Traditionally, data integration has meant compromise. No matter how rapidly data architects and developers could complete a project before its deadline, speed would always come at the expense of quality. On the other hand, if they focused on delivering a quality project, it would generally drag on for months thus exceeding its deadline. Finally, if the teams concentrated on both quality and rapid delivery, the costs would invariably exceed the budget. Regardless of which path you chose, the end result would be less than desirable. This led some experts to revisit the scope of data integration. This write up shall focus on the same issue.
Enhancing Data Staging as a Mechanism for Fast Data AccessEditor IJCATR
Most organizations rely on data in their daily transactions and operations. This data is retrieved from different source
systems in a distributed network hence it comes in varying data types and formats. The source data is prepared and cleaned by
subjecting it to algorithms and functions before transferring it to the target systems which takes more time. Moreover, there is pressure
from data users within the data warehouse for data to be availed quickly for them to make appropriate decisions and forecasts. This has
not been the case due to immense data explosion in millions of transactions resulting from business processes of the organizations. The
current legacy systems cannot handle large data levels due to processing capabilities and customizations. This approach has failed
because there lacks clear procedures to decide which data to collect or exempt. It is with this concern that performance degradation
should be addressed because organizations invest a lot of resources to establish a functioning data warehouse. Data staging is a
technological innovation within data warehouses where data manipulations are carried out before transfer to target systems. It carries
out data integration by harmonizing the staging functions, cleansing, verification, and archiving source data. Deterministic
Prioritization Approach will be employed to enhance data staging, and to clearly prove this change Experiment design is needed to test
scenarios in the study. Previous studies in this field have mainly focused in the data warehouses processes as a whole but less to the
specifics of data staging area.
Enhancing Data Staging as a Mechanism for Fast Data AccessEditor IJCATR
Most organizations rely on data in their daily transactions and operations. This data is retrieved from different source systems in a distributed network hence it comes in varying data types and formats. The source data is prepared and cleaned by subjecting it to algorithms and functions before transferring it to the target systems which takes more time. Moreover, there is pressure from data users within the data warehouse for data to be availed quickly for them to make appropriate decisions and forecasts. This has not been the case due to immense data explosion in millions of transactions resulting from business processes of the organizations. The current legacy systems cannot handle large data levels due to processing capabilities and customizations. This approach has failed because there lacks clear procedures to decide which data to collect or exempt. It is with this concern that performance degradation should be addressed because organizations invest a lot of resources to establish a functioning data warehouse. Data staging is a technological innovation within data warehouses where data manipulations are carried out before transfer to target systems. It carries out data integration by harmonizing the staging functions, cleansing, verification, and archiving source data. Deterministic Prioritization Approach will be employed to enhance data staging, and to clearly prove this change Experiment design is needed to test scenarios in the study. Previous studies in this field have mainly focused in the data warehouses processes as a whole but less to the specifics of data staging area.
Traditionally, data integration has meant compromise. No matter how rapidly data architects and developers could complete a project before its deadline, speed would always come at the expense of quality. On the other hand, if they focused on delivering a quality project, it would generally drag on for months thus exceeding its deadline. Finally, if the teams concentrated on both quality and rapid delivery, the costs would invariably exceed the budget. Regardless of which path you chose, the end result would be less than desirable. This led some experts to revisit the scope of data integration. This write up shall focus on the same issue.
Enhancing Data Staging as a Mechanism for Fast Data AccessEditor IJCATR
Most organizations rely on data in their daily transactions and operations. This data is retrieved from different source
systems in a distributed network hence it comes in varying data types and formats. The source data is prepared and cleaned by
subjecting it to algorithms and functions before transferring it to the target systems which takes more time. Moreover, there is pressure
from data users within the data warehouse for data to be availed quickly for them to make appropriate decisions and forecasts. This has
not been the case due to immense data explosion in millions of transactions resulting from business processes of the organizations. The
current legacy systems cannot handle large data levels due to processing capabilities and customizations. This approach has failed
because there lacks clear procedures to decide which data to collect or exempt. It is with this concern that performance degradation
should be addressed because organizations invest a lot of resources to establish a functioning data warehouse. Data staging is a
technological innovation within data warehouses where data manipulations are carried out before transfer to target systems. It carries
out data integration by harmonizing the staging functions, cleansing, verification, and archiving source data. Deterministic
Prioritization Approach will be employed to enhance data staging, and to clearly prove this change Experiment design is needed to test
scenarios in the study. Previous studies in this field have mainly focused in the data warehouses processes as a whole but less to the
specifics of data staging area.
