检索结果-内蒙古大学图书馆

作者： Chen, Mo Herbert, Sylvia L. Hu, Haimin Pu, Ye Fisac, Jaime Fernandez Bansal, Somil Han, Soojean Tomlin, Claire J. School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada Department of Mechanical and Aerospace Engineering University of California San Diego San DiegoCA92122 United States Department of Electrical Engineering Princeton University PrincetonNJ08536 United States Department of Electrical and Electronic Engineering University of Melbourne ParkvilleVIC3010 Australia Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94704 United States Control and Dynamical Systems Program California Institute of Technology Los AngelesCA90019 United States

Real-time, guaranteed safe trajectory planning is vital for navigation in unknown environments. However, real-time navigation algorithms typically sacrifice robustness for computation speed. Alternatively, provably safe trajectory planning tends to be too computationally intensive for real-time replanning. We propose FaSTrack, Fast and Safe Tracking, a framework that achieves both real-time replanning and guaranteed safety. In this framework, real-time computation is achieved by allowing any trajectory planner to use a simplified planning model of the system. The plan is tracked by the system, represented by a more realistic, higher dimensional tracking model. We precompute the tracking error bound (TEB) due to mismatch between the two models and due to external disturbances. We also obtain the corresponding tracking controller used to stay within the TEB. The precomputation does not require prior knowledge of the environment. We demonstrate FaSTrack using Hamilton-Jacobi reachability for precomputation and three different real-time trajectory planners with three different tracking-planning model pairs. © 1963-2012 IEEE.

关键词： Real time systems

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Developing a medical artificial intelligence course for high school students

Developing a medical artificial intelligence course for high...

引用

International Forum on Medical Imaging in Asia 2021, IFMIA 2021

作者： Huang, Chao-Jung Wu, Tinghui Lu, Jui-Ting Lin, Beatrice Chang, Dawei Wang, Pochuan Wang, Mei-Chi Lee, Peijung Wang, Weichung National Taiwan University-Stanford Joint Program Office of AI in Biotechnology Ministry of Science and Technology Joint Research Center for Artificial Intelligence Technology and All Vista Healthcare Taipei Taiwan Institute of Applied Mathematical Sciences National Taiwan University Taipei Taiwan Department of Mathematics Ecole Normale Supérieure de Lyon Lyon France Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Mathematics National Taiwan University Taipei Taiwan

ISBN: (数字)9781510644212

ISBN: (纸本)9781510644205

Artificial intelligence (AI) training courses often require prerequisites such as calculus or statistics. It is hence challenging to design and develop an introductory AI course for students of secondary education. This research intends to develop a medical AI course, provide high school students with an overview of deep learning applications in medical image analysis, and inspire them to pursue careers in the field of medical AI. We designed a 20-hour course, including lectures and two hands-on projects based on medical image classification. The proposed courses provided medical AI disciplines and built up their knowledge from basic to advanced levels. During the ten-day online courses, all the students were fully engaged and gave us positive feedback. The students endeavored to complete the experimental study in training, testing, and hypothesis of medical images application in the course. Their performance exceeded all expectations, for they did further analysis by tuning different hyperparameters. We designed a course evaluation form, which suggested that the students found it essential and expected to interact with the instructors. The results indicate that combining lectures with hands-on sessions would lead to evidently better achievement in terms of high school students' medical AI knowledge and positive attitudes while addressing real-world problems in the projects. Through this innovative education model, high school students regained their enthusiasm and were encouraged to cultivate their medical AI skills through self-learning while finishing the project. We conclude that this course could be successfully applied to interdisciplinary education in high school. © 2021 SPIE.

关键词： Students

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Scientometric Research Assessment of IEEE CSCWD Conference Proceedings: An Exploratory Analysis from 2001 to 2019

Scientometric Research Assessment of IEEE CSCWD Conference P...

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International Conference on computer Supported Cooperative Work in Design

作者： António Correia Dennis Paulino Hugo Paredes Benjamim Fonseca Shoaib Jameel Daniel Schneider Jano Moreira de Souza INESC TEC and University of Trás-os-Montes e Alto Douro UTAD Vila Real Portugal School of Computer Science and Electronic Engineering Colchester Campus University of Essex UK Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil

ISBN: (纸本)9781728165981

It has been a quarter of a century since the publication of the first edition of the IEEE International Conference on computer Supported Cooperative Work in Design (CSCWD) held in 1996 in Beijing, China. Despite some attempts to empirically examine the evolution and identity of the field of CSCW and its related communities and disciplines, the scarcity of scientometric studies on the IEEE CSCWD research productivity is noteworthy. To fill this gap, this study reports on an exploratory quantitative analysis of the literature published in the IEEE CSCWD conference proceedings with the purpose of visualizing and understanding its structure and evolution for the 2001-2019 period. The findings offer valuable insights into the paper and author distribution, country and citation-level productivity indicators, degree of collaboration, and collaboration index. Through this analysis we also expect to get an initial overview of the IEEE CSCWD conference concerning the main topics being presented, most cited papers, and variances in the number of keywords, full-text views, and references.

