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Journal articles on the topic 'Information-measuring systems'

Author: Grafiati

Published: 4 June 2025

Last updated: 23 June 2025

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1

Petrina, A. M., and G. M. Mainasheva. "Information measuring systems in nanotechnology." Automatic Documentation and Mathematical Linguistics 44, no. 4 (2010): 187–98. http://dx.doi.org/10.3103/s0005105510040023.

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2

Eilon, Samuel. "Measuring quality of information systems." Omega 21, no. 2 (1993): 135–38. http://dx.doi.org/10.1016/0305-0483(93)90046-n.

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3

Chua, Cecil, Lan Cao, Karlene Cousins, and Detmar Straub. "Measuring Researcher-Production in Information Systems." Journal of the Association for Information Systems 3, no. 1 (2002): 145–215. http://dx.doi.org/10.17705/1jais.00026.

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4

Kulchin, Yurii N. "Adaptive distributed optoelectronic information-measuring systems." Physics-Uspekhi 46, no. 8 (2003): 867–72. http://dx.doi.org/10.1070/pu2003v046n08abeh001654.

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5

Kul'chin, Yurii N. "Adaptive distributed optoelectronic information-measuring systems." Uspekhi Fizicheskih Nauk 173, no. 8 (2003): 894. http://dx.doi.org/10.3367/ufnr.0173.200308h.0894.

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6

Likhttsinder, Boris Ya. "Ensor networks – distributed information measuring and information control systems." Vestnik of Samara State Technical University. Technical Sciences Series 29, no. 2 (2021): 76–87. http://dx.doi.org/10.14498/tech.2021.2.5.

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The problems of control and management of geographically distributed objects are considered. The sensor networks operating on the ZigBee technology are considered. The characteristics of the 802.15.4 ZigBee standard are given. The advantages of this technology are shown when building networks that are not very critical to traffic delays. The elements of such a network are considered. The primary converters used in such networks and their energy characteristics are considered. The issues of reducing and compensating delays in control circuits are considered. It is shown that modern wireless sensor networks can be considered as distributed information measuring and information control systems.

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7

Tkhishev, A. N., P. S. Gorshkov, and A. P. Golovkin. "INFORMATION SECURITY IN MOBILE MODULAR MEASURING SYSTEMS." Civil Aviation High TECHNOLOGIES 20, no. 6 (2017): 99–110. http://dx.doi.org/10.26467/2079-0619-2017-20-6-99-110.

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8

Pletin, Sergey Aleksandrovich, and Alexander Nikolaevich Petrenko. "INFORMATION-MEASURING SYSTEMS FOR ELECTRICAL PROPULSION UNIT." Journal of Rocket-Space Technology 27, no. 4 (2019): 62–66. http://dx.doi.org/10.15421/451910.

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Considered electric propulsion systems (EPS) based on the Hall effect. Structure of EPS includes hall thruster (HT), xenon feed system (XFS), power processing unit and automatic control and monitoring system. The task is to design information-measuring system (IMS), which allows measurement of the current settings of EPS, conduct their mathematical processing and registration for further analysis. EPS is a complex physical system consisting of subsystems within which different physical processes. These processes are characterized by a large number of parameters and characteristics to be measured and controlled in the process EPS. Listed EPS parameters that should be measured in the operation of system. Determined informative signs of laboratory and onboard subsystems EPS. The necessity of development of information-measuring system and the need to create two different IMS - for use in laboratory equipment for research and testing of subsystems and EPS use in an onboard system management and control. Laboratory IMS provides measurement parameters and test conditions in a vacuum chamber; modes of power processing unit; XFS modes; EPS temperatures subsystem (anode, cathode, supply system, power conversion system); thrust of hall-effect thruster and forming control signals for power sources, flow of propellant and laboratory equipment. Onboard IMS provides measuring current and voltage discharge between the anode and the cathode; valve current of XFS; high and low level pressure of XFS; the temperature of the anode block, cathode, power processing unit.Formulated the tasks performed by the laboratory and on-board IMS. Made choice of hardware implementation of the IMS. The work on the creation of software for the operation of the IMS.

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9

Saunders, Carol Stoak, and Jack William Jones. "Measuring Performance of the Information Systems Function." Journal of Management Information Systems 8, no. 4 (1992): 63–82. http://dx.doi.org/10.1080/07421222.1992.11517939.

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10

Van Wegen, Bert, and Robert De Hoog. "Measuring the economic value of information systems." Journal of Information Technology 11, no. 3 (1996): 247–60. http://dx.doi.org/10.1080/026839696345298.

