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Design of Economical Fuzzy Logic Controller for Washing Machine

JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING 2023年第1-2期41卷 209-222页

作者： Dheerawat, Kriti Pirzada, Umme Salma M. Kataria, Haribhai R. Maharaja Sayajirao Univ Baroda Dept Math Vadodara 390002 Gujarat India Navrachana Univ Sch Engn & Technol Vadodara 391410 Gujarat India

The washing machine is the most commonly utilized household appliance. The automatic washing machines are developed using Fuzzy Logic Controller by several authors. The amount of water, electricity and detergent are more effective parameters for developing economical washing machines. In this work, we design an economical fuzzy logic controller for washing machine which optimize consumption of water, electricity and amount of detergent. We employ Mamdani approach to develop the algorithm as this approach gives higher accuracy. In the fuzzy-rule basis system, 36 rules are created based on expert user knowledge. The algorithm has been implemented and simulated using python programming language. The simulation results show that the proposed algorithm for washing machine provides better performance at a lower computational cost. The comparative analysis of proposed work with the previous work shows that our input-output control system optimises performance of the washing machine.

关键词： Artificial intelligence washing machine fuzzy rules fuzzy logic controller Mamdani approach python programming

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Experimental-numerical study of wrinkling in rotary-draw bending of Tight Fit Pipes

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THIN-WALLED STRUCTURES 2023年 183卷

作者： Naderi, Gholamali Torshizi, Seyed Ebrahim Moussavi Dibajian, Seyed Houssain Azarab Ind Co Boiler Design Dept Arak Iran Shahid Beheshti Univ Fac Mech & Energy Engn Tehran Iran

The paper focuses on tight fit pipe (TFP) wrinkling in rotary-draw bending (RDB), where TFP is a double -walled pipe. To this end, a corrosion-resistant alloy liner is fitted inside an outer carbon steel pipe through a thermal-hydraulic manufacturing process. A 3D elastic-plastic finite element (FE) model (the dynamic explicit FEM code ABAQUS/Explicit) is developed in this study. This model simulates the manufacturing process in the first step of the analysis and proceeds in the rotary-draw bending analysis of the lined pipe. Then, in the third step, the springback of the two-layer pipe is analyzed. This integrated three-stage process considers geometric nonlinearities, geometric and thickness imperfections, inelastic material behavior, and contact between the two pipes. Contact pressure during the process in the presence of various imperfections is monitored by the histogram to predict wrinkling. Furthermore, the best bending angle, with minimum possibility of wrinkling in the inner pipe, is obtained by these histograms. ABAQUS scripting via the python programming language is used to study the effect of various imperfections on RDB. Numerical results on imperfection sensitivity demonstrate the significant influence of imperfections' amplitude on liner wrinkling. The ovality and thinning of the lined pipe via bending angle were investigated. By comparing the experimental results with those of the simulation, an appropriate agreement is observed.

关键词： Tight Fit Pipe Wrinkling Rotary-draw bending Imperfection ABAQUS Explicit python programming

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Design and development of electronic stethoscope for early screening of valvular heart disease prediction

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第PartA期86卷

作者： Roy, Tanmay Sinha Roy, Joyanta Kumar Mandal, Nirupama Haldia Inst Technol Dept Elect Engn Haldia W Bengal India Narula Inst Technol Dept Elect & Commun Engn Kolkata India Indian Inst Technol ISM Dept Elect Engn Dhanbad India

