141 research outputs found
Research and Design of an X-Band UHF Power Amplifier
Introduction. A method for designing power amplifiers for use in the transmitting channels of X-band transceiver modules is investigated. The design process was aimed at optimizing the relationship between the basic amplifier characteristics, including the operating frequency band, output power level, output linearity, high harmonics suppression, etc. Β Aim. Investigation of a method for designing an X-band UHF power amplifier, which is capable of optimizing the relationship between its main characteristics. Β Materials and methods. Theoretical calculations were combined with experimental studies into the design of a UHF power amplifier. The stages of the design process are described in detail, including major ideas, principal circuits, and strip circuits. Evaluations were conducted using the Keysight ADS high frequency circuit simulation tool. Β Results. A method for designing X-band UHF power amplifiers on the basis of a close combination of theory, simulation, and experimental adjustment was described in detail. Β Conclusion. A prototype of an X-band PA was developed; an approach to developing a methodology for manufacturing, measuring, and testing X-band PAs is described.Introduction. A method for designing power amplifiers for use in the transmitting channels of X-band transceiver modules is investigated. The design process was aimed at optimizing the relationship between the basic amplifier characteristics, including the operating frequency band, output power level, output linearity, high harmonics suppression, etc. Β Aim. Investigation of a method for designing an X-band UHF power amplifier, which is capable of optimizing the relationship between its main characteristics. Β Materials and methods. Theoretical calculations were combined with experimental studies into the design of a UHF power amplifier. The stages of the design process are described in detail, including major ideas, principal circuits, and strip circuits. Evaluations were conducted using the Keysight ADS high frequency circuit simulation tool. Β Results. A method for designing X-band UHF power amplifiers on the basis of a close combination of theory, simulation, and experimental adjustment was described in detail. Β Conclusion. A prototype of an X-band PA was developed; an approach to developing a methodology for manufacturing, measuring, and testing X-band PAs is described
Impact of foaming conditions on quality for foam-mat drying of Butterfly pea flower by multiple regression analysis
In recent years, the Butterfly pea flower has been increasingly interested in its color and function. However, the preservation of the extract faced many difficulties; therefore, foam drying technology was applied to solve this problem. The study was conducted to determine the effect of foaming conditions, including albumin ratio, carboxymethyl cellulose (CMC) ratio, and whipping time on foam characteristics. At the same time, the multi-dimensional regression method was also used to determine the most suitable foaming conditions for the following process. The research results showed that all 3 factors strongly influenced the foaming process of pea flower extract. It could be concluded that the most suitable condition for foaming is to use 9.3% albumin, 0.79% CMC and stir for 19 min. Under these conditions, the foam expansion and stability were 584.79% and 96.44% respectively. The powder obtained from the foam drying of Butterfly pea flower extract was also analyzed for quality. The temperature of 65 oC for 4 hrs gave relatively high-quality powder with protein content, anthocyanin and antioxidant activity of 9.89 g/100g, 1.15 mg/g and 87.34% respectively. In conclusion, the foam-mat dried powder from butterfly pea flower extract is suitable for other processing processes, especially in the processing of folk cakes, pasta and bread industry
