcome methodological challenges such as large volumes of data and high dimensionality. However, the effective identification and analysis of latent stages of progression, which can allow for variation within different but similarly evolving event sequences, remain a significant challenge with important real-world motivations. In this paper, we propose an unsupervised stage analysis algorithm to identify semantically meaningful progression stages as well as the critical events which help define those stages. The algorithm follows three key steps: (1) event representation estimation, (2) event sequence warping and alignment, and (3) sequence segmentation. We also present a novel visualization system, ET2, which interactively illustrates the results of the stage analysis algorithm to help reveal evolution patterns across stages. Finally, we report three forms of evaluation for ET2: (1) case studies with two real-world datasets, (2) interviews with domain expert users, and (3) a performance evaluation on the progression analysis algorithm and the visualization design.

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Section: Industry 40mentioning

confidence: 99%

Visual Progression Analysis of Event Sequence Data

Guo

Jin

Gotz

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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“…Visualization has become an increasingly important research area due to its wide range of applications in many disciplines [19,23,25,26,31,38,41,[53][54][55][56][57][58][59][60]62]. Recently, the visualization and visual analytics community is eliciting considerable interests in blockchain data presentation and analysis, leading to several pioneering studies [45].…”

Section: Blockchain Data Visualizationmentioning

confidence: 99%

SilkViser: A Visual Explorer of Blockchain-based Cryptocurrency Transaction Data

Zhao¹

2020

Preprint

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<p>Many blockchain-based cryptocurrencies provide users with online blockchain explorers for viewing online transaction data. However, traditional blockchain explorers mostly present transaction information in textual and tabular forms. Such forms make understanding cryptocurrency transaction mechanisms difficult for novice users (NUsers). They are also insufficiently informative for experienced users (EUsers) to recognize advanced transaction information. This study introduces a new online cryptocurrency transaction data viewing tool called SilkViser. Guided by detailed scenario and requirement analyses, we create a series of appreciating visualization designs, such as paper ledger-inspired block and blockchain visualizations and ancient copper coin-inspired transaction visualizations, to help users understand cryptocurrency transaction mechanisms and recognize advanced transaction information. We also provide a set of lightweight interactions to facilitate easy and free data exploration. Moreover, a controlled user study is conducted to quantitatively evaluate the usability and effectiveness of SilkViser. Results indicate that SilkViser can satisfy the requirements of NUsers and EUsers. Our visualization designs can compensate for the inexperience of NUsers in data viewing and attract potential users to participate in cryptocurrency transactions.</p>

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“…Therefore, it is a good choice to predict surface-free lithium content by using the soft-sensor model. 2,3 There are two most commonly used methods for building soft-sensor models, namely, mechanism modeling and data-driven modeling. The mechanism modeling method starts with the physical, chemical, and biological knowledge of the object process.…”

Section: Introductionmentioning

confidence: 99%

An ensemble just‐in‐time learning soft‐sensor model for residual lithium concentration prediction of ternary cathode materials

Chen

Gui

Dai

et al. 2020

Journal of Chemometrics

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The surface-free lithium content, which is a critical index that reflecting the quality of ternary cathode materials, usually cannot be monitored in real time due to technical restriction. To this end, soft sensor technique is applied to predict the surface-free lithium content by readily available process variables, making timely control on operation parameters. In this paper, a modeling method based on ensemble just-in-time learning (JITL) soft-sensor is developed. In the model, data feature indices are firstly designed to properly extract the real-time and short-time features between the batching and the loading procedure. Accordingly, ensemble JITL soft-sensor model is constructed with the semisupervised local weighted probability principal component regression.Moreover, considering varying working conditions, an adaptive moving window technique is adopted to improve the adaptability of the model. The validation and the flexibility of the developed modeling method are testified with the practical manufacturing data. K E Y W O R D Sjust-in-time learning, moving window, prediction of residual lithium content, semisupervised learning | INTRODUCTIONWith the growing demand for electric energy reliability in modern society, as well as the widespread use of intermittent new energy sources such as wind power and photovoltaics, electric energy storage technology has received extensive attention from all over the world. 1 Lithium-ion batteries, which have many merits such as portability, recyclability, high energy density, and long cycle life, are nowadays recognized as a key power storage material. In the process of manufacturing lithium-ion batteries, surface-free lithium content, namely, the content of lithium-ion on the surface of cathode materials, plays a decisive role in determining the characteristic of this material. Thus, high requirements are put forward for the real-time monitoring of surface-free lithium content. However, surface-free lithium content cannot be directly measured since the mensuration is inefficient, hysteresis, and costly. Therefore, it is a good choice to predict surface-free lithium content by using the soft-sensor model. 2,3 There are two most commonly used methods for building soft-sensor models, namely, mechanism modeling and data-driven modeling. The mechanism modeling method starts with the physical, chemical, and biological knowledge of the object process. Then, mathematical equations based on conservation laws such as mass conservation are formed to find the relationship between process variables and the key variables. The mechanism models have more application

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Visually enhanced situation awareness for complex manufacturing facility monitoring in smart factories

Cited by 36 publications

References 14 publications

Visual Progression Analysis of Event Sequence Data

Visual Progression Analysis of Event Sequence Data

SilkViser: A Visual Explorer of Blockchain-based Cryptocurrency Transaction Data

An ensemble just‐in‐time learning soft‐sensor model for residual lithium concentration prediction of ternary cathode materials

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