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11

Oliver, R. "Measuring users' performance with interactive information systems." Journal of Computer Assisted Learning 12, no. 2 (1996): 89–102. http://dx.doi.org/10.1111/j.1365-2729.1996.tb00041.x.

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12

Gounaris, Spiros P., George G. Panigyrakis, and Kalliopi C. Chatzipanagiotou. "Measuring the effectiveness of marketing information systems." Marketing Intelligence & Planning 25, no. 6 (2007): 612–31. http://dx.doi.org/10.1108/02634500710819978.

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13

Timmerman, Yoram, and Antoon Bronselaer. "Measuring data quality in information systems research." Decision Support Systems 126 (November 2019): 113138. http://dx.doi.org/10.1016/j.dss.2019.113138.

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14

Van Wegen, Bert, and Robert De Hoog. "Measuring the Economic Value of Information Systems." Journal of Information Technology 11, no. 3 (1996): 247–60. http://dx.doi.org/10.1177/026839629601100306.

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The determination of value of information or information systems is a basic issue for information management. In order to solve it several questions must be answered like: what is the object of valuation; how is value defined and measured; and what constitutes a coherent and usable method for valuation. In this paper an approach is outlined that combines the information commodity approach, activity-based costing, and graph modelling. The first is used to define the object of analysis (an information commodity) and the nature of value (the demand value at the marketplace). The third allows the modelling of business processes in terms of activities and cost relations between activities. The second enables the assignment of costs to activities modelled in the graph. Together they constitute a coherent and usable method for determining the value of IS. This is illustrated by means of a case study.

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15

Lazovskaya, Tatyana V., Galina F. Malykhina, and Dmitry A. Tarkhov. "FROM INFORMATION-MEASURING TO CYBER-PHYSICAL SYSTEMS." SOFT MEASUREMENTS AND COMPUTING 1, no. 62 (2023): 73–84. http://dx.doi.org/10.36871/2618-9976.2023.01.007.

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The expansion of the capabilities of informationmeasuring systems in the framework of the fourth industrial revolution can be associated with the use of artificial intelligence methods and algorithms for measurement tasks. Approaches based on intelligent technologies are methodically most developed within the framework of the concept of cyberphysical systems, therefore it is advisable to analyze the possibilities of using these technologies in informationmeasuring systems. It is expedient to use measuring systems for obtaining digital twins of controlled physical objects, forecasting and prediction, diagnosing and classification. The authors propose, along with wellknown artificial intelligence technologies using classical and deep neural networks, to use physically informed neural networks, which reveal the black box model to a certain extent. Physically informed artificial neural networks are more suitable for measurement tasks, since they combine knowledge of physical laws and learning processes based on a constantly updated set of measurements and a digital twin of a physical object.

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16

Serediuk, Оrest, and Mykhailo Trufan. "THEORETICAL FOUNDATIONS OF THE DUAL CONTROL ALGORITHM FOR MULTI-AGENT INFORMATION-MEASURING SYSTEMS." Measuring Equipment and Metrology 86, no. 1 (2025): 5–12. https://doi.org/10.23939/istcmtm2025.01.005.

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This article examines the theoretical foundations of the dual control algorithm in the context of machine learning, focusing on its application for intelligent agents in multi-agent information-measuring systems. A proposed algorithm combines anomaly detection in data with telemetry-based sensor calibration, opening new possibilities for improving the accuracy and reliability of data in complex and dynamic environments. The advantages of the algorithm are analyzed concerning adaptability, forecasting, and data integration, comparing it with other machine learning algorithms. A scheme of the software algorithm for the sensor data acquisition module is presented. A machine learning model of the dual control algorithm is developed and compared with the isolation forest model, highlighting the advantages of applying the dual control algorithm for building multi-agent information-measuring systems.

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17

Powell, Philip. "Measuring the value of information technology." Journal of Strategic Information Systems 4, no. 3 (1995): 304–6. http://dx.doi.org/10.1016/0963-8687(95)90005-5.

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18

Alexey, Bukalin,, and Zagrebaev Andrey. "NEW DATA COMPRESSION ALGORITHM FOR TECHNOLOGICAL INFORMATION OF THE INFORMATION-MEASURING SYSTEM "SKALA-MICRO"." Eurasian Journal of Mathematical and Computer Applications 9, no. 2 (2021): 4–11. http://dx.doi.org/10.32523/2306-6172-2021-9-2-4-11.