Enhancing Data Staging as a Mechanism for Fast Data AccessEditor IJCATR
Most organizations rely on data in their daily transactions and operations. This data is retrieved from different source systems in a distributed network hence it comes in varying data types and formats. The source data is prepared and cleaned by subjecting it to algorithms and functions before transferring it to the target systems which takes more time. Moreover, there is pressure from data users within the data warehouse for data to be availed quickly for them to make appropriate decisions and forecasts. This has not been the case due to immense data explosion in millions of transactions resulting from business processes of the organizations. The current legacy systems cannot handle large data levels due to processing capabilities and customizations. This approach has failed because there lacks clear procedures to decide which data to collect or exempt. It is with this concern that performance degradation should be addressed because organizations invest a lot of resources to establish a functioning data warehouse. Data staging is a technological innovation within data warehouses where data manipulations are carried out before transfer to target systems. It carries out data integration by harmonizing the staging functions, cleansing, verification, and archiving source data. Deterministic Prioritization Approach will be employed to enhance data staging, and to clearly prove this change Experiment design is needed to test scenarios in the study. Previous studies in this field have mainly focused in the data warehouses processes as a whole but less to the specifics of data staging area.
Enhancing Data Staging as a Mechanism for Fast Data AccessEditor IJCATR
Most organizations rely on data in their daily transactions and operations. This data is retrieved from different source
systems in a distributed network hence it comes in varying data types and formats. The source data is prepared and cleaned by
subjecting it to algorithms and functions before transferring it to the target systems which takes more time. Moreover, there is pressure
from data users within the data warehouse for data to be availed quickly for them to make appropriate decisions and forecasts. This has
not been the case due to immense data explosion in millions of transactions resulting from business processes of the organizations. The
current legacy systems cannot handle large data levels due to processing capabilities and customizations. This approach has failed
because there lacks clear procedures to decide which data to collect or exempt. It is with this concern that performance degradation
should be addressed because organizations invest a lot of resources to establish a functioning data warehouse. Data staging is a
technological innovation within data warehouses where data manipulations are carried out before transfer to target systems. It carries
out data integration by harmonizing the staging functions, cleansing, verification, and archiving source data. Deterministic
Prioritization Approach will be employed to enhance data staging, and to clearly prove this change Experiment design is needed to test
scenarios in the study. Previous studies in this field have mainly focused in the data warehouses processes as a whole but less to the
specifics of data staging area.
Enhancing Data Staging as a Mechanism for Fast Data AccessEditor IJCATR
Most organizations rely on data in their daily transactions and operations. This data is retrieved from different source systems in a distributed network hence it comes in varying data types and formats. The source data is prepared and cleaned by subjecting it to algorithms and functions before transferring it to the target systems which takes more time. Moreover, there is pressure from data users within the data warehouse for data to be availed quickly for them to make appropriate decisions and forecasts. This has not been the case due to immense data explosion in millions of transactions resulting from business processes of the organizations. The current legacy systems cannot handle large data levels due to processing capabilities and customizations. This approach has failed because there lacks clear procedures to decide which data to collect or exempt. It is with this concern that performance degradation should be addressed because organizations invest a lot of resources to establish a functioning data warehouse. Data staging is a technological innovation within data warehouses where data manipulations are carried out before transfer to target systems. It carries out data integration by harmonizing the staging functions, cleansing, verification, and archiving source data. Deterministic Prioritization Approach will be employed to enhance data staging, and to clearly prove this change Experiment design is needed to test scenarios in the study. Previous studies in this field have mainly focused in the data warehouses processes as a whole but less to the specifics of data staging area.