Influence Of Fabrication Condition on the Microstructural and Optical Properties of Lead-Free Ferroelectric BiNaTiO Materials
Lead-free ferroelectric materials have attracted considerable attention due to the increasing potential application in environmental benign materials. Among lead-free ferroelectric materials, the Bi0.5Na0.5TiO3 (BNT) materials were more studied because it exhibited the good ferroelectric and piezoelectric properties which could be promising candidate materials replacing Pb(Zr,Ti)O3. In this work, the lead-free ferroelectric BNT materials were synthesized by sol-gel method. The effects of fabrication process to microstructural and optical properties were studied which includes Na precursor concentration and calcining temperature. The result indicated that the Na precursor concentration were higher 40 mol.% and the calcining temperature
Π‘ΠΈΠ½ΡΠ΅Π· Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΡΡΠ°Π΅ΠΊΡΠΎΡΠ½ΠΎΠΉ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌΠΈ ΡΠ΅ΠΎΡΠΈΠΈ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ Π΄Π°Π½Π½ΡΡ
Introduction.Β Requirements for the quality of information about the trajectory of moving objects provided by sensor networks are increasingly becoming more stringent. For Information and Data Processing Centers (DPC) at control and management command posts, the issue of information mapping and forming the true trajectories of moving objects in the area of intersection of network detection zones is of particular importance. The use of conventional approaches to solving this problem involves issuesΒ related to ensuring the efficient provision of users with complete and reliable information about trajectories in real time. In this article, wee propose a new approach to solving this problem using data mining theory, in particular, the methods of data clustering theory. Based on an analysis of the process of processing radar data in a DPC and its similarity with that of data clustering, we synthesized an algorithm for processing the trajectories of moving objects. The algorithm was verified by modelling and experimental research.Aim.Β To develop a generalized scheme for processing object trajectories (TP) in a DPC and to synthesized a TP algorithm using the methods of data clustering theory.MaterialsΒ andΒ methods.Β DataΒ ClusteringΒ theory,Β SystemsΒ Β EngineeringΒ theory,Β RadarΒ DataΒ processingΒ theory (RD), methods of mathematical modelling and experimental research.Results.Β Based on an analysis of the essence of radar data processing (RD) in a DPC and its similarity with the process of data clustering,Β an algorithm for processing the trajectories of moving objects was synthesized and verified by modelling and experimental research. A generalized scheme for processing the trajectories of moving objects in a DPC and a TP algorithm for a DPC were synthesized.Conclusions.Β An algorithm for processing object trajectories was proposed based on a new approach of data clustering theory. A generalized scheme and an algorithm for processing object trajectories (TP) in a DPC were suggested. These developments can beΒ effectively applied in various models, e.g. centralized, hierarchical and decentralized. The synthesized algorithm can provide output information about the true identified trajectories in terms of various indicators of data processing systems (DPS).ΠΠ²Π΅Π΄Π΅Π½ΠΈΠ΅.Β Π‘Β ΠΊΠ°ΠΆΠ΄ΡΠΌΒ Π³ΠΎΠ΄ΠΎΠΌΒ ΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡΒ ΡΠ»ΠΎΠΆΠ½Π΅Π΅Β ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°ΡΡΒ ΠΏΡΠΎΡΠ΅ΡΡΒ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈΒ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈΒ ΠΎ ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΡΡ
Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ², ΠΏΠΎΠ»ΡΡΠ°Π΅ΠΌΠΎΠΉΒ ΠΈΠ· ΡΠ΅Π½ΡΠΎΡΠ½ΠΎΠΉ ΡΠ΅ΡΠΈ. ΠΡΠΎΡ ΠΏΡΠΎΡΠ΅ΡΡ ΠΈΠΌΠ΅Π΅Ρ ΠΎΡΠΎΠ±ΠΎΠ΅ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ Π΄Π»ΡΒ ΡΠ΅Π½ΡΡΠΎΠ² ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΈ Π΄Π°Π½Π½ΡΡ
(Π¦ΠΠΠ), Π² ΠΊΠΎΡΠΎΡΡΡ
ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ΅ΡΡΡ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ ΠΈ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠ΅, ΡΠ°ΠΊ ΠΊΠ°ΠΊ Π²ΠΎΠ·Π½ΠΈΠΊΠ°Π΅Ρ Π²ΠΎΠΏΡΠΎΡ ΡΠΎΠΏΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΈ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΡΡΠΈΠ½Π½ΡΡ
ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΉ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΠΏΠ΅ΡΠ΅ΡΠ΅ΡΠ΅Π½ΠΈΡ Π·ΠΎΠ½ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ ΡΠ΅ΡΠΈ. ΠΡΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π½ΡΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ²Β ΠΊΒ ΡΠ΅ΡΠ΅Π½ΠΈΡΒ Π΄Π°Π½Π½ΠΎΠΉΒ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡΒ Π²ΠΎΠ·Π½ΠΈΠΊΠ°ΡΡΒ Π²ΠΎΠΏΡΠΎΡΡ,Β ΡΠ²ΡΠ·Π°Π½Π½ΡΠ΅Β ΡΒ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΠ΅ΠΌΒ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎΒ ΠΏΡΠ΅Π΄ΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΡΒ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Π΅ΠΌΒ Π²ΡΡ
ΠΎΠ΄Π½ΠΎΠΉΒ Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎΠΉΒ ΡΡΠ°Π΅ΠΊΡΠΎΡΠ½ΠΎΠΉΒ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈΒ Π²Β ΡΠ΅Π°Π»ΡΠ½ΠΎΠΌΒ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ.Β ΠΒ Π΄Π°Π½Π½ΠΎΠΉ ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ Π½ΠΎΠ²ΡΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΊ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΡΡΠΎΠ³ΠΎ Π²ΠΎΠΏΡΠΎΡΠ° Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠ΅ΠΎΡΠΈΠΈ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½ΡΡ
(data mining) Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ Π΄Π°Π½Π½ΡΡ
.Π¦Π΅Π»Ρ ΡΠ°Π±ΠΎΡΡ. Π Π°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΠΎΠ±ΠΎΠ±ΡΠ΅Π½Π½ΠΎΠΉ ΡΡ
Π΅ΠΌΡ ΡΡΠ°Π΅ΠΊΡΠΎΡΠ½ΠΎΠΉ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ (Π’Π) Π² Π¦ΠΠΠ ΠΈ ΡΠΈΠ½ΡΠ΅Π· Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π’Π Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ Π΄Π°Π½Π½ΡΡ
.ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ.Β Π’Π΅ΠΎΡΠΈΡ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ Π΄Π°Π½Π½ΡΡ
, ΡΠ΅ΠΎΡΠΈΡ ΡΠΈΡΡΠ΅ΠΌΠΎΡΠ΅Ρ
Π½ΠΈΠΊΠΈ,Β ΡΠ΅ΠΎΡΠΈΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠ°Π΄ΠΈΠΎΠ»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉΒ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈΒ (Π ΠΠ), ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ.Β ΠΠ° ΠΎΡΠ½ΠΎΠ²Π΅ Π°Π½Π°Π»ΠΈΠ·Π° ΡΡΡΠ½ΠΎΡΡΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈΒ Π ΠΠΒ Π² Π¦ΠΠΠ ΠΈ Π΅Π³ΠΎ ΡΡ
ΠΎΠ΄ΡΡΠ²Π° Ρ ΠΏΡΠΎΡΠ΅ΡΡΠΎΠΌ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈΒ Π΄Π°Π½Π½ΡΡ
