4 research outputs found
Π‘ΡΠ°Π²Π½Π΅Π½ΠΈΠ΅ ΠΈ ΠΎΡΠ±ΠΎΡ ΡΠΈΡΡΠ°ΡΠΈΠΉ Π² ΡΠΈΡΡΠ΅ΠΌΠ°Ρ Π²ΡΠ²ΠΎΠ΄Π° ΡΠ΅ΡΠ΅Π½ΠΈΠΉ Π½Π° ΠΏΡΠ΅ΡΠ΅Π΄Π΅Π½ΡΠ°Ρ Π΄Π»Ρ Β«ΡΠΌΠ½ΠΎΠΉΒ» ΡΠ΅ΡΠΌΡ
The trend of development of smart farms is aimed at their becoming fully autonomous, robotic enterprises. The prospects for the intellectualization of agricultural production and smart farms, in particular, today are associated with the development of technology systems used to detect, recognize complex production situations and search for effective solutions in these situations. The article presents the concept of such a decision support system on smart farms using the method of decision support based on case-based reasoning - CBR system. Its implementation requires a number of non-trivial tasks, which include, first of all, the tasks of formalizing the presentation of situations and creating methods for comparing and retrieving situations from the KB on this basis. In this study, a smart farm is presented as a complex technological object consisting of interrelated components, which are the technological subsystems of a smart farm, the products produced, the objects of the operational environment, as well as the relationships between them. To implement algorithms for situational decision-making based on precedents, a formalized representation of the situation in the form of a multivector is proposed. This allowed us to develop a number of models of the trained similarity function between situations. The conducted experiments have shown the operability of the proposed models, on the basis of which ensemble architecture of a neural network has been developed for comparing situations and selecting them from the knowledge base in decision-making processes. Of practical interest is monitoring the condition of plants by their video and photo images, which allows detecting undesirable plant conditions (diseases), which can serve as a signal to activate the process of searching for solutions in the knowledge base.Π’Π΅Π½Π΄Π΅Π½ΡΠΈΡ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΌΠ½ΡΡ
ΡΠ΅ΡΠΌ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π° Π½Π° ΠΈΡ
ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΠ΅ ΠΏΠΎΠ»Π½ΠΎΡΡΡΡ Π°Π²ΡΠΎΠ½ΠΎΠΌΠ½ΡΠΌΠΈ, ΡΠΎΠ±ΠΎΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΠΌΠΈ ΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΡΠΌΠΈ. ΠΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Ρ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΠ΅Π»ΡΡΠΊΠΎΡ
ΠΎΠ·ΡΠΉΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π° ΠΈ ΡΠΌΠ½ΡΡ
ΡΠ΅ΡΠΌ, Π² ΡΠ°ΡΡΠ½ΠΎΡΡΠΈ, ΡΠ΅Π³ΠΎΠ΄Π½Ρ ΡΠ²ΡΠ·ΡΠ²Π°ΡΡΡΡ Ρ ΡΠ°Π·Π²ΠΈΡΠΈΠ΅ΠΌ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ ΡΠΈΡΡΠ΅ΠΌ, ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΠΌΡΡ
Π΄Π»Ρ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ, ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΡΡ
ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π΅Π½Π½ΡΡ
ΡΠΈΡΡΠ°ΡΠΈΠΉ ΠΈ ΠΏΠΎΠΈΡΠΊΠ° ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
ΡΠ΅ΡΠ΅Π½ΠΈΠΉ Π² ΡΡΠΈΡ
ΡΠΈΡΡΠ°ΡΠΈΡΡ
. Π ΡΡΠ°ΡΡΠ΅ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ Π²ΠΎΠΏΡΠΎΡΡ ΡΠΎΠ·Π΄Π°Π½ΠΈΡ ΡΠΈΡΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠΈ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π½Π° ΡΠΌΠ½ΡΡ