The cumulative effect of decades of IT infrastructure investment around a diverse set of technologies and processes has stifled innovation at organizations around the globe. Layer upon layer of complexity to accommodate a staggering array of applications has created hardened processes that make changes to systems difficult and cumbersome.
BRIDGING DATA SILOS USING BIG DATA INTEGRATIONijmnct
With cloud computing, cheap storage and technology advancements, an enterprise uses multiple
applications to operate business functions. Applications are not limited to just transactions, customer
service, sales, finance but they also include security, application logs, marketing, engineering, operations,
HR and many more. Each business vertical uses multiple applications which generate a huge amount of
data. On top of that, social media, IoT sensors, SaaS solutions, and mobile applications record exponential
growth in data volume. In almost all enterprises, data silos exist through these applications. These
applications can produce structured, semi-structured, or unstructured data at different velocity and in
different volume. Having all data sources integrated and generating timely insights helps in overall
decision making. With recent development in Big Data Integration, data silos can be managed better and it
can generate tremendous value for enterprises. Big data integration offers flexibility, speed and scalability
for integrating large data sources. It also offers tools to generate analytical insights which can help
stakeholders to make effective decisions. This paper presents the overview on data silos, challenges with
data silos and how big data integration can help to stun them.
The effect of technology-organization-environment on adoption decision of bi...IJECEIAES
Big data technology (BDT) is being actively adopted by world-leading organizations due to its expected benefits. However, most of the organizations in Thailand are still in the decision or planning stage to adopt BDT. Many challenges exist in encouraging the BDT diffusion in businesses. Thus, this study develops a research model that investigates the determinants of BDT adoption in the Thai context based on the technology-organizationenvironment (TOE) framework and diffusion of innovation (DOI) theory. Data were collected through an online questionnaire. Three hundred IT employees in different organizations in Thailand were used as a sample group. Structural equation modeling (SEM) was conducted to test the hypotheses. The result indicated that the research model was fitted with the empirical data with the statistics: Normed Chi-Square=1.651, GFI=0.895, AFGI=0.863, NFI=0.930, TLI=0.964, CFI=0.971, SRMR=0.0392, and RMSEA=0.046. The research model could, at 52%, explain decision to adopt BDT. Relative advantage, top management support, competitive pressure, and trading partner pressure show significant positive relation with BDT adoption, while security negatively influences BDT adoption.
A Comprehensive Study on Big Data Applications and Challengesijcisjournal
Big Data has gained much interest from the academia and the IT industry. In the digital and computing
world, information is generated and collected at a rate that quickly exceeds the boundary range. As
information is transferred and shared at light speed on optic fiber and wireless networks, the volume of
data and the speed of market growth increase. Conversely, the fast growth rate of such large data
generates copious challenges, such as the rapid growth of data, transfer speed, diverse data, and security.
Even so, Big Data is still in its early stage, and the domain has not been reviewed in general. Hence, this
study expansively surveys and classifies an assortment of attributes of Big Data, including its nature,
definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a
data life cycle that uses the technologies and terminologies of Big Data. Map/Reduce is a programming
model for efficient distributed computing. It works well with semi-structured and unstructured data. A
simple model but good for a lot of applications like Log processing and Web index building.
Big data is a broad term for data sets so large or complex that tr.docxhartrobert670
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set.
Analysis of data sets can find new correlations, to "spot business trends, prevent diseases, combat crime and so on."[1] Scientists, practitioners of media and advertising and governments alike regularly meet difficulties with large data sets in areas including Internet search, finance and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[2]connectomics, complex physics simulations,[3] and biological and environmental research.[4]
Data sets grow in size in part because they are increasingly being gathered by cheap and numerous information-sensing mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers, and wireless sensor networks.[5]
HYPERLINK "http://en.wikipedia.org/wiki/Big_data" \l "cite_note-6" [6]
HYPERLINK "http://en.wikipedia.org/wiki/Big_data" \l "cite_note-7" [7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data were created;[9] The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization.[10]
Work with big data is necessarily uncommon; most analysis is of "PC size" data, on a desktop PC or notebook[11] that can handle the available data set.
