Journal articles: 'Fuzzy logic – Industrial applications – Congresses'

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Barrett, J. Douglas. "Advanced Fuzzy Logic Technologies in Industrial Applications." Technometrics 49, no. 4 (November 2007): 494–95. http://dx.doi.org/10.1198/tech.2007.s689.

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Bonissone, P. P., V. Badami, K. H. Chiang, P. S. Khedkar, K. W. Marcelle, and M. J. Schutten. "Industrial applications of fuzzy logic at General Electric." Proceedings of the IEEE 83, no. 3 (March 1995): 450–65. http://dx.doi.org/10.1109/5.364490.

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Meier, Willi, Richard Weber, and Hans-Jurgen Zimmermann. "Fuzzy data analysis — Methods and industrial applications." Fuzzy Sets and Systems 61, no. 1 (January 1994): 19–28. http://dx.doi.org/10.1016/0165-0114(94)90280-1.

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Turc, Cristian Gheorghe, and George Belgiu. "Fuzzy Logic Applications in Flanges Manufacturing." Advanced Materials Research 837 (November 2013): 223–27. http://dx.doi.org/10.4028/www.scientific.net/amr.837.223.

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The industrial engineering experts consider the product early design stage as one of the most important stage of the design process, because it influences all stages of the product life cycle. One of the capital questions for manufacturing or design engineering people is how simple or elaborated work piece to use for a given part, in a specific set of production conditions. The optimization problem consists of the choosing of the right work piece for the current production conditions. In many cases this problem is solved empirically, based on the experience of the manufacturer. This approach leads to results that are situated more or less close to the optimum for the technical and economical point of view. Fuzzy logic is a method that is used in system control and analysis design, because it shortens the time for engineering development and sometimes, in the case of highly complex systems, is the only way to solve the problem. For the parts manufacturing, the main idea of the algorithm is to formalize the knowledge regarding production volume, geometry, loading conditions and other factors, using fuzzy sets and then to take the work piece choosing decision through the inference rules that are specified to fuzzy logic methodology. The described fuzzy logic algorithm allows a rapid, argued choosing of the work piece type in the production. The paper describes the method implementation for the manufacturing of flanges, including the description of the software results.

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Turc, Cristian Gheorghe, Felicia Banciu, and George Belgiu. "Fuzzy Logic Applications in Gears Manufacturing." Advanced Materials Research 1036 (October 2014): 1028–32. http://dx.doi.org/10.4028/www.scientific.net/amr.1036.1028.

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The experts of industrial design field consider the product early design stage as one of the most important stage of the design process, because it influences all stages of the product life cycle. One of the capital questions for manufacturing or design engineering people is how simple or elaborated work piece to use for a given part, in a specific set of production conditions. The optimization problem consists of the choosing of the right work piece for the current production conditions. In many cases this problem is solved empirically, based on the experience of the manufacturer. This approach leads to results that are situated more or less close to the optimum for the technical and economical point of view. Fuzzy logic is a method that is used in system control and analysis design, because it shortens the time for engineering development and sometimes, in the case of highly complex systems, is the only way to solve the problem. For the parts manufacturing, the main idea of the algorithm is to formalize the knowledge regarding production volume, loading conditions and other factors, using fuzzy sets and then to take the work piece choosing decision through the inference rules that are specified to fuzzy logic methodology. The described fuzzy logic algorithm allows a rapid, argued choosing of the work piece type in the production. The paper describes the method implementation for the manufacturing of gears, including the description of the software results and their interpretation.

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Efstathiou, Janet. "Applications of fuzzy set methodologies in industrial engineering." Fuzzy Sets and Systems 36, no. 3 (July 1990): 405. http://dx.doi.org/10.1016/0165-0114(90)90116-n.

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Karwowski, Waldemar, and Anil Mital. "Potential applications of fuzzy sets in industrial safety engineering." Fuzzy Sets and Systems 19, no. 2 (June 1986): 105–20. http://dx.doi.org/10.1016/0165-0114(86)90031-x.

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Rana, Sudesh. "DEVELOPMENT OF PID LIKE FLC ALGORITHM FOR INDUSTRIAL APPLICATIONS." International Journal of Engineering Technologies and Management Research 2, no. 1 (January 29, 2020): 12–22. http://dx.doi.org/10.29121/ijetmr.v2.i1.2015.26.

