检索结果-内蒙古大学图书馆

1st ieee international conference on Autonomic computing and Self-Organizing systems (ACSOS)

作者： Roy, Shouvik Mehmood, Usama Grosu, Radu Smolka, Scott A. stoller, Scott D. Tiwari, Ashish SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA Tech Univ Wien Cyber Phys Syst Grp Vienna Austria Microsoft Res San Francisco CA USA

ISBN: (纸本)9781728172774

We show how a high-performing, fully distributed and symmetric neural V-formation controller can be synthesized from a Centralized MPC (Model Predictive Control) controller using Deep Learning. This result is significant as we also establish that under very reasonable conditions, it is impossible to achieve V-formation using a deterministic, distributed, and symmetric controller. The learning process we use for the neural V-formation controller is significantly enhanced by CEGkR, a Counterexample-Guided k-fold Retraining technique we introduce, which extends prior work in this direction in important ways. Our experimental results show that our neural V-formation controller generalizes to a significantly larger number of agents than for which it was trained (from 7 to 15), and exhibits substantial speedup over the MPC-based controller. We use a form of statistical model checking to compute confidence intervals for our neural V-formation controller's convergence rate and time to convergence.

关键词： V-Formation Model Predictive Control distributed Neural Controller Supervised Learning

来源：评论

学校读者我要写书评

暂无评论

Proceedings - 2020 international conference on Space-Air-Ground computing, SAGC 2020

Proceedings - 2020 International Conference on Space-Air-Gro...

引用

1st international conference on Space-Air-Ground computing, SAGC 2020

ISBN: (纸本)9780738124384

The proceedings contain 19 papers. The topics discussed include: a cloud storage framework for massive meteorological and oceanographic data and the application of virtualization technology;on link performance according to flight route in drone-based wide area wireless sensor network;multi-authority attribute based encryption with policy-hidden and accountability;distributed task offloading and resource allocation in vehicular edge computing;stackelberg game based computation offloading and resource allocation in mobile edge computing;and research and backoff algorithm improvement of statistical priority-based multiple access protocol.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Advancing Sustainable Manufacturing with IoT and Deep Reinforcement Learning in the Industry 4.0 Era

Advancing Sustainable Manufacturing with IoT and Deep Reinfo...

引用

Green Industrial Electronics and Sustainable Technologies (GIEst), ieee international conference on

作者： Franciskus Antonius Alijoyo Divya Nimma Saravanan B Jangam Divya P N V Syamala Rao M Sankari V Center for Risk Management and Sustainability Indonesia School of Business and Information Technology STMIK LIKMI Bandung Indonesia Data Analyst in UMMC University of Southern Mississippi USA Department of CSE Sri Eshwar College of Engineering Coimbatore India A.V. College of Arts Science and Commerce Hyderabad India Department of CSE SRM University AP Amaravati Andhra Pradesh India Department of AI&DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331540142

ISBN: (纸本)9798331540159

Nowadays with the emergence of Industry 4. 0, there is therefore the need to have sustainable manufacturing in order to increase operational efficiency as well as relieve the pressure on the environment. A potential weakness of conventional approaches is time-dependency while processing real-time data to prevent dysfunctions in the company’s resource management, energy consumption control, and predictive maintenance. Thus, in this work it is proposed an innovative approach to address these problems with the use of Deep Reinforcement Learning (DRL) and Internet of Things (IoT) technology. Energy consumption, machine performance and environmental data are collected by IoT sensors in real-time and fed into a DRL model. This concept effectively eliminates the vices that are characteristic of conventional approaches in that it seeks to optimize production processes by cell design. The approach suggested above attains an unprecedented 98. 2% accuracy rate. This is in a bid to exemplify its capability in predicting these phenomenal and balance forecasts, to improve the overall performance and sustainability of manufacturing. Thereby, the method offers a robust answer to the acute questions arising in front-line manufacturing today and sets a brand-new reference for intelligent and sustainable behavior in the context of the fourth industrial revolution 4.0.

关键词： Green manufacturing Energy consumption Production sensor phenomena and characterization Deep reinforcement learning Real-time systems Fourth Industrial Revolution Internet of Things Resource management Sustainable development

来源：评论

学校读者我要写书评

暂无评论

Invulnerability Analysis of Wireless sensor Networks based on Cellular Automata 1

Invulnerability Analysis of Wireless Sensor Networks based o...

引用

1st ieee international conference on Human-Machine systems (ICHMS)

作者： Fu, Xiuwen He, Xiaolin Yang, Yongsheng Shanghai Maritime Univ Inst Logist Sci & Engn Shanghai 201306 Peoples R China

ISBN: (纸本)9781728158716

Network invulnerability is widely considered as one of the main bottlenecks restricting the widespread application of wireless sensor networks (WSNs). Therefore, in this work, we present a 2-D cellular automata model to mimic the failure process of WSNs. In this model, we consider five failure events of sensor nodes (i.e., energy exhaustion, hardware/software malfunctions, intentional attacks, random attacks and isolation from the sink node). The experimental results have shown that the invulnerability of WSNs under random attacks is better than that under intentional attacks. In addition, through composition analysis of failed sensor nodes, we observe that energy exhaustion is mainly responsible for node failures in the case of random attacks. Isolation from the sink node is the main cause of node failures under intentional attacks. The obtained results can provide theoretical guidance to build a more invulnerable WSN.