ΡΠ΅ΡΠΌΠ°Ρ
Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ Π²ΡΠ²ΠΎΠ΄Π° ΡΠ΅ΡΠ΅Π½ΠΈΠΉ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠ°ΡΡΡΠΆΠ΄Π΅Π½ΠΈΠΉ ΠΏΠΎ ΠΏΡΠ΅ΡΠ΅Π΄Π΅Π½ΡΠ°ΠΌ (case-based reasoning). ΠΠ»Ρ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠ°ΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ ΡΡΠ΅Π±ΡΠ΅ΡΡΡ Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΠ΅ ΡΡΠ΄Π° Π½Π΅ΡΡΠΈΠ²ΠΈΠ°Π»ΡΠ½ΡΡ
Π·Π°Π΄Π°Ρ, ΠΊ ΠΊΠΎΡΠΎΡΡΠΌ ΠΎΡΠ½ΠΎΡΡΡΡΡ, ΠΏΡΠ΅ΠΆΠ΄Π΅ Π²ΡΠ΅Π³ΠΎ, Π·Π°Π΄Π°ΡΠΈ ΡΠΎΡΠΌΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠΈΡΡΠ°ΡΠΈΠΉ ΠΈ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ Π½Π° ΡΡΠΎΠΉ ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠΏΠΎΡΠΎΠ±ΠΎΠ² ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ ΠΈ ΠΎΡΠ±ΠΎΡΠ° ΡΠΈΡΡΠ°ΡΠΈΠΉ Π² Π±Π°Π·Π°Ρ
Π·Π½Π°Π½ΠΈΠΉ. Π Π΄Π°Π½Π½ΠΎΠΌ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΈ ΡΠΌΠ½Π°Ρ ΡΠ΅ΡΠΌΠ° ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π° ΠΊΠ°ΠΊ ΡΠ»ΠΎΠΆΠ½ΡΠΉ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΎΠ±ΡΠ΅ΠΊΡ, ΡΠΎΡΡΠΎΡΡΠΈΠΉ ΠΈΠ· Π²Π·Π°ΠΈΠΌΠΎΡΠ²ΡΠ·Π°Π½Π½ΡΡ
ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠΎΠ², ΠΊΠΎΡΠΎΡΡΠΌΠΈ ΡΠ²Π»ΡΡΡΡΡ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΠΎΠ΄ΡΠΈΡΡΠ΅ΠΌΡ ΡΠΌΠ½ΠΎΠΉ ΡΠ΅ΡΠΌΡ, ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠΌΠ°Ρ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΡ, ΠΎΠ±ΡΠ΅ΠΊΡΡ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΎΠΊΡΡΠΆΠ΅Π½ΠΈΡ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ ΠΌΠ΅ΠΆΠ΄Ρ Π½ΠΈΠΌΠΈ. ΠΠ»Ρ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² ΡΠΈΡΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π²ΡΠ²ΠΎΠ΄Π° ΡΠ΅ΡΠ΅Π½ΠΈΠΉ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΏΡΠ΅ΡΠ΅Π΄Π΅Π½ΡΠΎΠ² ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎ ΡΠΎΡΠΌΠ°Π»ΠΈΠ·ΠΎΠ²Π°Π½Π½ΠΎΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ ΡΠΈΡΡΠ°ΡΠΈΠΈ Π² Π²ΠΈΠ΄Π΅ ΠΌΡΠ»ΡΡΠΈΠ²Π΅ΠΊΡΠΎΡΠ°, ΠΊΠΎΡΠΎΡΡΠΉ ΡΠΎΠ΄Π΅ΡΠΆΠΈΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡΡ
ΡΡΠΈΡ
ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠΎΠ². ΠΡΠΎ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΎ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°ΡΡ ΡΡΠ΄ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΎΠ±ΡΡΠ°Π΅ΠΌΠΎΠΉ ΡΡΠ½ΠΊΡΠΈΠΈ ΡΡ
ΠΎΠΆΠ΅ΡΡΠΈ ΠΌΠ΅ΠΆΠ΄Ρ ΡΠΈΡΡΠ°ΡΠΈΡΠΌΠΈ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½Π½ΡΠ΅ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ ΡΠ°Π±ΠΎΡΠΎΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΡ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΡΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ, Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠ΅Π³ΠΎ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π° Π°Π½ΡΠ°ΠΌΠ±Π»Π΅Π²Π°Ρ Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΠ° Π½Π΅ΠΉΡΠΎΡΠ΅ΡΠΈ Π΄Π»Ρ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ ΡΠΈΡΡΠ°ΡΠΈΠΉ ΠΈ ΠΈΡ
ΠΎΡΠ±ΠΎΡΠ° ΠΈΠ· Π±Π°Π·Ρ Π·Π½Π°Π½ΠΈΠΉ Π² ΠΏΡΠΎΡΠ΅ΡΡΠ°Ρ
Π²ΡΠ²ΠΎΠ΄Π° ΡΠ΅ΡΠ΅Π½ΠΈΠΉ. ΠΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΈΠ½ΡΠ΅ΡΠ΅Ρ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΠ΅Ρ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΡΠ°ΡΡΠ΅Π½ΠΈΠΉ ΠΏΠΎ ΠΈΡ
