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6 pages, 630 KiB

Open AccessProceeding Paper

Analysis of One-Degree-of-Freedom Spring-Mass-Damper System with Nonlinear Spring Using Runge–Kutta Method

by Kuan-Bo Lin and Tzu-Li Tien

Eng. Proc. 2025, 92(1), 1; https://doi.org/10.3390/engproc2025092001 - 10 Apr 2025

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Most engineering problems are described using differential equations, yet only a few can be solved analytically. Nonlinear differential equations are generally difficult to solve. The goal of numerical analysis is to minimize the difference between the numerical solution and the exact solution as [...] Read more.

Most engineering problems are described using differential equations, yet only a few can be solved analytically. Nonlinear differential equations are generally difficult to solve. The goal of numerical analysis is to minimize the difference between the numerical solution and the exact solution as much as possible. The Runge–Kutta method, particularly the fourth-order Runge–Kutta method (RK4), is a highly accurate numerical analysis technique. We applied the RK4 method to the analysis of a spring-mass-damper system with a nonlinear spring. The results show that the numerical solution of the displacement time response function of the spring-mass-damper system is accurate and precise, with six significant figures. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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7 pages, 785 KiB

Open AccessProceeding Paper

Calculating Percentiles of T-Distribution Using Gaussian Integration Method

by Tzu-Li Tien

Eng. Proc. 2025, 92(1), 2; https://doi.org/10.3390/engproc2025092002 - 10 Apr 2025

Viewed by 100

Abstract

Statistical inference is used to estimate population parameters based on sample information and to quantify the sampling error based on the probability narrative. The population mean is inferred by its sample mean, but when using sample variance, the population variance is needed. In [...] Read more.

Statistical inference is used to estimate population parameters based on sample information and to quantify the sampling error based on the probability narrative. The population mean is inferred by its sample mean, but when using sample variance, the population variance is needed. In the quantitative analysis of the sampling error, the t-distribution is used. To determine the percentiles of the t-distribution, the cumulative probability density function is necessary. However, the analytic expression does not exist for the cumulative probability density function of the t-distribution. Its values are obtained using numerical integration. However, the percentiles of the t-distribution are not listed for degrees of freedom over 30, while only listed for every 10 data points in probability theory or mathematical statistics. This is inconvenient for research. Therefore, the cumulative probability density function of t-distribution was calculated using the Gaussian integration method in this study. The results show that the percentiles of the t-distribution are accurately estimated using the algorithm developed in this study. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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9 pages, 470 KiB

Open AccessProceeding Paper

Applying a Parameterized Quantum Circuit to Anomaly Detection

by Jehn-Ruey Jiang and Jyun-Sian Li

Eng. Proc. 2025, 92(1), 3; https://doi.org/10.3390/engproc2025092003 - 10 Apr 2025

Viewed by 153

Abstract

In this study, a parameterized quantum circuit (PQC) is applied for anomaly detection, a crucial process to identify unusual patterns or outliers in data. PQC is a quantum circuit with trainable parameters linked to quantum gates, which are iteratively optimized by classical optimizers [...] Read more.

In this study, a parameterized quantum circuit (PQC) is applied for anomaly detection, a crucial process to identify unusual patterns or outliers in data. PQC is a quantum circuit with trainable parameters linked to quantum gates, which are iteratively optimized by classical optimizers to ensure that the circuit’s output fulfills its objectives. This is analogous to the way of using trainable parameters, such as weights adjusted in classical machine learning and neural network models. We used the amplitude−embedding mechanism with classical data into quantum states of qubits. These states are fed into PQC, which contains strongly entangled layers, and the circuit is trained to determine whether an anomaly exists. As anomaly detection datasets are often imbalanced, resampling techniques, such as random oversampling, the synthetic minority oversampling technique (SMOTE), random undersampling, and Tomek-Link undersampling, are applied to reduce the imbalance. The proposed PQC and various resampling techniques were compared using the public Musk dataset for anomaly detection. Their combination was also compared with the combination of the classical autoencoder and the classical isolation forest model in terms of the F1 score. By analyzing the comparison results, the advantages and disadvantages of PQC for future research studies were determined. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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9 pages, 652 KiB

Open AccessProceeding Paper

Indirect Measurement of Tensile Strength of Materials by Grey Prediction Models GMC(1,n) and GM(1,n)

by Tzu-Li Tien

Eng. Proc. 2025, 92(1), 4; https://doi.org/10.3390/engproc2025092004 - 10 Apr 2025

Viewed by 87

Abstract

Grey theory is applied to forecasting, decision-making, and control as this theory is appropriate for predictive analysis. Incomplete information is a primary characteristic of the grey system, necessitating the supplementation of information to transform the relationships between various information elements from grey to [...] Read more.

