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Widi Aribowo
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vubeta@unesa.ac.id
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Jl. Prof. Moch Yamin, Ketintang, Kec. Gayungan, Surabaya, Jawa Timur 60231
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Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Published by Universitas Negeri Surabaya
ISSN : -     EISSN : 30640768     DOI : https://doi.org/10.26740/vubeta.v1i1
Core Subject : Science, Engineering,
Computer Science & IT Engineering Mechanical Engineering Transportation
Vokasi Unesa Bulletin Of Engineering, Technology and Applied Science is a peer-reviewed, Quarterly International Journal, that publishes high-quality theoretical and experimental papers of permanent interest, that have not previously been published in a journal, in the field of engineering, technology, and applied sciences that aim to promote the theory and practice of Engineering, Technology And Applied Science.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 95 Documents
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Zero-Shot Super-Resolution as a Test-Time Enhancer for Cross-Crop Plant Disease Recognition Malam, Sani Saminu Saleh; Ibrahim, Yusuf; Haruna, Zaharuddeen; Yusuf, Shehu Mohammed
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 2 (2026): (In Progress)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i2.45798

Abstract

Accurate plant disease diagnosis is central to precision agriculture, yet real-world performance degrades under blur, low resolution, and domain shift, weakening zero-shot recognition of unseen diseases. This paper investigates the integration of Coordinate Attention (CA) and Zero-Shot Super-Resolution (ZSSR) as test-time plug-ins to a standard Zero-Shot Learning (ZSL) pipeline without using any target labels. Using Plant Village tomato to potato transfer, each target image is super-resolved via a compact, self-supervised SR CNN (50 inner steps with self-ensemble and back-projection) and then standardized to 224×224×3 before feature extraction with MobileNetV2 (global average pooling). A lightweight CA module enhances spatial channel attention, focusing on lesion regions. The visual embeddings (1280-D) are projected into a 300-dimensional, L2-normalized semantic space through a dense, BN, ReLU to dropout head, and class logits are computed as cosine similarity to Word2Vec prototypes. On the target (potato) test set, the proposed ZSL + CA + ZSSR model achieved 86.33% accuracy, outperforming both ZSL + ZSSR (79.04%) and the ZSTL benchmark (78.34%, VGG16 + Triplet + DAC-300). Confusion matrices show fewer PEB↔PLB and PH to diseased confusions, while training curves exhibit faster, more stable convergence when ZSSR and CA are jointly applied. These results indicate that per-image, test-time ZSSR with CA attention sharpens lesion cues and enhances cross-crop transfer, providing a lightweight, label-free pathway to improved field robustness and diagnostics.
Performance evaluation of the fast forward quantum optimization algorithm in digital image clustering Singh, Sanjeev Kumar; Singh, Pawan Kumar
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 2 (2026): (In Progress)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i2.46206

Abstract

The primary objective of clustering in image analysis is to establish a meaningful correspondence between image features and clusters. This process is instrumental in extracting higher-level semantic information from digital images. In this study, we propose a novel image clustering approach that integrates the fast forward quantum optimization algorithm (FFQOA) with the K-means clustering (KMC) algorithm, forming a hybrid method referred to as FFQOA + KMC. The FFQOA + KMC initiates clustering based on the grayscale values of images using KMC and then refines the clustering outcome through FFQOA to achieve optimal segmentation. Subsequently, FFQOA + KMC is applied to several benchmark grayscale images, with results compared to those from alternative clustering techniques. Experimental findings confirm the robustness and superiority of FFQOA + KMC through both visual inspections and statistical metrics
Benz Limit Optimal Design for Double Fed Induction Generator and Kundru’s Multimachine integration Abel E. Airobman; Sabo Aliyu2; Musa, Engr.
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 2 (2026): (In Progress)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i2.46268