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This paper describes a computer program that implements an algorithm for com- pressing technological information with losses using archive data of the information-measuring system "Skala-micro" of the Kursk and Smolensk NPPs as an example. The presented al- gorithm allows to compress file archives at the data level with the possibility of their quick subsequent recovery within the limits of the acceptable error. The main goal of the algorithm is to significantly reduce the excess amount of memory when storing data for its subsequent more efficient use.

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19

Tambe, Prasanna, and Lorin M. Hitt. "Measuring Information Technology Spillovers." Information Systems Research 25, no. 1 (2014): 53–71. http://dx.doi.org/10.1287/isre.2013.0498.

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20

Fedotov, Vitaliy, and Sergey Solovykh. "Modernization of information-measuring systems for grain processing." E3S Web of Conferences 247 (2021): 01010. http://dx.doi.org/10.1051/e3sconf/202124701010.

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The paper discusses the basic operation principles of information-measuring systems for optimization wheat grain processing. The quality of grain processing products (cereals, flour, etc.) is influenced by both weather and climatic factors and grinding technologies. The modern development of information technologies makes it possible to modernize the existing information-measuring systems for grain processing and create new ones through the development of algorithms for analyzing the physical characteristics of the grain mass. During the study, test grinding of wheat grains of different varieties was carried out in a laboratory mill. To increase the yield of the finished product, digitalization of the selection of optimal grain separation modes was used. The obtained mathematical models allow predicting the quality of grain separation in separators of various types. The digitalization of the grain processing industry includes the use of artificial neural networks to analyze images of the grain mass using computer vision algorithms. It is promising to increase the information content of granulometric analysis using modern intelligent (information-measuring) systems. For the classification of wheat according to the milling properties, it is proposed to use the grain hardness. The studies used computer vision and artificial neural networks to find and organize the particles of grain grinding by geometric properties. The characteristics of the contours of the images of the grinding particles were taken into account. The values obtained by the developed information-measuring system were compared with that obtained using the Russian State Standard GOST methods. The error in assessing the grain hardness by the new method does not exceed 3.5%. The use of modern information tools allows improving the quality of wheat grain processing.

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21

Elias, Nur Fazidah. "Measuring the Impact of Information Systems in Malaysia." International Journal on Advanced Science, Engineering and Information Technology 1, no. 2 (2011): 206. http://dx.doi.org/10.18517/ijaseit.1.2.43.

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22

Eldrandaly, Khalid A., Soaad M. Naguib, and Mohammed M. Hassan. "A Model for Measuring Geographic Information Systems Success." Journal of Geographic Information System 07, no. 04 (2015): 328–47. http://dx.doi.org/10.4236/jgis.2015.74026.

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23

Sukumaran, A. K. S. "Measuring end user satisfaction in hospital information systems." International Journal of Management Concepts and Philosophy 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijmcp.2022.10051143.

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24

Guimaraes, Tor, Curtis P. Armstrong, and Brian M. Jones. "A New Approach to Measuring Information Systems Quality." Quality Management Journal 16, no. 1 (2009): 42–51. http://dx.doi.org/10.1080/10686967.2009.11918217.

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25

Miller, Jonathan. "Measuring and aligning information systems with the organization." Information & Management 25, no. 4 (1993): 217–28. http://dx.doi.org/10.1016/0378-7206(93)90070-a.

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26

Evans, Patricia A., James E. Bailey, William C. Moor, and Ardell L. Roberts. "An instrument for measuring effectiveness of information systems." Computers & Industrial Engineering 14, no. 3 (1988): 227–36. http://dx.doi.org/10.1016/0360-8352(88)90001-0.

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27

Sukumaran, Alagirisamy Kamatchi Subbiah. "Measuring end user satisfaction in hospital information systems." International Journal of Management Concepts and Philosophy 16, no. 3 (2023): 228–42. http://dx.doi.org/10.1504/ijmcp.2023.131752.

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28

Dubois, Didier, and Henri Prade. "Measuring and updating information." Information Sciences 57-58 (September 1991): 181–95. http://dx.doi.org/10.1016/0020-0255(91)90077-8.

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29

Pitoura, Evaggelia, Panayiotis Tsaparas, Giorgos Flouris, et al. "On Measuring Bias in Online Information." ACM SIGMOD Record 46, no. 4 (2018): 16–21. http://dx.doi.org/10.1145/3186549.3186553.

