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Input/Output Bank Programming and Interfacing
Published in A. Arockia Bazil Raj, FPGA-Based Embedded System Developer's Guide, 2018
The GLCD is a type of LCD which is used to display characters and graphs at relatively higher resolutions. A thin profile, low power consumption, easy interfacing, reasonable resolution and more pixels are the unique features of the GLCD. The graphical representation of any data presents a better understanding than just characters and/or numbers. A GLCD of size 128 × 64 and its pin details are shown in Figure 5.8. The illumination contrast of the GLCD can be adjusted through a POT. GLCD interfacing requires 8 data bits (D0–D7) and six control lines as register select (RS), read/write (R/W), enable (E), reset (RST) and segment select 1 and 2 (CS1 and CS2), whose function details are given in Table 5.3.
On application of machine learning method for history matching and forecasting of times series data from hydrocarbon recovery process using water flooding
Published in Petroleum Science and Technology, 2021
Data visualization is the graphical representation of information and data. By using visual elements, a data visualization tool provides an accessible way to see and understand trends, outliers, and patterns in the data. As provided data is with respect to time, where each observation has been recorded on a particular date, so visualizing injection rate and production rate on basis of time ends up with a time series plot and gives the flexibility to detect any anomaly, in data, with respect to time. These anomalies must be removed during data processing. An outlier may be due to variability in the measurement or it may indicate experimental error. Outlier can cause serious problems in statistical analyses of the data and must be removed.
Comparing Visual Encodings for the Task of Anomaly Detection
Published in International Journal of Human–Computer Interaction, 2022
Meirav Taieb-Maimon, Eden Ya’akobi, Nevo Itzhak, Yossi Zaltsman
Anomaly detection refers to the task of finding patterns in data that do not conform to the expected behavior. This is an important task, since data anomalies serve as valuable actionable information that can be leveraged in a wide variety of application domains (Chandola et al., 2009). Previous research showed that effective data presentation enhances decision making and insight generation, therefore likely to facilitate anomaly detection. Numerous studies (e.g., Gelman et al. (2002); Meyer (2000); Sopan et al. (2013); Vessey (1991)) compared the use of tabular representation to graphical representation. Visual representation is especially useful in tasks that required logical computation and the search for information (Larkin & Simon, 1987). Anomaly detection using graphical representation may be helpful for the straightforward identification of suspicious data or as a tool for gathering abnormal cases for in-depth analysis (Laurikkala et al., 2000). Effective graphical representation of data enable rapid visual exploration (Keim et al., 2006; Shneiderman, 2003). Furthermore, abstract information visualization has the power to reveal patterns and trends, clusters, gaps, or outliers (Shneiderman, 2003). Given the usefulness of graphs in comparing points and patterns, it is reasonable to hypothesize that graphical representation would be more efficient and preferable than tabular representation for anomaly detection tasks, yet Jarvenpaa and Dickson (1988) found that tables are preferable for reading individual exact data values. Therefore, tabular visualizations are still used by analysts for anomaly detection and may prove to be more effective for this purpose. In this study, we used real data, which analysts at a well-known company examined using a table visualization to detect anomalies. Our objective was to compare graphical representation (using position, size, and color saturation visualizations) and tabular representation in terms of effectiveness (anomaly detection accuracy), efficiency (performance time), and user satisfaction (ease of use and user’s preference) and investigate which visualization provides the best results for anomaly detection.