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检索条件"任意字段=2013 IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2013"

共 4106 条记录，以下是71-80 订阅

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A Low Cost and User Friendly Graphical User Interface for Programmable and Flexible FES

A Low Cost and User Friendly Graphical User Interface for Pr...

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ieee international conference on control system, computing and engineering (iccsce)

作者： M.A.M.A. Halim E. Noorsal Z. Hussain S.Z. Yahaya A.N.A. Rashid Y.M. Ali S.Z.M. Saad S. Arof Cawangan Pulau Pinang Permatang Pauh Campus Electrical Engineering Studies College of Engineering Universiti Teknologi MARA Pulau Pinang Malaysia Universiti Kuala Lumpur Malaysian Spanish Institute Kulim Hi-Tech Park Kulim Kedah Malaysia

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

This paper presents the development and testing of a Tkinter-based graphical user interface (GUI) for controlling a functional electrical stimulation (FES) system. The FES device, which is used for individuals with spinal cord injuries or neurological disorders to aid muscle re-education, prevent atrophy, and improve circulation, is crucial in rehabilitation exercise. The existing FES devices in the market often lack a user-friendly interface, require complex settings to generate stimulus pulse, and are costly. This study develops a low-cost, user-friendly GUI using Python’s Tkinter, which communicates with an Arduino microcontroller via a COM port, allowing users to adjust various waveform types and parameters. Validated using Proteus simulation, the system demonstrated accurate data transfer and correct waveform generation. Results highlight the Tkinter-based GUI’s effectiveness in providing an intuitive and efficient interface for biomedical applications, with potential future enhancements in real-time data visualization and advanced waveform customization.

关键词： Wireless communication Microcontrollers Data visualization control systems Iron Real-time systems Neuromuscular stimulation Spinal cord injury Graphical user interfaces Testing

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Analysis of Power Consumption and Maximum Demand on Power Factor Using Support Vector Machine Regression

Analysis of Power Consumption and Maximum Demand on Power Fa...

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ieee international conference on control system, computing and engineering (iccsce)

作者： Siti Nursyafiqah Md Isa Kamarulazhar Daud Mohammad Nizam Ibrahim Ahmad Asri Abd Samat Saodah Omar Mohd Affandi Shafie Centre of Electrical Engineering Studies Universiti Teknologi MARA Cawangan Pulau Pinang Permatang Pauh Pulau Pinang Malaysia

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

This paper analyzes the power consumption and maximum demand to evaluate the effect of the power factor and forecast the value of the power consumption and maximum demand using support vector machine (SVM) regression in MATLAB software. The data on power consumption and maximum demand are collected from the previous 12-month electricity bills for the year 2019 until 2022 at Universiti Teknologi Mara (UiTM) campuses: Pulau Pinang, Arau, and Tapah. The graph of the maximum demand, power consumption, and power factor has been plotted to show the relationship between these three variables for the year 2022. From the graph, it was found that when the power factor is low, more current is needed to supply the same amount of power to a load. This means that more power is being drawn from the electrical supply system, which increases the power consumption. The power factor also shows the efficiency of the electricity at the UiTM. The quadratic SVM in regression learning is used to forecast the value of power consumption and maximum demand in MATLAB software without or with the use of the Principal Component Analysis (PCA). Therefore, it can show which one is better to use to make a prediction. This study successfully achieved the objective of analyzing power consumption, maximum demand, and power factor and forecasting the value of power consumption and maximum demand.

关键词： Support vector machines Reactive power Power demand Accuracy Electricity control systems Software Forecasting Principal component analysis Matlab

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Design of Digital system Identification controller for a Nonlinear Knee Model in Closed-Loop Functional Electrical Stimulator (FES)

Design of Digital System Identification Controller for a Non...

引用

ieee international conference on control system, computing and engineering (iccsce)

作者： N.H.M. Muhan E. Noorsal S. Arof M.K. Safie Z. Hussain S.Z. Yahaya S.S.M. Sallah Electrical Engineering Studies College of Engineering Universiti Teknologi MARA Cawangan Permatang Pauh Campus Pulau Pinang Malaysia Universiti Kuala Lumpur Malaysian Spanish Institute Kulim Hi-Tech Park Kulim Kedah Malaysia Intel PG14 Plot 6 Medan Bayan Lepas Bayan Lepas Technoplex Bayan Lepas Pulau Pinang Malaysia

