6 research outputs found
ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠ΅ΡΡ ΡΠ΅Π½Π½ΠΎΡΡΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π‘ΡΡΠ°ΡΠΎΠ½ΠΎΠ²ΠΈΡΠ° Π΄Π»Ρ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ Π³ΠΈΠ±ΠΊΠΈΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ΅Ρ Π½ΠΈΡΠ΅ΡΠΊΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ²
Π‘ΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ Π²ΡΡΠΈΡΠ»Π΅Π½ΠΈΡ ΡΠ΅Π½Π½ΠΎΡΡΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ, ΡΠΈΡΠΊΡΠ»ΠΈΡΡΡΡΠ΅ΠΉ Π² Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ², Π½Π΅ ΡΡΠΈΡΡΠ²Π°ΡΡ ΠΏΠΎΡΠ΅ΡΠΈ (Π²ΡΠΈΠ³ΡΡΡΠΈ), ΡΠ²ΡΠ·Π°Π½Π½ΡΠ΅ Ρ ΠΏΡΠΈΠ½ΡΡΠΈΠ΅ΠΌ Π½Π΅ΠΏΡΠ°Π²ΠΈΠ»ΡΠ½ΡΡ
ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΏΡΠΈ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΡΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ.
Π¦Π΅Π»Ρ ΡΠ°Π±ΠΎΡΡ β ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° Π°Π»Π³ΠΎΡΠΈΡΠΌΠ°, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ΅Π³ΠΎ ΡΠ΅ΡΠΈΡΡ Π·Π°Π΄Π°ΡΡ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ, Π² ΠΊΠΎΡΠΎΡΠΎΠΌ Π½Π°Ρ
ΠΎΠ΄ΠΈΡΡΡ Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΠ΅ΠΌΡΠΉ ΠΎΠ±ΡΠ΅ΠΊΡ, ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΎΠΏΡΠΈΠΌΠΈΠ·ΠΈΡΡΠ΅ΠΌΠΎΠ³ΠΎ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ ΡΠ΅Π½Π½ΠΎΡΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ. Π Π΅ΡΠ΅Π½ΠΈΠ΅ Π·Π°Π΄Π°ΡΠΈ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΎ Π½Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ ΠΌΠ΅ΡΡ ΡΠ΅Π½Π½ΠΎΡΡΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π . Π.Β Π‘ΡΡΠ°ΡΠΎΠ½ΠΎΠ²ΠΈΡΠ°, ΠΌΠΎΠ΄ΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΠΏΡΠΈΠΌΠ΅Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎ ΠΊ ΠΏΡΠ΅Π΄ΠΌΠ΅ΡΠ½ΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΏΡΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ², ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π½ΡΡ
Π² Π²ΠΈΠ΄Π΅ ΠΈΠ½ΡΠ΅ΡΠ²Π°Π»ΠΎΠ² Π½Π° Π²Π΅ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΡΠΈΡΠ»ΠΎΠ²ΠΎΠΉ ΠΎΡΠΈ. ΠΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½Π°Ρ ΡΠ΅Π½Π½ΠΎΡΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π΄ΠΎΡΡΠΈΠ³Π°Π΅ΡΡΡ ΠΏΡΠΈ ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΡΠ΅Π΄Π½ΠΈΡ
ΠΏΠΎΡΠ΅ΡΡΒ (ΠΌΠ°ΠΊΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΡΠ΅Π΄Π½ΠΈΡ
Π²ΡΠΈΠ³ΡΡΡΠ΅ΠΉ), ΠΏΠΎΠ»ΡΡΠ°Π΅ΠΌΡΡ
ΠΏΡΠΈ Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΠΈ ΠΏΡΠΎΠ²Π΅ΡΠΎΠΊ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ², Π² ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠ°.