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Now a day, in many industries different types of controllers (PD, PID, PLC, FLC etc.) are used. One of them is fuzzy logic controller. Here we develop a PID like fuzzy logic controller for industrial application, such application is water purification plant. For developing the PID like FLC, first we have to design a PID algorithm than we develop an algorithm for fuzzy logic controller. By comparing this two of controller we will develop a PID like FLC. A simple PID controller is sum of three type of controller proportional, integral and derivative controller, after simulated on MATLAB. Same cases we can be develop a structure of FLC for water purification plant. In the water purification plant raw water or ground water is promptly purified by injecting chemical rates at rates, related to water quality [13][2]. The feed of chemical rate judged and determined by the skilled operator. Here we try to develop an FLC algorithm so that the feed rate of coagulant is can be judged automatically without any skilled operator, than compose a PID like FLC for water purification plant process.

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Katayama, Ryu. "Applications of Neuro Fuzzy Technology in Consumer Electronics Products." Journal of Robotics and Mechatronics 7, no. 1 (February 20, 1995): 2–8. http://dx.doi.org/10.20965/jrm.1995.p0002.

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In recent years, intelligent industrial systems and consumer electronic products have been widely and intensively developed. Fuzzy logic, neural network, and neuro fuzzy technology, which integrates both approaches, are now regarded as an effective method to realize such intelligent features. In this paper, a review of the fuzzy boom in the consumer electronics market of Japan is presented. Typical applications of home appliances using fuzzy logic and neuro fuzzy technology are then described. Finally, methods and tools for developing fuzzy systems such as self-tuning and fuzzy modeling are reviewed.

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Sultan. "Development of Software Reliability Growth Models for Industrial Applications Using Fuzzy Logic." Journal of Computer Science 7, no. 10 (October 1, 2011): 1574–80. http://dx.doi.org/10.3844/jcssp.2011.1574.1580.

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Profumo, Francesco, Peter Kosc, and Viliam Fedak. "AC Drives for high Performance Applications Using Fuzzy Logic Controllers." IEEJ Transactions on Industry Applications 114, no. 7/8 (1994): 734–40. http://dx.doi.org/10.1541/ieejias.114.734.

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Rafibakhsh, Nima, H. Felix Lee, Jie Gong, Hoo Sang Ko, and Arefeh Mohammadi. "Applying Fuzzy Logic for Optimal Placement of XBOX Kinect Sensors for Industrial Applications." Advanced Materials Research 628 (December 2012): 433–39. http://dx.doi.org/10.4028/www.scientific.net/amr.628.433.

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Automated acquisition of sensing data is an active research area in manufacturing domain. A great deal of research work has been focused on automated data acquisitionapplied to various subjects related to manufacturing operations such as safety, performance improvement, monitoring and layout planning. Laser scanners including Time of Flight cameras play a significant role in real time or near real time decision makings in manufacturing automation. To establish an automated sensing system in a work place, enough test data should be available regarding the performance characteristics. This paper investigates the performance of Microsoft XBOX Kinect on spatial modeling in large jobsites, and employs fuzzy logic to find optimal placement of Kinect sensors that gives the best resolution.

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Mohaghegh, Shahab. "Virtual-Intelligence Applications in Petroleum Engineering: Part 3—Fuzzy Logic." Journal of Petroleum Technology 52, no. 11 (November 1, 2000): 82–87. http://dx.doi.org/10.2118/62415-jpt.

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Lehal, Manpreet Singh. "Fuzzy in the Real World." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 7, no. 1 (May 30, 2013): 473–77. http://dx.doi.org/10.24297/ijct.v7i1.3476.

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The Fuzzy Logic tool was introduced in 1965, by LotfiZadeh, and is a mathematical tool for dealing with uncertainty. It offers to a soft computing partnership the important concept of computing with words. It provides a technique to deal with imprecision and information granularity. Fuzzy Logic (FL) is a multi valued logic that allows intermediate values to be defined between conventional evaluations like true/false, yes/no, high/low, etc. Notions like rather tall or very fast can be formulated mathematically and processed by computers, in order to apply a more human like way of thinking in the programming of computers. Fuzzy Logic has emerged as a a profitable tool for the controlling and steering of systems and complex industrial processes, as well as for household and entertainment electronics, as well as for other expert systems and applications like the classification of SAR data.

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Ordoobadi, Sharon M. "Fuzzy logic and evaluation of advanced technologies." Industrial Management & Data Systems 108, no. 7 (August 22, 2008): 928–46. http://dx.doi.org/10.1108/02635570810898008.

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Khalifa, Banafsj, Ahmed Khedr, Zaher Al Aghbari, and Jemal Abawajy. "Fuzzy logic approach to repair coverage holes in internet of things monitoring applications." IET Wireless Sensor Systems 9, no. 4 (August 1, 2019): 227–35. http://dx.doi.org/10.1049/iet-wss.2018.5174.