Relational database management systems and desktop statistics and visualization packages often have difficulty handling big data. The work instead requires "massively parallel software running on tens, hundreds, or even thousands of servers".[12] What is considered "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make Big Data a moving target. Thus, what is considered to be "Big" in one year will become ordinary in later years. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."[13]
Contents
· 1 Definition
· 2 Characteristics
· 3 Architecture
· 4 Technologies
· 5 Applications
· 5.1 Government
· 5.1.1 United States of America
· 5.1.2 India
· 5.1.3 United Kingdom
· 5.2 International development
· 5.3 Manufacturing
· 5.3.1 Cyber-Physical Models
· 5.4 Media
· 5.4.1 Internet of Things (IoT)
· 5.4.2 Technology
· 5.5 Private sector
· 5.5.1 Retail
· 5.5.2 Retail Banking
· 5.5.3 Real Estate
· 5.6 Science
· 5.6.1 Science and Resear ...
Enhancing Data Staging as a Mechanism for Fast Data AccessEditor IJCATR
Most organizations rely on data in their daily transactions and operations. This data is retrieved from different source
systems in a distributed network hence it comes in varying data types and formats. The source data is prepared and cleaned by
subjecting it to algorithms and functions before transferring it to the target systems which takes more time. Moreover, there is pressure
from data users within the data warehouse for data to be availed quickly for them to make appropriate decisions and forecasts. This has
not been the case due to immense data explosion in millions of transactions resulting from business processes of the organizations. The
current legacy systems cannot handle large data levels due to processing capabilities and customizations. This approach has failed
because there lacks clear procedures to decide which data to collect or exempt. It is with this concern that performance degradation
should be addressed because organizations invest a lot of resources to establish a functioning data warehouse. Data staging is a
technological innovation within data warehouses where data manipulations are carried out before transfer to target systems. It carries
out data integration by harmonizing the staging functions, cleansing, verification, and archiving source data. Deterministic
Prioritization Approach will be employed to enhance data staging, and to clearly prove this change Experiment design is needed to test
scenarios in the study. Previous studies in this field have mainly focused in the data warehouses processes as a whole but less to the
specifics of data staging area.
Enhancing Data Staging as a Mechanism for Fast Data AccessEditor IJCATR
Most organizations rely on data in their daily transactions and operations. This data is retrieved from different source systems in a distributed network hence it comes in varying data types and formats. The source data is prepared and cleaned by subjecting it to algorithms and functions before transferring it to the target systems which takes more time. Moreover, there is pressure from data users within the data warehouse for data to be availed quickly for them to make appropriate decisions and forecasts. This has not been the case due to immense data explosion in millions of transactions resulting from business processes of the organizations. The current legacy systems cannot handle large data levels due to processing capabilities and customizations. This approach has failed because there lacks clear procedures to decide which data to collect or exempt. It is with this concern that performance degradation should be addressed because organizations invest a lot of resources to establish a functioning data warehouse. Data staging is a technological innovation within data warehouses where data manipulations are carried out before transfer to target systems. It carries out data integration by harmonizing the staging functions, cleansing, verification, and archiving source data. Deterministic Prioritization Approach will be employed to enhance data staging, and to clearly prove this change Experiment design is needed to test scenarios in the study. Previous studies in this field have mainly focused in the data warehouses processes as a whole but less to the specifics of data staging area.
The cumulative effect of decades of IT infrastructure investment around a diverse set of technologies and processes has stifled innovation at organizations around the globe. Layer upon layer of complexity to accommodate a staggering array of applications has created hardened processes that make changes to systems difficult and cumbersome.
BRIDGING DATA SILOS USING BIG DATA INTEGRATIONijmnct
With cloud computing, cheap storage and technology advancements, an enterprise uses multiple
applications to operate business functions. Applications are not limited to just transactions, customer
service, sales, finance but they also include security, application logs, marketing, engineering, operations,
HR and many more. Each business vertical uses multiple applications which generate a huge amount of
data. On top of that, social media, IoT sensors, SaaS solutions, and mobile applications record exponential
growth in data volume. In almost all enterprises, data silos exist through these applications. These
applications can produce structured, semi-structured, or unstructured data at different velocity and in
different volume. Having all data sources integrated and generating timely insights helps in overall
decision making. With recent development in Big Data Integration, data silos can be managed better and it
can generate tremendous value for enterprises. Big data integration offers flexibility, speed and scalability
for integrating large data sources. It also offers tools to generate analytical insights which can help
stakeholders to make effective decisions. This paper presents the overview on data silos, challenges with
data silos and how big data integration can help to stun them.
