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10th International Conference on Control, Instrumentation and Automation, ICCIA 2024

作者： Zibandehpoor, Mobina Rahmati, Fatemeh Delrobaei, Mehdi Nikoofard, Amirhossein K. N. Toosi University of Technology Faculty of Electrical Engineering Department of Mechatronics Tehran Iran K. N. Toosi University of Technology Faculty of Electrical Engineering Department of Systems and Control Tehran Iran

ISBN: (纸本)9798331509972

Pneumatic muscle actuators (PMAs) are efficient and versatile, mimicking natural muscle-Tendon systems. They are widely used in manufacturing, automotive, and aerospace industries due to their lightweight design, high force-To-weight ratio, and flexibility, making them suitable for delicate tasks and powerful exertions. Their application in industrial and biomechatronic systems highlights their potential to revolutionize automation and human-machine interaction. This research aimed to develop a PMA regulator using the Model Predictive Control (MPC) framework. Researchers identified key PMA characteristics and defined attributes for the regulator, ensuring accurate signal tracking, effective uncertainty handling, and real-Time operation. The initial stages involved implementing a proportional-integral-derived (PID) controller, and by integrating a PID block with a linear MPC block, a tube MPC controller was developed to enhance performance. The controllers' responses were tested in noisy environments using a low-pass filter for evaluation. The tube MPC controller demonstrated optimal control, robustness, and expertise in managing uncertainties, making it a reliable choice for system control. © 2024 IEEE.

关键词： Model predictive control

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Data Augmentation and Random Multi-Model Deep Learning for Data Classification

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Computers, Materials & Continua 2023年第3期74卷 5191-5207页

作者： Fatma Harby Adel Thaljaoui Durre Nayab Suliman Aladhadh Salim EL Khediri Rehan Ullah Khan Computer Science Department Future Academy-Higher Future Institute for Specialized Technological StudiesEgypt Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawar25120Pakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Sciences Faculty of Sciences of GafsaUniversity of GafsaGafsaTunisia

In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML *** increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen *** learning(DL)models have a lot of parameters,and they frequently ***,to avoid overfitting,data plays a major role to augment the latest improvements in ***,reliable data collection is a major limiting ***,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in *** this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for *** present a methodology for using Generative Adversarial Networks(GANs)to generate images for data *** experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model *** across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models.

关键词： Data augmentation generative adversarial networks classification machine learning random multi-model deep learning

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A Robust Optimization Approach for Agricultural Waste Supply Chain Network Design: A Real Case Study 3rd

A Robust Optimization Approach for Agricultural Waste Supply...

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3rd International Conference on Intelligent systems and Applications, ICISA 2024

作者： Kianypor, Hossein Husseinzadeh Kashan, Ali Nikbakhsh, Ehsan Hosseinpour, Maedeh Faculty of Operations Management IÉSEG School of Management Paris France Faculty of Industrial and Systems Engineering Tarbiat Modares University Tehran Iran Faculty of Industrial Engineering K. N. Toosi University of Technology Tehran Iran

ISBN: (纸本)9789819798384

Nowadays, the preservation of natural resources and their use in optimal ways has attracted both academic study and industrial implementation. The design of supply chain networks plays a pivotal role in effective supply chain management. Optimal decisions in this area ensure the flexibility and responsiveness of the entire system. Also, the role of supply chains in waste management is crucial as it enables the efficient movement and handling of materials, ensuring proper disposal or recycling. An effective supply chain helps the collection, processing, and distribution of waste, optimizing resource use and minimizing environmental impact. This study developed a mixed-integer linear programming (MILP) model to manage agricultural waste in the palm date industry, serving as a case study for producing medium-density fiberboards (MDF). It employs a robust optimization approach to enhance the network's adaptability in accommodating uncertainties related to customer demands. Computational analysis is also provided to present the significance of the model. Also, managerial insights are extracted from the final results, and sensitivity analyses are presented to handle agricultural wastes adequately. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sensitivity analysis

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Synthetic Image Generation for Visual Particle Size Distribution Estimation based on U-NET Convolutional Networks

Synthetic Image Generation for Visual Particle Size Distribu...

