The Human Element in Automation Systems

Soviet Review ◽  
1961 ◽  
Vol 2 (12) ◽  
pp. 33-46
Author(s):  
D. A. Oshanin ◽  
D. Y. Panov
Keyword(s):  
Human Element ◽  
Automation Systems

scholarly journals Possible dangers of automation failures on board and measures to avoid the negative effects of these failures

2019 ◽  
Vol XXII (2) ◽  
pp. 22-35
Author(s):  
Demirel E.
Keyword(s):  
Human Life ◽  
Automated System ◽  
Automated Systems ◽  
Negative Effects ◽  
Critical Elements ◽  
Human Element ◽  
Training Systems ◽  
Technology Products ◽  
Manpower Requirement ◽  
Automation Systems

The first priority of the IMO (International Maritime Organization) is to ensure safety at sea. The ships are the critical elements of the shipping. The ships are the largest vehicles of the world and equipped with state of art management systems which are mostly furnished with highly automated systems. Any automated system failure may cause fatal accidents which hamper the human life, ship and environment as well as ports and terminals. Automated systems facilitate management of the ships and reduce manpower requirement. Unfortunately the seafarers assume these high technology products are very reliable and mostly neglects any possible failure on these systems. If we investigate seafarer’s education and training systems, curricula do not cover sufficient knowledge on operation principles and limitations of automated systems. This insufficiency negatively affects their counter actions in case of an automation failure which endangers safety of sea. The application of automation systems on board speedily pervades following the rapid technologic developments on merchant ships as well as on naval ships. That means automation failure will become rather important in the future. This study investigates possible reasons for automation failures and proposals to prevent it and reduce the negative effects of such incidents. Finally, it is intended to define measures covering automation failure versus human element on board.

Implementaon of digital economy elements in electric power industry

2018 ◽  
Vol 11 (2) ◽  
pp. 94-102 ◽  
Author(s):  
A. G. Filimonov ◽  
N. D. Chichirova ◽  
A. A. Chichirov ◽  
A. A. Filimonovа
Keyword(s):  
Electric Power ◽  
Energy Production ◽  
Predictive Analytics ◽  
Digital Economy ◽  
Digital Transformation ◽  
Cloud Services ◽  
Small Scale ◽  
Economic Activities ◽  
Industrial Enterprises ◽  
Automation Systems

Energy generation, along with other sectors of Russia’s economy, is on the cusp of the era of digital transformation. Modern IT solutions ensure the transition of industrial enterprises from automation and computerization, which used to be the targets of the second half of the last century, to digital enterprise concept 4.0. The international record of technological and structural solutions in digitization may be used in Russia’s energy sector to the full extent. Specifics of implementation of such systems in different countries are only determined by the level of economic development of each particular state and the attitude of public authorities as related to the necessity of creating conditions for implementation of the same. It is shown that a strong legislative framework is created in Russia for transition to the digital economy, with research and applied developments available that are up to the international level. The following digital economy elements may be used today at enterprises for production of electrical and thermal energy: — dealing with large amounts of data (including operations exercised via cloud services and distributed data bases); — development of small scale distributed generation and its dispatching; — implementation of smart elements in both electric power and heat supply networks; — development of production process automation systems, remote monitoring and predictive analytics; 3D-modeling of parts and elements; real time mathematic simulation with feedback in the form of control actions; — creating centres for analytical processing of statistic data and accounting in financial and economic activities with business analytics functions, with expansion of communication networks and computing capacities. Examples are presented for implementation of smart systems in energy production and distribution. It is stated in the paper that state-of art information technologies are currently being implemented in Russia, new unique digital transformation projects are being launched in major energy companies. Yet, what is required is large-scale and thorough digitization and controllable energy production system as a multi-factor business process will provide the optimum combination of efficient economic activities, reliability and safety of power supply.

Addressing the human element in Unmanned Aerial Vehicles

1998 ◽  
Author(s):  
Conrad Monson ◽  
Craig Fong ◽  
Richard Marsh ◽  
Michael Haas
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Human Element ◽  
Aerial Vehicles

EXPERT MANAGEMENT SYSTEMS OF AGROTECHNOLOGIES IN CLOUD INFORMATION TECHNOLOGY

1970 ◽  
pp. 12-17
Author(s):  
I. М. Mikhaylenko ◽  
V. N. Timoshin
Keyword(s):  
Decision Making ◽  
Pattern Recognition ◽  
Information System ◽  
Knowledge Base ◽  
Information Technologies ◽  
Agricultural Sector ◽  
Perennial Grasses ◽  
Management Decision ◽  
Agricultural Technologies ◽  
Automation Systems

The transition to "intellectual" agriculture is the main vector of modernization of the agricultural sector of the economy. It is based on integrated automation and robotization of production, the use of automated decision-making systems. This is inevitably accompanied by a significant increase in data flow from sensors, monitoring systems, meteorological stations, drones, satellites and other external systems. Farm management has the opportunity to use various online applications for accurate recommendations and making various kinds of management decisions. In this regard, the most effective use of cloud information technologies, allowing implementing the most complex information and technical level of automation systems for management of agricultural technologies. The purpose of this work is to test the approach to creating expert management decision support systems (DSS) through the knowledge base (KB), formed in the cloud information system. For this, we consider an example of constructing a DSS for choosing the optimal date for preparing forage from perennial grasses. A complete theoretical and algorithmic database of the analytical DSS implemented in the data processing center of the cloud information system is given. On its basis, a KB is formed for a variety of different decision-making conditions. This knowledge base is transmitted to the local DSS. To make decisions about the optimal dates for the preparation of the local DSS, two variants of algorithms are used. The first option is based on management models, and the second uses the pattern recognition method. The approbation of the algorithms was carried out according to the BZ from 50 cases. According to the results of testing, the method of pattern recognition proved to be more accurate, which provides a more flexible adjustment of the situation on the local DSS to a similar situation in the KB. The considered technique can be extended to other crops.

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