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30

Bandurian, Boris, and Volodymyr Kovalevskyj. "INFORMATION MEASURING SYSTEM FOR SAFETY STATE ASSESSMENT." Technogenic and Ecological Safety, no. 11(1/2022) (April 7, 2022): 3–7. http://dx.doi.org/10.52363/2522-1892.2022.1.1.

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The article is a continuation of a series of articles united by a single theme of improving the organization of the country’s security management system. Materials from previous articles were used. The article outlines the principles of building the hardware component of the system of obtaining objective information, which will provide a unified approach to improving the organization of the management system, set out in previous articles. Emphasis is placed on infrared spectrometers and the authors’ method of spectrometry, as a priority area for the development of objective information systems. The development of methodology and a specific example of the use of infrared systems and technologies is given.

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31

Asadov, Hikmat, Sevindj Abdullayeva, and Ulviya Tarverdiyeva. "Questions on Optimization of Izomorphic-Holonomic Information-Measuring Systems." Известия высших учебных заведений. Электромеханика 63, no. 6 (2020): 51–56. http://dx.doi.org/10.17213/0136-3360-2020-6-51-56.

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Questions on optimization of isomorphic-holonomic information –measuring systems, characterized by in-ternal holomorphic relation are considered. It is shown that isomorphic-holonomic property of information measuring and mechatronic systems make it possible to carry out optimization of them transforming of this task to Lagrange task where the target functional (Lagrange functional) is sum of initial target functional and integral of function of holonomic relation function with limitation imposed on it multiplied by Lagrange multi-plier. It is proved that if searched for function of holonomic relation with imposed limitation condition pro-vides for minimum (maximum) of target functional and if inegrant of initial target functional can be linearized so the function is always exists and is featured as inversed to link function upon which maximum (minimum) can be achieved.

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32

Staatz, John, Andrew Kizito, Michael Weber, and Niama Nango Dembélé. "Challenges in measuring the impact of Market Information Systems." Cahiers Agricultures 23, no. 4-5 (2014): 317–24. http://dx.doi.org/10.1684/agr.2013.0631.

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33

Headrick, R. Wayne, and George W. Morgan. "Measuring the Impact of Information Systems on Organizational Behavior." Journal of Organizational and End User Computing 11, no. 4 (1999): 16–21. http://dx.doi.org/10.4018/joeuc.1999100102.

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34

Grabowski, O. V., Т. І. Nakonechna, and S. L. Volkov. "QUALITY INDICATORS AND LIFE CYCLES OF INFORMATION-MEASURING SYSTEMS." Key title Zbìrnik naukovih pracʹ Odesʹkoï deržavnoï akademìï tehnìčnogo regulûvannâ ta âkostì -, no. 1(1) (2012): 17–23. http://dx.doi.org/10.32684/2412-5288-2012-1-1-17-23.

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35

Ivanov, M. A. "CONTROL AND MONITORING MODULES OF INFORMATION-MEASURING AVIONICS SYSTEMS." Vestnik of Ryazan State Radio Engineering University 58, no. 4 (2016): 140–45. http://dx.doi.org/10.21667/1995-4565-2016-58-4-140-145.

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36

CHANG, JERRY CHA-JAN, and WILLIAM R. KING. "Measuring the Performance of Information Systems: A Functional Scorecard." Journal of Management Information Systems 22, no. 1 (2005): 85–115. http://dx.doi.org/10.1080/07421222.2003.11045833.

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37

Holohan, James. "Use of executive information systems in measuring business performance." Journal of Information Technology 7, no. 3 (1992): 177–86. http://dx.doi.org/10.1057/jit.1992.24.

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38

Petter, Stacie, William DeLone, and Ephraim McLean. "Measuring information systems success: models, dimensions, measures, and interrelationships." European Journal of Information Systems 17, no. 3 (2008): 236–63. http://dx.doi.org/10.1057/ejis.2008.15.

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39

Chikara, Toshinori, and Takenori Takahashi. "Research of measuring the customer satisfaction for information systems." Computers & Industrial Engineering 33, no. 3-4 (1997): 639–42. http://dx.doi.org/10.1016/s0360-8352(97)00211-8.

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40

Nikitaev, V. G. "Expert Systems in Information Measuring Complexes of Oncological Diagnoses." Measurement Techniques 58, no. 6 (2015): 719–23. http://dx.doi.org/10.1007/s11018-015-0781-4.

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41

Teleshevskii, V. I., and D. A. Masterenko. "Recursive robust parameter estimation in automated information—Measuring systems." Measurement Techniques 40, no. 4 (1997): 319–23. http://dx.doi.org/10.1007/bf02504123.

