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4th Novel Intelligent and Leading Emerging Sciences Conference, NILES 2022

作者： Rabie, Ahmed Thabet, Doha Abdellatif, Ibrahim Elsayed, Kariman Elbadawy, Marwa Shalaby, Raafat Mechanical Engineering Program School of EAS Nile University ElsheikhZayed Egypt SESC Center & MECT Program School of EAS Nile University ElsheikhZayed Egypt Menofia Uni. Faculty of Electronic Engineering Menouf Egypt

ISBN: (数字)9781665452410

ISBN: (纸本)9781665452410

This paper illustrates a humanlike self-balancing robot's control and sensor fusion with an inherently imbalanced dynamical model. Using a state space model, a two-wheeled self-balancing robot (TWSBR) is modelled as an inverted pendulum. A comparative study of the system performance is conducted, with different control techniques, namely, PD, PID, and Linear Quadratic Regulator (LQR). The system status is monitored and fed back to the controller using an internal measurement unit (IMU) with data filtering through sensor fusion. The controlled system is simulated using MATLAB/ Simulink and Simscape. The quantitative performance of different controllers is calculated using seven different indices: maximum overshooting, settling time, rise time, root-mean-square error (RMSE), steady-state error, controller effort, and max control action. The results proved that the LQR gives the best performance with the lowest RMSE and reasonable rise, settling time, energy consumption, and saturation level;therefore, it is the most suited for our system requirements. © 2022 IEEE.

关键词： Controllers

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Development of LED Lighting for Microalgae Propagation with Emission Spectrum Having Broader Emission Peaks

Development of LED Lighting for Microalgae Propagation with ...

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Sustainable Smart Lighting World Conference & Expo (LS18), IEEE

作者： Nobuaki Hosoda Takashi Murata Takayuki Misu Graduate School of Engineering Master's Degree Program in Electrical and Electronic Engineering Kanagawa Institute of Technology Atsugi Kanagawa Japan Department of Applied Chemistry and Bioscience Faculty of Engineering Kanagawa Institute of Technology Atsugi Kanagawa Japan Department of Electrical and Electronic Engineering Faculty of Engineering Kanagawa Institute of Technology Atsugi Kanagawa Japan

ISBN: (数字)9798331540197

ISBN: (纸本)9798331540203

In this study, we aimed to develop LED lighting with an emission spectrum effective for microalgae propagation, and fabricated LED lighting with Phosphor layer RGB, which has peaks in the red, green, and blue wavelength bands, and Phosphor layer RG’B’, which has broader peaks in the red, green, and blue wavelength bands. In order to examine the effect of microalgae propagation, Closterium ehrenbergii samples prepared at different dilution rates were irradiated with LED illumination light of Phosphor layer RG’B’ and Phosphor layer RGB, respectively, for 7 days, and their propagation rates were compared. The results of the experiment showed that Phosphor layer RG’B’ produced a higher propagation rate than Phosphor layer RGB.

关键词： Phosphors Lighting Light emitting diodes

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Comparison of Algebraic Reconstruction Technique Methods and Generative Adversarial Network in Image Reconstruction of Magnetic Induction Tomography (MIT)

Comparison of Algebraic Reconstruction Technique Methods and...

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2021 International Conference on Biomedical engineering, ICoBE 2021

作者： Lubis, A.J. Nasir, N.F. Mohd Zakaria, Z. Jusoh, M. Azizan, M.M. Zakaria, M.Z. Kazim, M.N.F.M. Rahman, S. Faculty of Engineering and Computer Science Universitas Harapan Medan Medan Indonesia Computer Engineering Program Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Biomedical Electronic Engineering Program Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Universiti Malaysia Perlis Perlis Malaysia Department of Electrical and Electronic Department Faculty of Engineering and Built Environment Universiti Sains Islam Malaysia Negeri Sembilan Malaysia Faculty of Mechanical Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Mechatronic Engineering Program Faculty of Electrical Engineering Technology Universiti Malaysia Perlis Perlis Malaysia

Magnetic induction tomography (MIT) is a technique used for imaging electromagnetic properties of objects using eddy current effects. The non-linear characteristics had led to more difficulties with its solution especially in dealing with low conductivity imaging materials such as biological tissues. Two methods that could be applied for MIT image processing which is the Generative Adversarial Network (GAN) and the Algebraic Reconstruction Technique (ART). ART is widely used in the industry due to its ability to improve the quality of the reconstructed image at a high scanning speed. GAN is an intelligent method which would be able to carry out the training process. In the GAN method, the MIT principle is used to find the optimum global conductivity distribution and it is described as a training process and later, reconstructed by a generator. The output is an approximate reconstruction of the distribution's internal conductivity image. Then, the results were compared with the previous traditional algorithm, namely the regularization algorithm of BPNN and Tikhonov Regularization method. It turned out that GAN had able to adjust the non-linear relationship between input and output. GAN was also able to solve non-linear problems that cannot be solved in the previous traditional algorithms, namely Back Propagation Neural Network (BPNN) and Tikhonov Regularization method. There are several other intelligent algorithms such as CNN (Convolution Neural Network) and K-NN (K-Nearest Neighbor), but such algorithms have not been able to produce the expected image quality. Thus, further study is still needed for the improvement of the image quality. The expected result in this study is the comparison of these two techniques, namely ART and GAN to get the best results on the image reconstruction using MIT. Thus, it is shown that GAN is a better candidate for this purpose. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Generative adversarial networks

