Data Updating and Deleting with Visual Basic.NET

2021 ◽  
pp. 355-400
Keyword(s):  
Data Updating

Navigational Data Change Detection Based on Rasterizing Features & Attributes

2012 ◽  
Vol 256-259 ◽  
pp. 2279-2284
Author(s):  
Lian Ying Li ◽  
Zhang Huang ◽  
Xiao Lan Xu
Keyword(s):  
Change Detection ◽  
Detection Methods ◽  
Vehicle Navigation ◽  
Digital Map ◽  
Navigation Data ◽  
Data Production ◽  
History Of ◽  
Map Data ◽  
Data Updating

A necessary updating degree is vital for the digital map data in a vehicle navigation system. Only when the digital map data are well updated, can the quality of the navigation be assured. Today the companies devoting to the production of digital map data for vehicle navigation have to cost much labor, material and capital to collect and update data in order to maintain a necessary updating degree. Throughout the history of electronic navigation data updating, they have made considerable progress both on the methods and processes of data production, and the way of map management. Updating from the CD to the network, from the wired to the wireless, from the replacing to the incremental way, each of the technical changes is a power source to enhance the data updating rate. As we all know, the change detection is a prerequisite and base for the electronic navigation data updating. By rapidly developing the area with changes and using the appropriate updating method, we can scientifically maintain the original database of navigation data and terminal physical data. In view of this, starting from application needs for dynamic data updating, this paper analyses change detection methods of navigation data in different versions used for generating incremental data, and focuses on that of rasterizing features and attributes, exploring a new approach to quickly get the incremental data between versions.

scholarly journals Adjacency-Hash-Table Based Public Auditing for Data Integrity in Mobile Cloud Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Wenqi Chen ◽  
Hui Tian ◽  
Chin-Chen Chang ◽  
Fulin Nan ◽  
Jing Lu
Keyword(s):  
Cloud Computing ◽  
High Speed ◽  
Security Analysis ◽  
Hash Table ◽  
The State ◽  
Public Auditing ◽  
The Arts ◽  
High Speed Data ◽  
And Performance ◽  
Data Updating

Cloud storage, one of the core services of cloud computing, provides an effective way to solve the problems of storage and management caused by high-speed data growth. Thus, a growing number of organizations and individuals tend to store their data in the cloud. However, due to the separation of data ownership and management, it is difficult for users to check the integrity of data in the traditional way. Therefore, many researchers focus on developing several protocols, which can remotely check the integrity of data in the cloud. In this paper, we propose a novel public auditing protocol based on the adjacency-hash table, where dynamic auditing and data updating are more efficient than those of the state of the arts. Moreover, with such an authentication structure, computation and communication costs can be reduced effectively. The security analysis and performance evaluation based on comprehensive experiments demonstrate that our protocol can achieve all the desired properties and outperform the state-of-the-art ones in computing overheads for updating and verification.

scholarly journals Adaptive Solar Power Forecasting based on Machine Learning Methods

2018 ◽  
Vol 8 (11) ◽  
pp. 2224 ◽  
Author(s):  
Yu Wang ◽  
Hualei Zou ◽  
Xin Chen ◽  
Fanghua Zhang ◽  
Jie Chen
Keyword(s):  
Power Systems ◽  
Solar Power ◽  
Machine Learning Methods ◽  
Load Demand ◽  
Forecasting Method ◽  
Combining Data ◽  
Data Updating ◽  
Solar Power Forecasting ◽  
Forecasting Errors ◽  
Power Forecasting

Due to the existence of predicting errors in the power systems, such as solar power, wind power and load demand, the economic performance of power systems can be weakened accordingly. In this paper, we propose an adaptive solar power forecasting (ASPF) method for precise solar power forecasting, which captures the characteristics of forecasting errors and revises the predictions accordingly by combining data clustering, variable selection, and neural network. The proposed ASPF is thus quite general, and does not require any specific original forecasting method. We first propose the framework of ASPF, featuring the data identification and data updating. We then present the applied improved k-means clustering, the least angular regression algorithm, and BPNN, followed by the realization of ASPF, which is shown to improve as more data collected. Simulation results show the effectiveness of the proposed ASPF based on the trace-driven data.

Bridge life-cycle prediction through visual inspection data updating

2016 ◽  
pp. 1518-1525
Author(s):  
Mariano Angelo Zanini ◽  
Flora Faleschini ◽  
Carlo Pellegrino
Keyword(s):  
Life Cycle ◽  
Visual Inspection ◽  
Inspection Data ◽  
Data Updating

An Intelligent Interactive Layout CAD System for Industrial Plants

1990 ◽  
Author(s):  
Ken-ichi Kameyama ◽  
Koichi Kondo ◽  
Koichi Ohtomi
Keyword(s):  
Industrial Plant ◽  
Production Rules ◽  
Design Environment ◽  
Plant Layout ◽  
Industrial Plants ◽  
Major Function ◽  
Cad System ◽  
Constraint Checking ◽  
Data Updating ◽  
Interactive User

Abstract This paper describes an industrial plant layout system with an intelligent interactive user-interface. This system offers a powerful design environment, in which a designer can concurrently draw and check a layout plan, which can not be realized on a conventional plant layout CAD system. The major function of the system is the automatic constraint checking of each designer’s drafting action. This function is achieved by the integration of a geometric modeler, frame representation, and production rules. The geometric modeler is used for extracting the information for data updating and constraint checking from graphical objects, which are directly manipulated by a designer. Frame representation is used for representing the attributes of the equipments and the areas, and these attributes are handled by production rules. Production rules are used for representing the constraint checking and data updating procedures, and can be executed as required.

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