THE USA JOURNALS
THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN
–
2689-0984)
VOLUME 06 ISSUE09
34
https://www.theamericanjournals.com/index.php/tajet
PUBLISHED DATE: - 11-09-2024
https://doi.org/10.37547/tajet/Volume06Issue09-05
PAGE NO.: - 34-37
METROLOGICAL SUPPORT OF INFORMATION
MEASUREMENT SYSTEMS
Obidov Jamshidbek
Associate professor of Fergana polytechnic institute, Uzbekistan
INTRODUCTION
The rapid technological evolution of recent years in
the field information and communication
technologies have made it possible to form a
significant backlog in terms of developed software
and hardware infrastructure that supports the
accumulation and constant replenishment of data
archives of various natures and purposes.
Increasing competition in various areas of human
activity
-
business,
medicine,
corporate
management, etc.
–
and the complexity of the
external environment make approaches to the
expert use of existing data to improve the validity
and efficiency of adoption management decisions.
At the same time, it is not always possible today to
directly effectively use a well-developed and well-
known
apparatus
probability
theory
or
mathematical statistics without taking into account
the characteristics of a specific subject area,
computer science, computational complexity of
known and common algorithms (including details
of data storage, transmission and processing,
machine learning algorithms, etc.), the current and
future state of information systems and
technologies.
METHODS
Unit systems and dimensions emerged in the late
19th century. However, efforts to adapt these ideas
for use in modern digital systems are proving a
challenge. We provide an interpretation of units
and dimensions that clarifies the main reasons for
difficulties. We then suggest how a digital system
would provide adequate support for quantities and
units. A layer of metrological information is
envisaged that would track details and allow
familiar unit formats to be rendered. Three
independent aspects of the data should be
captured: 1) the quantity; 2) the measurement
scale, scale type and conversion functions; and 3)
RESEARCH ARTICLE
Open Access
Abstract
THE USA JOURNALS
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the semantics of numerical data [1].
One of the most well-established methods to
integrate research findings and assess the
cumulative knowledge Measuring information
systems success Stacie Petter et al 239 European
Journal of Information Systems within a domain is
a qualitative literature review (Oliver, 1987). This
method allows a researcher to analyze and
evaluate both quantitative and qualitative
literature within a domain to draw conclusions
about the state of the field. As with any research
technique, there are limitations.
The primary limitation with this approach is that
when conflicting findings arise, it becomes difficult
to determine the reason for the conflicting results.
Some also perceive that because the literature
review is qualitative, it is subjective in nature and
provides little’hard evidence’ to support a finding.
To counter these shortcomings, the research
technique of meta-analysis has become quite
popular in the social sciences and now in IS. Meta-
analysis is an interesting and useful technique to
synthesize the literature using quantitative data
reported across research studies.
The result of a meta-
analysis is an’effect size’
statistic that states the magnitude of the
relationship and whether or not the relationship
between variables is statistically significant. This
approach too has its limitations. A key limitation is
the need to exclude studies that use qualitative
techniques to examine success or studies that fail
to report the information required for the
statistical calculations for the meta-analysis.
While the meta-analysis produces a quantified
result regarding the relationship between two
variables, the need to exclude some studies may
not present a complete picture of the literature.
Furthermore, a meta-analysis does not examine the
direction of causality, because the effect size is an
adjusted correlation between two variables [2].
There have been meta-analyses examining one or
more of the elements of IS success therefore, this
paper seeks to obtain a different, qualitative view
of the literature to answer a different set of
research questions. While a meta-analysis is aimed
at answering the question:’Is
there a correlation
between two variables?’, a qualitative literature
review is better equipped to explain how the
relationships have been studied in the literature, if
there appears to be support for a causal
relationship between two variables, and examines
if there are any potential boundary conditions for
the model.
THE USA JOURNALS
THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN
–
2689-0984)
VOLUME 06 ISSUE09
36
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Figure 1. Meta-analysis vs Systematic review
It means combining the results of several studies
using statistical methods (that is, quantitative
methods of assessment) to test one or more
interrelated scientific hypotheses.
A meta-analysis uses either primary data from
original studies or summarizes published
(secondary) results from studies devoted to one
problem. Meta-analysis is a common, but not
required, component of a systematic review of
empirical studies.
RESULTS
Intelligent measurement systems are capable of
performing all measurement and control functions
in real time. This allows “high level” measurement
and control functions to be carried out without the
need for large computers. When operating
autonomously, such an IC provides continuous
measurements
and
control
of
specified
parameters, data collection and signal processing
[3].
Intelligent measuring systems have significant
advantages over traditional ones, namely:
-
high speed of control loops for measurement
processes, as well as high speed of data acquisition;
-
versatility - standard interfaces provide easy
connection to any systems and equipment;
-
high reliability at each system level - the use
of universal methods ensures trouble-free
operation;
-
interchangeability; Since intelligent systems
are standard devices, individually programmed for
their specific functions, each of them can be
replaced by another device of the same functional
purpose; each system can be considered as a
backup for any type of system of the same class,
which reduces the number of additional redundant
measuring, monitoring, control and adjustment
equipment and minimizes the emergency period in
the unlikely event of failure of any element.
DISCUSSION
Before implementing a new, newly invented
THE USA JOURNALS
THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN
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2689-0984)
VOLUME 06 ISSUE09
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apparatus (device), or a new version of improving
the circuits of a device, you need to make sure that
the updated device will work better than the old
one. For these purposes, the designers of a new
device or device always began by creating some
kind of prototype or mock-up, which would allow
them to verify the functionality or advantages of
the new device over the old one without great
expense[4]. Professionals often call the creation of
such a prototype a process of physical modeling.
With the advent and widespread use of
professional computers, individual companies
have developed computer programs that allow
computer (mathematical) modeling of various
electronic circuits.
Physical modeling is associated with large material
costs, since it requires the production of models
and their labor-intensive research. Often physical
modeling is simply not possible due to the extreme
complexity of the device, for example in the design
of large and ultra-large integrated circuits. In this
case, they resort to mathematical modeling using
computer tools and methods.
REFERENCES
1.
B.D. Hall, M. Kuster. Metrological support for
quantities and units in digital systems.
–
Available online//Version of Record 17
September 2021.
2.
Approaches to measuring the intelligence of
machines
by
quantifying
them.Prerna
Kapoor//Article. 2015.
3.
Obidov, J.G. Virtual process modeling
technologies based on imitation-variability in
technical higher education institutions//E3S
Web of Conferences, 2023, 452, 07017
https://doi.org/10.1051/e3sconf/202345207
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4.
Erkaboev, A., Obidov, J., Madmarova, U.,
Alikhonov, E.//Analysis of the ISO 9001
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