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42

Aggelidis, Vassilios P., and Prodromos D. Chatzoglou. "Hospital information systems: Measuring end user computing satisfaction (EUCS)." Journal of Biomedical Informatics 45, no. 3 (2012): 566–79. http://dx.doi.org/10.1016/j.jbi.2012.02.009.

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43

Bashirov, Z. A., and Ya S. Uretskii. "Optimization of multichannel systems for transmission of measuring information." Measurement Techniques 37, no. 5 (1994): 522–25. http://dx.doi.org/10.1007/bf00980438.

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44

Pilipovich, V. A., A. K. Esman, I. A. Goncharenko, and V. K. Kuleshov. "Optical data switching in information processing and measuring systems." Measurement Techniques 47, no. 9 (2004): 879–83. http://dx.doi.org/10.1007/s11018-005-0034-z.

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45

Taneja, Harsh, and Utsav Mamoria. "Measuring Media Use Across Platforms: Evolving Audience Information Systems." International Journal on Media Management 14, no. 2 (2012): 121–40. http://dx.doi.org/10.1080/14241277.2011.648468.

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46

Holohan, James. "Use of Executive Information Systems in Measuring Business Performance." Journal of Information Technology 7, no. 3 (1992): 177–86. http://dx.doi.org/10.1177/026839629200700307.

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Much effort and research has concentrated on the need to formulate and implement business strategy, and the need to monitor performance against strategic plans. This paper, based on research carried out from July to September 1990, identifies the methodologies which organizations applied to define performance indicators presented on their Executive Information Systems and if these indicators helped steer the organizations towards their strategic objectives. Organizations applied either a critical success factor, an alternative top-down, or an evolutionary/prototype methodology to build their EISs. Unfortunately, not one organization which took part in the research modelled its EIS on its overall business strategy, thus limiting the use of their EIS to that of a glorified fire extinguisher rather than using it to help bridge the gap between formulating and implementing a business strategy.

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47

Lopatovska, Irene. "Measuring experienced utility of information retrieval systems: Experimental approach." Proceedings of the American Society for Information Science and Technology 42, no. 1 (2006): n/a. http://dx.doi.org/10.1002/meet.14504201223.

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48

FitzGerald, Chris. "Environmental management information systems: New tools for measuring performance." Environmental Quality Management 4, no. 2 (1994): 21–33. http://dx.doi.org/10.1002/tqem.3310040205.

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49

Kantor, Paul B. "Measuring information: An information services perspective." Information Processing & Management 32, no. 3 (1996): 389–91. http://dx.doi.org/10.1016/s0306-4573(96)90103-x.

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50

Herasimov, Sergey, Oleksandr Tymochko, Oleksii Kolomiitsev, et al. "FORMATION ANALYSIS OF MULTI-FREQUENCY SIGNALS OF LASER INFORMATION MEASURING SYSTEM." EUREKA: Physics and Engineering 5 (September 17, 2019): 19–28. http://dx.doi.org/10.21303/2461-4262.2019.00984.

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Improving the accuracy of measuring motion parameters and the volume of information exchange with aircraft requires the modern development of multifunctional radio systems that allow the combination of information and measuring channels due to frequency (time) signal selection. However, radio systems have limitations on the number and accuracy of the measurement of aircraft motion parameters, as well as information transfer rates. These restrictions will make it possible to remove laser systems for measuring with high accuracy the parameters of motion of aircraft. Similar laser systems are widely used in transmitting information to stationary or quasi-stationary objects. As a result, there was a need to overcome the contradiction between the requirements for the technical characteristics of radio systems and the capabilities of the existing scientific and methodological apparatus for synthesizing the signals of laser information-measuring systems for monitoring the parameters of aircraft motion. The theoretical foundations of the formation and selection of laser signals for combining the information and measuring channels of laser information-measuring systems are proposed and investigated. A comparison is made with known methods of combining (combining) the information and measuring channels of radio engineering systems and the features of using a spectrum of laser radiation formed by pairs of longitudinal modes that “color” each channel signal for their selection at reception. When receiving optical pairs of longitudinal modes, the intermode beat frequencies turn into radio engineering subcarriers that are well filtered and selectable. Based on the results of mathematical modeling and combinations of longitudinal modes, the choice of elements of a modified selector of longitudinal modes of laser information-measuring systems is substantiated. This determines the importance and usefulness of work for applied systems for measuring the parameters of movement of aircraft.

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