关键词： Productivity Visualization Statistical analysis Conferences Bibliometrics Collaboration Collaborative work

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Scalable Crystal Structure Relaxation Using an Iteration-Free Deep Generative Model with Uncertainty Quantification

arXiv

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arXiv 2024年

作者： Yang, Ziduo Zhao, Yi-Ming Wang, Xian Liu, Xiaoqing Zhang, Xiuying Li, Yifan Lv, Qiujie Chen, Calvin Yu-Chian Shen, Lei Department of Mechanical Engineering National University of Singapore Singapore117575 Singapore Artificial Intelligence Medical Research Center School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China Department of Physics National University of Singapore Singapore117551 Singapore -Preferred Program School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China State Key Laboratory of Chemical Oncogenomics School of Chemical Biology and Biotechnology Peking University Shenzhen Graduate School Shenzhen518055 China Department of Medical Research China Medical University Hospital Taichung40447 Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung41354 Taiwan Guangdong L-Med Biotechnology Co. Ltd. Guangdong Meizhou514699 China Research Institute Chongqing401123 China

In computational molecular and materials science, determining equilibrium structures is the crucial first step for accurate subsequent property calculations. However, the recent discovery of millions of new crystals and complex twisted structures has challenged traditional computational methods, both ab initio and machine-learning-based, due to their computationally intensive iterative processes. To address these scalability issues, here we introduce DeepRelax, a deep generative model capable of performing geometric crystal structure relaxation rapidly and without iterations. DeepRelax learns the equilibrium structural distribution, enabling it to predict relaxed structures directly from their unrelaxed ones. The ability to perform structural relaxation at the millisecond level per structure, combined with the scalability of parallel processing, makes DeepRelax particularly useful for large-scale virtual screening. We demonstrate DeepRelax's reliability and robustness by applying it to five diverse databases, including oxides, Materials Project, two-dimensional materials, van der Waals crystals, and crystals with point defects. DeepRelax consistently shows high accuracy and efficiency, validated by density functional theory calculations. Finally, we enhance its trustworthiness by integrating uncertainty quantification. This work significantly accelerates computational workflows, offering a robust and trustworthy machine-learning method for material discovery and advancing the application of AI for science. Code for DeepRelax is available at https://***/Shen-Group/DeepRelax. Copyright © 2024, The Authors. All rights reserved.

关键词： Point defects

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TargetNet: Functional microRNA target prediction with deep neural networks

arXiv

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arXiv 2021年

作者： Min, Seonwoo Lee, Byunghan Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of LG AI Research Seoul07796 Korea Republic of Department of Electronic and IT Media Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Interdisciplinary Program in Artificial Intelligence and Interdisciplinary Program in Bioinformatics Seoul National University Seoul08826 Korea Republic of

Motivation: MicroRNAs (miRNAs) play pivotal roles in gene expression regulation by binding to target sites of messenger RNAs (mRNAs). While identifying functional targets of miRNAs is of utmost importance, their prediction remains a great challenge. Previous computational algorithms have major limitations. They use conservative candidate target site (CTS) selection criteria mainly focusing on canonical site types, rely on laborious and time-consuming manual feature extraction, and do not fully capitalize on the information underlying miRNA-CTS interactions. Results: In this paper, we introduce TargetNet, a novel deep learning-based algorithm for functional miRNA target prediction. To address the limitations of previous approaches, TargetNet has three key components: (1) relaxed CTS selection criteria accommodating irregularities in the seed region, (2) a novel miRNA-CTS sequence encoding scheme incorporating extended seed region alignments, and (3) a deep residual network-based prediction model. The proposed model was trained with miRNA-CTS pair datasets and evaluated with miRNA-mRNA pair datasets. TargetNet advances the previous state-of-theart algorithms used in functional miRNA target classification. Furthermore, it demonstrates great potential for distinguishing high-functional miRNA targets. © 2021, CC BY-NC-ND.