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LEUNG, F. H. F., L. K. WONG, P. K. S. TAM, and H. K. LAM. "REALIZATION OF ANALOG FUZZY LOGIC CONTROL FOR PWM BOOST CONVERTERS." Journal of Circuits, Systems and Computers 08, no. 03 (June 1998): 411–19. http://dx.doi.org/10.1142/s0218126698000213.

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Fuzzy logic controllers (FLCs) have been widely used in many applications. However, they are usually implemented digitally by expensive digital signal processors (DSPs), and the performance is restricted by the sampling period. These disadvantages have limited the applications of FLCs to industrial systems such as switch mode power converters. In this paper, an analog FLC implemented by low-cost devices is proposed. Through analog circuitry, features such as membership function shaping, rule inference and defuzzification are realized. The controller is successfully applied to regulate a PWM boost converter with satisfactory performance.

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Bilkay, O., O. Anlagan, and S. E. Kilic. "Job shop scheduling using fuzzy logic." International Journal of Advanced Manufacturing Technology 23, no. 7-8 (April 1, 2004): 606–19. http://dx.doi.org/10.1007/s00170-003-1771-2.

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Apen, Ajisman, Ker Shao Jiun, and Siti Nursyuhada Mahsahirun. "Fuzzy Logic Controller Implementation on Separately Excited DC-Motor." ICONIET PROCEEDING 2, no. 3 (February 13, 2019): 134–43. http://dx.doi.org/10.33555/iconiet.v2i3.25.

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Various industrial and domestic applications such as automotive, aerospace,appliances and many others are electrically driven. Conventional methods of motor control had failed to produce desired performance of DC-motor due the system parameters variation and load changes. The Fuzzy-Logic-controller is one of the controllers that can handle non-linear system. This project is aimed to control the speed of a separately-excited DC-motor using fuzzy logic control. The system is simulated on MATLAB-Simulink and implemented on ARDUINO Uno development board.

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Tay, Kai Meng, and Chee Peng Lim. "On the use of fuzzy inference techniques in assessment models: part II: industrial applications." Fuzzy Optimization and Decision Making 7, no. 3 (June 25, 2008): 283–302. http://dx.doi.org/10.1007/s10700-008-9037-y.

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CHABNI, Fayçal, Rachid TALEB, Abderrahmen BENBOUALI, and Mohammed Amin BOUTHIBA. "Digital Control Fuzzy Logic for a Water Tank Level Using Arduino." Algerian Journal of Signals and Systems 1, no. 1 (February 1, 2021): 2–10. http://dx.doi.org/10.51485/ajss.v1i1.12.

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Fuzzy logic control has been successfully utilized in various industrial applications; it is generally used in complex control systems, such as chemical process control. Today, most of fuzzy logic controls are still implemented on expensive high performance processors. This paper analyzes the effectiveness of a fuzzy logic control using a low cost controller applied to water level control system. The paper also gives a low cost hardware solution and practical procedure for system identification and control. We started, first by identifying the process to obtain its mathematical model. Then we used two methods to control our system (PI and fuzzy control). Simulation and experimental results are presented.

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Dzitac, Ioan, Florin Gheorghe Filip, and Misu-Jan Manolescu. "Fuzzy Logic Is Not Fuzzy: World-renowned Computer Scientist Lotfi A. Zadeh." International Journal of Computers Communications & Control 12, no. 6 (December 4, 2017): 748. http://dx.doi.org/10.15837/ijccc.2017.6.3111.

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In 1965 Lotfi A. Zadeh published "Fuzzy Sets", his pioneering and controversialpaper, that now reaches almost 100,000 citations. All Zadeh’s papers were citedover 185,000 times. Starting from the ideas presented in that paper, Zadeh foundedlater the Fuzzy Logic theory, that proved to have useful applications, from consumerto industrial intelligent products. We are presenting general aspects of Zadeh’s contributionsto the development of Soft Computing(SC) and Artificial Intelligence(AI),and also his important and early influence in the world and in Romania. Severalearly contributions in fuzzy sets theory were published by Romanian scientists, suchas: Grigore C. Moisil (1968), Constantin V. Negoita & Dan A. Ralescu (1974), DanButnariu (1978). In this review we refer the papers published in "From Natural Languageto Soft Computing: New Paradigms in Artificial Intelligence" (2008, Eds.: L.A.Zadeh, D. Tufis, F.G. Filip, I. Dzitac), and also from the two special issues (SI) of theInternational Journal of Computers Communications & Control (IJCCC, founded in2006 by I. Dzitac, F.G. Filip & M.J. Manolescu; L.A. Zadeh joined in 2008 to editorialboard). In these two SI, dedicated to the 90th birthday of Lotfi A. Zadeh (2011), andto the 50th anniversary of "Fuzzy Sets" (2015), were published some papers authoredby scientists from Algeria, Belgium, Canada, Chile, China, Hungary, Greece, Germany,Japan, Lithuania, Mexico, Pakistan, Romania, Saudi Arabia, Serbia, Spain,Taiwan, UK and USA.