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Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

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Computers, Materials & Continua 2022年第1期70卷 1297-1313页

作者： C.S.S.Anupama L.Natrayan E.Laxmi Lydia Abdul Rahaman Wahab Sait Jose Escorcia-Gutierrez Margarita Gamarra Romany F.Mansour Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Archives and Communication King Faisal University31982Kingdom of Saudi Arabia Electronic and Telecommunicacions Program Universidad Autonoma del CaribeBarranquilla08001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla08001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare *** of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare *** entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor *** advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making *** cancer is the deadliest disease which affects people across the *** skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin *** current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare ***,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further ***,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image *** by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature *** addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic *** extensive set of simulations was conducted to validate the performance of the presented method using benchmark *** experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.

关键词： Intelligent models skin lesion dermoscopic images smart healthcare internet of things

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Applied Depthwise Separable-based Convolutional Neural Network Classification on MobileNet Algorithm for Human Fall Detection

Applied Depthwise Separable-based Convolutional Neural Netwo...

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International Electrical engineering Congress (iEECON)

作者： Pipat Sakarin Suchada Sitjongsataporn The Electrical Engineering Graduate Program Faculty of Engineering and Technology Mahanakorn University of Technology Bangkok Thailand Department of Electronic Engineering Mahanakorn Institute of Innovation (MII) Mahanakorn University of Technology Bangkok Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

This paper presents the human fall detection by convolutional neural network (CNN) classification and MobileNet algorithm. In order to reduce the number of parameter, we propose a vision-based fall detection model, that is called “Applied Depthwise separable-based CNN-MobileNet (ACM) algorithm”. This model is based on CNN, deptwise convolution and pointwise convolution of MobileNet algorithm. Experimental results show the accuracy of proposed ACM model about 97.68% and number of parameters of proposed ACM model is less than conventional CNN model.

关键词： Electrical engineering Convolution Classification algorithms Convolutional neural networks Fall detection Standards

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The Design and Optimisation of W-Angular Plate on Quadrilateral Surface

The Design and Optimisation of W-Angular Plate on Quadrilate...

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Communication engineering and Emerging Technologies (ICoCET), IEEE International Conference on

作者： Riya Letchumi Rajan Nashrul Fazli Bin Mohd Nasir Normahira Binti Mamat Sahran Bin Yahaya Eugene Tan Lim Chee Chin Cheng Ee Meng Che Wan Sharifah Robiah Mohd Rafiq Bin Abdul Kadir Biomedical Electronic Engineering Program Faculty of Electronic Engineering and Technology Universiti Malaysia Perlis Perlis Malaysia Department of Orthopaedics Universiti Sains Malaysia Kubang Kerian Malaysia Leonix Sdn. Bhd. Batu Maung Malaysia Biomedical Engineering Department Faculty of Engineering Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798331504144

ISBN: (纸本)9798331504151

This study focuses on the design, optimization, and biomechanical evaluation of a W-angular hip plate intended for the quadrilateral surface of the human pelvis. The W angular configuration is chosen for its potential to enhance stability and load distribution across the complex pelvic anatomy. The primary objective is to investigate and refine the screw insertion technique that maximizes the plate's stability on the quadrilateral surface. This involves evaluating various parameters such as screw placement angles and depths to achieve optimal fixation. Mechanical behavior of the W-angular hip plate is comprehensively assessed under diverse loading conditions simulating activities essential to daily living, including walking, standing, and sitting. The evaluation encompasses the analysis of stress distribution, strain patterns, and deformation characteristics within the implant structure. Both male and female anatomical variations are considered to ensure a thorough assessment of performance across different patient demographics. The outcomes of this study aim to deepen the understanding of how the W-angular hip plate interacts with the pelvic bone and surrounding tissues under realistic conditions.

关键词： Legged locomotion Pelvic bones Loading Fasteners Stability analysis Pelvis Hip Stress Optimization Strain

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Evaluation of time-series database on microservice architecture for health monitoring system

Evaluation of time-series database on microservice architect...

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2022 International Symposium on electronics and Smart Devices, ISESD 2022

作者： Simanjuntak, Eko Surantha, Nico Isa, Sani Muhamad Bina Nusantara University Binus Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia Tokyo City University Electronic and Communication Engineering Faculty of Engineering Department of Electrical Tokyo Japan

ISBN: (数字)9798350396607

ISBN: (纸本)9798350396607

Recently, the research on daily health monitoring using a wearable sensor has been continually evolving. In the future, when this system is actually implemented, a vast amount of data transmission will be conducted from multiple sensors to the cloud server. A system capable of transmitting, storing, processing, and visualizing massive volumes of data is necessary for health monitoring. This research aims to develop a health monitoring system that can accommodate a high amount of data with high performance. The proposed techniques include numerous modifications to system architecture and technology. In the system architecture, a multi-database is proposed to enhance the reliability of the system. As an enhancement to the technology aspect, a time series database and MQTT server are proposed. As the result, the proposed system achieves significantly better performance in terms of throughput, database-writing speed, and database-reading speed compared to the existing system. © 2022 IEEE.

