检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Jenkins, Odest Chadwicke Grizzle, Jessy Atkins, Ella Stirling, Leia Rouse, Elliott Guzdial, Mark Provost, Damen Mann, Kimberly Millunchick, Joanna Robotics and Electrical Engineering & Computer Science United States Undergraduate Education Robotics Institute United States Robotics Institute United States Graduate Programs Robotics Institute United States Robotics and Industrial Engineering United States Robotics and Mechanical Engineering United States Electrical Engineering & Computer Science United States Engineering Education Research Program United States Undergraduate Education College of Engineering United States University of Michigan United States

The Robotics Major at the University of Michigan was successfully launched inthe 2022-23 academic year as an innovative step forward to better servestudents, our communities, and our society. Building on our guiding principleof "Robotics with Respect" and our larger Robotics Pathways model, the MichiganRobotics Major was designed to define robotics as a true academic disciplinewith both equity and excellence as our highest priorities. Understanding thattalent is equally distributed but opportunity is not, the Michigan RoboticsMajor has embraced an adaptable curriculum that is accessible through adiversity of student pathways and enables successful and sustained career-longparticipation in robotics, AI, and automation professions. The results afterour planning efforts (2019-22) and first academic year (2022-23) have beenhighly encouraging: more than 100 students declared Robotics as their major,completion of the Robotics major by our first two graduates, soaringenrollments in our Robotics classes, thriving partnerships with HistoricallyBlack Colleges and Universities. This document provides our original curricularproposal for the Robotics Undergraduate program at the University of Michigan,submitted to the Michigan Association of State Universities in April 2022 andapproved in June 2022. The dissemination of our program design is in the spiritof continued growth for higher education towards realizing equity andexcellence. The most recent version of this document is also available on Google Docsthrough this link: https://***/robotics_majorMSC Codes 97 © 2023, CC BY.

关键词： Curricula

来源：评论

学校读者我要写书评

暂无评论

Enhancing UAV Spraying Efficiency through Payload Sensor Integration

Enhancing UAV Spraying Efficiency through Payload Sensor Int...

引用

IEEE Latin American Robotics Symposium, LARS

作者： Celso O. Barcelos Leonardo A. Fagundes-Júnior Daniel C. Gandolfo Martin Saska Alexandre S. Brandão Department of Electrical Engineering Graduate Program in Computer Science Núcleo de Especialização em Robótica (NERo) Universidade Federal de Viçosa Viçosa MG Brazil Instituto de Automática National University of San Juan San Juan Argentine Department of Cybernetics Czech Technical University in Prague Prague Czech Republic

ISBN: (数字)9798331508807

ISBN: (纸本)9798331508814

The use of UAVs has grown in recent years, leading to the prominence of various applications such as monitoring and inspection, search and rescue operations, dam and civil structure inspection, military applications, among others. One of the most relevant applications today is related to Precision Agriculture, which is the subject of this work. Our study analyzes the behavior of an Unmanned Autonomous Vehicle (UAV) used for spraying tasks. The variation of the payload is measured through a load sensor. This information indicates to the UAV the right time to return to the base for refueling or completion of the application. Experimental validation is carried out to assess the functionality of the payload sensor during flight operations. In addition, the dynamic compensation controller improves the navigation of the UAV, even in the presence of payload variations during the entire mission duration.

关键词： Precision agriculture Navigation Spraying Inspection Robot sensing systems Autonomous aerial vehicles Vehicle dynamics Monitoring Payloads Diseases

来源：评论

学校读者我要写书评

暂无评论

Measure the Level of Success in Using Google Drive with the Kuder Richardson (KR) Reliability Method

Measure the Level of Success in Using Google Drive with the ...

引用

2019 International Congress on Applied Information Technology, AIT 2019

作者： Nugroho, Andi Warnars, Harco Leslie Hendric Spits Heriyadi, Yaya Tanutama, Lukas Mercu Buana University and Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta Indonesia Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728158624

The Google Sheets is a cloud technology part of Google Drive. Google Drive is a cloud operating system owned by Google's search engine. National Elementary School (SDN) to make a report book or Basic Education Databook (Dapodik) still use Microsoft Excel, which has the effect of delaying the delivery of report results to parents. This happens because to fill in the DAPODIK score must be filled by teachers who teach subjects in class. This study aims to provide knowledge to teachers in collaborating to fill DAPODIK and measure how reliable the delivery of material received by each teacher. Using the method of reliability Kuder Richardson KR-20 and KR-21 produces a value before the training on reliability values KR-20 = 0.6328, and KR-21 = 0.6000, after the training, the reliability values KR-20 = 0.5788 and KR-21 = 0.6231. From these results, it is known that the value after training for KR-21 has increased and according to the conventional correlation coefficient. In future research can be re-developed by making application system recommendations in determining teachers in schools to become instructors in accordance with the capabilities possessed based on the reliability testing results of the KR-20 and KR-21. © 2019 IEEE.