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García-Martínez, José R., Edson E. Cruz-Miguel, Roberto V. Carrillo-Serrano, Fortino Mendoza-Mondragón, Manuel Toledano-Ayala, and Juvenal Rodríguez-Reséndiz. "A PID-Type Fuzzy Logic Controller-Based Approach for Motion Control Applications." Sensors 20, no. 18 (September 17, 2020): 5323. http://dx.doi.org/10.3390/s20185323.

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Motion control is widely used in industrial applications since machinery, robots, conveyor bands use smooth movements in order to reach a desired position decreasing the steady error and energy consumption. In this paper, a new Proportional-Integral-Derivative (PID) -type fuzzy logic controller (FLC) tuning strategy that is based on direct fuzzy relations is proposed in order to compute the PID constants. The motion control algorithm is composed by PID-type FLC and S-curve velocity profile, which is developed in C/C++ programming language; therefore, a license is not required to reproduce the code among embedded systems. The self-tuning controller is carried out online, it depends on error and change in error to adapt according to the system variations. The experimental results were obtained in a linear platform integrated by a direct current (DC) motor connected to an encoder to measure the position. The shaft of the motor is connected to an endless screw; a cart is placed on the screw to control its position. The rise time, overshoot, and settling time values measured in the experimentation are 0.124 s, 8.985% and 0.248 s, respectively. These results presented in part 6 demonstrate the performance of the controller, since the rise time and settling time are improved according to the state of the art. Besides, these parameters are compared with different control architectures reported in the literature. This comparison is made after applying a step input signal to the DC motor.

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Muñoz, Maria J., Juana M. Rivera, and Jose M. Moneva. "Evaluating sustainability in organisations with a fuzzy logic approach." Industrial Management & Data Systems 108, no. 6 (June 27, 2008): 829–41. http://dx.doi.org/10.1108/02635570810884030.

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Kannamma, R., and K. S. Umadevi. "Neuro-Fuzzy-Based Frame Pre-Emption Using Time-Sensitive Networking for Industrial Ethernet." Journal of Information & Knowledge Management 20, Supp01 (February 2021): 2140008. http://dx.doi.org/10.1142/s0219649221400086.

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IEEE802.1 Time-Sensitive Networking (TSN) makes it conceivable to convey the data traffic of time as well as critical applications using Ethernet shared by different applications having diversified Quality of Service (QoS) requirements for both TSN and non-TSN. TSN assures a guaranteed data delivery with limited latency, low jitter, and amazingly low loss of data for time-critical traffic. By holding networking resources for basic traffic, and applying different queuing and traffic shaping strategies, TSN accomplishes zero congestion loss for basic time-critical traffic. In proposed system, backpropagation algorithm is used to train the training set and fuzzy inference system methodologies such as Mamdani fuzzy inference system which has fuzzy inputs and fuzzy outputs, Sugeno FIS which has fuzzy inputs and a crisp output and adaptive-network-based fuzzy inference system has obtained from the neural network and fuzzy logic. The proposed system uses neuro-fuzzy techniques to handle frame pre-emption and reduces the time taken for decision making. It presents a decision making process using the traffic class.

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A. Ali, Methaq, Abbas H. Miry, and Tariq M. Salman. "IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE IN CONTROLLING THE TEMPERATURE OF INDUSTRIAL PANEL." Journal of Engineering and Sustainable Development 25, no. 01 (January 1, 2021): 92–99. http://dx.doi.org/10.31272/jeasd.25.1.8.

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Artificial intelligence has been widely used in various applications such as health and safety, smart homes, greenhouses, and industrial application. It has been increasingly utilized in the industry owing to its benefits in terms of enhancing the overall performance of a given system. This study appeared from a real need in many local industries. In this paper, a prototype system has been implemented for artificial control on the temperature of the industrial panel. The paper includes two control systems executed; classical PID (Proportional Integral Derivative) and fuzzy logic with a comparison between them. Fuzzy control algorithm is developing based on Sugeno method inside PLC (Programmable Logic Controller). The connection of PLC with sensors is used by the Modbus protocol. Arduino UNO and Ethernet shield are used to connect the sensor to the router and then to PLC by Modbus.

