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Accelerating Space Commerce, Exploration, and New Discovery Conference, ASCEND 2020

作者： Tellier, Daniel Millman, Meyer McClelland, Brian Go, Kate Beatrix Balayan, Alice Munje, Michael J. Dewey, Kyle Ho, Nhut Havelund, Klaus Ingham, Michel Department of Computer Science California State University Northridge NorthridgeCA91330 United States Department of Electrical and Computer Engineering California State University Northridge NorthridgeCA91330 United States Department of Mechanical Engineering California State University Northridge NorthridgeCA91330 United States Laboratory for Reliable Software Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Systems Engineering Division Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States

ISBN: (纸本)9781624106088

In space applications, hierarchical state machines (HSMs) are often used for writing simulation and control software, including that of the Curiosity rover. At the Jet Propulsion Lab (JPL), multiple programming languages have been developed specifically for writing HSM-based software, and these have been used in practice. However, we observe that the existing languages developed have significant issues with one or more of usability, performance, and safety, making them problematic for HSM-based development. To address these concerns, we are taking lessons learned from these languages and developing a new programming language named Proteus. Proteus builds HSM support directly into the language, and permits complex HSMs to be defined which communicate with each other. Proteus is designed with a look and feel similar to C/C++, making it usable and overall approachable for use by JPL systems engineers. Proteus itself compiles to C++, allowing it to fit easily into existing development toolchains, and making it amenable to embedded real-time systems. To ensure that Proteus will be of use to its target audience, it is being developed iteratively through a series of prototypes which are regularly evaluated by key stakeholders at JPL, allowing us to refine its design if we go off course. While Proteus is still very young in its development, we demonstrate its basic viability on an example utilizing multiple independent HSMs communicating with each other, and relevant execution traces. In the future, we plan to apply Proteus to larger HSMs taken from real space applications, and many additional relevant features are planned. © 2020 The MITRE Corporation. All Rights Reserved.

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Recent Advances in Triboelectric Nanogenerators: From Technological Progress to Commercial Applications

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ACS Nano 2023年第12期17卷 11087-11219页

作者： Choi, Dongwhi Lee, Younghoon Lin, Zong-Hong Cho, Sumin Kim, Miso Ao, Chi Kit Soh, Siowling Sohn, Changwan Jeong, Chang Kyu Lee, Jeongwan Lee, Minbaek Lee, Seungah Ryu, Jungho Parashar, Parag Cho, Yujang Ahn, Jaewan Kim, Il-Doo Jiang, Feng Lee, Pooi See Khandelwal, Gaurav Kim, Sang-Jae Kim, Hyun Soo Song, Hyun-Cheol Kim, Minje Nah, Junghyo Kim, Wook Menge, Habtamu Gebeyehu Park, Yong Tae Xu, Wei Hao, Jianhua Park, Hyosik Lee, Ju-Hyuck Lee, Dong-Min Kim, Sang-Woo Park, Ji Young Zhang, Haixia Zi, Yunlong Guo, Ru Cheng, Jia Yang, Ze Xie, Yannan Lee, Sangmin Chung, Jihoon Oh, Il-Kwon Kim, Ji-Seok Cheng, Tinghai Gao, Qi Cheng, Gang Gu, Guangqin Shim, Minseob Jung, Jeehoon Yun, Changwoo Zhang, Chi Liu, Guoxu Chen, Yufeng Kim, Suhan Chen, Xiangyu Hu, Jun Pu, Xiong Guo, Zi Hao Wang, Xudong Chen, Jun Xiao, Xiao Xie, Xing Jarin, Mourin Zhang, Hulin Lai, Ying-Chih He, Tianyiyi Kim, Hakjeong Park, Inkyu Ahn, Junseong Huynh, Nghia Dinh Yang, Ya Wang, Zhong Lin Baik, Jeong Min Choi, Dukhyun Kyung Hee University Gyeonggi Yongin17104 Korea Republic of Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Mechanical Engineering Soft Robotics Research Center Seoul National University Seoul08826 Korea Republic of Department of Mechanical Engineering Gachon University Seongnam13120 Korea Republic of Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu30013 Taiwan School of Advanced Materials Science & Engineering Sungkyunkwan University Suwon16419 Korea Republic of Sungkyunkwan University 2066 Seobu-ro Gyeonggi Suwon16419 Korea Republic of Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 117585 Singapore Division of Advanced Materials Engineering Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Department of Physics Inha University 100 Inha-ro Incheon22212 Korea Republic of School of Materials Science & Engineering Yeungnam University Gyeongbuk Gyeongsan38541 Korea Republic of 291 Daehak-ro Daejeon34141 Korea Republic of School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Institute of Flexible Electronics Technology of Tsinghua Zhejiang Jiaxing314000 China Nanomaterials and System Lab Major of Mechatronics Engineering Faculty of Applied Energy System Jeju National University Jeju632-43 Korea Republic of School of Engineering University of Glasgow GlasgowG128QQ United Kingdom Seoul02792 Korea Republic of Department of Physics Inha University Incheon22212 Korea Republic of Suwon16419 Korea Republic of Department of