Grey theory is applied to forecasting, decision-making, and control as this theory is appropriate for predictive analysis. Incomplete information is a primary characteristic of the grey system, necessitating the supplementation of information to transform the relationships between various information elements from grey to white and improve the accuracy of predictive models. However, for the first-order grey prediction model with n variables, specifically the traditional GM(1,n) model, modelling values are derived using a rough approximation method. It is assumed in this method that the elements of the one-order accumulated generating series of each associated series are constant, leading to an unreasonable relationship between the forecast series and the associated series, which is fundamentally an incorrect model. The elements of a non-negative series’s one-order accumulated generating series cannot be constants; even if they are constant series, this is not true. Consequently, the traditional GM(1,n) model yields significant errors. There have been few papers addressing the errors of this model. To improve the GM(1,n) model, correct algorithms must be used by incorporating convolution algorithms or fitting system action quantities with basic functions to derive particular solutions. The modelling procedure of the grey convolution prediction model GMC(1,n) demonstrates that the traditional grey prediction model GM(1,n) is incorrect. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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7 pages, 726 KiB

Open AccessProceeding Paper

by Takuto Nishi, Yuki Aikawa, Kyosuke Kato, Miki Kaneko and Ken Kiyono

Eng. Proc. 2025, 92(1), 5; https://doi.org/10.3390/engproc2025092005 - 10 Apr 2025

Viewed by 150

Abstract

Many females experience physical problems caused by menstruation, such as menstrual cramps and premenstrual syndrome, which disrupt their daily lives and work. Knowing when menstruation begins is essential for managing such physical conditions. However, menstrual periods are not always cyclic and can be [...] Read more.

Many females experience physical problems caused by menstruation, such as menstrual cramps and premenstrual syndrome, which disrupt their daily lives and work. Knowing when menstruation begins is essential for managing such physical conditions. However, menstrual periods are not always cyclic and can be extended by physical and mental stress. Currently used menstrual management applications rely on self-reported cycle length and basal body temperature (BBT), which makes it challenging to predict irregular periods. Advances in smart wearables have made continuous, non-invasive health monitoring accessible, such as heart rate variability (HRV). HRV characteristics reflect autonomic nervous system activity and are used as physical and mental health status indices. This study aims to explore the relationship between HRV indices and the menstrual cycle using smart wearables. A total of 13 females aged from 18 to 20 participated in this study and measured their indices using an underwear-type wearable device for six months. The device measured HRV and body acceleration. Participants recorded their BBT every morning and answered questionnaires about their physical and mental status every morning and evening. They also reported the start and end dates of menstruation. The HRV data were split into sleep and wake phases using acceleration and calculated time- and frequency-domain HRV indices. Cross-correlation and regression analysis were conducted to assess the relation between the menstrual cycle and phases, such as follicular and luteal, and the HRV indices. A significant relationship between HRV indices and the menstrual cycle length was found, particularly in the difference between the follicular and luteal phases of HRV indices. This difference showed a relatively high association with menstrual cycle length. Importantly, the regression analysis results suggested that HRV indices can be used to predict the length of the menstrual cycle and potential physical and mental disorders. These findings significantly contributed to menstrual health management and the Femtech industry. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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7 pages, 1998 KiB

Open AccessProceeding Paper

Monitoring Leg Muscle Strength Symmetry via Electromyography

by Fu-Jung Wang, Liang-Sian Lin, Chun-Kai Tseng, Cheng-Hsiang Chan, Zhe-Yu Lee and Ting-An Yeh

Eng. Proc. 2025, 92(1), 6; https://doi.org/10.3390/engproc2025092006 - 14 Apr 2025

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Abstract

Many movements of the human body’s muscles rely on the leg muscles for power or weight-bearing. However, leg muscle symmetry is often ignored. Therefore, it is necessary to monitor uneven or asymmetric muscle strength between the legs. We developed a system using electromyography [...] Read more.