Π²ΠΈΠ΄Π΅ΠΎ-, ΡΠΎΡΠΎ- ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΠΌ, ΡΡΠΎ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΎΠ±Π½Π°ΡΡΠΆΠΈΠ²Π°ΡΡ Π½Π΅ΠΆΠ΅Π»Π°ΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΡΠ°ΡΡΠ΅Π½ΠΈΠΉ (Π±ΠΎΠ»Π΅Π·Π½ΠΈ), ΠΊΠΎΡΠΎΡΡΠ΅ ΠΌΠΎΠ³ΡΡ ΡΠ»ΡΠΆΠΈΡΡ ΡΠΈΠ³Π½Π°Π»ΠΎΠΌ Π΄Π»Ρ Π°ΠΊΡΠΈΠ²ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΠΏΠΎΠΈΡΠΊΠ° ΡΠ΅ΡΠ΅Π½ΠΈΠΉ Π² Π±Π°Π·Π΅ Π·Π½Π°Π½ΠΈΠΉ
Case-Based Decision Support for Disaster Management
Disasters are characterized by severe disruptions of the societyβs functionality and adverse impacts on humans, the environment, and economy that cannot be coped with by society using its own resources. This work presents a decision support method that identifies appropriate measures for protecting the public in the course of a nuclear accident. The method particularly considers the issue of uncertainty in decision-making as well as the structured integration of experience and expert knowledge
Recommended from our members
The design and development of a knowledge-based lean six sigma maintenance system for sustainable buildings. The design and development of a hybrid Knowledge-based (KB)/Gauging Absence of Pre-requisites (GAP)/Analytic Hierarchy Process (AHP) model for implementing lean six sigma maintenance system in sustainable buildings' environment
The complexity of sustainable building maintenance environment requires managers to define and implement appropriate quality benchmark system suitable for this function. Lean Six Sigma (LSS) is one of the most effective process improvement and optimization philosophy that maintenance organisations can implement in their environment. However, literature review has shown that 90% of failures in LSS implementations are due to lack of readiness to change, the unawareness of the required benchmark organisation capabilities, and improper control of priorities.
The contribution of the current research approach is in developing a hybrid Knowledge-Based (KB)/GAP/AHP System, consisting of three stages (Planning, Designing and Implementation) and containing over 2500 KB rules. The KB System can assist the decision-makers in identifying the obstacles behind the organisation readiness to change into a benchmark LSS maintenance environment. Thus the KB System will be used to achieve benchmark standards by determining the gap existing between the current environment and the benchmark goal, and then suggest a detailed plan to overcome these hurdles in a prioritised and structured manner, thus achieving cost benefits.
To ensure its consistency and reliability, the KB System was validated in three Oman-based maintenance organisations, and one published case study for a UK-based organisation. The results from the validation were positive with the System output suggesting list of top priorities and action plans for achieving benchmark LSS standards for these organisations. The research concludes that the developed KB System is a consistent and reliable methodology for assisting decision-makers in designing, planning, and implementing LSS for benchmark sustainable building maintenance