The effect of technology-organization-environment on adoption decision of bi...IJECEIAES
Big data technology (BDT) is being actively adopted by world-leading organizations due to its expected benefits. However, most of the organizations in Thailand are still in the decision or planning stage to adopt BDT. Many challenges exist in encouraging the BDT diffusion in businesses. Thus, this study develops a research model that investigates the determinants of BDT adoption in the Thai context based on the technology-organizationenvironment (TOE) framework and diffusion of innovation (DOI) theory. Data were collected through an online questionnaire. Three hundred IT employees in different organizations in Thailand were used as a sample group. Structural equation modeling (SEM) was conducted to test the hypotheses. The result indicated that the research model was fitted with the empirical data with the statistics: Normed Chi-Square=1.651, GFI=0.895, AFGI=0.863, NFI=0.930, TLI=0.964, CFI=0.971, SRMR=0.0392, and RMSEA=0.046. The research model could, at 52%, explain decision to adopt BDT. Relative advantage, top management support, competitive pressure, and trading partner pressure show significant positive relation with BDT adoption, while security negatively influences BDT adoption.
A Comprehensive Study on Big Data Applications and Challengesijcisjournal
Big Data has gained much interest from the academia and the IT industry. In the digital and computing
world, information is generated and collected at a rate that quickly exceeds the boundary range. As
information is transferred and shared at light speed on optic fiber and wireless networks, the volume of
data and the speed of market growth increase. Conversely, the fast growth rate of such large data
generates copious challenges, such as the rapid growth of data, transfer speed, diverse data, and security.
Even so, Big Data is still in its early stage, and the domain has not been reviewed in general. Hence, this
study expansively surveys and classifies an assortment of attributes of Big Data, including its nature,
definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a
data life cycle that uses the technologies and terminologies of Big Data. Map/Reduce is a programming
model for efficient distributed computing. It works well with semi-structured and unstructured data. A
simple model but good for a lot of applications like Log processing and Web index building.
Big data is a broad term for data sets so large or complex that tr.docxhartrobert670
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set.
Analysis of data sets can find new correlations, to "spot business trends, prevent diseases, combat crime and so on."[1] Scientists, practitioners of media and advertising and governments alike regularly meet difficulties with large data sets in areas including Internet search, finance and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[2]connectomics, complex physics simulations,[3] and biological and environmental research.[4]
Data sets grow in size in part because they are increasingly being gathered by cheap and numerous information-sensing mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers, and wireless sensor networks.[5]
HYPERLINK "http://en.wikipedia.org/wiki/Big_data" \l "cite_note-6" [6]
HYPERLINK "http://en.wikipedia.org/wiki/Big_data" \l "cite_note-7" [7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data were created;[9] The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization.[10]
Work with big data is necessarily uncommon; most analysis is of "PC size" data, on a desktop PC or notebook[11] that can handle the available data set.
Relational database management systems and desktop statistics and visualization packages often have difficulty handling big data. The work instead requires "massively parallel software running on tens, hundreds, or even thousands of servers".[12] What is considered "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make Big Data a moving target. Thus, what is considered to be "Big" in one year will become ordinary in later years. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."[13]
Contents
· 1 Definition
· 2 Characteristics
· 3 Architecture
· 4 Technologies
· 5 Applications
· 5.1 Government
· 5.1.1 United States of America
· 5.1.2 India
· 5.1.3 United Kingdom
· 5.2 International development
· 5.3 Manufacturing
· 5.3.1 Cyber-Physical Models
· 5.4 Media
· 5.4.1 Internet of Things (IoT)
· 5.4.2 Technology
· 5.5 Private sector
· 5.5.1 Retail
· 5.5.2 Retail Banking
· 5.5.3 Real Estate
· 5.6 Science
· 5.6.1 Science and Resear ...
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.