Β ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½Β Π°Π»Π³ΠΎΡΠΈΡΠΌΒ ΡΡΠ°Π΅ΠΊΡΠΎΡΠ½ΠΎΠΉΒ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈΒ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ²,Β ΠΏΡΠΎΠ²Π΅ΡΠ΅Π½Π½ΡΠΉΒ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ΠΌΒ ΠΈΒ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΠΎ.Β ΠΠΎΠΌΠΈΠΌΠΎΒ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ°Β ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½Π°Β ΡΡΡΡΠΊΡΡΡΠ½Π°ΡΒ ΡΡ
Π΅ΠΌΠ°Β Π’Π Β Π΄Π»ΡΒ Π¦ΠΠΠ, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΠΎΠΉ ΠΈΠ· ΡΠ΅Π½ΡΠΎΡΠ½ΠΎΠΉ ΡΠ΅ΡΠΈ.ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅.Β ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Ρ ΠΎΠ±ΠΎΠ±ΡΠ΅Π½Π½Π°ΡΒ ΡΡΡΡΠΊΡΡΡΠ½Π°ΡΒ ΡΡ
Π΅ΠΌΠ°Β ΠΈΒ Π°Π»Π³ΠΎΡΠΈΡΠΌΒ Π’ΠΒ Π΄Π»ΡΒ Π¦ΠΠΠ.Β ΠΠ½ΠΈΒ ΠΌΠΎΠ³ΡΡΒ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎ ΠΏΡΠΈΠΌΠ΅Π½ΡΡΡΡΡ Π΄Π»Ρ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌΠ½ΡΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Π’Π, ΡΠ°ΠΊΠΈΡ
, ΠΊΠ°ΠΊΒ ΡΠ΅Π½ΡΡΠ°Π»ΠΈΠ·ΠΎΠ²Π°Π½Π½Π°Ρ, ΠΈΠ΅ΡΠ°ΡΡ
ΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΈ Π΄Π΅ΡΠ΅Π½ΡΡΠ°Π»ΠΈΠ·ΠΎΠ²Π°Π½Π½Π°Ρ.Β Π‘ΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½Π½ΡΠΉΒ Π°Π»Π³ΠΎΡΠΈΡΠΌΒ ΠΌΠΎΠΆΠ΅ΡΒ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡΒ ΠΏΡΠ΅Π΄ΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅Β Π²ΡΡ
ΠΎΠ΄Π½ΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΎΠ±Β ΠΈΡΡΠΈΠ½Π½ΡΡ
ΠΎΡΠΎΠΆΠ΄Π΅ΡΡΠ²Π»Π΅Π½Π½ΡΡ
ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΡΡ
ΠΏΠΎ ΠΌΠ½ΠΎΠ³ΠΈΠΌ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΠΌ ΡΠΈΡΡΠ΅ΠΌΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ (Π‘ΠΠ)
An Aquaculture Water Checker--design and Manufacture
A real-time, mobile aquaculture water checker is presented. The configuration of double integrating spheres is developed for simultaneously measuring backward scattering , forward scattering Β and transmitted light . Based on Kubelka-Munk model, a set of optical parameters including absorption coefficient , scattering coefficient and anisotropy Β Β are calculated. The obtained results for diluted milk standard samples with different milk concentrations and aquaculture water samples with different densities of Psexdo-Nitzschia-delicatissium algae are also reported
Autologous Transplantation of Adipose-Derived Stem Cells to Treat Acute Spinal Cord Injury: Evaluation of Clinical Signs, Mental Signs, and Quality of Life
BACKGROUD: Spinal cord injury (SCI) is damage that can cause a temporary or permanent change in spinal cord functions
AIM: This work evaluates clinical signs, mental signs, and quality of life (QoL) after autologous adipose-derived stem cells (ADSCs) transplantation to treat acute spinal cord injury (SCI).
MATERIAL AND METHODS: In this study, 47 SCI patients were recruited and divided into two groups: intervention and control. ADSCs were isolated and cultured under the cell culture quality control procedure. All patients in both groups underwent neurosurgery with or without ADSC transplantation. The recovery regarding neurological muscle, QoL, neurogenic bladder, and mental improvement was assessed after transplantation.
RESULTS: All patients had improved in terms of motor function, bladder function, and daily living. No patients reported any side effect. MRI imaging showed significant changes in the lesion length of the spinal canal and the thickening of the spinal cord. Mental improvement was highest at six months after transplantation and lowest at one month after transplantation. The proportion of patients whose quality of life improved after treatment was 100%, while 80% of patients were satisfied with treatment outcomes.