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

Functional Electrical Stimulation (FES) devices represent a valuable rehabilitation intervention for patients with spinal cord injury (SCI). SCI can result in significant functional impairments, including the loss of movement in the lower body. Currently, closed-loop stimulation strategies are preferred over open-loop systems, as the latter’s trial-and-error approach can lead to premature muscle fatigue. However, while closed-loop systems offer promise, feedback controllers often exhibit inadequate performance when addressing nonlinear effects in knee muscles, such as stiffness, spasticity, and fatigue. To address these challenges, an adaptive feedback controller equipped with a system identification controller is crucial for accurately detecting these nonlinear parameters in knee muscles. This paper proposes the development of a digital system identification controller designed to detect nonlinear parameters from the knee extension model’s response. The system identification was implemented and simulated using hardware description language (HDL) Verilog code, with its performance evaluated through HDL co-simulation in MATLAB Simulink. The simulation results indicate that all nonlinear effects including stiffness, spasticity and fatigue are identifiable once the rate-of-change response stabilizes at 4 s.

关键词： Digital systems Software packages Muscles control systems Fatigue Mathematical models Iron system identification Hardware design languages Adaptive control

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Facial Expression Recognition (FER) system Using Deep Learning 1

Facial Expression Recognition (FER) System Using Deep Learni...

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1st ieee Karachi Section Humanitarian Technology conference, Khi-HTC 2024

作者： Yan, Ooi Juin Ashraf, Humaira Ihsan, Uswa Jhanjhi, Nz Ray, Sayan Kumar Kavita Taylor's University Masters of Applied Computing Selangor Malaysia Taylor's University School of Computer Science SCS Malaysia International Islamic University Department of Computer Science Islamabad Pakistan Uttaranchal University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350373042

Sentiment analysis of both text and images have been widely implemented machine learning application. Facial expression recognition (FER) is a widely known application of sentiment analysis of image data. This application has already been implemented in various industry, but challenges such as the complexities of facial features and different image angle still remain. This project aims to develop a FER system capable of classifying human expressions through facial images using deep learning techniques. A CNN-based architecture was proposed and training using the FER2013 dataset, which is a commonly used facial expression dataset. Overall, the trained model achieved an accuracy of 63.97% through the dataset. The trained CNN model was then employed to develop a web-based prototype of a FER system through Python Flask. © 2024 ieee.

关键词： Sentiment analysis

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Dysgraphia Handwriting Image Augmentation for CNN Model Classification

Dysgraphia Handwriting Image Augmentation for CNN Model Clas...

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ieee international conference on control system, computing and engineering (iccsce)

作者： Nurhani Harun Iza Sazanita Isa Siti Azura Ramlan Muhammad Khusairi Osman Mohd Ikmal Fitri Maruzuki Electrical Engineering Studies College of Engineering Universiti Teknologi MARA Cawangan Pulau Pinang Permatang Pauh Pulau Pinang Malaysia

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

Dysgraphia affects a person’s ability to write consistently and properly especially among school children. It is a challenging condition as it needs effective intervention to help the affected children succeed academically and socially. With the advancement in technology in artificial intelligence (AI), various methods and approaches have been developed using convolutional neural networks (CNN) model to overcome several limitations to assess dysgraphia symptoms. However, there are major concerns about the difficulties of getting large data of dysgraphia handwriting images for CNN attributes model. Thus, this study is aimed to develop dysgraphia handwriting recognition model based on augmentation method. In this study, image augmentation is addressed by creating new data by using rotation and brightness technique to generate a set of synthetic images. The augmented data is trained and tested using CNN classification model to classify four classes of dysgraphia handwriting. The results show a significant improvement with $77 \%$ accuracy using augmented as compared to without augmented data only $\mathbf{7 3 \%}$. This study indicated that augmentation method is significant for inclusion in CNN classification model particularly for dysgraphia potential risk recognition. This study is further recommended to implement intelligence-based augmentation method which can be incorporated into a computer-assisted dysgraphia screening system to provide a rapid, accurate, and unbiased dysgraphia detection.

关键词： Handwriting recognition Accuracy Computational modeling Brightness control systems Data models Image augmentation Convolutional neural networks Artificial intelligence

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PID-DTC controller for Nonlinear Knee Extension Model with Time Delay Effect in Closed-Loop FES

PID-DTC Controller for Nonlinear Knee Extension Model with T...