ΠΠ»Ρ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π·Π°Π΄Π°ΡΠΈ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎ ΠΎΠ±Π»Π°Π΄Π°ΡΡΠ΅Π΅ Π½Π°ΡΡΠ½ΠΎΠΉ Π½ΠΎΠ²ΠΈΠ·Π½ΠΎΠΉ ΡΠ΅ΠΊΡΡΡΠ΅Π½ΡΠ½ΠΎΠ΅ Π²ΡΡΠ°ΠΆΠ΅Π½ΠΈΠ΅, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ΅Π΅ Π²ΡΡΠΈΡΠ»ΡΡΡ ΡΠ΅Π½Π½ΠΎΡΡΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ, ΠΏΠΎΠ»ΡΡΠ°Π΅ΠΌΠΎΠΉ ΠΏΡΠΈ Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΠΈ ΠΏΡΠΎΠ²Π΅ΡΠΎΠΊ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π² ΠΊΠ°ΠΆΠ΄ΠΎΠΌ ΠΈΠ· Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΠ΅ΠΌΡΡ
ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠΎΡΡΠΎΡΠ½ΠΈΠΉ ΠΏΡΠΎΡΠ΅ΡΡΠ° Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ. Π ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΈ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΠΈ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠ° Π²ΠΎΠ·ΠΌΠΎΠΆΠ½Ρ ΠΊΠ°ΠΊ ΠΏΠΎΡΠ΅ΡΠΈ, ΡΠ°ΠΊ ΠΈ Π²ΡΠΈΠ³ΡΡΡΠΈ. Π Π°Π·Π½ΠΎΡΡΡ ΠΈΡ
Π°ΠΏΡΠΈΠΎΡΠ½ΡΡ
ΠΈ Π°ΠΏΠΎΡΡΠ΅ΡΠΈΠΎΡΠ½ΡΡ
ΡΡΠ΅Π΄Π½ΠΈΡ
Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ ΡΠΈΡΠ»Π΅Π½Π½ΠΎ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΠ΅Ρ ΡΠ΅Π½Π½ΠΎΡΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ. ΠΠ΅Π»ΠΈΡΠΈΠ½Π° ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ ΡΠ΅Π½Π½ΠΎΡΡΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π·Π°Π²ΠΈΡΠΈΡ ΠΎΡ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ΅ΠΉ ΠΈΡΡ
ΠΎΠ΄ΠΎΠ² ΠΏΡΠΎΠ²Π΅ΡΠΎΠΊ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² ΠΈ ΠΏΡΠΎΠΏΠΎΡΡΠΈΠΎΠ½Π°Π»ΡΠ½Π° ΡΠ°Π·Π½ΠΎΡΡΠΈ Π°ΠΏΠΎΡΡΠ΅ΡΠΈΠΎΡΠ½ΡΡ
ΠΈ Π°ΠΏΡΠΈΠΎΡΠ½ΡΡ
Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ΅ΠΉ Π΄ΠΎΡΡΠΈΠΆΠ΅Π½ΠΈΡ ΡΠ΅Π»ΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ.
ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠ³ΠΎ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°ΡΡ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΡΡ ΠΏΠΎ ΠΊΡΠΈΡΠ΅ΡΠΈΡ ΠΌΠ°ΠΊΡΠΈΠΌΡΠΌΠ° ΡΠ΅Π½Π½ΠΎΡΡΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π³ΠΈΠ±ΠΊΡΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π² Π²ΠΈΠ΄Π΅ ΠΎΡΠΈΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ Π³ΡΠ°ΡΠ° ΠΈΠ»ΠΈ ΡΠΏΠΎΡΡΠ΄ΠΎΡΠ΅Π½Π½ΡΡ
ΠΏΠΎ ΠΎΡΠ΅ΡΠ΅Π΄Π½ΠΎΡΡΠΈ ΠΈΡ
Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΡ Π½Π°Π±ΠΎΡΠΎΠ² ΠΏΡΠΎΠ²Π΅ΡΠΎΠΊ, Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΡΡ
Π΄Π»Ρ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΠ³ΠΎ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ, Π² ΠΊΠΎΡΠΎΡΠΎΠΌ Π½Π°Ρ
ΠΎΠ΄ΠΈΡΡΡ ΠΎΠ±ΡΠ΅ΠΊΡ.
