scholarly journals A Fuzzy Logic Module to Estimate a Driver’s Fuel Consumption for Reality-Enhanced Serious Games

2018 ◽  
Vol 5 (4) ◽  
pp. 45-62 ◽  
Author(s):  
Rana Massoud ◽  
Stefan Poslad ◽  
Francesco Bellotti ◽  
Riccardo Berta ◽  
Kamyar Mehran ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Fuel Consumption ◽  
Serious Games ◽  
Rotation Speed ◽  
Expert Knowledge ◽  
Coefficient Of Determination ◽  
Fuzzy Models ◽  
Logic Module ◽  
Driver Performance ◽  
Target Environment

Reality-enhanced gaming is an emerging serious game genre, that could contextualize a game within its real instruction-target environment. A key module for such games is the evaluator, that senses a user performance and provides consequent input to the game. In this project, we have explored an application in the automotive field, estimating driver performance in terms of fuel consumption, based on three key vehicular signals, that are directly controllable by the driver: throttle position sensor (TPS), engine rotation speed (RPM) and car speed. We focused on Fuzzy Logic, given its ability to embody expert knowledge and deal with incomplete information availability. The fuzzy models – that we iteratively defined based on literature expertise and data analysis – can be easily plugged into a reality-enhanced gaming architecture. We studied four models with all the possible combinations of the chosen variables (TPS and RPM; RPM and speed; TPS and speed; TPS, speed and RPM). Input data were taken from the enviroCar database, and our fuel consumption predictions compared with their estimated values. Results indicate that the model with the three inputs outperforms the other models giving a higher coefficient of determination (R2), and lower error. Our study also shows that RPM is the most important fuel consumption predictor, followed by TPS and speed.

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

scholarly journals Modeling the seam strength of denim garments by using fuzzy expert system

2021 ◽  
Vol 16 ◽  
pp. 155892502198897
Author(s):  
Joy Sarkar ◽  
Md Abdullah Al Faruque ◽  
Moni Sankar Mondal
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Linear Density ◽  
Unit Length ◽  
Coefficient Of Determination ◽  
Sewing Thread ◽  
External Forces ◽  
Fuzzy Logic Expert System ◽  
The Relationship ◽  
Sufficient Strength

The main purpose of this study is to predict and develop a model for forecasting the Seam Strength (SS) of denim garments with respect to the thread linear density (tex) and Stitches Per Inch (SPI) by using a Fuzzy Logic Expert System (FLES). The seam strength is an important factor for the serviceability of any garments. As seams bound the fabric pieces together in a garment, the seams must have sufficient strength to execute this property even in the unexpected severe conditions where the garments are subjected to loads or any additional internal or external forces. Sewing thread linear density and number of stitches in a unit length of the seam are the two of the most important factors that affect the seam strength of any garments. But the relationship among these two specific variables and the seam strength is complex and non-linear. As a result, a fuzzy logic based model has been developed to demonstrate the relationship among these parameters and the developed model has been validated by the experimental trial. The coefficient of determination ( R2) was found to be 0.98. The mean relative error also lies withing acceptable limit. The results have suggested a very good performance of the model in the case of the prediction of the seam strength of the denim garments.

scholarly journals Experts versus Algorithms? Optimized Fuzzy Logic Energy Management of Autonomous PV Hybrid Systems with Battery and H2 Storage

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1777
Author(s):  
Lisa Gerlach ◽  
Thilo Bocklisch
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Fuzzy Logic Controller ◽  
Expert Knowledge ◽  
Large Power ◽  
Hybrid Energy ◽  
Knowledge Based ◽  
Long Term Storage ◽  
Fuzzy Logic Approach

Off-grid applications based on intermittent solar power benefit greatly from hybrid energy storage systems consisting of a battery short-term and a hydrogen long-term storage path. An intelligent energy management is required to balance short-, intermediate- and long-term fluctuations in electricity demand and supply, while maximizing system efficiency and minimizing component stress. An energy management was developed that combines the benefits of an expert-knowledge based fuzzy logic approach with a metaheuristic particle swarm optimization. Unlike in most existing work, interpretability of the optimized fuzzy logic controller is maintained, allowing the expert to evaluate and adjust it if deemed necessary. The energy management was tested with 65 1-year household load datasets. It was shown that the expert tuned controller is more robust to changes in load pattern then the optimized controller. However, simple readjustments restore robustness, while largely retaining the benefits achieved through optimization. Nevertheless, it was demonstrated that there is no one-size-fits-all tuning. Especially, large power peaks on the demand-side require overly conservative tunings. This is not desirable in situations where such peaks can be avoided through other means.