The significant human population of the world is suffering from valvular heart diseases and dies due to the lack of a simple predictive diagnosis system. Identifying the abnormalities in heart sound needs excellent auscultation skills and experience. Thus an electronic stethoscope is designed and developed using different microphonic heads. In this research work, hardware development of an electronic stethoscope interfaced with raspberry pi 4B and software development of the proposed CNN-based deep learning model is carried out. The Bluetooth-enabled electronic stethoscope is used to auscultate the heart sound analyzed with a developed deep learning Con-volutional Neural Network-based Efficient Network model. The developed CNN model is designed with classifiers to predict valvular diseases accurately. The analysis result is almost in real-time processed and stored in the Cloud. The design provides a better way of studying PCG signal analysis, which eventually reduces the cost and makes the system compact. The proposed model has been trained through a standard and validated heart sound bank of normal and abnormal diseases and predicts the abnormality with accuracy. The proposed modified Efficient Net -B3 model scored an accuracy of 99.35 & PLUSMN;0.34% on the test dataset with a sensitivity of 98.84 & PLUSMN;0.07% and specificity of 98.23 & PLUSMN;0.52%. The Selenium Web Driver tool with Google Drive API is used to automate the web application PCG signal analysis. Finally, the SQLite database has been used as a back-end server to store the patient record. The system is low-cost and portable, with data remotely accessible and tested with volunteers. The developed system can be used in rural areas where there is a lack of medical facilities exists and can be used to initiate primary screening of valvular diseases.

关键词： Valvular heart disease prediction Machine learning Convolutional neural network Residual network Electronic stethoscope python programming SQLite Real-time data acquisition Selenium web driver Cloud storage Remote telemedicine Graphical user interface Feature extraction Classification PCG signal

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Creating Instructional Elementary programming Videos for Use in an Adaptive Testing and Remediation System 2024

Creating Instructional Elementary Programming Videos for Use...

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Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 2

作者： Abigail Jones Manogya Aryal Justin Selby Seth Adjei Northern Kentucky University Highland Heights KY USA

ISBN: (纸本)9798400704246

In computer science undergraduate programs, the first course often focuses on elementary computer programming. However, students' backgrounds can be diverse, leading to challenges in understanding the course material. We discuss our initial steps to improve elementary programming instruction by using instructional videos. A team of three content creators developed a series of instructional videos, covering forty different programming skills. These videos span various topics, from creating and running basic python programs to understanding and creating functions. To assist elementary programming students, we plan to integrate these videos with PLACEments, an adaptive assessment and remediation system designed to help students bridge knowledge gaps. If a student answers a question incorrectly, a relevant video is provided as support. The three content creators peer reviewed the others' instructional videos according to an educational video assessment framework. These results were also standardized through a z-score to account for any reviewer biases. The initial results from the peer review are promising. However, more information will be collected as the videos are deployed through PLACEments and tested by students currently taking an elementary programming course.

关键词： elementary programming python programming undergraduate education video instruction

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Empirical Study of the Evolution of python Questions on Stack Overflow

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E-INFORMATICA SOFTWARE ENGINEERING JOURNAL 2023年第1期17卷

作者： Syam, Gopika Lal, Sangeeta Chen, Tao Keele Univ Sch Comp Sci & Math Keele England Loughborough Univ Dept Comp Sci Loughborough England

Background: python is a popular and easy-to-use programming language. It is constantly expanding, with new features and libraries being introduced daily for a broad range of applications. This dynamic expansion needs a robust support structure for developers to effectively utilise the language. Aim: In this study we conduct an in-depth analysis focusing on several research topics to understand the theme of python questions and identify the challenges that developers encounter, using the questions posted on Stack Overflow. Method:We perform a quantitative and qualitative analysis of python questions in Stack Overflow. Topic Modelling is also used to determine the most popular and difficult topics among developers. Results: The findings of this study revealed a recent surge in questions about scientific computing libraries pandas and TensorFlow. Also, we observed that the discussion of Data Structures and Formats is more popular in the python community, whereas areas such as Installation, Deployment, and IDE are still challenging. Conclusion: This study can direct the research and development community to put more emphasis on tackling the actual issues that python programmers are facing.

