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Explore chapters and articles related to this topic
General Reservoir Management Practices, Aberrations, and Consequences
Published in Ashok K. Pathak, Petroleum Reservoir Management, 2021
Data purging removes obsolete or superfluous data from the system to avoid unnecessary crowding of the active database. The purged data is not permanently deleted from the system; it is backed up and archived in a separate storage device to be recalled later if required. At this point, let us differentiate between the backup data and archiving. Database backup is usually a periodic short-term measure mandated often by both the organization and the government. It ensures that the operational database is always functional, and critical/essential business data is protected from accidental system failures, outages, or crashes. Database backup is performed with a database management software that creates duplicate or multiple copies of the same data stored locally or on a backup server.
dBASE IV
Published in Paul W. Ross, The Handbook of Software for Engineers and Scientists, 2018
So far we have gone through the basics of creating a database and then searching through it. Now, as we look at the Forms Column we can begin to customize how you interact with dBASE IV. Everyone has different and unique needs in terms of working with data. The Third Column is the Forms Column; in this column you can “paint” the screen the way you want it to look when you are working with your database. Highlight the (create) under the Forms Column and press enter. This moves us to the Screen Generator. You can give your screen a title like “Invoice Data Entry screen.” Using the Layout menu you can draw boxes and lines, add and delete text, and save your screen. The Fields menu gives you a list of all the fields in the active database. You can define where on the screen you want to put them. dBASE IV even checks to make sure the data are being entered according to editing and formatting rules. Forms are the way to format the way data appear on the screen as the data are being entered and edited.
Wind Energy Resource
Published in D. Yogi Goswami, Frank Kreith, Energy Conversion, 2017
Sensor data that have not been subjected to a validation or verification process are commonly referred to as raw data. There is a constant risk of raw data loss or alteration during any measurement program. Aside from the data logger programming requirements, the actual data collection process requires minimal human intervention, and data are adequately protected by following recommended installation and operation procedures, including grounding all equipment. These field data will eventually be transferred to a personal computer for analysis; while this may be the primary location of the working database, it should not be the storage area for the archived or raw database, as frequent usage of a computer increases the likelihood of electrical surges, static discharges, and other events that may damage hard drives and destroy any databases. Preserve the original raw data; make at least two copies of that data set on removable media and store the original and all but one of the copies in separate locations (not in the same building). Then, apply the validation and processing steps to the remaining copy. Back up this active database on a regular schedule during the validation process. Once the database is fully validated, create multiple copies of it and again store each copy in a separate location (not in the same building).
Under-frequency load shedding of microgrid systems: a review
Published in International Journal of Modelling and Simulation, 2022
T. Madiba, R.C. Bansal, N.T. Mbungu, M. Bettayeb, R.M. Naidoo, M.W. Siti
In an extensive scale power system application, the combination of UFLS and UVLS is well coordinated to monitor overload in the electrical network [106]. Figure 7 presents the block diagram for a given load shedding scheme. This scheme presumes that some external optimal control application will provide the amount of load to be shed. The load prioritisation module (LPM) uses the various systems’ electrical parameters, the critical nature of loads, and the control variable’s demand level. These features come from the active database. At any provided time, an integrated circuit may perform one mission or a combining mission to coordinate the LSS. Taking these control variables, the LPM orders the loads in SPS in increasing order of priority and passes this list to the control action module (CAM). The CAM holds numerous circuit breakers (CBs) and bus transfers and switches status from the dynamic database. The load (Pshed) is considered the system input, which the CAM also holds. Using these control variables, the CAM optimally defines which loads to shed and the lowest number of control actions that will de-energise [107,108].
Hypervolemia Screening for Dialysis Patient Healthcare Using Meta Learning Model-Based Intelligent Scaler
Published in Smart Science, 2019
Wei-Ling Chen, Hsiang-Yueh Lai, Pi-Yun Chen, Chung-Dann Kan, Chia-Hung Lin
In addition, the numbers of current training patterns was 260 (N =10) in the current database, and 130 new training patterns (5 subjects, 11#–15#) were added as shown in Figure 8. In the meta-learning stage, by feeding the incremental training patterns, the connecting weights wnki and wnkj, n =11, 12, 13, …, 15, could be set from the input nodes to the new pattern nodes, (g111, g112, g113, …, g1126), (g121, g122, g123, …, g1226), …, (g151, g152, g153,…, g1526) and from those to the respective summation nodes. Then, the PSO algorithm was used to tune the smoothing parameter and could also rapidly reach the convergent condition for ≤ 20 iteration computations. The optimal parameter, sopt ≈0.003758, was also obtained to minimize the mean squared error. While new subjects were enrolled in the current database, the proposed meta-learning model had the capability of processing numerical computations for expandable training patterns. Therefore, the database could be automatically enhanced by updating new training patterns or adding incremental training patterns to the current active database.
Exploring servitization and digital transformation of manufacturing enterprises: evidence from an industrial internet platform in China
Published in International Journal of Production Research, 2023
Yi Liu, Justin Zuopeng Zhang, Sajjad Jasimuddin, M. Zied Babai
We test the hypothesis using data collected in spring 2021 with the help of CEOs or senior executives of several industrial Internet platforms. These managers are members of our alumni association. We selected them as key entry points for our survey because we are familiar with these managers and we know they provide technical support for thousands of manufacturing firms. An active database of manufacturing industries afforded by the primary industrial Internet platform provider in China was used as the source of sampling for the servitization survey.