关键词： Search engines

来源：评论

学校读者我要写书评

暂无评论

A deep learning approach to Lung Nodule Growth Prediction using CT image combined with Demographic and image features 23

A deep learning approach to Lung Nodule Growth Prediction us...

引用

7th International Conference on Medical and Health Informatics, ICMHI 2023

作者： Li, Ai-Hsien Adams Nguyen, Duc-Khanh Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Yang, Pan-Chyr Division of Cardiology Far Eastern Memorial Hospital Taipei220 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Information Management Yuan Ze University Taoyuan320 Taiwan Department of Radiology Far Eastern Memorial Hospital Taipei220 Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital Taipei Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan320 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei106 Taiwan Department of Internal Medicine College of Medicine National Taiwan University Taipei100 Taiwan Center of Genomic Medicine National Taiwan University Taipei100 Taiwan

ISBN: (纸本)9798400700712

Lung cancer remains a significant global public health concern, being the leading cause of cancer-related deaths worldwide. Despite recent medical advancements, the disease still has a high mortality rate, making early detection and treatment critical for improving patient outcomes. Accurate prediction of nodule growth is key to early lung cancer treatment, but current assessments offer unclear indications of future growth, leading physicians to recommend costly and anxiety-provoking follow-up appointments every 3 to 12 months. To address this need, this study develops an ensemble deep learning approach that combines demographic data with existing CT images to predict whether lung nodules will grow, potentially reducing unnecessary examinations and easing the burden on patients. Our study, conducted on 862 patients with 1004 nodules, produced promising preliminary results with Accuracy, Sensitivity, Precision, F1 Score, and AUC of 0.66, 0.66, 0.67, 0.66, and 0.71, respectively. The proposed method provides a promising support system to empower patients to make informed decisions about seeking medical attention and helps physicians facilitate early treatment regimens, leading to improved patient outcomes and potentially saving lives. © 2023 ACM.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

An Efficient Algorithm to Update Incremental Attributes in Morphological Trees

SSRN

引用

SSRN 2022年

作者： Gobber, Charles Ferreira Hashimoto, Ronaldo Fumio Alves, Wonder Alexandre Luz Informatics and Knowledge Management Graduate Program Universidade Nove de Julho São Paulo Brazil Department of Computer Science Institute of Mathematics and Statistics Universidade de São Paulo São Paulo Brazil

Attribute Filters are powerful image simplification operators that have very good contour-preservation properties. They can be efficiently computed using morphological trees. The most common strategy to compute attribute filters using morphological trees is based on three steps: (i) tree construction: the tree is built along with its attributes;(ii) filtering: we simplify the tree by removing some of its nodes based on some attribute and a filtering rule defined on a threshold value;and (iii) image reconstruction: this is the final phase where the tree is converted back to an image leading to an attribute filter. However, after filtering a few attributes in tree may change and they must be updated, since some attribute filters are computed by filtering the simplified tree again, but with a different threshold. In this paper it is presented an efficient algorithm to update incremental attributes in morphological trees with low memory consumption and fast computation. © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Dynamic Healthcare Embeddings for Improving Patient Care

Dynamic Healthcare Embeddings for Improving Patient Care

引用

International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Hankyu Jang Sulyun Lee D. M. Hasibul Hasan Philip M. Polgreen Sriram V. Pemmaraju Bijaya Adhikari Department of Computer Science University of Iowa Iowa City Iowa USA Interdisciplinary Graduate Program in Informatics University of Iowa Iowa City Iowa USA Department of Internal Medicine University of Iowa Iowa City Iowa USA

As hospitals move towards automating and integrating their computing systems, more fine-grained hospital operations data are becoming available. These data include hospital architectural drawings, logs of interactions between patients and healthcare professionals, prescription data, procedures data, and data on patient admission, discharge, and transfers. This has opened up many fascinating avenues for healthcare-related prediction tasks for improving patient care. However, in order to leverage off-the-shelf machine learning software for these tasks, one needs to learn structured representations of entities involved from heterogeneous, dynamic data streams. Here, we propose DECENT, an auto-encoding heterogeneous co-evolving dynamic neural network, for learning heterogeneous dynamic embeddings of patients, doctors, rooms, and medications from diverse data streams. These embeddings capture similarities among doctors, rooms, patients, and medications based on static attributes and dynamic interactions. DECENT enables several applications in healthcare prediction, such as predicting mortality risk and case severity of patients, adverse events (e.g., transfer back into an intensive care unit), and future healthcare-associated infections. The results of using the learned patient embeddings in predictive modeling show that DECENT has a gain of up to 48.1% on the mortality risk prediction task, 12.6% on the case severity prediction task, 6.4% on the medical intensive care unit transfer task, and 3.8% on the Clostridioides difficile (***) Infection (CDI) prediction task over the state-of-the-art baselines. In addition, case studies on the learned doctor, medication, and room embeddings show that our approach learns meaningful and interpretable embeddings.