关键词： python programming Software Development Stack Overflow Topic Modelling

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Analytical analyzing mixed convection flow of nanofluid in a vertical channel using python approach

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RESULTS IN PHYSICS 2023年 52卷

作者： Jalili, Payam Shateri, Amirali Ganji, Ali Mirzagoli Jalili, Bahram Ganji, Davood Domiri Islamic Azad Univ Dept Mech Engn North Tehran Branch Tehran Iran Babol Noshirvani Univ Technol Dept Mech Engn POB 484 Babol Iran

This study uses analytical methods to investigate the impact of parameters on the mixed convection flow of nanofluid in a vertical channel. The research aims to explore the heat transfer characteristics of nanofluids for more efficient heat transfer in devices. The study analyzes the influence of the Reynolds, Grashof, and Prandtl numbers on nanofluid flows. It also examines the effects of temperature and nanoparticle concentration distri-butions on parameters such as Brownian motion (Nb), thermophoresis (Nt), and Lewis number (Le). The presence of nanoparticles significantly enhances the heat transfer characteristics in the flow problem. Reynolds number, Grashof number, and Prandtl number significantly impact flow behavior. Velocity, temperature, and nano-particle volume fraction profile trends are thoroughly investigated and found to vary. Akbari-Ganji's method (AGM) and the homotopy perturbation method (HPM) demonstrate the potential of computational tools in analyzing nanotechnology fluid dynamics. The accuracy and reliability of the proposed analytical techniques are confirmed through comparisons with a numerical method. The novelty of this research lies in its comprehensive analysis of parameter effects on the mixed convection flow of nanofluid, the enhanced heat transfer character-istics in the presence of nanoparticles, and the unique contributions offered by the utilization of python pro-gramming in this field. The outcomes of this study provide insights for designing and optimizing heat transfer systems using nanofluids, contributing to advancements in nanotechnology.

关键词： Mixed convection Heat transfer Vertical channel Nanofluid python programming

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Smart Robotic Personal Assistant Vehicle Using Raspberry Pi and Zero UI Technology 6

Smart Robotic Personal Assistant Vehicle Using Raspberry Pi ...

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6th International Conference for Convergence in Technology (I2CT)

作者： Sunehra, Dhiraj Jhansi, B. Sneha, R. JNTUH Coll Engn Dept ECE Jagtial India

ISBN: (纸本)9781728188768

This paper presents a prototype of a smart robotic personal assistant vehicle based on Raspberry Pi and Zero-UI technology. Zero UI uses sensory experiences such as gestures, voice and movement to control the devices. A voice controlled robot vehicle implemented in this paper performs three functions, viz. movement of the robot is controlled using voice commands;it has the ability to articulate the text from a captured image using optical character recognition and present the equivalent audio to the user by using a built-in speaker or headset;it accepts voice commands from the user and uses Google Assistant API for any query processing and presents information searched on the Internet to the user in audio form using the built-in speaker or headset. This robotic personal assistant vehicle is a substitute to screen-based communication and makes use of Zero UI for its operation. In addition to the Raspberry Pi board, we use two DC motors to form the wheels of the robot, a webcam with built in microphone, a headset and motor driver IC to implement this robotic personal assistant vehicle. This system enables the visually impaired people to have access to useful information in the public domain by giving voice commands to the robot assistant. The robot can be realized as a wheel chair for the physically challenged. python programming language is used for the development of software code.

关键词： Raspberry Pi3 Robotic Personal Assistant Vehicle Zero-UI Technology Optical Character Recognition Tool Google Assistant API python programming

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Grid dependency minimization of a microgrid using Single and Multi agent Reinforcement Learning

Grid dependency minimization of a microgrid using Single and...

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IEEE Region 10 Symposium (TENSYMP) - Good Technologies for Creating Future

作者： Perera, M. K. Hemapala, K. T. M. U. Wijayapala, W. D. A. S. Univ Moratuwa Dept Elect Engn Moratuwa Sri Lanka

ISBN: (纸本)9781665400268

Distributed generation has become an emerging concept with the people's concern towards the options for environmentally friendly electricity generation. However, the penetration of renewable-based distributed generation resources into the main power network is challenging due to their continuous controlling and monitoring requirements. Therefore, microgrids have been identified as suitable integrating platforms for distributed generation resources and loads. As a novel approach, these microgrids come up with distributed control systems as multi-agent-based systems instead of the conventional centralized control architecture. These control agents are social, reactive, proactive, and autonomous in nature. These agents can be advanced by introducing intelligence through machine learning-based approaches. Therefore, this study focuses on applying Reinforcement Learning as an experimental machine learning method to optimize the energy generation function of a grid-connected microgrid such that the grid dependency of the microgrid is minimized as the agent learns. The performance of the proposed microgrid is evaluated under single and multi-agent Reinforcement learning models in python to identify the appropriate approach for system optimization. For the completion of the study, hardware implementation of the proposed microgrid with Reinforcement Learning applicability is entitled.