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade. © 2023 The Authors. Published by American Chemical Society

关键词： Nanogenerators

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Embedding 3D models in offline physical environments

Embedding 3D models in offline physical environments

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作者： Ertugrul, Egemen Zhang, Han Zhu, Fang Lu, Ping Li, Ping Sheng, Bin Wu, Enhua Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China ZTE Corporation China State Key Laboratory of Mobile Network and Mobile Multimedia Technology China Department of Computing The Hong Kong Polytechnic University Hong Kong State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Faculty of Science and Technology University of Macau China

This article introduces a novel approach for embedding 3D models in offline physical environments using quick response (QR) codes. Unlike conventional methods, we consider settings where 3D models cannot be retrieved from a remote server. Our method involves generating octree models from voxelized 3D models and storing them in QR codes using a space-efficient data structure. This allows storing 3D models that are both intelligible and purposeful on standard QR codes while addressing the major storage constraint that is present in offline situations. Furthermore, we explore 3D convolutional neural networks (CNN) and autoencoders (AE) to compress 3D models with high resolutions where using octrees alone does not suffice. To the best of our knowledge, our AE network is the first to employ octrees to further compress its encoded data. Through user-friendly desktop and mobile applications, we allow users to encode, decode and visualize 3D models in augmented reality (AR) using QR codes, thus experiment with our methods. The proposed approach enables unique applications and future research in ubiquitous computing, 3D data compression and transmission, 3D AEs, AR and Virtual Reality, low-cost autonomous robots, and 3D printing. © 2020 John Wiley & Sons, Ltd.

关键词： Digital storage

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Automating Deep-Sea Video Annotation Using Machine Learning

Automating Deep-Sea Video Annotation Using Machine Learning

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International Conference on Semantic Computing

作者： Lubomir Stanchev Hanson Egbert Benjamin Ruttenberg Computer Science and Software Engineering Department California Polytechnic State University San Luis Obispo CA USA Benjamin Ruttenberg Biological Sciences Department California Polytechnic State University San Luis Obispo CA USA

ISBN: (数字)9781728163321

ISBN: (纸本)9781728163338

As the world explores opportunities to develop offshore renewable energy capacity, there will be a growing need for pre-construction biological surveys and post-construction monitoring in the challenging marine environment. Underwater video is a powerful tool to facilitate such surveys, but the interpretation of the imagery is costly and time-consuming. Emerging technologies have improved automated analysis of underwater video, but these technologies are not yet accurate or accessible enough for widespread adoption in the scientific community or industries that might benefit from these tools. To address these challenges, we developed a website that allows us to: (1) Quickly play and annotate underwater videos, (2) Create a short tracking video for each annotation that shows how an annotated concept moves in time, (3) Verify the accuracy of existing annotations and tracking videos, (4) Create a neural network model from existing annotations, and (5) Automatically annotate unwatched videos using a model that was previously created. The website was seeded with 50 hours of high-resolution underwater videos that were generously provided by the Monterey Bay Aquarium Research Institute (MBARI). The biology students that were part of the project created more than 30,000 annotations that range over more than 20 concepts. About 3,000 of these annotations were then verified for accuracy by our marine biology experts. Using both validated and unvalidated annotations and automatically generated annotations from trackings, our software was able to count the number of Rathbunaster californicus (starfish) and Strongylocentrotus fragilis (sea urchin) with count accuracy of 97% and 99%, respectively, and F1 score accuracy of 0.90 and 0.81, respectively.

关键词： Tools Convolutional neural networks Biology software Image annotation Machine learning Monitoring

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An Immersive and Interactive Visualization of Gravitational Waves

An Immersive and Interactive Visualization of Gravitational ...

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Immersive Learning Research Network (iLRN), Conference of the

作者： Stefan Lontschar Krzysztof Pietroszek Irene Humer Christian Eckhardt Software Development Graz University of Technology Graz Austria Computer Science California Polytechnic State University San Luis Obispo California USA

ISBN: (数字)9781734899504

ISBN: (纸本)9781728152264

In this work we are presenting a novel virtual learning environment for understanding the phenomena of gravitational waves. Emphasizing on the popular aspect of this topic, we developed an interactive and immersive gravitational wave simulation using a field density representation. We identified three main areas of understanding gravitational waves such as wave source, spatial irradiation distribution and wave type, which the virtual learning environment supports and was designed for. Participant were tested on their knowledge post and prior to the VLE experience as well as on their perceived immersion with the Game Experience Questionnaire. We found striking results in improving the understanding for gravitational waves, that underlines the feasibility of your virtual learning environment.