Π Π΅Π°Π»ΠΈΠ·Π°ΡΠΈΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π²ΠΎΠ·ΠΌΠΎΠΆΠ½Π° Π² ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎ-Π°Π»Π³ΠΎΡΠΈΡΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΠΈ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ²
ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠ΅ΡΡ ΡΠ΅Π½Π½ΠΎΡΡΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π‘ΡΡΠ°ΡΠΎΠ½ΠΎΠ²ΠΈΡΠ° Π΄Π»Ρ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ Π³ΠΈΠ±ΠΊΠΈΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ΅Ρ Π½ΠΈΡΠ΅ΡΠΊΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ²
Existing methods of calculating of the value of diagnostic information circulating in the automated systems of monitoring of technical condition of objects do not take into account "losses" ("gains") resulting from making βwrongβ decisions when identifying this state. The purpose of the work is to develop an algorithm that allows to solve the problem of recognizing the technical state of the object being analyzed by means of dynamic programming, the value of the diagnostic information as an optimized indicator being used. The solution to the optimization problem of a diagnostic procedure is based on the use of a measure of the information value proposed by R. L. Stratonovich. It is modified according to the subject area of the technical diagnostics and in the case when the diagnostic features presented in the form of intervals on the real numerical axis are used. The maximum value of the diagnostic information is achieved by minimizing the average "losses" (maximizing the average "gains") obtained when performing tests of diagnostic signs in the process of recognizing the technical condition of an object. To solve the problem, a recurrent expression possessing a scientific novelty has been proposed. It allows to calculate the value of the information obtained when performing tests of diagnostic signs in each of the analyzed information states of the diagnostic process. In the process of the diagnostics program implementation when recognizing the technical condition of the object both βlossesβ and βwinningsβ are possible. The difference between their a priori and a posteriori means values characterizes the value of the diagnostic information numerically. The magnitude of the information value indication depends on the probabilities of the results of the diagnostic signs checks and is proportional to the difference between the a posteriori and a priori probabilities of achieving the diagnostic goal. By using the proposed solution, it is possible to synthesize the flexible diagnostics program that is optimal according to the maximum value of diagnostic information in the form of a oriented graph or sets of tests in proper sequence of their execution. This is necessary in order to recognize the specific technical state in which the object is located. The implementation of the algorithm developed is possible in the software and algorithmic support of the automated systems for monitoring the state of complex technical objects.Π‘ΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ Π²ΡΡΠΈΡΠ»Π΅Π½ΠΈΡ ΡΠ΅Π½Π½ΠΎΡΡΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ, ΡΠΈΡΠΊΡΠ»ΠΈΡΡΡΡΠ΅ΠΉ Π² Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ², Π½Π΅ ΡΡΠΈΡΡΠ²Π°ΡΡ ΠΏΠΎΡΠ΅ΡΠΈ (Π²ΡΠΈΠ³ΡΡΡΠΈ), ΡΠ²ΡΠ·Π°Π½Π½ΡΠ΅ Ρ ΠΏΡΠΈΠ½ΡΡΠΈΠ΅ΠΌ Π½Π΅ΠΏΡΠ°Π²ΠΈΠ»ΡΠ½ΡΡ
ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΏΡΠΈ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΡΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ.
