Advanced Mechanical and Mechatronic Systems

The Effect of Natural Stone Grain Size on Indoor Temperature Reduction Through the Coating Process on Galvanised Roofs

Thu, 12 Jun 2025 00:00:00 +0000

The capacity of a roof to absorb heat played a vital role in maintaining indoor temperature stability. Employing composite coatings made from natural materials presented a promising solution for contemporary roofing systems. This study explored the impact of incorporating natural stone powder combined with epoxy as a coating on galvalume roofing, focusing on its effects on thermal conductivity and indoor temperature reduction based on powder sizes. Temperature data were gathered from a small structure featuring a roof treated with the composite coating, which included andesite natural stones. Thermocouples were placed 20 cm above the roof, on the coated surface, beneath the galvalume layer, and inside the room to monitor heat transfer. The findings revealed that adding natural stone powder to roofing materials effectively lowered thermal conductivity and indoor temperature. The degree of temperature reduction varied depending on the size of the stone powder. Ultimately, the study confirmed that the inherent characteristics of natural stone powder size contributed significantly to enhancing a roof's insulation properties and reducing heat buildup indoors.

Finite Element Analysis and Vibration Signal Processing Techniques To Determine the Frequency Response in Bridge Health Monitoring Study

Tue, 01 Jul 2025 00:00:00 +0000

Ensuring the structural integrity of large-scale bridges is critical worldwide, particularly in Indonesia. The integration of modern digital technologies significantly enhances this effort. A bridge health monitoring system is a vital tool for collecting data, allowing authorities to assess bridge conditions and refine inspection methods. Vibration responses measured using accelerometers, offer valuable insights into a bridge’s structural health. However, the complexity of vibration signals requires advanced signal processing techniques to extract meaningful information. Empirical Mode Decomposition (EMD) and Wavelet Packet Decomposition (WPD) are two promising methods for analyzing such complex signals. Given the large scale of bridge structures and the limited number of sensors typically available, researchers often use Finite Element Analysis (FEA) to simulate and predict vibration responses. For example, a study on the Cisomang Bridge in Bandung, Indonesia, employed FEA to model the bridge’s vibration characteristics. The first natural frequency identified was approximately 4.732 Hz, which served as a reference for further analysis. By integrating FEA models with advanced signal processing methods, the system aims to deliver reliable tools for monitoring and maintaining bridge health, thereby improving infrastructure safety and longevity.

Stilleto Type High Heel Shoe Design and Presesure Analysis with Adjustable Height

Dwi Basuki Wibowo, Adham Adhwa Adibawa, Rudiansyah Harahap, Yudhi Ariadi — Wed, 09 Jul 2025 00:00:00 +0000

Wearing high heels has been a consistent component in fashion trends for women in a variety of endeavors ranging from business to social settings. Research into the design of height-adjustable stilleto-type high heels is a response to shifting demands in the fashion industry. Consumer demand was not only focused on aesthetic appeal but also on comfort. Conventional high heels, especially the stiletto type, often had limitations in terms of long-term comfort due to their fixed height. This led to the need for innovative designs that allowed users to customize heel height according to their preference and comfort. This study presented the steps taken to develop adjustable high heels and analyzed how pressure was distributed on the sole of the foot. The pressure distribution on the soles of the feet while wearing adjustable high heels was measured using the FSR 400 device available at a shoe orthotics facility. The study aimed to develop an adjustable high-heel design that enhanced both fashion and user comfort through integrated design and pressure analysis. The manufacture of an adjustable high heel shoe model in this study was successfully completed by implementing an unloading system, where the heel featured two height options, 3 cm and 5 cm, and a screw-based locking mechanism. The subject of this research was was a 20-year-old female Mechanical Engineering student at Diponegoro University, with a shoe size of 39, a height of 159 cm, and a body weight of 45 kg. Test results revealed that heel pressure decreased as the heel height increased.

A Classification and Prediction Method of Electric Battery Condition during Discharging Process Utilizing Adaptive Neuro-Fuzzy Inference System and Support Vector Machine

Mon, 23 Jun 2025 00:00:00 +0000

This paper presents a data-driven prediction method for electric battery condition monitoring with different loads. The prediction is subject to the usage time of the electric battery during the discharge condition. Two variables are selected as the prediction input i.e. load (Watt) and discharge voltage (Volt) to predict how much time (hour) has left during the discharge period. Adaptive Neuro-Fuzzy Inference System (ANFIS) serves dual purposes of classification and prediction of battery discharge conditions, while Support Vector Machine (SVM) is implemented for classification comparison. While SVM demonstrates superior classification performance with 95% accuracy compared to ANFIS's 88%, ANFIS provides the added value of precise time prediction. The time-series data was collected from the discharge battery experiment for a few hours that uses the electric rechargeable battery from a fully charged capacity to an empty capacity. The experiments were conducted on four different load conditions i.e. 130, 180, 200, and 220 Watts. The prediction result of ANFIS was compared with the result of the Support Vector Machine (SVM). The ANFIS was used to predict how many hours the battery has been used based on two inputs i.e. load (Watt), and discharge volt (Volt). Five different prediction targets i.e. 1, 2, 3, 4, and 5 hours are selected for the ANFIS prediction. This prediction target is according to the deterioration of the discharge voltage during the measurement. The rate of voltage drop varies under different load conditions, with specific discharge profiles observed for each tested load. The result shows that ANFIS can predict the target hour based on the present load and voltage data input during the discharge operation. From the hour prediction, it can estimate the remaining useful life of the battery because the total duration of the battery is known initially. SVM is used as a comparison classifier to the ANFIS. Although SVM demonstrates superior classification accuracy (95% versus ANFIS's 88%), ANFIS's ability to predict time with two-digit precision enables more accurate remaining useful life estimation in EV applications, where even minutes of battery life can be critical for route planning and operational decisions.

Explainable Artificial Intelligence (XAI) in Medical Imaging: Techniques, Applications, Challenges, and Future Directions

Purwono Purwono, Annastasya Nabila Elsa Wulandari, Khoirun Nisa — Tue, 01 Jul 2025 00:00:00 +0000

The integration of Explainable Artificial Intelligence (XAI) into medical imaging is pivotal in addressing the “black-box” limitations of deep learning models, which often hinder clinical trust and regulatory approval. This review provides a comprehensive examination of XAI techniques that enhance interpretability and transparency in diagnostic imaging applications. Key approaches such as feature visualization (Grad-CAM, Integrated Gradients), attention mechanisms, symbolic reasoning, and example-based methods—are explored alongside their practical implementations. Specific cases in cardiac imaging, cancer diagnostics, and brain lesion segmentation illustrate the value of XAI in improving clinical decision-making and patient care. Moreover, the review highlights major challenges, including the trade-off between accuracy and interpretability, ethical and legal constraints, integration barriers within clinical workflows, and the complexity of medical data. To address these issues, future research directions are proposed, including the development of more robust example-based models, ethical frameworks, generalizable architectures, advanced visualization techniques, and interdisciplinary collaboration. With continued refinement and responsible deployment, XAI systems can enable AI models to become not only accurate but also interpretable and clinically relevant. This paper underscores the transformative potential of XAI in building trustworthy, transparent, and effective AI-driven diagnostic tools aligned with the practical demands of modern healthcare systems.