关键词： Gravity Space vehicles Extraterrestrial measurements Earth Visualization Graphics processing units

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Exciton Superposition across Moiré states in a Semiconducting Moiré Superlattice

arXiv

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arXiv 2023年

作者： Lian, Zhen Chen, Dongxue Meng, Yuze Chen, Xiaotong Su, Ying Banerjee, Rounak Taniguchi, Takashi Watanabe, Kenji Tongay, Sefaattin Zhang, Chuanwei Cui, Yong-Tao Shi, Su-Fei Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Physics University of Texas DallasTX75083 United States School for Engineering of Matter Transport and Energy Arizona State University TempeAZ85287 United States International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Physics and Astronomy University of California RiversideCA92521 United States Department of Electrical Computer & Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

Moiré superlattices of semiconducting transition metal dichalcogenides (TMDCs) enable unprecedented spatial control of electron wavefunctions in an artificial lattice with periodicities more than ten times larger than that of atomic crystals, leading to emerging quantum states with fascinating electronic and optical properties1,2. The breaking of translational symmetry further introduces a new degree of freedom inside each moiré unit cell: high symmetry points of energy minima called moiré sites, behaving as spatially separated quantum dots3–5. The superposition of a quasiparticle’s wavefunction between different moiré sites will enable a new platform for quantum information processing but is hindered by the suppressed electron tunneling between moiré sites. Here we demonstrate the superposition between two moiré sites by constructing an angle-aligned trilayer WSe2/monolayer WS2 moiré heterojunction. The two moiré sites with energy minimum allow the formation of two different interlayer excitons, with the hole residing in either moiré site of the first WSe2 layer interfacing the WS2 layer and the electron in the third WSe2 layer. An external electric field can drive the hybridization of either of the interlayer excitons with the intralayer excitons in the third WSe2 layer, realizing the continuous tuning of interlayer exciton hopping between two moiré sites. Therefore, a superposition of the two interlayer excitons localized at different moiré sites can be realized, which can be resolved in the electric-field-dependent optical reflectance spectra, distinctly different from that of the natural trilayer WSe2 in which the moiré modulation is absent. Our study illustrates a strategy of harnessing the new moiré site degree of freedom for quantum information science, a new direction of twistronics. © 2023, CC BY.

关键词： Selenium compounds

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Learning visually guided latent actions for assistive teleoperation

arXiv

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arXiv 2021年

作者： Karamcheti, Siddharth Zhai, Albert J. Losey, Dylan P. Sadigh, Dorsa Department of Computer Science Stanford University United States Department of Computing & Mathematical Sciences California Institute of Technology United States Department of Mechanical Engineering Virginia Polytechnic Institute State University United States

It is challenging for humans - particularly those living with physical disabilities - to control high-dimensional, dexterous robots. Prior work explores learning embedding functions that map a human's low-dimensional inputs (e.g., via a joystick) to complex, high-dimensional robot actions for assistive teleoperation;however, a central problem is that there are many more high-dimensional actions than available low-dimensional inputs. To extract the correct action and maximally assist their human controller, robots must reason over their context: for example, pressing a joystick down when interacting with a coffee cup indicates a different action than when interacting with knife. In this work, we develop assistive robots that condition their latent embeddings on visual inputs. We explore a spectrum of visual encoders and show that incorporating object detectors pretrained on small amounts of cheap, easy-to-collect structured data enables i) accurately and robustly recognizing the current context and ii) generalizing control embeddings to new objects and tasks. In user studies with a high-dimensional physical robot arm, participants leverage this approach to perform new tasks with unseen objects. Our results indicate that structured visual representations improve few-shot performance and are subjectively preferred by users. © 2021, CC BY.

关键词： Remote control

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Refine-Net: Normal Refinement Neural Network for Noisy Point Clouds

arXiv

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arXiv 2022年

作者： Zhou, Haoran Chen, Honghua Zhang, Yingkui Wei, Mingqiang Xie, Haoran Wang, Jun Lu, Tong Qin, Jing Zhang, Xiao-Ping State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China Department of Computing and Decision Sciences Lingnan University Hong Kong School of Nursing The Hong Kong Polytechnic University Hong Kong Department of Electrical Computer and Biomedical Engineering Ryerson University Toronto Canada