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Podržaj, Primož, and Samo Simončič. "Resistance spot welding control based on fuzzy logic." International Journal of Advanced Manufacturing Technology 52, no. 9-12 (July 3, 2010): 959–67. http://dx.doi.org/10.1007/s00170-010-2794-0.

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Rehman, Waheed UR, Yuanxin Luo, Yongqin Wang, Guiyun Jiang, Nadeem Iqbal, Shafiq UR Rehman, and Shamsa Bibi. "Fuzzy logic–based intelligent control for hydrostatic journal bearing." Measurement and Control 52, no. 3-4 (March 2019): 229–43. http://dx.doi.org/10.1177/0020294019830110.

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Most of the researchers are focusing to bring automation in mechanical systems. So, current research work presents a mechatronic system called active hydrostatic journal bearing that is a higher demand of fast-growing industrial needs. The objective is to improve the dynamics and static characteristics of hydrostatic journal bearing. To achieve the objective, a feedback servo controlled system is presented. A mathematical model is proposed for newly proposed active hydrostatic journal bearing with servo control. A comparison has been performed between the active hydrostatic journal bearing and traditional/conventional hydrostatic journal bearing under the influence of different dynamic conditions of load, viscosity, speed, and pressure. The results demonstrate that proposed active hydrostatic journal bearing has better performance, including controllability, stability, faster response, higher stiffness as well as better disturbance rejection. To further enhance the performance of proposed active hydrostatic journal bearing, two different control strategies are proposed such as proportional–integral–derivative and intelligent control. The results show that fuzzy logic–based intelligent control has faster response, greater amplitude reduction, and good stiffness against load. So, it shows that role of active lubrication in hydrostatic journal bearing will have potential applications in high load and high speed.

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Scheffler, Michael, Riccardo Rovatti, and Alberto Bellini. "A Development Tool for Analog Fuzzy Controllers: Features and Applications." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 06, no. 02 (April 1998): 211–20. http://dx.doi.org/10.1142/s0218488598000197.

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This paper describes an integrated development tool for analog fuzzy controllers which exploits analog hardware implementation and fuzzy logic flexibility to allow an easy and extremely low-cost synthesis of non-linear control laws. The core of the final implementation is a modular general-purpose analog fuzzy engine (AFE) with good speed performance and built-in temperature compensation. To ease field prototyping, a digitally-programmable version of AFE is provided with a software layer computing the biasing voltages needed by the circuit to fit numerical data and/or linguistic rules specified by the users. Once that the application is fine-tuned, a further software module is able to generate an optimized version of AFE dedicated to the task. An example application shows that this approach can lead to the implementation of effective analog controllers fulfilling industrial requirements.

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Hadi Amoozgar, Mohammad, Khalil Alipour, and Seyed Hossein Sadati. "A fuzzy logic‐based formation controller for wheeled mobile robots." Industrial Robot: An International Journal 38, no. 3 (May 3, 2011): 269–81. http://dx.doi.org/10.1108/01439911111122770.

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Bilgiç, Hilal, Yusuf Kuvvetli, and Pınar Duru Baykal. "Determination of Difficulty Level for Garment Model with Fuzzy Logic Method." Tekstil ve Mühendis 28, no. 121 (March 31, 2021): 39–47. http://dx.doi.org/10.7216/1300759920212812105.

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The purpose of this study is a rule-based fuzzy logic approach is proposed for determining model difficulty in manufacturing top clothing for ladies. A decision framework concerned with different scenarios (main pattern types and material types) is proposed for determining the model difficulty. Each scenario modeled as a Mamdani type fuzzy inference system which is known as one of the best approximator fuzzy logic models. The fuzzified input variables are unit operation time, second quality rate and fabric weight. Moreover, two different defuzzification methods which are centroid and middle of maxima are compared for finding best fuzzy logic structure over the six different test instances. According to the results, both deffuzzification methods find similar model difficulty determinations. A graphical user interface of the proposed decision framework is designed in order to apply this to real-life applications. Finally, six different clothing models are identified to be simple, medium-hard, hard and very hard. The results of this study showed that defuzzification methods is not significantly effected the model difficulty decisions off is systems regarding different test instances. The model difficulty values range between 0-10. In order to find a useful difficulty assignment (linguistic), the model difficulty is determined by using the closeness to center value (a2) of membership functions. This research offers a solution to determine the difficulty levels of the garment models.