关键词： Database systems

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Initial Results on Primary Field Cancellation of Magnetic Induction Spectroscopy Technique for Fetal Acidosis Detection using COMSOL Multiphysics

Initial Results on Primary Field Cancellation of Magnetic In...

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2021 International Conference on Biomedical engineering, ICoBE 2021

作者： Halim, S.F. Abdul Zakaria, M.H. Zakaria, Z. Norali, A.N. Noor, A. Mohd Ahmed, A.A. Pusppanathan, J. Rahiman, M.H. Fazalul Muji, S.Z. Mohd Rahim, R. Abdul Biomedical Electronic Engineering Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Arau Perlis 02600 Malaysia Universiti Teknologi Malaysia Skudai Johor81310 Malaysia Mechatronic Engineering Program Faculty of Electrical Engineering Technology Universiti Malaysia Perlis Malaysia Electronic Engineering Department Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia School of Electrical Engineering Faculty of Engineering Universiti Teknologi Malaysia Skudai Johor81310 Malaysia

Monitoring of fetal condition during labor could save hundred lives in a single year. During labor, fetus is at critical condition as acidosis may occur suddenly without any early symptoms. Invasive method such as Fetal Blood Sampling (FBS) has been used to detect the decline in pH level of fetus. However, fetal loss rate after FBS may range from 1.4% up to 25%. In this paper, magnetic field induction spectroscopy was implemented to determine fetal acidosis by using primary magnetic field cancellation technique. Magnetic Induction Spectroscopy (MIS) probe was design where transmitter coil (TX) is perpendicular to receiver coil (RX). The result shows that the secondary magnetic field produced have been successfully measured without any interruption from primary magnetic field. By using transmitter input 1A, it shows that voltage is inversely proportional to the blood pH due to the conductivity properties of blood. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Spectroscopy

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Coordinated-axis Weight for Real-Time Trace Transform

Coordinated-axis Weight for Real-Time Trace Transform

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International Electrical engineering Congress (iEECON)

作者： Nattapong Jundang Suchada Sitjongsataporn The Electrical Engineering Graduate Program Faculty of Engineering and Technology Mahanakorn University of Technology Bangkok Thailand Department of Electronic Engineering Faculty of Engineering and Technology Mahanakorn Institute of Innovation (MII) Mahanakorn University of Technology Bangkok Thailand

This paper presents a method for building Coordinated-axis Weight (CW) that has the advantage can be used to increase the speed for converting image data into the domain of real-time trace transforms. The main idea for this method is to use a feature reduction method for image data that must be used in cooperation with supervised machine learning outcome predictions. But the obtained result still retains the distinctive features of the data image in the form of a trace transform. Start by reducing the number of features from the image data, which will be used to reduce the size of the image to the appropriate image size, which in this article will reduce the image size to 45x45 pixels. The next step is to train the data using a Decision Tree (DT) method by aligning the reduced image data in a 1D model. In addition to the image data, two more features are added, i.e. the $\phi$ and $\rho$ positions, to inform the prediction that each position has a different value. And the last step is to bring the data to create a model for predicting the answer. The weights obtained from the model are called Coordinated-axis Weights (CW). The experiments within this paper were experimented using the Caltech-256 database, which was able to measure the MSE, RMSE, and PNSR of the images compared to the original trace transform response images at 4.1, 2.02, and 42.00, respectively, including the highest predictive speed of 0.2 ms per image.

关键词： Weight measurement Training Transforms Machine learning Predictive models Prediction algorithms Real-time systems

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Reinforced Deep Learning By Discriminant Feature Trace Transform

Reinforced Deep Learning By Discriminant Feature Trace Trans...

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International Electrical engineering Congress (iEECON)

Classification of objects using YOLO deep learning is currently very popular. When the discovered object is rotated, however, the pre-trained YOLO model frequently generates incorrect answer predictions. In order to improve the accuracy of the YOLO deep learning pre-trained model, we present in this paper a method for rotating an object's image back to its initial gain using the DFTF algorithm. The DFTF algorithm is designed to take the results of the trace transform and accumulate the values for each column in order to predict the object's degree of rotation. The results of this calculation are then used to rotate the object back to its normal axis. The experiment is therefore a collaboration between YOLO and DFTF with the aim of more accurately predicting the object's responses. Five groups of data will be chosen for experimentation, each of which can be used to determine the precision of the experimental image with rotational issues. The average of accuracy is 98.56%.

关键词： Deep learning Electrical engineering Collaboration Transforms Predictive models Prediction algorithms Data models

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