Many movements of the human body’s muscles rely on the leg muscles for power or weight-bearing. However, leg muscle symmetry is often ignored. Therefore, it is necessary to monitor uneven or asymmetric muscle strength between the legs. We developed a system using electromyography (EMG) and an HW827 sensor for detecting leg muscles and monitoring the heart rate. In the system, the data are displayed on the Node-RED dashboard and are stored in the SQLite database. These experimental results show that for two subjects at a moderate level of exercise intensity, their non-dominant leg EMG values are higher than those for the dominant leg. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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10 pages, 3359 KiB

Open AccessProceeding Paper

Guarded Diagnosis: Preserving Privacy in Cervical Cancer Detection with Convolutional Neural Networks on Pap Smear Images

by Sanmugasundaram Ravichandran, Hui-Kai Su, Wen-Kai Kuo, Manikandan Mahalingam, Kanimozhi Janarthanan, Kabilan Saravanan and Bruhathi Sathyanarayanan

Eng. Proc. 2025, 92(1), 7; https://doi.org/10.3390/engproc2025092007 - 11 Apr 2025

Viewed by 75

Abstract

Advancements in image processing have advanced medical diagnostics, especially in image classification, impacting healthcare by offering faster and more accurate analyses of magnetic resonance imaging (MRI) and X-rays. The manual examination of these images is slow, error-prone, and costly. Therefore, we propose a [...] Read more.

Advancements in image processing have advanced medical diagnostics, especially in image classification, impacting healthcare by offering faster and more accurate analyses of magnetic resonance imaging (MRI) and X-rays. The manual examination of these images is slow, error-prone, and costly. Therefore, we propose a new method focusing on the Pap smear exam for early cervical cancer detection. Using a convolutional neural network (CNN) and the SIPaKMeD dataset, cervical cells are classified into normal, precancerous, and benign cells after segmentation. The CNN’s architecture is simple yet efficient, achieving a 91.29% accuracy. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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11 pages, 4387 KiB

Open AccessProceeding Paper

Revolutionizing Prenatal Care: Harnessing Machine Learning for Gestational Diabetes Anticipation

by Sanmugasundaram Ravichandran, Hui-Kai Su, Wen-Kai Kuo, Manikandan Mahalingam, Kanimozhi Janarthanan, Bruhathi Sathyanarayanan and Kabilan Saravanan

Eng. Proc. 2025, 92(1), 8; https://doi.org/10.3390/engproc2025092008 - 11 Apr 2025

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Abstract

We implemented a robust framework for diabetes prediction, leveraging a diverse array of machine learning algorithms. Through an analysis of diabetes-related characteristics, we identified the most accurate classifier. Diverse algorithms were tested to compare their accuracies with the complexities of data: K-nearest neighbors [...] Read more.

We implemented a robust framework for diabetes prediction, leveraging a diverse array of machine learning algorithms. Through an analysis of diabetes-related characteristics, we identified the most accurate classifier. Diverse algorithms were tested to compare their accuracies with the complexities of data: K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), logistic regression (LR), Naïve Bayes (NB), and decision tree (DT). The decision tree algorithm demonstrated the best accuracy in predicting diabetes. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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8 pages, 4426 KiB

Open AccessProceeding Paper

Application of Image Analysis Technology in Detecting and Diagnosing Liver Tumors

by Van-Khang Nguyen, Chiung-An Chen, Cheng-Yu Hsu and Bo-Yi Li

Eng. Proc. 2025, 92(1), 9; https://doi.org/10.3390/engproc2025092009 - 16 Apr 2025

Viewed by 474

Abstract

We applied processing technology to detect and diagnose liver tumors in patients. The cancer imaging archive (TCIA) was used as it contains images of patients diagnosed with liver tumors by medical experts. These images were analyzed to detect and segment liver tumors using [...] Read more.