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Marques, Gonçalo, Akash Kumar Bhoi, Raksha K. Patel, Himal B. Chitara, and Tejas V. Bhatt. "Fuzzy logic system for diabetic eye morbidity prediction." International Journal of Computer Applications in Technology 64, no. 4 (2020): 339. http://dx.doi.org/10.1504/ijcat.2020.10034780.

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Bhatt, Tejas V., Raksha K. Patel, Himal B. Chitara, Gonçalo Marques, and Akash Kumar Bhoi. "Fuzzy logic system for diabetic eye morbidity prediction." International Journal of Computer Applications in Technology 64, no. 4 (2020): 339. http://dx.doi.org/10.1504/ijcat.2020.112680.

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Arshinder, A. Kanda, and S. G. Deshmukh. "Coordination in supply chains: an evaluation using fuzzy logic." Production Planning & Control 18, no. 5 (June 25, 2007): 420–35. http://dx.doi.org/10.1080/09537280701430994.

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Boubertakh, Hamid, Mohamed Tadjine, Pierre-Yves Glorennec, and Salim Labiod. "A simple goal seeking navigation method for a mobile robot using human sense, fuzzy logic and reinforcement learning." Journal of Automatic Control 18, no. 1 (2008): 23–27. http://dx.doi.org/10.2298/jac0801023b.

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This paper proposes a new fuzzy logic-based navigation method for a mobile robot moving in an unknown environment. This method allows the robot obstacles avoidance and goal seeking without being stuck in local minima. A simple Fuzzy controller is constructed based on the human sense and a fuzzy reinforcement learning algorithm is used to fine tune the fuzzy rule base parameters. The advantages of the proposed method are its simplicity, its easy implementation for industrial applications, and the robot joins its objective despite the environment complexity. Some simulation results of the proposed method and a comparison with previous works are provided.

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Livinti, Petru. "Speed control of the asynchronous motor using LabVIEW." International Journal of ADVANCED AND APPLIED SCIENCES 8, no. 5 (May 2021): 59–66. http://dx.doi.org/10.21833/ijaas.2021.05.007.

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This paper was presented a comparative study on the methods of adjusting the speed of a three-phase asynchronous motor with a rotor in a short circuit. For the same structure of the experimental stand used, two programs were created, implemented, and validated in LabVIEW. For the first method, the program in LabVIEW was made with the PI (proportional-integrative) controller and for the second method, the program in LabVIEW was made with the Fuzzy Logic controller. Following the analysis of the resulting graphs, it was found that the speed control system made with the fuzzy logic controller ensures an increase in its performance compared to the speed control system made with the conventional PI type controller. The indicial responses of the adjustment system of the three-phase asynchronous motor speed with PI controller or Fuzzy Logic controller have been determined in real-time by means of the experimental stand. The override of the speed adjustment system is decreased from the value of 26.9% corresponding to the PI controller to the value of 2.3% corresponding to the Fuzzy Logic controller and the duration of the transient time is decreased from the value of 2.2 s related to the PI controller to the value of 0.5 s, related to the Fuzzy Logic controller. By using the Fuzzy Logic controller, the amount of electrical energy required to supply the electric drive system made with a three-phase asynchronous motor will be reduced. This three-phase asynchronous motor speed adjustment algorithm can be implemented for other electric drive systems from different industrial applications.

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Alsammak, Ahmed Nasser, and Hasan Adnan Mohammed. "Power quality improvement using fuzzy logic controller based unified power flow controller." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (January 1, 2021): 1. http://dx.doi.org/10.11591/ijeecs.v21.i1.pp1-9.

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<p>The Power quality of the electrical system is an important issue for industrial, commercial, and housing uses. An increasing request for high quality electrical power and an increasing number of distorting loads had led to increase the consideration of power quality by customers and utilities. The development and use of flexible alternating current transmission system (FACTs) controllers in power transmission systems had led to many applications of these controllers. A unified power flow controller (UPFC) is one of the FACTs elements which is used to control both active and reactive power flow of the transmission line. This paper tried to improve power quality using a fuzzy logic controller (FLC) based UPFC, where it used to control both active and reactive power flow, decreas the total harmonic distortion (THD), correct power factor, regulate line voltage and enhance transient stability. A comparison study of the performance between the system with a conventional PID controller and FLC has been done. The theoretical analysis has been proved by implementing the system using MATLAB/SIMULINK package.The Power quality of the electrical system is an important issue for industrial, commercial, and housing uses. An increasing request for high quality electrical power and an increasing number of distorting loads had led to increase the consideration of power quality by customers and utilities. The development and use of flexible alternating current transmission system (FACTs) controllers in power transmission systems had led to many applications of these controllers. A unified power flow controller (UPFC) is one of the FACTs elements which is used to control both active and reactive power flow of the transmission line. This paper tried to improve power quality using a fuzzy logic controller (FLC) based UPFC, where it used to control both active and reactive power flow, decreas the total harmonic distortion (THD), correct power factor, regulate line voltage and enhance transient stability. A comparison study of the performance between the system with a conventional PID controller and FLC has been done. The theoretical analysis has been proved by implementing the system using MATLAB/SIMULINK package.</p>