Π¦Π΅Π»Ρ ΡΠ°Π±ΠΎΡΡ β ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° Π°Π»Π³ΠΎΡΠΈΡΠΌΠ°, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ΅Π³ΠΎ ΡΠ΅ΡΠΈΡΡ Π·Π°Π΄Π°ΡΡ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ, Π² ΠΊΠΎΡΠΎΡΠΎΠΌ Π½Π°Ρ
ΠΎΠ΄ΠΈΡΡΡ Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΠ΅ΠΌΡΠΉ ΠΎΠ±ΡΠ΅ΠΊΡ, ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΎΠΏΡΠΈΠΌΠΈΠ·ΠΈΡΡΠ΅ΠΌΠΎΠ³ΠΎ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ ΡΠ΅Π½Π½ΠΎΡΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ. Π Π΅ΡΠ΅Π½ΠΈΠ΅ Π·Π°Π΄Π°ΡΠΈ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΎ Π½Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ ΠΌΠ΅ΡΡ ΡΠ΅Π½Π½ΠΎΡΡΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π . Π. Π‘ΡΡΠ°ΡΠΎΠ½ΠΎΠ²ΠΈΡΠ°, ΠΌΠΎΠ΄ΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΠΏΡΠΈΠΌΠ΅Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎ ΠΊ ΠΏΡΠ΅Π΄ΠΌΠ΅ΡΠ½ΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΏΡΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ², ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π½ΡΡ
Π² Π²ΠΈΠ΄Π΅ ΠΈΠ½ΡΠ΅ΡΠ²Π°Π»ΠΎΠ² Π½Π° Π²Π΅ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΡΠΈΡΠ»ΠΎΠ²ΠΎΠΉ ΠΎΡΠΈ. ΠΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½Π°Ρ ΡΠ΅Π½Π½ΠΎΡΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π΄ΠΎΡΡΠΈΠ³Π°Π΅ΡΡΡ ΠΏΡΠΈ ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΡΠ΅Π΄Π½ΠΈΡ
ΠΏΠΎΡΠ΅ΡΡ (ΠΌΠ°ΠΊΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΡΠ΅Π΄Π½ΠΈΡ
Π²ΡΠΈΠ³ΡΡΡΠ΅ΠΉ), ΠΏΠΎΠ»ΡΡΠ°Π΅ΠΌΡΡ
ΠΏΡΠΈ Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΠΈ ΠΏΡΠΎΠ²Π΅ΡΠΎΠΊ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ², Π² ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠ°.
ΠΠ»Ρ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π·Π°Π΄Π°ΡΠΈ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎ ΠΎΠ±Π»Π°Π΄Π°ΡΡΠ΅Π΅ Π½Π°ΡΡΠ½ΠΎΠΉ Π½ΠΎΠ²ΠΈΠ·Π½ΠΎΠΉ ΡΠ΅ΠΊΡΡΡΠ΅Π½ΡΠ½ΠΎΠ΅ Π²ΡΡΠ°ΠΆΠ΅Π½ΠΈΠ΅, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ΅Π΅ Π²ΡΡΠΈΡΠ»ΡΡΡ ΡΠ΅Π½Π½ΠΎΡΡΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ, ΠΏΠΎΠ»ΡΡΠ°Π΅ΠΌΠΎΠΉ ΠΏΡΠΈ Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΠΈ ΠΏΡΠΎΠ²Π΅ΡΠΎΠΊ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π² ΠΊΠ°ΠΆΠ΄ΠΎΠΌ ΠΈΠ· Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΠ΅ΠΌΡΡ
ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠΎΡΡΠΎΡΠ½ΠΈΠΉ ΠΏΡΠΎΡΠ΅ΡΡΠ° Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ. Π ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΈ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΠΈ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠ° Π²ΠΎΠ·ΠΌΠΎΠΆΠ½Ρ ΠΊΠ°ΠΊ ΠΏΠΎΡΠ΅ΡΠΈ, ΡΠ°ΠΊ ΠΈ Π²ΡΠΈΠ³ΡΡΡΠΈ. Π Π°Π·Π½ΠΎΡΡΡ ΠΈΡ
Π°ΠΏΡΠΈΠΎΡΠ½ΡΡ
ΠΈ Π°ΠΏΠΎΡΡΠ΅ΡΠΈΠΎΡΠ½ΡΡ
ΡΡΠ΅Π΄Π½ΠΈΡ
Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ ΡΠΈΡΠ»Π΅Π½Π½ΠΎ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΠ΅Ρ ΡΠ΅Π½Π½ΠΎΡΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ. ΠΠ΅Π»ΠΈΡΠΈΠ½Π° ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ ΡΠ΅Π½Π½ΠΎΡΡΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π·Π°Π²ΠΈΡΠΈΡ ΠΎΡ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ΅ΠΉ ΠΈΡΡ
ΠΎΠ΄ΠΎΠ² ΠΏΡΠΎΠ²Π΅ΡΠΎΠΊ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² ΠΈ ΠΏΡΠΎΠΏΠΎΡΡΠΈΠΎΠ½Π°Π»ΡΠ½Π° ΡΠ°Π·Π½ΠΎΡΡΠΈ Π°ΠΏΠΎΡΡΠ΅ΡΠΈΠΎΡΠ½ΡΡ
ΠΈ Π°ΠΏΡΠΈΠΎΡΠ½ΡΡ
Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ΅ΠΉ Π΄ΠΎΡΡΠΈΠΆΠ΅Π½ΠΈΡ ΡΠ΅Π»ΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ.
ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠ³ΠΎ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°ΡΡ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΡΡ ΠΏΠΎ ΠΊΡΠΈΡΠ΅ΡΠΈΡ ΠΌΠ°ΠΊΡΠΈΠΌΡΠΌΠ° ΡΠ΅Π½Π½ΠΎΡΡΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π³ΠΈΠ±ΠΊΡΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π² Π²ΠΈΠ΄Π΅ ΠΎΡΠΈΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ Π³ΡΠ°ΡΠ° ΠΈΠ»ΠΈ ΡΠΏΠΎΡΡΠ΄ΠΎΡΠ΅Π½Π½ΡΡ
ΠΏΠΎ ΠΎΡΠ΅ΡΠ΅Π΄Π½ΠΎΡΡΠΈ ΠΈΡ
Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΡ Π½Π°Π±ΠΎΡΠΎΠ² ΠΏΡΠΎΠ²Π΅ΡΠΎΠΊ, Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΡΡ
Π΄Π»Ρ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΠ³ΠΎ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ, Π² ΠΊΠΎΡΠΎΡΠΎΠΌ Π½Π°Ρ
ΠΎΠ΄ΠΈΡΡΡ ΠΎΠ±ΡΠ΅ΠΊΡ.
Π Π΅Π°Π»ΠΈΠ·Π°ΡΠΈΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π²ΠΎΠ·ΠΌΠΎΠΆΠ½Π° Π² ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎ-Π°Π»Π³ΠΎΡΠΈΡΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΠΈ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ²
RANCANG BANGUN APLIKASI SMARTWATCH βSCHEDULLING ACTIVITIESβ MENGGUNAKAN MODEL MOBILE-D
Smartwatch merupakan salah satu teknologi yang berkembang didunia. Perkembangan smartwatch dunia juga mempengaruhi penggunaannya di Indonesia. Pengguna Smartwatch didunia hanya sebesar 0.1% dari pengguna smartphone didunia yang sebesar 1914.6 juta pengguna, padahal perkembangan teknologi smartwatch sendiri semakin hari semakin bertambah. Smartwatch merupakan jam tangan pintar yang menggunakan sistem operasi android wear. Dengan menggunakan smartwatch pengguna mendapatkan fungsi utama jam tangan beserta kelebihan lainnya seperti kalkulator, pedometer, informasi waktu minum air dan lain-lainnya. Penggunaan smartwatch juga dapat membantu penggunya dalam berbagai kegiatan, salah satunya adalah penjadualan aktifitas setiap hari. Aplikasi penjadualan aktifitas berbasikan smartwatch dapat membantu pengguna dalam mengingat dan menentukan kegiatan setiap harinya dengan memasukkan jadual kegiatan pada sebuah smartphone dan informasi kegiatan akan dimunculkan pada smartwatch yang digunakan pengguna. Dengan keadaan seperti ini, maka pengguna selalu dapat melihat informasi yang muncul setiap ada janji yang akan berlangsung setiap harinya. Mobile-D merupakan salah satu model pengembangan apikasi yang digunakan dalam pengembangan aplikasi ini. Dengan lima kegiatan utama: Explore, Initialize Productionize, Stabilize dan System Test & Fix menjadikan mobile-d sebagai model pengembangan yang cocok dalam pengembangan aplikasi berbasiskan android. Aplikasi Smartwatch yang dikembangkan telah berhasil dan diuji pada dua buah smartwatch Lemfo Lem5 dan Asus Zenwatch. Uji aplikasi menggunakan teknologi bluetooth yang ada pada smartwatch dan dihubungkan dengan bluetooth dari smartphone.Dari hasil uji, aplikasi dapat berjalan baik pada kedua smartwatch yang berbeda dan fungsi-fungsi aplikasi juga telah berjalan sesuai dengan rencan
Urban planning, public participation and digital technology: App development as a method of generating citizen involvement in local planning processes
There has been a recent shift in England towards empowering citizens to shape their neighbourhoods. However, current methods of participation are unsuitable or unwieldy for many people. In this paper, we report on ChangeExplorer, a smart watch application to support citizen feedback, to investigate the extent to which digital wearables can address barriers to participation in planning. The research contributes to both technology-mediated citizen involvement and urban planning participation methods. The app leverages in-situ, quick interactions encouraging citizens to reflect and comment on their environment. Taking a case study approach, the paper discusses the design and deployment of the app in a local planning authority through interviews with 19 citizens and three professional planners. The paper discusses the potential of the ChangeExplorer app to address more conceptual issues, and concludes by assessing the degree to which the technology raises awareness of urban change and whether it could serve as a gateway to more meaningful participatory methods
Geospatial Semantics
Geospatial semantics is a broad field that involves a variety of research
areas. The term semantics refers to the meaning of things, and is in contrast
with the term syntactics. Accordingly, studies on geospatial semantics usually
focus on understanding the meaning of geographic entities as well as their