Point normal, as an intrinsic geometric property of 3D objects, not only serves conventional geometric tasks such as surface consolidation and reconstruction, but also facilitates cutting-edge learning-based techniques for shape analysis and generation. In this paper, we propose a normal refinement network, called Refine-Net, to predict accurate normals for noisy point clouds. Traditional normal estimation wisdom heavily depends on priors such as surface shapes or noise distributions, while learning-based solutions settle for single types of hand-crafted features. Differently, our network is designed to refine the initial normal of each point by extracting additional information from multiple feature representations. To this end, several feature modules are developed and incorporated into Refine-Net by a novel connection module. Besides the overall network architecture of Refine-Net, we propose a new multi-scale fitting patch selection scheme for the initial normal estimation, by absorbing geometry domain knowledge. Also, Refine-Net is a generic normal estimation framework: 1) point normals obtained from other methods can be further refined, and 2) any feature module related to the surface geometric structures can be potentially integrated into the framework. Qualitative and quantitative evaluations demonstrate the clear superiority of Refine-Net over the state-of-the-arts on both synthetic and real-scanned datasets. Our code is available at https://***/hrzhou2/refinenet. Copyright © 2022, The Authors. All rights reserved.

关键词： Domain Knowledge

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A High-Fidelity Virtual Liver Model Incorporating Biological Characteristics

SSRN

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SSRN 2022年

作者： Zhang, Xiaorui Zhang, Wenzheng Sun, Wei Song, Aiguo Xu, Tong Wuxi Research Institute Nanjing University of Information Science & Technology Wuxi214100 China Engineering Research Center of Digital Forensics Ministry of Education Jiangsu Engineering Center of Network Monitoring School of Computer and Software Nanjing University of Information Science & Technology Nanjing210044 China School of Automation Nanjing University of Information Science & Technology Nanjing210044 China State Key Laboratory of Bioelectronics Jiangsu Key Lab of Remote Measurement and Control School of Instrument Science and Engineering Southeast University Nanjing210096 China University of Southern California Los AngelesCA United States

Flexible tissue modeling plays an important role in the field of telemedicine. It is related to whether the soft tissue deformation process can be accurately, real-time and vividly simulated during surgery. Most existing models lack the unique biological characteristics. To enhance visual perception ability, a high-fidelity virtual liver model incorporating biological characteristics, such as the viscoelastic, anisotropic and nonlinear biological characteristics, is proposed based on the finite element model with the fourth-order Runge-Kutta method. When simulating the resection of diseased livers, the model undergoes a plastic deformation process where the model loses elasticity due to excessive deformation, but does not reach the cutting fracture threshold. To improve the fidelity of the liver model, this study is the first to introduce the viscoplasticity of biological tissues to the liver model. The 3DMax2020 and OpenGL4.6 were used to build a liver surgery simulation platform, and the PHANTOM OMNI manual controller was used to sense the feedback force during the operation. The proposed model was verified from three aspects of accuracy, fidelity and real-time performance. The experimental results show that the proposed virtual liver model can achieve real visual reproduction, with natural deformation effect, accurate simulation and high fidelity. © 2022, The Authors. All rights reserved.

关键词： Simulation platform

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Cardiac Evidence Mining for Eating Monitoring using Collocative Electrocardiogram Imagining

TechRxiv

引用

TechRxiv 2022年

作者： Zhang, Xu-Lu Yang, Zhen-Qun Jiang, Dong-Mei Liao, Ga Li, Qing Jain, Ramesh Wei, Xiao-Yong College of Computer Science Sichuan University Chengdu610065 China Department of Biomedical Engineering Chinese University of Hong Kong Kowloon Hong Kong School of Computer Science Northwestern Polytechnical University Xi’an China State Key Laboratory of Oral Diseases National Clinical Research Center for Oral Diseases West China Hospital of Stomatology of Sichuan University Chengdu610041 China Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Institute for Future Health University of California IrvineCA United States Donald Bren School of Information and Computer Sciences University of California IrvineCA United States

Eating monitoring has remained an open challenge in medical research for years due to the lack of non-invasive sensors for continuous monitoring and the reliable methods for automatic behavior detection. In this paper, we present a pilot study using the wearable 24-hour ECG for sensing and tailoring the sophisticated deep learning for ad-hoc and interpretable detection. This is accomplished using a collocative learning framework in which 1) we construct collocative tensors as pseudo-images from 1D ECG signals to improve the feasibility of 2D image-based deep models;2) we formulate the cardiac logic of analyzing the ECG data in a comparative way as periodic attention regulators so as to guide the deep inference to collect evidence in a human comprehensible manner;and 3) we improve the interpretability of the framework by enabling the backtracking of evidence with a set of methods designed for Class Activation Mapping (CAM) decoding and decision tree/forest generation. The effectiveness of the proposed framework has been validated on the largest ECG dataset of eating behavior with superior performance over conventional models, and its capacity of cardiac evidence mining has also been verified through the consistency of the evidence it backtracked and that of the previous medical studies. © 2022, CC BY-NC-SA.

关键词： Electrocardiograms

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