Programmable filament: Printed filaments for multi-material 3D printing

From full-color objects to functional capacitive artifacts, 3D printing multi-materials became essential to broaden the application areas of digital fabrication. We present Programmable Filament, a novel technique that enables multi-material printing using a commodity FDM 3D printer, requiring no hardware upgrades. Our technique builds upon an existing printing technique in which multiple filament segments are printed and spliced into a single threaded filament. We propose an end-to-end pipeline for 3D printing an object in multi-materials, with an introduction of the design systems for end-users. Optimized for low-cost, single-nozzle FDM 3D printers, the system is built upon our computational analysis and experiments to enhance its validity over various printers and materials to design and produce a programmable filament. Finally, we discuss application examples and speculate the future with its potential, such as custom filament manufacturing on-demand.Haruki Takahashi, Parinya Punpongsanon, and Jeeeun Kim. 2020. Programmable Filament: Printed Filaments for Multi-material 3D Printing. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology (UIST '20). Association for Computing Machinery, New York, NY, USA, 1209–1221. DOI:https://doi.org/10.1145/3379337.3415863.UIST '20: The 33rd Annual ACM Symposium on User Interface Software and Technology [October 20 - 23, 2020

スマートフォンを用いた視覚障碍者向け移動支援システムアーキテクチャに関する研究

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 坂村 健, 東京大学教授 越塚 登, 東京大学教授 暦本 純一, 東京大学教授 中尾 彰宏, 東京大学教授 石川 徹University of Tokyo(東京大学

A High-Resolution Dataset for Instance Detection with Multi-View Instance Capture

Instance detection (InsDet) is a long-lasting problem in robotics and computer vision, aiming to detect object instances (predefined by some visual examples) in a cluttered scene. Despite its practical significance, its advancement is overshadowed by Object Detection, which aims to detect objects belonging to some predefined classes. One major reason is that current InsDet datasets are too small in scale by today's standards. For example, the popular InsDet dataset GMU (published in 2016) has only 23 instances, far less than COCO (80 classes), a well-known object detection dataset published in 2014. We are motivated to introduce a new InsDet dataset and protocol. First, we define a realistic setup for InsDet: training data consists of multi-view instance captures, along with diverse scene images allowing synthesizing training images by pasting instance images on them with free box annotations. Second, we release a real-world database, which contains multi-view capture of 100 object instances, and high-resolution (6k x 8k) testing images. Third, we extensively study baseline methods for InsDet on our dataset, analyze their performance and suggest future work. Somewhat surprisingly, using the off-the-shelf class-agnostic segmentation model (Segment Anything Model, SAM) and the self-supervised feature representation DINOv2 performs the best, achieving >10 AP better than end-to-end trained InsDet models that repurpose object detectors (e.g., FasterRCNN and RetinaNet).Comment: Accepted by NeurIPS 2023, Datasets and Benchmarks Trac

Elastomeric Core/Conductive Sheath Fibers for Tensile and Torsional Strain Sensors

Motion sensing, aimed at detecting and monitoring mechanical deformation, has received significant attention in various industrial and research fields. In particular, fiber-structured mechanical strain sensors with carbon-based materials have emerged as promising alternatives for wearable applications owing to their wearability and adaptability to the human body. Various materials, structures, sensing mechanisms, and fabrication methods have been used to fabricate high-performance fiber strain sensors. Nevertheless, developing multi-modal strain sensors that can monitor multiple deformations remains to be accomplished. This study established core/sheath fiber multi-modal strain sensors using polymer and carbon nanotubes (CNTs). Specifically, a flexible and conductive CNT sheet was wrapped onto the elastomeric core fiber at a certain angle. This wrapping angle allowed the CNTs to mechanically deform under tensile and torsional deformations without fatal structural damage. The CNTs could sense both tensile and torsional strains through reversible structural changes during deformations. The fiber strain sensor exhibited an increase of 124.9% and 9.6% in the resistance during tensile and torsional deformations of 100% and 1250 rad/m, respectively

Elastomeric Core/Conductive Sheath Fibers for Tensile and Torsional Strain Sensors