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2023 IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2023

作者： Chavan, Akshay Schmidt, Antonius Klopries, Hendrik Aufderheide, Dominik Faculty of Electrical Engineering Soest Germany South Westphalia University of Applied Sciences Automation Technology and Learning Systems Faculty of Electrical Engineering Soest Germany

ISBN: (纸本)9781665474924

Many applications in the field of bulk material handling require the determination of the Particle Size Distribution (PSD) of the handled materials. Typically, this can only be achieved by manual sampling and a subsequent laboratory analysis. However, the availability of novel image segmentation algorithms based on Convolutional Neural Networks (CNNs) leads to the possibility for an implementation of visual PSD estimation techniques. One prominent example of such approaches is the U-Net image segmentation architecture. However, one fundamental problem for the training of the underlying CNN is the difficulty to gather ground truth data, since the exact correspondence between a set of images and the underlying particle sizes cannot be achieved in practice. This paper introduces a novel synthetic image generation procedure for the visual PSD estimation by using the U-NET structure. The overall approach is validated and verified by using a prototypic implementation of a U-NET segmentation pipeline. It is shown, that the proposed synthetic image dataset generation can be applied for all visual PSD estimation based on machine learning algorithms. © 2023 IEEE.

关键词： Particle size analysis

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A Parallel Hybrid Testing Technique for Tri-Programming Model-Based Software systems

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Computers, Materials & Continua 2023年第2期74卷 4501-4530页

作者： Huda Basloom Mohamed Dahab Abdullah Saad AL-Ghamdi Fathy Eassa Ahmed Mohammed Alghamdi Seif Haridi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia KTH Royal Institute of Technology StockholmSweden

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple *** different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve *** the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these *** studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming *** the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been ***,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot *** the first time,this paper presents a classification of operational errors that can result from the integration of the three *** paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and *** hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic *** hybrid technique can detect more errors because it combines two distinct *** proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environme

关键词： Software testing hybrid testing technique OpenACC OpenMP MPI tri-programming model exascale computing

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Gentle Approach With a Potential Field Based on Personal Space∗

Gentle Approach With a Potential Field Based on Personal Spa...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Saegusa, Ryo Mongkolphinyopas, Apichaya Chancharoen, Ratchatin Kanagawa Institute of Technology Department. of Information Systems Atsugi Japan Chulalongkorn University Faculty of Engineering Bangkok10330 Thailand

ISBN: (纸本)9781665481090

In the context of a globally aging population and declining birth rates, the demand for elderly care is increasing, while the availability of caregivers and nurses is decreasing. To mitigate this challenge, healthcare robots are being developed to assist with various caregiving tasks. However, it is crucial for these robots to interact with elderly patients in a manner that maintains their comfort and well-being. This study aims to create an appropriate approach pathway for healthcare robots based on three key factors: personal space, field of view, and speed of approach. Utilizing the potential field method with principles of heat diffusion, a pathway design is developed and simulated in 3D. The pathway is then tested with elderly individuals, revealing an optimal approach distance of 1.2 meters. By prioritizing patient comfort and friendly interaction, this research contributes to addressing the evolving challenges in elderly care and the integration of robotics in healthcare settings. © 2024 IEEE.

关键词： Robots

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Investigation of Control Algorithm for Electromechanical Vehicle Active Anti-Roll Bar

Investigation of Control Algorithm for Electromechanical Veh...

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2023 International Conference on Electrical engineering and Informatics, ICEEI 2023

作者： Zhang, Zien Rahman, Abdul Hadi Abd Zulkarnain, Noraishikin Center for Artificial Intelligence Technology Faculty of Information Science & Technology Selangor Bangi Malaysia Faculty of Engineering & Built Environment Department of Electrical Electronic & Systems Engineering Selangor Bangi Malaysia

ISBN: (纸本)9798350381290

The present paper aims to address the issue of improving the roll stability of vehicles by employing an active anti-roll bar (AARB) controller. The AARB controller is established using MATLAB/SIMULINK based on the principle of Proportional-Integral-Derivative (PID) Controller, and the CARSIM software is used to build a testing environment. The evaluation is conducted through specific indicators of vehicle roll stability. A comparative simulation was conducted on the control system by changing the vehicle speed. The joint simulation of SIMULINK and CARSIM software verified the feasibility and correctness of the established model and control system. The simulation results analyzed using MATLAB software show that the AARB control system can improve the vehicle's stability. The significance of the AARB control algorithm for other active safety controllers in vehicles was also briefly explained. We highlight the future research direction of the control algorithm for the AARB controller on the importance and impact of intelligent algorithms on future research. © 2023 IEEE.

