Fuzzy Logic Simulation for Classifying Abrasive Wheels in Pendulum Grinding

2021 ◽  
pp. 1-14
Author(s):  
Van Canh Nguyen ◽  
Van Chi Hoang ◽  
Vanthan Nguyen ◽  
Ngoc Hue Nguyen
Keyword(s):  
Fuzzy Logic ◽  
Logic Simulation

scholarly journals Fuzzy logic simulation of oat yield after using hydrogel and nitrogen biopolymer management

2020 ◽  
pp. 1319-1327
Author(s):  
Osmar Bruneslau Scremin ◽  
José Antonio Gonzalez da Silva ◽  
Ivan Ricardo Carvalho ◽  
Ângela Teresinha Woschinski De Mamann ◽  
Odenis Alessi ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Air Temperature ◽  
Biomass Yield ◽  
New Technologies ◽  
Block Design ◽  
Fuzzy Model ◽  
Logic Simulation ◽  
Efficient Tool ◽  
Randomized Block Design ◽  
Maximum Air Temperature

The fuzzy logic is an efficient tool for simulation and validation of new technologies in agriculture. The objective of the study is to adapt the fuzzy logic model for simulation of biomass and oat grain yield by nitrogen involving the nonlinearity of the maximum air temperature in the conditions of use of the biopolymer hydrogel, considering high succession systems and low release of residual N. The study was conducted in 2014 and 2015, in a randomized block design with four replicates in a 5 x 5 factorial. Five hydrogel doses (0, 30, 60, 90 and 120 kg ha-1) were added in the groove next to the seed; and 5 doses of N-fertilizer (0, 30, 60, 90 and 120 kg ha-1) applied at the fourth expanded leaf stage, respectively. The cultivar was URS Corona. The pertinence functions and the linguistic values established in the input and output variables to simulate the biomass yield and oat grains in the succession systems are adequate observed productivity. The fuzzy model makes it possible to estimate the biomass and oat grains productivity efficiently under the conditions of use of the hydrogel as a function of the nitrogen doses and maximum air temperature, adding to the existing models of simulation.

Fuzzy Logic Simulation to Process Control Systems

2002 ◽  
Vol 1 (3) ◽  
pp. 272-279 ◽  
Author(s):  
M. Shaharurrizal B.M.W. . ◽  
Farrah D. Herman . ◽  
A. Arunagiri . ◽  
Stella Morris .
Keyword(s):  
Fuzzy Logic ◽  
Process Control ◽  
Control Systems ◽  
Logic Simulation ◽  
Process Control Systems

Quantifying effects of accidents by fuzzy-logic- and simulation-based analysis

2006 ◽  
Vol 33 (3) ◽  
pp. 219-226 ◽  
Author(s):  
Sangyoub Lee ◽  
Daniel W Halpin ◽  
Hoon Chang
Keyword(s):  
Fuzzy Logic ◽  
Simulation Analysis ◽  
Productivity Loss ◽  
Indirect Costs ◽  
Safety Factors ◽  
Logic Simulation ◽  
Training And Supervision ◽  
Simulation Based ◽  
The Relationship ◽  
Productive Time

This study quantifies the effects of accidents by defining one of the indirect costs, the productive time lost owing to accidents in utility trenching operations. The probability of accidents, estimated by fuzzy-logic-based analysis of the performance of the factors (training, supervision, and preplanning) affecting safety in utility trenching operations, was used to quantify, based on simulation analysis, the productivity loss due to process delays resulting from accidents during excavation and pipe installation. It was determined that the productivity loss resulting from accidents during excavation is greater than that resulting from accidents during pipe installation. During excavation, the "very poor" condition of preplanning is most critical to productivity loss due to accidents, whereas during pipe installation, the condition of training and supervision affects the productivity loss more than that of preplanning. This paper provides insights into the relationship between the condition of the safety factors and the possible productivity loss by concomitant probability of accidents to quantify the effects of the accidents.Key words: effect of accidents, probability of accidents, productivity, fuzzy logic, simulation.