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ieee international conference on control system, computing and engineering (iccsce)

作者： S. Arof E. Noorsal Z. Hussain S.Z. Yahaya Y.M. Ali A.N.A. Rashid S.Z.M. Saad S.S.M. Sallah H. Arof Universiti Kuala Lumpur Malaysian Spanish Institute Kulim Hi-Tech Park Kulim Kedah Malaysia Electrical Engineering Studies College of Engineering Universiti Teknologi MARA Cawangan Pulau Pinang Permatang Pauh Campus Pulau Pinang Malaysia Engineering Department Universiti Malaya Jalan Universiti Kuala Lumpur Malaysia

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

Functional Electrical Stimulation (FES) is a crucial device in neuromuscular applications, facilitating restoring functionality of paralyzed muscles and limbs. However, its practical utilization encounters significant nonlinear challenges, predominantly arising from factors like muscle fatigue and time delay. These challenges impede the optimal performance of FES controllers, such as proportional-integral-derivative (PID), sometimes resulting in suboptimal outcomes or complete failure. The nonlinearities introduced by these factors necessitate system adaptability, but traditional controller retuning is often impractical due to its adverse effects on muscle performance. Moreover, introducing time delays can induce instability and oscillations in the system. This study explores the application of direct torque control (DTC) with PID controller to leverage FES-induced muscle torque for knee rehabilitation. Through a simulation model developed in MATLAB/Simulink, we empirically demonstrate the effectiveness of the PID-DTC approach in managing the knee extension model with nonlinear time delay in closed-loop FES. The results demonstrate the absence of oscillations, reduced overshoot, and minimal steady-state error, suggesting potential for successful real-world hardware implementation in rehabilitation applications.

关键词： Performance evaluation Torque Delay effects Torque control Iron Mathematical models Stability analysis Steady-state PD control Oscillators

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An Optimized Harris Hawks Algorithm for Enhancing ANN Performance in Prediction Tasks Applied in Sales Domain

An Optimized Harris Hawks Algorithm for Enhancing ANN Perfor...

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ieee international conference on control system, computing and engineering (iccsce)

作者： Bibi Aamirah Shafaa Emambocus Muhammed Basheer Jasser Shou Heng Tan Samuel Mofoluwa Ajibade Hui Na Chua Richard T. K. Wong Ahmad Sahban Rafsanjani School of Engineering and Technology Sunway University Petaling Jaya Selangor Malaysia

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

Sales prediction, the process of predicting future sales performance based on past information, offers valuable information to businesses, allowing them to make informed and profitable decisions. This can be done using machine learning, specifically Artificial Neural Networks (ANNs) which have proven to be effective for prediction tasks. A requisite process before using ANNs as prediction models is its training which is commonly done using back-propagation algorithms. Recently, it has been noticed that swarm intelligence algorithms are better training algorithms for ANNs than backpropagation. In this paper, we propose to employ a high-performing swarm intelligence algorithm, Harris Hawks Optimization (HHO) for the training of ANNs applied to the prediction of iPhone sales in Malaysia. Moreover, we propose an optimized Harris Hawks algorithm and apply it for training the same prediction models. Both the original and optimized HHO algorithms are found to be able to better train the prediction models compared to the usual backpropagation algorithm. Furthermore, the proposed optimized HHO algorithm is found to have higher effectiveness than the original HHO as the ANNs trained by the optimized HHO can achieve a root mean square error of 89.6789 during the training as compared to 112.2448 by those trained using the original HHO.

关键词： Training Machine learning algorithms Artificial neural networks Machine learning Predictive models Prediction algorithms control systems Particle swarm optimization Root mean square Optimization

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Monitoring Water Quality for Catfish Ponds Using PID control with Internet of Thing

Monitoring Water Quality for Catfish Ponds Using PID Control...

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ieee international conference on control system, computing and engineering (iccsce)

作者： Zuraida Muhammad Nuramirah Md Jafri Shabinar Abd Hamid Siti Juliana Abu Bakar Nor Adni Mat Leh Cawangan Pulau Pinang Electrical Engineering Studies College of Engineering Universiti Teknologi MARA Permatang Pauh Pulau Pinang MALAYSIA

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

Catfish is one type of fish that holds a prominent position due to ease of cultivation, the popularity of its high protein and economic significance in aquaculture or fish farming that has become a significant sector in the global food industry, meeting the increasing demand for seafood. However, maintaining of clean water parameters in the rearing environment is crucial for a catfish farm to be successful. African Catfish is one of Malaysia’s most popular and favored freshwater food fish species. The ideal hatching and the greatest larval survival of African catfish is a pH level of within the range 6.7-7.5. The family Claridae includes the African catfish (Clarias gariepinus). This project focuses on the design and implementation of a water quality monitoring system for catfish ponds using PID control and Internet of Things (IoT) technology. The pH level fluctuations can have adverse effects on the catfish’s health. The PID Water Quality Monitoring for Catfish Pond are designed to see the results of the pH data using MATLAB Software and ThingSpeak software. The circuit is designed by using WEMOS D1 R32 microcontroller, Sensor E-201-C and 12V Solenoid Valve. The experiment is conducted by throwing bread into the plastic basin. PID controller is implemented to control the opening of valve for water flow to achieve setpoint. The sensor performance was evaluated. The sensor can read pH level of the water and can stabilise the pH water using PID controller.

