FIEMA, a system of fuzzy inference and emission analytics for sustainability-oriented chemical process design

Guzman-Urbina, Alexander; Ouchi, Kakeru; Ohno, Hiroki; Fukushima, Yasuhiro

doi:10.1016/j.asoc.2022.109295

Cited by 6 publications

(4 citation statements)

References 62 publications

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“…Control system errors are recorded, and poor effects are observed. Inverse models for control systems cannot be accurately established by the PSO identifier at this time 28 . At this time, the PSO controller output also has a high level of uncertainty.…”

Section: Fuzzy Supervisory Controlmentioning

confidence: 99%

An intelligent fuzzy-particle swarm optimization supervisory-based control of robot manipulator for industrial welding applications

Kumar¹,

Naveen

Vijayakumar

et al. 2023

Sci Rep

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The propensity of manufacturers to produce goods at affordable cost, with more accuracy, and at a faster rate force them to search for novel solutions, such as deploying robots in place of people in a sector that can accommodate their needs. Welding is one of the most crucial processes in the automotive industry. This process is time-consuming, subject to error, and demands skilled professionals. The robotic application can improve this area of production and quality. Other industries, such as painting and material handling, can also profit from the use of robots. This work describes the fuzzy DC linear servo controller, which functions as a robotic arm actuator. Robots have been widely employed in most productive sectors in recent years, including assembly plates, welding, tasks at higher temperatures, etc. Controlling a robot accurately is a difficult undertaking as a robot is very nonlinear with many joints that are often organized and unstructured. To carry out the effective task, an effective PID control based on fuzzy logic has been employed together with the method of Particle Swarm Optimization (PSO) approach for the estimate of the parameter. This offline technique determines the lowest number of optimal robotic arm control parameters. To verify the controller design with computer simulation, a comparative assessment of controllers is given by means of a fuzzy surveillance controller with PSO which improves the parameter gain to provide a rapid climb, a smaller overflow, no steady condition error signal, and effective torque control of the robot arm.

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Section: Fuzzy Supervisory Controlmentioning

confidence: 99%

An intelligent fuzzy-particle swarm optimization supervisory-based control of robot manipulator for industrial welding applications

Kumar¹,

Naveen

Vijayakumar

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Kemudian, dengan menggunakan operasi AND, OR, dan NOT, semua kemungkinan kombinasi aturan fuzzy dan fungsi keanggotaan digunakan untuk menghasilkan keluaran fuzzy. Pada defuzzifikasi, pendekatan apapun digunakan untuk mengubah semua keluaran fuzzy menjadi nilai yang tajam [28]. Keluaran dari perhitungan Fuzzy pada penelitian ini adalah besarnya duty cycle kipas AC yang didistribusikan oleh PWM Dimmer AC.…”

Section: Pendahuluanunclassified

Optimasi Pengendalian Suhu dan Kelembapan Ruangan di Kota Yogyakarta Menggunakan Metode Fuzzy

2022

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The dry season is a season where most regions in Indonesia experience an increase in temperature. This unstable temperature can have a negative effect on the human body, so a control device is needed according to the needs of the body automatically. This study focuses on optimizing room temperature and humidity control in Yogyakarta City using a fan duty cycle unit with the Fuzzy Tsukamoto method. The ideal temperature and humidity range is obtained from measurements by the Indonesian Board of Meteorology, Climatology, and Geophysics (BMKG). The purpose of this study is to reduce the hot temperature in the room to normal temperature conditions. The calculation results with a temperature of 28.29°C and humidity of 79.06% resulted in a duty cycle of 40.92%. Based on 50 sample data taken each fan rotated for five minutes showed that the average change in temperature was -0.01°C and humidity -0.032%, meaning it could lower 0.01°C and humidity 0.032% every five minutes. This result is considered inefficient considering the very small changes, so in subsequent studies it is recommended to use technology such as air conditioning as a control tool

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“…FISs can help to identify patterns in complex datasets, make accurate predictions based on historical data, and make informed decisions in uncertain and dynamic environments [9,10]. Furthermore, FISs can be integrated into control systems to regulate the behavior of complex systems, such as robots, vehicles, or industrial processes [5,11,12]. Additionally, FISs play a pivotal role in financial applications by predicting stock prices and analyzing market trends.…”

Section: Introductionmentioning

confidence: 99%

A Hardware Realization Framework for Fuzzy Inference System Optimization

Gorgin,

Karvandi,

Moghari

et al. 2024

Electronics

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Fuzzy inference systems (FISs) are a key focus for decision-making in embedded systems due to their effectiveness in managing uncertainty and non-linearity. This study demonstrates that optimizing FIS hardware enhances performance, efficiency, and capabilities, improving user experience, heightened productivity, and cost savings. We propose an ultra-low power FIS hardware framework to address power constraints in embedded systems. This framework supports optimizations for conventional arithmetic and Most Significant Digit First (MSDF) computing, ensuring compatibility with MSDF-based sensors. Within the MSDF-computing FIS, fuzzification, inference, and defuzzification processes occur on serially incoming data bits. To illustrate the framework’s efficiency, we implemented it using MATLAB, Chisel3, and Vivado, starting from high-level FIS descriptions and progressing to hardware synthesis. A Scala library in Chisel3 was developed to connect these tools seamlessly, facilitating design space exploration at the arithmetic level. We applied the framework by realizing an FIS for autonomous mobile robot navigation in unknown environments. The synthesis results highlight the superiority of our designs over the MATLAB HDL code generator, achieving a 43% higher clock frequency, and 46% and 67% lower resource and power consumption, respectively.

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FIEMA, a system of fuzzy inference and emission analytics for sustainability-oriented chemical process design

Cited by 6 publications

References 62 publications

An intelligent fuzzy-particle swarm optimization supervisory-based control of robot manipulator for industrial welding applications

An intelligent fuzzy-particle swarm optimization supervisory-based control of robot manipulator for industrial welding applications

Optimasi Pengendalian Suhu dan Kelembapan Ruangan di Kota Yogyakarta Menggunakan Metode Fuzzy

A Hardware Realization Framework for Fuzzy Inference System Optimization

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