counterparts in the cognitive and digital world, such as cognitive geographic
concepts and digital gazetteers. Geospatial semantics can also facilitate the
design of geographic information systems (GIS) by enhancing the
interoperability of distributed systems and developing more intelligent
interfaces for user interactions. During the past years, a lot of research has
been conducted, approaching geospatial semantics from different perspectives,
using a variety of methods, and targeting different problems. Meanwhile, the
arrival of big geo data, especially the large amount of unstructured text data
on the Web, and the fast development of natural language processing methods
enable new research directions in geospatial semantics. This chapter,
therefore, provides a systematic review on the existing geospatial semantic
research. Six major research areas are identified and discussed, including
semantic interoperability, digital gazetteers, geographic information
retrieval, geospatial Semantic Web, place semantics, and cognitive geographic
concepts.Comment: Yingjie Hu (2017). Geospatial Semantics. In Bo Huang, Thomas J. Cova,
and Ming-Hsiang Tsou et al. (Eds): Comprehensive Geographic Information
Systems, Elsevier. Oxford, U
Technology-enabled planning participation: Designing & deploying digital technology to encourage citizen participation in urban planning
Ph. D. Thesis.Citizens increasingly want to formally engage with the governmental and policy processes that
manage how places change. Whilst enhancing the role of citizens in urban planning has been
a longstanding objective for academics and communities, translating these aspirations into
practice has proved to be more challenging. Although a range of conceptual ideas and
practical techniques have been developed in planning to provide opportunities to enhance
citizen involvement, these ideas and methods have faced several challenges. These include the
strict legalistic and policy parameters that determine what sort of comments that are
permissible, the governmental initiators of public engagement, and the need to understand
and utilise the often complex language of planning. And yet citizens and communities are
increasingly resorting to social media and digital communication to express their views about
urban change.
This research assesses the degree to which new digital technology can be designed and
deployed to enhance citizen engagement within urban planning and identify whether it offers
one potential method to address and overcome some of the challenges being experienced in
citizen engagement. Through designing, deploying and evaluating speculative digital
technologies, the research aims to understand the potential role of technology in facilitating
enhanced citizen participation in planning. Working with citizens, community organisations
and planners, the research explores the factors at play when innovative and bespoke
engagement methods are used to amplify citizensβ voices in urban change. An action research
approach was taken, which uses a continual cycle of designing and planning, deploying
different types of technologies and reflections to inform design. Three technologies were
piloted in different settings and contexts: a social media example addressing a complex
planning issues; a smart watch application to support in-place engagement; and an interactive
digital device that encourages people to communicate their feelings and aspirations through
visual and oral means. Across the three examples, over 1400 citizens participated in the
research.
Findings demonstrate how the three digital initiatives encouraged people to be expressive
when communicating complicated feelings towards urban change, and the influence different
methods have on what people communicate. They illustrate how different participation
methods can support differing levels of engagement, and how digital technologies might better
align with how citizens would like to participate.
The research critiques the suitability of current participation methods, and the extent to
which they can support a genuine discussion about where people live and what they care
about. It concludes by questioning whether current planning engagement methods can
adequately equip non-experts with the tools to participate. The overall conclusion is that by
employing digital technologies, a much more productive and fruitful conversation can be
designed to facilitate citizen participation in planning compared to traditional methods