scholarly journals Prediction of cold start hydrocarbon emissions of air cooled two wheeler spark ignition engines by simple fuzzy logic simulation

2014 ◽  
Vol 18 (1) ◽  
pp. 179-191 ◽  
Author(s):  
Raja Samuel ◽  
Arasu Valan
Keyword(s):  
Fuzzy Logic ◽  
Cold Start ◽  
Spark Ignition ◽  
Hydrocarbon Emissions ◽  
Spark Ignition Engines ◽  
Logic Simulation ◽  
Warm Start ◽  
Air Supply ◽  
Hydrocarbon Emission ◽  
Secondary Air

The cold start hydrocarbon emission from the increasing population of two wheelers in countries like India is one of the research issues to be addressed. This work describes the prediction of cold start hydrocarbon emissions from air cooled spark ignition engines through fuzzy logic technique. Hydrocarbon emissions were experimentally measured from test engines of different cubic capacity, at different lubricating oil temperature and at different idling speeds with and without secondary air supply in exhaust. The experimental data were used as input for modeling average hydrocarbon emissions for 180 seconds counted from cold start and warm start of gasoline bike engines. In fuzzy logic simulation, member functions were assigned for input variables (cubic capacity and idling rpm) and output variables (average hydrocarbon emission for first 180 seconds at cold start and warm start). The knowledge based rules were adopted from the analyzed experimental data and separate simulations were carried out for predicting hydrocarbon emissions from engines equipped with and without secondary air supply. The simulation yielded the average hydrocarbon emissions of air cooled gasoline engine for a set of given input data with accuracy over 90%.

Fuzzy-logic simulation of the development of a high-tech manufacturing enterprise

2015 ◽  
Vol 35 (5) ◽  
pp. 363-368 ◽  
Author(s):  
S. I. Solomennikova ◽  
B. A. Yakimovich ◽  
V. A. Tenenev
Keyword(s):  
Fuzzy Logic ◽  
High Tech ◽  
Logic Simulation ◽  
Manufacturing Enterprise

scholarly journals Fuzzy mamdani logic inference model in the loading of distribution substation transformer SCADA system

2021 ◽  
Vol 10 (2) ◽  
pp. 298
Author(s):  
Rahma Farah Ningrum ◽  
Riki Ruli A. Siregar ◽  
Darma Rusjdi
Keyword(s):  
Fuzzy Logic ◽  
Service Life ◽  
Data Acquisition ◽  
Logic Simulation ◽  
Life Duration ◽  
Scada System ◽  
Input Variables ◽  
Logic Inference ◽  
Monitoring Application ◽  
Data Period

<span id="docs-internal-guid-152e332e-7fff-7073-a541-1d420decb47b"><span>The research objective of supervisory control and data acquisition (SCADA), with fuzzy Mamdani logic simulation on the loading section of distribution transformer substations. Data acquisition is available when saving SAIFI SAIDI data and storing the results of monitoring equipment. The method used is Mamdani fuzzy logic, there are two input variables, namely current and voltage devices. The membership function in Mamdani fuzzy logic has been created based on the input current and voltage variables. Currently: parameter {0, 600} low is created {0, 350, 450, 600}, normal {400-650} parameter is created {400, 500, 550, 650}, parameter high {≥600} is created {600, 650, 750, 1000}, when determining the voltage: low {≤10.5} parameters {0 4 7 10.5}, normal {9-14} parameters {9, 10, 13, 14} and high {≥13} - parameters {13, 14, 15, 16}. Based on the results of the Mamdani logic rule test on the output current containing a transformer and a voltage sensor, the results obtained are IF (normal current; (630) AND voltage (high); (13.2) (high load transformer). The components in the simulation tool include miniature substations made with the 1A travel substation model, 3A substation as the main substation, the relay as distribution substation as the monitoring application. Telestatus and Telecontrol use a microcontroller. Initial scenario. After substation is resumed, data is stored after downtime, service life, duration, and data period. Initial scenario After substation is resumed, data is stored after downtime, service life, duration, and data period.</span></span>

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