关键词： RNA

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Export- and import-based economic models for predicting global trade using deep learning

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Expert Systems with Applications 2023年 218卷

作者： Yang, Cheng-Hong Lee, Cheng-Feng Chang, Po-Yin Department of Information Management Tainan University of Technology Tainan City710302 Taiwan Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung80778 Taiwan Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung80708 Taiwan Ph. D. Program in Biomedical Engineering Kaohsiung Medical University Kaohsiung80708 Taiwan School of Dentistry Kaohsiung Medical University Kaohsiung80708 Taiwan Department of Business Administration National Kaohsiung University of Science and Technology Kaohsiung80778 Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen County 892 Taiwan

Forecasting global foreign trade is essential for developing government trade policies and management strategies for multinational corporations. However, achieving an accurate trade forecast is challenging because of the complex structural relationships between exports, imports and other economic variables. Many traditional forecasting models, such as time series, econometric, and machine learning, provide less accurate forecasts for trade data. This paper proposes an ensemble learning approach to improve forecasting performance by hybridizing the structural relationships between trade and deep learning models to predict foreign trade for ten major countries. The proposed method first establishes a cointegration relationship between exports and imports and their structural variables. The cointegrated models are then used to predict the future of trade, which is used as a benchmark model for comparison. A hybrid deep learning algorithm uses the cointegrated variables as input variables to predict trade data, and then are compared with time-series forecasts and economic structural models. The experimental results reveal that the ensemble learning method can achieve excellent forecasting performance for the tested periods of trade data. In most cases, the root means square error and mean absolute percentage error values are smaller than the time series and economic structural models. © 2023 Elsevier Ltd

关键词： Forecasting

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Internet of Things in disaster management systems: A systematic review

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AIP Conference Proceedings 2023年第1期2594卷

作者： G. P. L. Barnabas H. Citrawati T. A. Santoso N. Surantha Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Department of Electrical Electronic and Communication Engineering Faculty of Engineering Tokyo City University Tamazutsumi 1-28-1 Setagaya City Tokyo 158-8557 Japan

Disasters are unexpected, sudden events that may cause monumental damages to both materials and human lives. Hence, the unpredictability and destruction that follow when a disaster happens to pose a dire challenge for researchers, scientists, and governments to find ways to effectively handle the situation and flow of information when such an event strikes. The Internet of Things (IoT) presents a solution that involves using one or more mobile devices connected over the network, thus making the transfer of data and information more effective and efficient. This paper presents a holistic analysis of the state-of-the-art IoT technologies which can answer the challenge posed above, in addition to the demand for efficiency, mobility, accuracy, as well as better communications between the command center and all actors involved in disaster operations. The applications of IoT have varied for many purposes. We divide those purposes into two broad strokes of pre-disaster management and post-disaster management IoT-aided systems. For pre-disaster management, the IoT-aided systems are mostly used to predict the possible disaster that might happen using sensors placed in strategic places. Finally, for post-disaster management, the IoT-aided systems aid in improving the effectiveness and accuracy of evacuation, relief, and victim-tracking efforts.

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Room-temperature multiple ligands-tailored SnO_(2) quantum dots endow in situ dual-interface binding for upscaling efficient perovskite photovoltaics with high V_(OC)

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Light(Science & Applications) 2021年第12期10卷 2467-2481页

作者： Zhiwei Ren Kuan Liu Hanlin Hu Xuyun Guo Yajun Gao Patrick W.K.Fong Qiong Liang Hua Tang Jiaming Huang Hengkai Zhang Minchao Qin Li Cui Hrisheekesh Thachoth Chandran Dong Shen Ming-Fai Lo Annie Ng Charles Surya Minhua Shao Chun-Sing Lee Xinhui Lu Frédéric Laquai Ye Zhu Gang Li Department of Electronic and Information Engineering Research Institute for Smart Energy(RISE)Guangdong-Hong Kong-Macao(GHM)Joint Laboratory for Photonic-Thermal-Electrical Energy Materials and DevicesThe Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina Department of Electrical and Computer Engineering Nazarbayev UniversityNur-SultanKazakhstan The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen 518057China Hoffmann Institute of Advanced Materials Shenzhen Polytechnic7098 Liuxian BoulevardShenzhen 518055China Department of Applied Physics The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina Department of Chemical and Biological Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina King Abdullah University of Science and Technology(KAUST) KAUST Solar Center(KSC)Physical Sciences and Engineering Division(PSE)Material Science and Engineering Program(MSE)Thuwal 23955-6900Kingdom of Saudi Arabia Department of Physics The Chinese University of Hong KongShatin 999077 Hong KongChina Center of Super-Diamond and Advanced Films(COSDAF) Department of ChemistryCity University of Hong KongHong KongChina