Abstract

The study explores the integration of an optimal Benz limit Doubly Fed Induction Generator (DFIG) with IEEE Kundur's test multi-machine power system, emphasizing the use of meta-heuristic algorithms and controllers.his work is scoped at the DFIG power coefficient, output voltage, and phase angle oscillations during integration. The controllers’ performances were compared with three techniques: the hippopotamus (HO), Sine cosine (SC), and Morth flame (MFO) algorithms to verify the competence of the proposed method in achieving better system stability. To improve the proposed Hybrid Multi-source integration of DFIG, Hydrogen Fuel Cell (HFC) to augment (Wind, Solar cell, Battery energy storage system), the proposed work presents the mathematical formulation of DAE, the designed models, and the implementation of wind aerodynamic/mechanical coupling shaft. ODE as solver in MATLAB 2021a Simulink environment as presented. The results presented an optimal Benz limit for the blade tip speed ratio  = 8.1, blade pitch angle =0, rotor power coefficient =  =0.48, and turbine output power  =5 MW described by equation 7. A symmetrical fault was set up on bus 2 at t = 1 second; the governor load reference increased by 1%, the system loading by 1%; and a nonlinear time-domain simulation was carried out on the integrated network to assess controllers’ robustness Likewise, the result validates the usefulness of the proposed SC, HO, and MFO tuned Tilt, PID for DFIG output voltage and phase angle control that outperforms the traditional MFO tuning techniques in terms of resilience, efficiency, and convergence.
A Literature Review on Brain Tumour Detection Approaches Using MRIs Ajay; Singh, Pritpal
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 2 (2026): (In Progress)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i2.46521

Abstract

Brain tumours are among the most common malignant tumours, making their accurate detection and precise evaluation crucial for effective treatment planning and strategic regimens. Recent advancements in machine learning (ML) and deep learning (DL) have significantly increased tumour identification precision, enabling the automatic pro cessing of complex imaging data and substantially reducing the needfor time-consuming manual intervention. However, persistent challenges in automated detection approaches stem from pervasive imaging artifacts, variations in image quality, and diverse tumor appearances. This comprehensive review addresses these challenges by highlighting key innovations and their clinical relevance across various automated approaches, including clustering, soft computing, and deep learning techniques for the classification and segmentation of brain tumours using magnetic resonance imaging (MRI). Furthermore, we synthesize the quantitative results of state-of-the-art models, summarizing performance measures such as the Dice Score and Sensitivity. Ultimately, this review outlines the critical future research pathways necessary to effectively address remaining obstacles and enhance the precision of automated segmentation and classification.
Development and Performance Evaluation of Silicone-Based Hydrophobic Coatings for Anti-Soiling Applications on Solar PV Glass Thaiubudeen, Ahmad A.; Sulaiman, Shaharin A.; Awais, Syed Awais Ali
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 2 (2026): (In Progress)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i2.48779

Abstract

Soiling is a major cause of performance degradation in solar photovoltaic (PV) systems, particularly in tropical environments characterized by high humidity and persistent dust accumulation. This study presents the development and experimental evaluation of silicone-based hydrophobic coatings designed to enhance the anti-soiling and water-repellent properties of PV glass substrates. Three coating formulations with varying silicon dioxide (SiO2) nanoparticle contents, 2.03 wt% (HC1), 3.34 wt% (HC2), and 0 wt% (HC3), were prepared and systematically characterized. Coating performance was assessed using water droplet mobility tests, static water contact angle (WCA) measurements, and controlled dust-accumulation experiments. Among the formulations, HC2 exhibited the best overall performance, achieving the highest average WCA (94.3°), fastest droplet runoff time (0.8 s), and lowest dust accumulation (4.4 mg). The results confirm that increasing SiO2 nanoparticle concentration enhances surface hydrophobicity and reduces dust adhesion by modifying surface roughness. These findings highlight the potential of optimized silicone-SiO2 coatings as a cost-effective, passive anti-soiling solution for improving the operational efficiency of solar PV systems, particularly in dust-prone environments.

Page 10 of 10 | Total Record : 95

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