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All Journal Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Journal of Applied Data Sciences
Poltak Sihombing
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Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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Analyzed New Design Data Driven Modelling of Piezoelectric Power Generating System Solly Aryza; Zulkarnain Lubis; M. Isa Indrawan; Syahril Efendi; Poltak Sihombing
Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences Vol 4, No 3 (2021): Budapest International Research and Critics Institute August
Publisher : Budapest International Research and Critics University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birci.v4i3.2350

Abstract

The piezoelectric speed bumps designed in this study consist of a speed bump mechanical system that functions to receive input from motorized vehicle pressure, piezoelectric cantilever system as a component producing electrical energy and energy harvesting system as energy harvester from piezoelectric material. One system module consists of a piezoelectric parallel circuit connected to the MB39C811 buck converter. In this paper, we will discuss the mechanism for analyzing the design and modeling of a piezoelectric power plant system on a bluff body-based speed bump with a cantilever system for variations in motor vehicle speed by means of physical modeling. In the piezoelectric speed bump in the form of a bluff body, it can be seen that the air flow when passing through the triangular cross section has an increase in speed so that the resulting vortex has a high speed. This is what causes high vibrations in the piezoelectric so that the maximum voltage and the average voltage are the highest. Piezoelectric speed bumps are capable of generating electrical power with an input of 60 times the motorized vehicle track of 2.166mWh with an efficiency of 2.87% compared to manual input.
Diffusion2D and Anchored Inference for Asymptotic Stabilization of Diffusion-Convolutional Neural Networks in Multidomain Medical Image Classification Hanna Willa Dhany; Sutarman Sutarman; Poltak Sihombing; Mohammad Andri Budiman
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1215

Abstract

Medical image classification across heterogeneous domains remains challenging due to domain shift, spatial variability, and unstable inference behavior. This study proposes a diffusion-stabilized Diffusion-Convolutional Neural Network (DCNN) framework that integrates Diffusion2D and post-hoc Anchored Diffusion to improve inference stability, probabilistic consistency, and robustness in multidomain medical image classification. The main contribution of this work is the introduction of a two-stage stabilization mechanism in which Diffusion2D performs controlled intra-image diffusion on feature representations before graph construction, while Anchored Diffusion refines uncertain predictions in the logit space through a k-nearest neighbors graph without retraining. The framework was evaluated on heterogeneous medical imaging datasets consisting of brain MRI, leukemia microscopy, and COVID-19 chest radiographs. Experimental results show that the proposed approach maintained baseline classification performance with an accuracy of 64.70% while improving the Macro-F1 score from 0.7045 to 0.7061. The diffusion mechanism reduced the average Laplacian value from 0.864355 to 0.187525, corresponding to a 78.23% reduction in spatial gradient variability. Internal analysis further demonstrated stable diffusion coefficients with a mean value of 0.141734 and a standard deviation of 0.003757, indicating controlled diffusion behavior. Anchored Diffusion selectively refined uncertain predictions, affecting only 0.6% of evaluated samples while preserving overall decision consistency. Repeated inference experiments across 40 iterations also revealed highly stable confidence trajectories with no observable variance after diffusion stabilization. The novelty of this research lies in combining feature-level diffusion stabilization, post-hoc anchored inference, and asymptotic regularization within a unified DCNN framework, providing a theoretically grounded and uncertainty-aware approach for robust multidomain medical image classification.
Co-Authors Efendi, Syahril Hanna Willa Dhany M. Isa Indrawan Mohammad Andri Budiman Sutarman Sutarman Zulkarnain Lubis