Expert Knowledge-Based Traction Control of a Truck Using Fuzzy Logic

1996 ◽  
Vol 29 (1) ◽  
pp. 7867-7872
Author(s):  
Ka C. Cheok ◽  
Kazuyuki Kobayashi ◽  
Francis B. Hoogterp
Keyword(s):  
Fuzzy Logic ◽  
Expert Knowledge ◽  
Traction Control ◽  
Knowledge Based

Low Cost Engine Management System (EMS) for the Cost Sensitive Two-Wheeler Application: Idle Speed and A/F Ratio Control Using PID and Fuzzy Logic Control Algorithms

2007 ◽  
Author(s):  
P. V. Manivannan ◽  
A. Ramesh
Keyword(s):  
Fuzzy Logic ◽  
Fuel Consumption ◽  
Fuzzy Logic Controller ◽  
Low Cost ◽  
Engine Management System ◽  
Position Sensor ◽  
Idle Speed ◽  
The Cost ◽  
Two Wheeler ◽  
Speed Fluctuations

In this work an Engine Management System (EMS) using a low cost 8-bit microcontroller specifically for the cost sensitive small two-wheeler application was designed and developed. Only the Throttle Position Sensor (TPS) and the cam position sensor (also used for speed measurement) were used. A small capacity 125CC four stroke two-wheeler was converted into a Port Fuel Injected (PFI) engine and was coupled to a fully instrumented Eddy Current Dynamometer. Air-fuel ratio was controlled using the open loop, lookup-table [speed (N) and throttle (α)] based technique. Spark Time was controlled using a proportional / fuzzy logic based close loop control algorithm for the idle speed control to reduce fuel consumption and emissions. Test results show a significant improvement in engine performance over the original carbureted engine, in terms of fuel consumption, emissions and idle speed fluctuations. The Proportional controller resulted in significantly lower speed fluctuations and HC / CO emissions than the fuzzy logic controller. Though the fuzzy logic controller resulted in low cycle by cycle variations than the original carbureted engine, it leads to significantly higher HC levels. The performance fuzzy logic can be improved by modifying the membership function shapes with more engine test data.

FUZZY DYNAMIC LOGIC

2006 ◽  
Vol 02 (01) ◽  
pp. 43-55 ◽  
Author(s):  
LEONID I. PERLOVSKY
Keyword(s):  
Fuzzy Logic ◽  
Dynamic Logic ◽  
Learning System ◽  
Dynamic Adaptation ◽  
Fuzzy Models ◽  
Uncertain Knowledge ◽  
Model Improvement ◽  
The Mind ◽  
Fuzzy State ◽  
Theory Of Forms

Fuzzy logic is extended toward dynamic adaptation of the degree of fuzziness. The motivation is to explain the process of learning as a joint model improvement and fuzziness reduction. A learning system with fuzzy models is introduced. Initially, the system is in a highly fuzzy state of uncertain knowledge, and it dynamically evolves into a low-fuzzy state of certain knowledge. We present an image recognition example of patterns below clutter. The paper discusses relationships to formal logic, fuzzy logic, complexity and draws tentative connections to Aristotelian theory of forms and working of the mind.

scholarly journals Gaudii: An Automated Graphic Design Expert System

2021 ◽  
Vol 24 (2) ◽  
pp. 1775-1780
Author(s):  
Carlos Glez-Morcillo ◽  
Victor Martin ◽  
David Vallejo Fernandez ◽  
Jose Castro-Schez ◽  
Javier Albusac
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Evolutionary Computation ◽  
Graphic Design ◽  
Expert Knowledge ◽  
Trial And Error ◽  
Design Elements ◽  
Design Expert ◽  
Novel Approach ◽  
Design Activities

Graphic design is the process of creating graphics to meet specific commercial needs based on knowledge of layout principles and esthetic concepts. This is usually an iterative trial and error process which requires a lot of time even for expert designers. This expert knowledge can be modelled, represented and used by a computer to perform design activities. This paper describes a novel approach named Gaudii (standing for "Intelligent Automated Graphic Design Generator") which utilizes principles and techniques known from the fields of Evolutionary Computation and Fuzzy Logic to automatically obtain design elements. Experimental results that demonstrate the potential of the proposed approach are presented in the area of poster design.

scholarly journals DETERMINATION OF COST OF PRODUCTION IN CONDITIONS OF UNCERTAINTY OF ORDERS

2016 ◽  
Vol 4 (4) ◽  
pp. 499-504
Author(s):  
Ольга Глод ◽  
Olga Glod ◽  
Виктор Ланкин ◽  
Viktor Lankin
Keyword(s):  
Fuzzy Logic ◽  
Expert Knowledge ◽  
Small Enterprises ◽  
Methodological Basis ◽  
Cost Of Production ◽  
The Cost

The purpose of this paper is to examine the model to determine the cost of production of small enterprises. The methodological basis for the model is a fuzzy logic based on expert knowledge. As a result, in this paper we consider an example of the model, determined the cost of production of confectionery

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