CONCLUSIONS: Thus, our data suggested that ADSCs transplantation was safe and effective for the treatment of SCI patients. Neurological muscle and neurogenic bladder were improved significantly after transplantation
Π‘ΠΈΠ½ΡΠ΅Π· ΠΎΠ±ΠΎΠ±ΡΠ΅Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄Π°Π½Π½ΡΡ ΠΏΠΎ ΠΎΡΡΠ°ΠΆΠ΅Π½Π½ΡΠΌ ΡΠΈΠ³Π½Π°Π»Π°ΠΌ ΠΎΡ ΡΠ»ΠΎΠΆΠ½ΡΡ ΡΠ΅Π»Π΅ΠΉ
Introduction. The quality of input information for trajectory processing (TP) systems can be improved by increasing the measurement accuracy of radar sensors (RS). However, in such a case, radar targets acquire the characteristics of complex targets having several marks at the output of the detector. This makes it difficult to accurately assess the kinetic parameters of targets in a TP system. In this respect, the development of a generalized algorithm for processing and generating data from the reflected signals of complex targets seems a relevant research task.Aim. To investigate reasons for the formation of complex targets and, using the theory of radar image processing, to synthesize an algorithm for processing and generating data on reflected signals from a complex target.Materials and methods. The following methodological approaches were used: the theory of digital signal processing; applied theory of radar image processing; MATLAB Simulink Toolboxes for simulating radar image processing; some prerequisites for fuzzy clustering methods.Results. Following an analysis of some characteristics of complex targets and the theory of radar image processing, an generalized algorithm was synthesized for processing and generating data of reflected signals from this class of targets. The results can be used to improve the measurement accuracy of their representative point when solving the TP problem.Conclusion. Reasons for the formation of complex targets in radar technology were analyzed. Their specific features consist in the need to accurately assess a true mark. A generalized algorithm for processing and generating these signals reflected from complex targets was proposed. The results can serve as a basis for solving the TP problem.ΠΠ²Π΅Π΄Π΅Π½ΠΈΠ΅. ΠΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° Π²Ρ
ΠΎΠ΄Π½ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π΄Π»Ρ ΡΠΈΡΡΠ΅ΠΌΡ ΡΡΠ°Π΅ΠΊΡΠΎΡΠ½ΠΎΠΉ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ (Π’Π) Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΡΠΎΡΠ½ΠΎΡΡΠΈ ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΠΉ ΡΠ°Π΄ΠΈΠΎΠ»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΡ
(Π Π) ΡΠ΅Π½ΡΠΎΡΠΎΠ² ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΎΠ΄Π½ΠΈΠΌ ΠΈΠ· ΠΎΡΠ΅Π²ΠΈΠ΄Π½ΡΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ². ΠΠ΄Π½Π°ΠΊΠΎ ΠΏΡΠΈ ΡΡΠΎΠΌ Π Π-ΡΠ΅Π»ΠΈ ΠΌΠΎΠ³ΡΡ ΡΡΠ°ΡΡ "ΡΠ»ΠΎΠΆΠ½ΡΠΌΠΈ ΡΠ΅Π»ΡΠΌΠΈ", ΠΈΠΌΠ΅ΡΡΠΈΠΌΠΈ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΎ ΠΎΡΠΌΠ΅ΡΠΎΠΊ Π½Π° Π²ΡΡ