Motion sensing, aimed at detecting and monitoring mechanical deformation, has received significant attention in various industrial and research fields. In particular, fiber-structured mechanical strain sensors with carbon-based materials have emerged as promising alternatives for wearable applications owing to their wearability and adaptability to the human body. Various materials, structures, sensing mechanisms, and fabrication methods have been used to fabricate high-performance fiber strain sensors. Nevertheless, developing multi-modal strain sensors that can monitor multiple deformations remains to be accomplished. This study established core/sheath fiber multi-modal strain sensors using polymer and carbon nanotubes (CNTs). Specifically, a flexible and conductive CNT sheet was wrapped onto the elastomeric core fiber at a certain angle. This wrapping angle allowed the CNTs to mechanically deform under tensile and torsional deformations without fatal structural damage. The CNTs could sense both tensile and torsional strains through reversible structural changes during deformations. The fiber strain sensor exhibited an increase of 124.9% and 9.6% in the resistance during tensile and torsional deformations of 100% and 1250 rad/m, respectively

Web-Based Visualization of Scientific Research Findings: National-Scale Distribution of Air Pollution in South Korea

Background: As scientific findings of air pollution and subsequent health effects have been accumulating, public interest has also been growing. Accordingly, web visualization is suggested as an effective tool to facilitate public understanding in scientific evidence and to promote communication between the public and academia. We aimed to introduce an example of easy and effective web-based visualization of research findings, relying on predicted concentrations of particulate matter ≤ 10 µg/m3 (PM10) and nitrogen dioxide (NO2) obtained from our previous study in South Korea and Tableau software. Our visualization focuses on nationwide spatial patterns and temporal trends over 14 years, which would not have been accessible without our research results. Methods: Using predicted annual average concentrations of PM10 and NO2 across approximately 250 districts and maps of administrative divisions in South Korea during 2001–2014, we demonstrate data preprocessing and design procedures in the Tableau dashboard, comprising maps, time-series plots, and bar charts. Results: Our visualization allows one to identify high concentration areas, a long-term temporal trend, and the contrast between two pollutants. The application of easy tools for user-interactive options in Tableau suggests possible easy access to the scientific knowledge of non-experts. Conclusion: Our example contributes to future studies that develop the visualization of research findings in further intuitive designs

Development of Korean Smartphone addiction proneness scale for youth.

This study developed a Smartphone Addiction Proneness Scale (SAPS) based on the existing internet and cellular phone addiction scales. For the development of this scale, 29 items (1.5 times the final number of items) were initially selected as preliminary items, based on the previous studies on internet/phone addiction as well as the clinical experience of involved experts. The preliminary scale was administered to a nationally representative sample of 795 students in elementary, middle, and high schools across South Korea. Then, final 15 items were selected according to the reliability test results. The final scale consisted of four subdomains: (1) disturbance of adaptive functions, (2) virtual life orientation, (3) withdrawal, and (4) tolerance. The final scale indicated a high reliability with Cronbach's α of .880. Support for the scale's criterion validity has been demonstrated by its relationship to the internet addiction scale, KS-II (r  =  .49). For the analysis of construct validity, we tested the Structural Equation Model. The results showed the four-factor structure to be valid (NFI  =  .943, TLI  =  .902, CFI  =  .902, RMSEA  =  .034). Smartphone addiction is gaining a greater spotlight as possibly a new form of addiction along with internet addiction. The SAPS appears to be a reliable and valid diagnostic scale for screening adolescents who may be at risk of smartphone addiction. Further implications and limitations are discussed

Stretchy Electrochemical Harvesters for Binarized Self-Powered Strain Gauge-Based Static Motion Sensors

The human monitoring system has motivated the search for new technology, leading to the development of a self-powered strain sensor. We report on the stretchable and soft stretchy electrochemical harvester (SECH) bilayer for a binarized self-powered strain gauge in dynamic and static motion. The active surface area participating in the electrochemical reaction was enhanced after stretching the SECH in the electrolyte, leading to an increase in the electrochemical double-layer capacitance. A change in the capacitance induced a change in the electrical potential of the bilayer, generating electrical energy. The SECH overcomes several challenges of the previous mechano-electrochemical harvester: The harvester had high elasticity (50%), which satisfied the required strain during human motion. The harvester was highly soft (modulus of 5.8 MPa), 103 times lower than that of the previous harvester. The SECH can be applied to a self-powered strain gauge, capable of measuring stationary deformation and low-speed motion. The SECH created a system to examine the configuration of the human body, as demonstrated by the human monitoring sensor from five independent SECH assembled on the hand. Furthermore, the sensing information was simplified through the binarized signal. It can be used to assess the hand configuration for hand signals and sign language