We applied processing technology to detect and diagnose liver tumors in patients. The cancer imaging archive (TCIA) was used as it contains images of patients diagnosed with liver tumors by medical experts. These images were analyzed to detect and segment liver tumors using advanced segmentation techniques. Following segmentation, the images were converted into binary images for the automatic detection of the liver’s shape. The tumors within the liver were then localized and measured. By employing these image segmentation techniques, we accurately determined the size of the tumors. The application of medical image processing techniques significantly aids medical experts in identifying liver tumors more efficiently. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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7 pages, 1709 KiB

Open AccessProceeding Paper

Developing Frugal Internet of Things with Backpropagation Neural Network for Predicting Impact of Gemini Artificial Intelligence on Student Meditation and Relaxation

by Chun-Kai Tseng, Cheng-Hsiang Chan, Liang-Sian Lin, Fu-Jung Wang, Kai-Hsuan Yao and Chao-Wei Hsu

Eng. Proc. 2025, 92(1), 10; https://doi.org/10.3390/engproc2025092010 - 17 Apr 2025

Viewed by 58

Abstract

With the rapid development of generative artificial intelligence (AI) technologies, large language models have been developed and used in education. In this study, we employ the Google Gemini AI tool (version 1.0) to annotate teachers’ programming of teaching materials. When students learned these [...] Read more.

With the rapid development of generative artificial intelligence (AI) technologies, large language models have been developed and used in education. In this study, we employ the Google Gemini AI tool (version 1.0) to annotate teachers’ programming of teaching materials. When students learned these annotated teaching materials, the ThinkGear ASIC module (TGAM) and galvanic skin response (GSR) sensors were deployed to measure student mindfulness meditation, relaxation levels, and learning stress. We constructed a backpropagation neural network (BPNN) model with three hidden layers to predict student concentration and relaxation levels using GSR data and the time that students spent answering questions. In the developed system, we deployed a Node-Red dashboard to monitor all sensing data and predict results for mindfulness meditation and relaxation levels. The results were stored in an SQLite database. The BPNN model effectively predicted students’ mindfulness meditation and relaxation levels. For multiple-choice questions about teaching materials, the mean absolute error (MAE) of the BPNN model was 14.29 for mindfulness meditation and 10.54 for relaxation. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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7 pages, 891 KiB

Open AccessProceeding Paper

Networked Symphony Orchestra in Internet of Things Courses

by Franklin Parrales-Bravo, Rosangela Caicedo-Quiroz, Julio Barzola-Monteses and Lorenzo Cevallos-Torres

Eng. Proc. 2025, 92(1), 11; https://doi.org/10.3390/engproc2025092011 - 23 Apr 2025

Abstract

Internet of Things (IoT) education is hindered by a deficiency of dynamic and interactive courses, in addition to a lack of components and difficulty in device configuration. These difficulties diminish students’ enthusiasm for IoT initiatives and reduce their drive and involvement. We designed [...] Read more.

Internet of Things (IoT) education is hindered by a deficiency of dynamic and interactive courses, in addition to a lack of components and difficulty in device configuration. These difficulties diminish students’ enthusiasm for IoT initiatives and reduce their drive and involvement. We designed and constructed a networked symphony orchestra using the Lego Mindstorms EV3 package as a project belonging to the IoT subject. Lego Mindstorms EV3 was selected due to its easy configuration. In this study, the knowledge obtained during the subject was utilized. In IoT courses at the University of Guayaquil, there is strong encouragement to apply the studied material to new initiatives. Through the design, the assessment of multiple technologies, and the final implementation of the project described within this paper, students were motivated for the practical application of concepts related to IoT. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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8 pages, 2046 KiB

Open AccessProceeding Paper

Classification of Salmon Freshness In Situ Using Convolutional Neural Network

by Juan Miguel L. Valeriano and Carlos C. Hortinela IV

Eng. Proc. 2025, 92(1), 12; https://doi.org/10.3390/engproc2025092012 - 23 Apr 2025

Abstract

Fish is an important food resource, an economic contributor, and a staple food for Filipinos. For the safety and satisfaction of consumers, fish freshness must be determined. Using the convolutional neural network (CNN) algorithm, we determined salmon fillet freshness in this study. In [...] Read more.

Fish is an important food resource, an economic contributor, and a staple food for Filipinos. For the safety and satisfaction of consumers, fish freshness must be determined. Using the convolutional neural network (CNN) algorithm, we determined salmon fillet freshness in this study. In total, 7000 images were used for training and 40 for testing the CNN model. The deep learning technique, specifically ResNet50 architecture, was used with Raspberry Pi 4B, and Raspberry Pi camera V₂ was employed to take images of fish. The model showed a 92.5% accuracy, highlighting the CNN model’s accurate evaluation of seafood quality. Full article

(This article belongs to the Proceedings of 2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering)

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