关键词： Proteins PI control Water quality Fish Valves Software Internet of Things PD control Solenoids Monitoring

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system Reconfiguration to control Knee Extension with Time Delay Nonlinearity in Closed-Loop FES

System Reconfiguration to Control Knee Extension with Time D...

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ieee international conference on control system, computing and engineering (iccsce)

作者： S. Arof E. Noorsal S.Z. Yahaya Z. Hussain Y.M. Ali A.N.A. Rashid S.Z.M. Saad S.S.M. Sallah M.K. Safie Universiti Kuala Lumpur Malaysian Spanish Institute Kulim Hi-Tech Park Kulim Kedah Malaysia Electrical Engineering Studies College of Engineering Universiti Teknologi MARA Cawangan Pulau Pinang Permatang Pauh Campus Pulau Pinang Malaysia Intel PG14 Plot 6 Medan Bayan Lepas Bayan Lepas Technoplex Bayan Lepas Pulau Pinang Malaysia

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

Functional Electrical Stimulation (FES) assists individuals with neuromuscular impairments during rehabilitation exercises conducted in open-loop and closed-loop. Typically, during the design phase, the closed-loop FES system mainly consists of a feedback controller, a second-order model, or a knee extension model to represent the actual knee. Closed-loop FES or feedback controllers are generally better, but their performance drops in real-world scenarios due to nonlinear factors like fatigue, time delay, spasm, and spasticity. These lead to suboptimal performance of the feedback controllers and even failure, diminishing their effectiveness in assisting patients. In response to these challenges, this paper investigates four different techniques of system reconfiguration, which involves changing the closed-loop system structure to mitigate the adverse effects of time delay in real-world FES applications. To evaluate the effectiveness of the four techniques, a feedback PID controller, knee extension model or second-order critically damped system were developed and tested using MATLAB/Simulink software. The simulation results demonstrate that the first system reconfiguration technique improves system behaviour and enhances FES controller performance. This research marks a significant step towards improving the practicality and adaptability of FES technology for individuals with neuromuscular impairments.

关键词： Neuromuscular Delay effects Iron Mathematical models Software Steady-state Mirrors Closed loop systems Adaptive control Matlab

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Machine Learning Approaches for Sentiment Analysis on Balanced and Unbalanced Datasets

Machine Learning Approaches for Sentiment Analysis on Balanc...

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ieee international conference on control system, computing and engineering (iccsce)

作者： Ahmed M. ElMassry Abdulla Alshamsi Ahmed F. Abdulhameed Nazar Zaki Abdelkader Nasreddine Belkacem College of Information Technology United Arab Emirates University AlAin UAE

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

Sentiment analysis, sometimes referred to as opinion mining, is essential for understanding public opinion and attitudes toward various social topics and trends. This study aims to explore the effectiveness of machine learning (ML) models, namely support vector machine (SVM), long short-term memory (LSTM), and bidirectional encoder representations from transformers (BERT), in analyzing a dataset obtained from Kaggle, which contains 37,000 user reviews on the Instagram Threads app. After initial data cleaning and preprocessing, the dataset was partitioned into ${7 0 \%}$ for training and ${3 0 \%}$ for testing. Subsequently, the training set was used to create three datasets: a balanced dataset and two unbalanced datasets, one featuring $90 \%$ positive instances and the other featuring ${9 0 \%}$ negative instances. Subsequently, these datasets were used to train the three machine learning models mentioned above, resulting in nine different models. Evaluation metrics, including accuracy, precision, recall, and F1 score, were applied to assess model performance. The finetuned BERT model on the balanced dataset outperformed all the other models with an accuracy of $86 \%$, precision of $85 \%$, recall of $87 \%$, and F1-score of ${8 6 \%}$. Furthermore, these findings underscore the effectiveness of diverse ML techniques, particularly transformers, and the crucial role of data balancing in optimizing sentiment analysis tasks.

关键词： Training Support vector machines Measurement Sentiment analysis Analytical models Accuracy Machine learning Bidirectional control Transformers Encoding

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