ΠΎΠ΄Π΅ ΠΎΠ±Π½Π°ΡΡΠΆΠΈΡΠ΅Π»Ρ. ΠΡΠΎ Π·Π°ΡΡΡΠ΄Π½ΡΠ΅Ρ ΡΠΎΡΠ½ΡΡ ΠΎΡΠ΅Π½ΠΊΡ ΠΊΠΈΠ½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΡΠ΅Π»Π΅ΠΉ Π² ΡΠΈΡΡΠ΅ΠΌΠ΅ Π’Π. Π ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠΈΠ½ΡΠ΅Π·Π° ΠΎΠ±ΠΎΠ±ΡΠ΅Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄Π°Π½Π½ΡΡ
ΠΈΠ· ΠΎΡΡΠ°ΠΆΠ΅Π½Π½ΡΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠ΅Π»Π΅ΠΉ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ΅Π³ΠΎ ΡΠΎΡΠ½ΠΎ ΠΎΡΠ΅Π½ΠΈΡΡ ΠΊΠΈΠ½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡ Π΄Π»Ρ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π·Π°Π΄Π°ΡΠΈ Π’Π.Π¦Π΅Π»Ρ ΡΠ°Π±ΠΎΡΡ. ΠΡΠ°ΡΠΊΠΎΠ΅ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΠΏΡΠΈΡΠΈΠ½ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ "ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠ΅Π»Π΅ΠΉ". Π‘ΠΈΠ½ΡΠ΅Π· ΠΎΠ±ΠΎΠ±ΡΠ΅Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄Π°Π½Π½ΡΡ
ΠΏΠΎ ΠΎΡΡΠ°ΠΆΠ΅Π½Π½ΡΠΌ ΡΠΈΠ³Π½Π°Π»Π°ΠΌ ΠΎΡ ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠ΅Π»Π΅ΠΉ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠ΅ΠΎΡΠΈΠΈ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π Π-ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ.ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. Π’Π΅ΠΎΡΠΈΡ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠΈΠ³Π½Π°Π»ΠΎΠ²; ΠΏΡΠΈΠΊΠ»Π°Π΄Π½Π°Ρ ΡΠ΅ΠΎΡΠΈΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π Π-ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ; MATLAB Simulink Toolboxes Π΄Π»Ρ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π Π-ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ; ΠΌΠ΅ΡΠΎΠ΄Ρ Π½Π΅ΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΠ° ΠΎΡΠ½ΠΎΠ²Π΅ Π°Π½Π°Π»ΠΈΠ·Π° Π½Π΅ΠΊΠΎΡΠΎΡΡΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠ΅Π»Π΅ΠΉ ΠΈ ΡΠ΅ΠΎΡΠΈΠΈ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π Π-ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½ ΠΎΠ±ΠΎΠ±ΡΠ΅Π½Π½ΡΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄Π°Π½Π½ΡΡ
ΠΎΡΡΠ°ΠΆΠ΅Π½Π½ΡΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΠΎΡ ΡΡΠΎΠ³ΠΎ ΠΊΠ»Π°ΡΡΠ° ΡΠ΅Π»Π΅ΠΉ, ΡΠ²Π»ΡΡΡΠΈΡ
ΡΡ ΠΏΡΠ΅Π΄ΠΏΠΎΡΡΠ»ΠΊΠΎΠΉ Π΄Π»Ρ ΡΠΎΡΠ½ΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΈΡ
"ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΎΡΠΌΠ΅ΡΠΊΠΈ" ΠΏΡΠΈ ΡΠ΅ΡΠ΅Π½ΠΈΠΈ Π·Π°Π΄Π°ΡΠΈ Π’Π.ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. Π ΡΡΠ°ΡΡΠ΅ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ Π°Π½Π°Π»ΠΈΠ· ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠ΅ΠΉ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠ΅Π»Π΅ΠΉ Π² Π Π-ΡΠ΅Ρ
Π½ΠΈΠΊΠ΅ ΠΈ ΠΈΡ
ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠ΅ΠΉ ΠΏΡΠΈ ΡΠΎΡΠ½ΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠ΅ ΠΈΡΡΠΈΠ½Π½ΠΎΠΉ ΠΎΡΠΌΠ΅ΡΠΊΠΈ; ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½ ΠΎΠ±ΠΎΠ±ΡΠ΅Π½Π½ΡΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π Π-ΡΠΈΠ³Π½Π°Π»ΠΎΠ², ΠΎΡΡΠ°ΠΆΠ΅Π½Π½ΡΡ
ΠΎΡ ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠ΅Π»Π΅ΠΉ, ΡΠ²Π»ΡΡΡΠΈΠΉΡΡ ΠΎΡΠ½ΠΎΠ²ΠΎΠΉ ΠΏΡΠΈ ΡΠ΅ΡΠ΅Π½ΠΈΠΈ Π·Π°Π΄Π°Ρ Π’Π
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