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Hilletofth, Per, Movin Sequeira, and Wendy Tate. "Fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions." Industrial Management & Data Systems 121, no. 5 (March 25, 2021): 965–92. http://dx.doi.org/10.1108/imds-05-2020-0290.

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PurposeThis paper investigates the suitability of fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.Design/methodology/approachTwo fuzzy-logic-based support tools are developed together with experts from a Swedish manufacturing firm. The first uses a complete rule base and the second a reduced rule base. Sixteen inference settings are used in both of the support tools.FindingsThe findings show that fuzzy-logic-based support tools are suitable for initial screening of manufacturing reshoring decisions. The developed support tools are capable of suggesting whether a reshoring decision should be further evaluated or not, based on six primary competitiveness criteria. In contrast to existing literature this research shows that it does not matter whether a complete or reduced rule base is used when it comes to accuracy. The developed support tools perform similarly with no statistically significant differences. However, since the interpretability is much higher when a reduced rule base is used and it require fewer resources to develop, the second tool is more preferable for initial screening purposes.Research limitations/implicationsThe developed support tools are implemented at a primary-criteria level and to make them more applicable, they should also include the sub-criteria level. The support tools should also be expanded to not only consider competitiveness criteria, but also other criteria related to availability of resources and strategic orientation of the firm. This requires further research with regard to multi-stage architecture and automatic generation of fuzzy rules in the manufacturing reshoring domain.Practical implicationsThe support tools help managers to invest their scarce time on the most promising reshoring projects and to make timely and resilient decisions by taking a holistic perspective on competitiveness. Practitioners are advised to choose the type of support tool based on the available data.Originality/valueThere is a general lack of decision support tools in the manufacturing reshoring domain. This paper addresses the gap by developing fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.

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Huang, S. J., and C. C. Lin. "A Self-Organising Fuzzy Logic Controller for a Coordinate Machine." International Journal of Advanced Manufacturing Technology 19, no. 10 (June 12, 2002): 736–42. http://dx.doi.org/10.1007/s001700200084.

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Zhe, Xu, Gao Junyao, Li Hui, Liu Huaxin, Li Xin, Liu Yi, Sun Wentao, and Zhao Wenxue. "The modeling and controlling of electrohydraulic actuator for quadruped robot based on fuzzy Proportion Integration Differentiation controller." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 228, no. 14 (January 21, 2014): 2557–68. http://dx.doi.org/10.1177/0954406213519613.

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Hydraulic actuators are widely used in various kinds of industrial applications. High-power density is key parameters for engineering applications especially for quadruped robots applied in the outdoor environment. Therefore, an increasing number of advanced robots are equipped with hydraulic actuators. In this paper, to compensate the inherent nonlinearities and enhance the performance of the quadruped robot, a hybrid fuzzy controller composed of fuzzy logic controller and Proportion Integration Differentiation controller is evaluated both in simulations and experiments. The control strategy is developed based on the accurate mathematical model. The Matlab Simulink and Fuzzy Logic Toolbox are implemented to accomplish the simulations under flexible loads. Single hydraulic actuator and single leg experiments are accomplished on the specific platforms. Both the simulations and the experimental results indicate that the fuzzy Proportion Integration Differentiation control strategy is capable of fulfilling the specific position tracking under diverse loads. Compared with the conventional Proportion Integration Differentiation controller, the fuzzy Proportion Integration Differentiation controller provided a desirable performance under heavy load with comparatively little response and settling time. Results show that the fuzzy Proportion Integration Differentiation control strategy can effectively achieve the objective of enhancing position tracking robustness under flexible loads and improve the performance of quadruped robot.

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Touati, Y., and Y. Amirat. "Fuzzy logic controller design methodology for Cartesian robot control." International Journal of Computer Applications in Technology 27, no. 2/3 (2006): 85. http://dx.doi.org/10.1504/ijcat.2006.011135.

