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All Journal IAES International Journal of Robotics and Automation (IJRA) Buletin Ilmiah Sarjana Teknik Elektro
Mohammad Ikhsan
Universitas Indonesia
Automotive Engineering Electrical & Electronics Engineering

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Development of a low pressure Pneu-Nets actuator using room temperature vulcanizing silicon rubber Nur Rahmah Abdullah; Sylvi Febriana Rachmawati Irnadiastputri; Mohammad Ikhsan
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i2.pp267-280

Abstract

Soft robotics offers potential advantages in achieving safer human-robot interaction compared to conventional rigid robots, making it relevant for stroke rehabilitation applications. A major challenge in developing soft actuators lies in selecting materials that balance mechanical performance and practical fabrication. This study investigates room-temperature vulcanizing (RTV) silicone rubber as an alternative to platinum-cured silicone rubber for Pneumatic-Networks (Pneu-Nets) actuators fabrication. The actuator was developed through mold casting with 3D-printed molds and characterized by its contact force and bending angle. This actuator produced a maximum force of 0.93 N and a bending angle of 244.5° at 52 kPa. Finite element analysis (FEA) was performed to simulate its mechanical behavior and validate experimental results. The simulation errors were quantified as 8.3% for contact force and 19.3% for bending angle at 30 kPa, confirming the feasibility of using condensation-cured silicone rubber for efficient soft actuator production.
Grid-Aligned Patchification for Deep Learning-Based Macrophage Detection in Unstained Brightfield Haemocytometer Images Mohammad Ikhsan; Zino Ramdani Suharto; Rizal Azis; Basari Basari
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 3 (2026): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i3.16005

Abstract

Manual cell counting from haemocytometer images is slow, subjective, and operator-dependent, especially in unstained brightfield microscopy where cell boundaries and viability-related morphology are difficult to distinguish. Although prior cell detection models have mainly been evaluated on stained or fluorescence images, systematic comparisons between fine-tuned detectors and zero-shot cell segmentation models remain limited for unstained brightfield haemocytometer images. This study presents a controlled 2×2 factorial benchmark of patchification and augmentation across five detection approaches, with variance-decomposition analysis and comparison of fine-tuned versus zero-shot deployment modes. Using 24 unstained brightfield RAW 264.7 macrophage images with 6,307 polygon-level annotations, including 28.8% dead cells, we evaluated four preprocessing scenarios under six-fold stratified cross-validation. Faster R-CNN, Mask R-CNN, and YOLOv11n-Seg were fine-tuned within each fold, whereas Cellpose and StarDist were applied zero-shot. Grid-aligned patchification improved bounding-box mAP50 by 2.6–8.4× across all fine-tuned architectures (paired Wilcoxon p = 0.016, Cohen’s d > 3). A 2×2 ANOVA attributed 99.2–99.4% of explained variance to patchification, while augmentation and interaction effects each contributed less than 0.1%, suggesting that performance gains were driven mainly by scale rescaling rather than sample count. On patchified data, fine-tuned models converged to 85.5–86.4% mAP50. YOLOv11n-Seg achieved the highest mAP50-95 of 51.1%, with 6× faster inference and 17× fewer parameters. In contrast, zero-shot Cellpose and StarDist reached only 45.3–51.2% class-agnostic F1@0.5. These findings show that structure-aware patchification is critical for reliable cell detection in this modality.
Co-Authors Basari Basari Nur Rahmah Abdullah Rizal Azis Sylvi Febriana Rachmawati Irnadiastputri Zino Ramdani Suharto