关键词： Hospitals Social networking (online) Neural networks Machine learning Predictive models Back Software

来源：评论

学校读者我要写书评

暂无评论

High-Performance and Resource-Efficient IoT-based Sleep Monitoring System

High-Performance and Resource-Efficient IoT-based Sleep Moni...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Nico Surantha Oei Kurniawan Utomo Sani Muhamad Isa Computer Science Department BINUS Graduate Program-Master of Computer Science Jakarta Indonesia

ISBN: (数字)9781728152073

ISBN: (纸本)9781728140537

Recently, the number of people who have trouble sleeping is on the rise. It results in an increase in the number of car accidents, lower productivity and a higher risk of chronic disease. Advancing wearable and contactless health sensors offers the possibility of daily sleep quality monitoring that can be done on a regular basis by patients in their homes. The goal of this research is to propose the design of an Internet-of-Things (IoT) sleep quality monitoring system that can be integrated with a wearable or contactless sensor. The proposed platform architecture is based on micro-services and is event-driven. The simulation results show that the implementation of the IoT sleep quality monitoring system based on the proposed design decreases the response time by 55.85 per cent while increasing the throughput by 34.76 per cent compared to the traditional approach. On the other hand, the rate of increase in memory usage per instance replication for proposed method is lower than the traditional method.

关键词： Monitoring Logic gates Time factors computer architecture Sensor systems Throughput

来源：评论

学校读者我要写书评

暂无评论

Automated database indexing using model-free reinforcement learning

arXiv

引用

arXiv 2020年

作者： Licks, Gabriel Paludo Meneguzzi, Felipe Brazil Graduate Program in Computer Science School of Technology

Configuring databases for efficient querying is a complex task, often carried out by a database administrator. Solving the problem of building indexes that truly optimize database access requires a substantial amount of database and domain knowledge, the lack of which often results in wasted space and memory for irrelevant indexes, possibly jeopardizing database performance for querying and certainly degrading performance for updating. We develop an architecture to solve the problem of automatically indexing a database by using reinforcement learning to optimize queries by indexing data throughout the lifetime of a database. In our experimental evaluation, our architecture shows superior performance compared to related work on reinforcement learning and genetic algorithms, maintaining near-optimal index configurations and efficiently scaling to large databases. Copyright © 2020, The Authors. All rights reserved.

关键词： Domain Knowledge

来源：评论

学校读者我要写书评

暂无评论

Constructing A Creative Service Software with Semantic Web 8

Constructing A Creative Service Software with Semantic Web

引用

8th International Conference on Dependable Systems and Their Applications, DSA 2021

作者： Huang, Pei-Shu Fahmi, Faisal Wang, Feng-Jian Yang, Hongji Dept. of Computer Science National Yang-Ming Chiao-Tung University Hsinchu City Taiwan EECS Int’l Graduate Program National Yang-Ming Chiao-Tung University Hsinchu City Taiwan Information and Library Science Airlangga University Surabaya City Indonesia Department of Informatics University of Leicester Leicester United Kingdom

ISBN: (纸本)9781665443913

In software development, Service Oriented Architecture (SOA) and creative computing can be adopted to utilize multiple-domain knowledges to construct service software possessing creative properties, i.e., novel, useful, and surprising. In the past, several theoretical evaluation metrics have been proposed to measure creativity of a software system. However, a systematic practical method to construct creative service software is rarely considered in current researches. In this paper, we propose a model for creative service software development based on semantic web, which is applied in two phases: domain-creative requirement specification and semantic-based service design. The model can reduce communication work between domain experts and software engineers, improve traceability of the specifications, and improve machine readability during the generation of creativity. After the model of service design is validated for completeness and consistency, the creative service software is well-designed and can be implemented and reused effectively without losing of creativity. © 2021 IEEE.

关键词： Semantic Web

来源：评论

学校读者我要写书评

暂无评论

Detection of Service Provider Hardware Over-commitment in Container Orchestration Environments

Detection of Service Provider Hardware Over-commitment in Co...

引用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Pedro Horchulhack Eduardo K. Viegas Altair O. Santin Pontifícia Universidade Católica do Paraná (PUCPR) — Graduate Program in Computer Science (PPGIa) Curitiba Paraná Brazil Secure Systems Research Center Technology Innovation Institute Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665435413

The deployment of container-based services continues to increase as time passes, mainly due to its fast provision time and lower allocation overheads. Yet, the literature still neglects the performance degradation in containers due to multi-tenancy and service provider hardware over-commitment. This paper proposes a new hardware over-commitment detection for container orchestration environments, implemented twofold. First, the containerized hardware usage of deployed containers is continuously monitored in a non-intrusive manner, leveraging the container engine resource management interface. Second, collected features are used by a recurrent neural network model for detecting both container and service level hardware over-commitment, following a time-series rationale. Experiments run on a containerized Apache Spark distribution have shown that multi-tenancy and hardware over-commitment significantly affect its performance. In addition, our proposed model is able to detect hardware over-commitment with up to 91% of true-positive at the container level, and up to 93% true-positive at the service level.

关键词： Degradation Recurrent neural networks Cluster computing Containers Feature extraction Hardware Resource management

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：