关键词： Agent-based Artificial neural network Energy management Microgrid python programming Q learning Reinforcement learning

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Modeling assessment of groundwater vulnerability to contamination risk in a typical basement terrain using TOPSIS-entropy developed vulnerability data mining technique

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HELIYON 2023年第7期9卷 e18371页

作者： Atenidegbe, Olanrewaju Fred Mogaji, Kehinde Anthony Fed Univ Technol Akure Dept Appl Geophys Akure Nigeria Univ Stirling Comp Sci & Math Stirling FK9 4LA Scotland

This study involved a comparative analysis in the groundwater vulnerability domain, which is a crucial component of groundwater management decision support systems (DSS). This was achieved by creating models that covered the range of algorithms from the subjective to the datadriven. The study was conducted in a basement complex area. Databases of climatic, remote sensing, and geophysical datasets were created using varieties of data acquisition techniques. The datasets included in this assessment were: rainfall (R), land use (LU), bedrock topography (BT), recharge rate (Re), and slope (S). The slope and rainfall were determined to have the highest (0.78) and lowest (0.01) weighted factors, respectively, using the entropy method. For the development of the TOPSIS-Entropy model algorithm, the weights results were combined with the TOPSIS outranking method. To generate the Groundwater Vulnerability Model map of the study area, the hybrid model was applied to griddled raster layers of the factors. Also, the TOPSIS and Entropy-WLA model algorithms were also explored and used to generate groundwater vulnerability maps. The TOPSIS-Entropy algorithms produced an accuracy of 70%, while TOPSIS and Entropy-WLA produced accuracy of 50 and 47%, respectively. The resulting model maps were validated by using correlation technique on the produced map and the longitudinal conductance map of the study area. The TOPSIS-Entropy, which followed an object-oriented model pattern, demonstrates greater accuracy and has the potential to provide appropriate insights and alternatives to decision-making in the field of groundwater hydrology in the study area and other regions of the world with comparable geology.

关键词： Groundwater vulnerability Geophysics Remote sensing and GIS TOPSIS Entropy python programming

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Applying Machine Learning to Predict the Rate of Penetration for Geothermal Drilling Located in the Utah FORGE Site

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ENERGIES 2022年第12期15卷 4288页

作者： Ben Aoun, Mohamed Arbi Madarasz, Tamas Polytech Montreal Dept Civil Geol & Min Engn 2500 Chemin Polytech Montreal PQ H3T 1J4 Canada Univ Miskolc Inst Environm Management H-3515 Miskolc Egyet Varos Hungary

Well planning for every drilling project includes cost estimation. Maximizing the rate of penetration (ROP) reduces the time required for drilling, resulting in reducing the expenses required for the drilling budget. The empirical formulas developed to predict ROP have limited field applications. Since real-time drilling data acquisition and computing technologies have improved over the years, we implemented the data-driven approach for this purpose. We investigated the potential of machine learning and deep learning algorithms to predict the nonlinear behavior of the ROP. The well was drilled to confirm the geothermal reservoir characteristics for the FORGE site. After cleaning and preprocessing the data, we selected two models and optimized their hyperparameters. According to our findings, the random forest regressor and the artificial neural network predicted the behavior of our field ROP with a maximum absolute mean error of 3.98, corresponding to 19% of the ROP's standard deviation. A tool was created to assist engineers in selecting the best drilling parameters that increase the ROP for future drilling tasks. The tool can be validated with an existing well from the same field to demonstrate its capability as an ROP predictive model.

关键词： rate of penetration (ROP) predictive modeling geothermal energy machine learning deep learning random forests artificial neural network python programming

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