关键词： Controllers

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Optimizing Random Forest Classifier for Detection of Alzheimer's Using OASIS

Optimizing Random Forest Classifier for Detection of Alzheim...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Sanjana Reddy, Karri Sai Reddy C, Kishor Kumar Doss, Srinath Lippert, Kari Stanley College of Engineering and Technology for Women Dept. of Computer Science and Engineering Hyderabad India Botho University Faculty of Engineering and Technology Botswana University of South Alabama Dept. of Systems Engineering Alabama United States

ISBN: (纸本)9798331540821

Alzheimer's disease (AD) is a progressive brain disorder impacting behavior, memory, and cognition, with over a million cases reported annually in India. The risk significantly increases beyond age 65. Early diagnosis and treatment can result in better recovery. We propose a predictive model using the Random Forest algorithm and the OASIS dataset for early AD diagnosis, leveraging MRI data, clinical notes, genetic markers, and cognitive test results. Our model was evaluated against several others, including Decision Tree, AdaBoost, SVM, and Logistic Regression. With a 97.3% accuracy and a 2.7% error rate, our Random Forest Classifier o utperformed t he others, demonstrating superior predictive power for early AD diagnosis and potentially improving patient care. © 2024 IEEE.

关键词： Logistic regression

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Optimizing Composting Efficiency and Odor Control in Intensive Tunnel Composting systems: A Case Study of the Arad County Composting Facility 11

Optimizing Composting Efficiency and Odor Control in Intensi...

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11th International Conference on ENERGY and ENVIRONMENT, CIEM 2023

作者： Raul, Lile Apostol, Tiberiu Stan, Constantin Faculty of Biotechnical Systems Engineering National University of Science and Technology POLITEHNICA Bucharest Bucharest Romania Faculty of Energy Engineering National University of Science and Technology POLITEHNICA Bucharest Bucharest Romania

ISBN: (纸本)9798350340785

—This study details the design of a municipal waste management facility that combines mechanical-biological treatment methods. Capable of bio-drying 195,000 tons of mixed solid waste and composting 10,000 tons of green waste annually, the facility leverages closed bio-cell technology and the Gore Cover system to optimize mass reduction and minimize environmental impact. Key operational parameters such as density, moisture content, and pH are strictly controlled. The technology choice allows year-round operations, emission reductions, and process automation. The resulting compost product is expected to enhance soil health, contributing to waste reduction and overall environmental sustainability. ©2023 IEEE.

关键词： Composting

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Effectiveness of generative adversarial networks in denoising medical imaging (CT/MRI images)

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Multimedia Tools and Applications 2024年 1-25页

作者： Ramadass, Sudhir Narayanan, Sreekumar Kumar, Rajiv K, Thilagavathi Data Analytics Sterck Systems Private Limited Chennai Nungambakkam India Network and Infrastructure Management Faculty of Engineering and Technology Botho University Gaborone Botswana School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Department of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India

For clinical analysis or medical intrusion, imaging technology is a potent tool for producing visual images of a body's inside. However, several existing methods include restricted access to high-quality and diverse datasets, overfitting, a lack of interpretability, high computational costs, varying medical imaging data, and ethical issues concerning patient privacy, consent, and bias. To overcome these issues, the effectiveness of generative adversarial networks in denoising medical imaging (CT/MRI images) is proposed. The input CT images are first gathered and preprocessed to reduce undesired noise using Altered Phase Preserving Dynamic Range Compression (APPDRC). The General Adversarial Network (GAN) is then given these preprocessed images to cope with the noisy areas of CT/MRI while also complying with maintaining image structures such as boundary details while preventing over-smoothing, and the weight parameters for the GAN are optimized using the Golden Eagle Optimization (GEO) technique. General Adversarial Network (GAN) has piqued the interest of academics studying medical image-denoising mechanisms and developing sophisticated medical image-denoising algorithms based on deep learning. In light of reducing the difficulty in feature learning and improving final diagnosis accuracy, data preprocessing is necessary and crucial in gan-based medical image denoising methods. This work focuses on GAN-based effective denoising approaches in CT/MRI. The potential challenges and research objectives are in the context of data preprocessing in medical image denoising. Because of how CT/MRI images are acquired, it was observed that noise in CT/MRI data follows the distribution. Generative Adversarial Networks (GANs) for denoising medical imaging, mainly CT and MRI images, have yielded promising results of 99% accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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