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Pandian, A., and R. Dhanasekaran. "Hybrid Anti-Windup Fuzzy PI Controller Based Direct Torque Control of Three Phase Induction Motor." Applied Mechanics and Materials 573 (June 2014): 155–60. http://dx.doi.org/10.4028/www.scientific.net/amm.573.155.

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This paper presents improved Fuzzy Logic Controller (FLC) of the Direct Torque Control (DTC) of Three-Phase Induction Motor (IM) for high performance and torque control industrial drive applications. The performance of the IM using PI Controllers and general fuzzy controllers are meager level under load disturbances and transient conditions. The FLC is extended to have a less computational burden which makes it suitable for real time implementation particularly at constant speed and torque disturbance operating conditions. Hybrid control has advantage of integrating a superiority of two or more control techniques for better control performances. A fuzzy controller offers better speed responses for startup and large speed errors. If the nature of the load torque is varied, the steady state speed error of DTC based IM drive with fuzzy logic controller becomes significant. To improve the performance of the system, a new control method, Hybrid fuzzy PI control is proposed. The effectiveness of proposed method is verified by simulation based on MATLAB. The proposed Hybrid fuzzy controller has adaptive control over load toque variation and can maintain constant speed.

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Cigolini, R., and T. Rossi. "Evaluating supply chain integration: a case study using fuzzy logic." Production Planning & Control 19, no. 3 (March 17, 2008): 242–55. http://dx.doi.org/10.1080/09537280801916249.

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Ali Lilo, Moneer, and Maath Jasem Mahammad. "Design and implementation of wireless system for vibration fault detection using fuzzy logic." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 3 (September 1, 2020): 545. http://dx.doi.org/10.11591/ijai.v9.i3.pp545-552.

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This paper aims at constructing the wireless system for fault detecting and monitoring by computer depending on the wireless and fuzzy logic technique. Wireless applications are utilized to identify, classify, and monitor faults in the real time to protect machines from damage .Two schemes were tested; first scheme fault collected X-Y-Z-axes mode while the second scheme collected Y-axis mode, which is utilized to protect the induction motor (IM) from vibrations fault. The vibration signals were processed in the central computer to reduce noise by signal processing stage, and then the fault was classified and monitored based on Fuzzy Logic (FL). The wireless vibration sensor was designed depending on the wireless techniques and C++ code. A fault collection, noise reduction, vibration fault classification and monitoring were implemented by MATLAB code. In the second scheme the processed real time was reduced to 60%, which is included collection, filtering, and monitoring fault level. Results showed that the system has the ability to early detect the fault if appears on the machine with time processing of 1.721s. This work will reduce the maintenance cost and provide the ability to utilize the system with harsh industrial applications to diagnose the fault in real time processing.

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Nia, Hossein Farid Ghassem, osheng Hu, and John Q. Gan. "A novel fuzzy logic approach to online exposure time calculation of line scan cameras in industrial inspection." International Journal of Modelling, Identification and Control 21, no. 1 (2014): 8. http://dx.doi.org/10.1504/ijmic.2014.059388.

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Melba Mary, P., and N. S. Marimuthu. "Design of self-tuning fuzzy logic controller for the control of an unknown industrial process." IET Control Theory & Applications 3, no. 4 (April 1, 2009): 428–36. http://dx.doi.org/10.1049/iet-cta.2007.0334.

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Kuo, Hsing-Chia, and Li-Jen Wu. "An image tracking system for welded seams using fuzzy logic." Journal of Materials Processing Technology 120, no. 1-3 (January 2002): 169–85. http://dx.doi.org/10.1016/s0924-0136(01)01155-4.

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Chin, Jih-Hua, Yuan-Ming Cheng, and Jin-Huei Lin. "Improving contour accuracy by Fuzzy-logic enhanced cross-coupled precompensation method." Robotics and Computer-Integrated Manufacturing 20, no. 1 (February 2004): 65–76. http://dx.doi.org/10.1016/j.rcim.2003.06.001.

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Jouili, Khalil, Houssem Jerbi, and Naceur Benhadj Braiek. "An advanced fuzzy logic gain scheduling trajectory control for nonlinear systems." Journal of Process Control 20, no. 4 (April 2010): 426–40. http://dx.doi.org/10.1016/j.jprocont.2010.01.001.

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Arghavani, J., M. Derenne, and L. Marchand. "Prediction of Gasket Leakage Rate and Sealing Performance Through Fuzzy Logic." International Journal of Advanced Manufacturing Technology 20, no. 8 (October 18, 2002): 612–20. http://dx.doi.org/10.1007/s001700200198.

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Journal articles on the topic 'Fuzzy logic – Industrial applications – Congresses'

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