The benchmark tin oxide(SnO_(2))electron transporting layers(ETLs)have enabled remarkable progress in planar perovskite solar cell(PSCs).However,the energy loss is still a challenge due to the lack of“hidden interface”*** report a novel ligand-tailored ultrafine SnO_(2) quantum dots(QDs)via a facile rapid room temperature ***,the ligand-tailored SnO_(2) QDs ETL with multi-functional terminal groups in situ refines the buried interfaces with both the perovskite and transparent electrode via enhanced interface binding and perovskite *** novel ETLs induce synergistic effects of physical and chemical interfacial modulation and preferred perovskite crystallization-directing,delivering reduced interface defects,suppressed non-radiative recombination and elongated charge carrier *** conversion efficiency(PCE)of 23.02%(0.04 cm^(2))and 21.6%(0.98 cm^(2),V_(OC) loss:0.336 V)have been achieved for the blade-coated PSCs(1.54 eV E_(g))with our new ETLs,representing a record for SnO_(2) based blade-coated ***,a substantially enhanced PCE(V_(OC))from 20.4%(1.15 V)to 22.8%(1.24 V,90 mV higher V_(OC),0.04 cm^(2) device)in the blade-coated 1.61 eV PSCs system,via replacing the benchmark commercial colloidal SnO_(2) with our new ETLs.

关键词： perovskite interface quantum

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Winter Cabin Water Tap Monitor and Release System to Reduce Risk of Freezing 10

Winter Cabin Water Tap Monitor and Release System to Reduce ...

引用

10th IEEE Annual Ubiquitous Computing, electronics and Mobile Communication Conference, UEMCON 2019

作者： Kaprielian, Paul L. Pham, Thach Young, Alex Dow, Douglas E. Grenquist, Scott A. Decker, Bruce M. Program in Electronic Eng. Technology Wentworth Institute of Technology Dept. of Electrical and Computer Engineering BostonMA United States

ISBN: (纸本)9781728138855

Frozen water pipes lead to leaks that result in property damage and repair costs. Frozen water pipes not only occur in northern climates, but also in more moderate climates that have occasional periods of temperatures below the freezing point. Water pipes at high risk for ice formation include ones outside of buildings, in poorly insulated structures or in seasonal buildings that are not heated during the winter months. Methods of protection include heating the building, insulation and heat tape on exposed water pipes, turning off the water supply and draining all pipes of water, or turning on a water valve to allow a continual flow of water through the pipes during the entire period having risk of low temperatures. The flow would hinder ice formation in the pipes. These protective measures have associated costs for equipment, heating fuel, and water loss. If the low temperature drops further or is sustained for longer periods than expected, there would still be a risk of frozen pipes. The purpose of this project was to develop and test a prototype system to monitor temperature, and initiate intermittent bursts of water flow only when temperatures are low enough to have risk of ice formation in the water pipes. The prototype utilized a temperature sensor, Arduino microcontroller, power supply, relay switch, and solenoid valve. The solenoid valve was incorporated into the water facet of a sink. When the sensor detected temperatures below the threshold of 25°C, intermittent bursts of flow were initiated, with 20 seconds of water flow followed by 2 minutes of no flow. Each flow event was recorded in a web service. A HTTP Get request was transmitted to a Google App Script program that stored the date and time in a Google Sheet. The owner or manager could remotely monitor the intermittent flow events. © 2019 IEEE.

关键词： Temperature

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Lightweight equivariant model for efficient machine learning interatomic potentials

arXiv

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arXiv 2023年

作者： Yang, Ziduo Wang, Xian Li, Yifan Lv, Qiujie Chen, Calvin Yu-Chian Shen, Lei Department of Mechanical Engineering National University of Singapore Singapore117575 Singapore Artificial Intelligence Medical Research Center School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China Department of Physics National University of Singapore Singapore117551 Singapore -Preferred Program School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China State Key Laboratory of Chemical Oncogenomics School of Chemical Biology and Biotechnology Peking University Shenzhen Graduate School Shenzhen518055 China Guangdong L-Med Biotechnology Co. Ltd Guangdong Meizhou514699 China Research Institute Chongqing401123 China

In modern computational materials science, deep learning has shown the capability to predict interatomic potentials, thereby supporting and accelerating conventional simulations. However, existing models typically sacrifice either accuracy or efficiency. Moreover, lightweight models are highly demanded for offering simulating systems on a considerably larger scale at reduced computational costs. Here, we introduce a lightweight equivariant interaction graph neural network (LEIGNN) that can enable accurate and efficient interatomic potential and force predictions for molecules and crystals. Rather than relying on higher-order representations, LEIGNN employs a scalar-vector dual representation to encode equivariant features. By learning geometric symmetry information, our model remains lightweight while ensuring prediction accuracy and robustness through the equivariance. Our results show that LEIGNN consistently outperforms the prediction performance of the representative baselines and achieves significant efficiency across diverse datasets, which include catalysts, molecules, and organic isomers. Furthermore, we conduct molecular dynamics (MD) simulations using the LEIGNN force field across solid, liquid, and gas systems. It is found that LEIGNN can achieve the accuracy of ab initio MD across all examined systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Prediction models

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