关键词： wireless sensor network network invulnerability cellular automata failure event

来源：评论

学校读者我要写书评

暂无评论

PerDL: 1st international Workshop on Deep Learning in Pervasive computing 2021 - Welcome and Committees

PerDL: 1st International Workshop on Deep Learning in Pervas...

引用

ieee Annual conference on Pervasive computing and Communications Workshops (PerCom)

The ieee PerDL workshop aims to address all relevant technologies, researches and discoveries in the field of distributed and pervasive Deep Learning (DL) techniques. The usage of deep learning techniques is becoming more and more widespread and needs to be properly incorporated into pervasive and distributed computing and communication architectures, in order to foster a fully connected environment of intelligent systems. The great development of Internet of Things devices and applications is fostering more and more pervasive communication paradigms, but smart devices and gateways need further capabilities to become fully connected intelligent things. What is still lacking is an in-depth study of the modalities and technologies to properly integrate all deep learning aspects and algorithms in all possible distributed and pervasive systems, making them actual intelligent distributed systems. The main aim is to support fast and on-the-field DL-based computations that may foster novel and fast services in the near future contexts, such as smart cities, smart agriculture, smart health, smart distance education, smart automotive, industry 4.0, and many more. On the other hand, DL could help in finding out better distributed and parallel configurations to increase the performance of pervasive computing itself. The objective of the PerDL workshop is to encourage the integration between the distributed and pervasive computing community and the deep artificial neural network community. This with the aim to foster the development of more and more widespread deep learning techniques in all computing and communication systems, as well as to increase pervasive computing performance by means of deep learning strategies.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Proceedings - 2020 ieee international conference on Autonomic computing and Self-Organizing systems, ACSOS 2020

Proceedings - 2020 IEEE International Conference on Autonomi...

引用

1st ieee international conference on Autonomic computing and Self-Organizing systems, ACSOS 2020

ISBN: (纸本)9781728172774

The proceedings contain 28 papers. The topics discussed include: DeCrypto Pro: deep learning based cryptomining malware detection using performance counters;understanding uncertainty in self-adaptive systems;assessing adaptations based on change impacts;automated management of collections of autonomic systems;self-patch: beyond patch Tuesday for containerized applications;how far should I watch? quantifying the effect of various observational capabilities on long-range situational awareness in multi-robot teams;taming resource heterogeneity in distributed ml training with dynamic batching;automating GUI testing with image-based deep reinforcement learning;and coevolutionary learning of neuromodulated controllers for multi-stage and gamified tasks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Characterization of On-Foil sensors and Ultra-Thin Chips for HySiF Integration

引用

ieee sensorS JOURNAL 2020年第14期20卷 7595-7604页

作者： Elsobky, Mourad Deuble, Thomas Ferwana, Saleh Albrecht, Bjorn Harendt, Christine Ottaviani, Alessandro Alomari, Mohammed Burghartz, Joachim N. Inst Mikroelekt Stuttgart IMS CHIPS D-70569 Stuttgart Germany

The characterization of multiple mechanically flexible passive and active electronic components, namely on-foil temperature sensor, relative humidity sensor, and ultra-thin microcontroller unit (MCU) integrated circuit (IC) is presented. These components are readily available for Hybrid System-in-Foil (HySiF) integration, which combines the merits of the high-performance silicon chips with the large-area electronics. The 60-nm thin-film resistance temperature detector (RTD) is fabricated on different substrates and the extracted temperature coefficient ranges from 0.002 to 0.003(Omega/Omega)/degrees C. Furthermore, the capacitive relative humidity sensor is fabricated by utilizing a polymeric sensing dielectric material that is compatible with those polyimides used in ultra-thin chip embedding. The 1-mu m thick sensor achieves a fast adsorption rise time of 54 ms and a spontaneous desorption fall time of 750 ms. For flexible ICs packaging and HySiF integration, microcontroller chips are back-thinned to a thickness of about 30 mu m. The characterization of the MCU after the thinning process step showed undegraded performance of the integrated analog-to-digital converter in terms of linearity. However, a drop in the on-chip temperature sensor sensitivity is observed for the thin chips and significant degradation in the period jitter of the high-frequency RC oscillator is measured. Finally, continuity tests are performed to validate the functionality of the ultra-thin MCUs when embedded using the Chip-Film Patch (CFP) flexible package.

关键词： Temperature sensors sensor phenomena and characterization Temperature measurement Substrates Resistance Flexible electronics flexible packaging hybrid system-in-foil humidity sensor microcontrollers system-on-package smart sensor systems temperature sensor ultra-thin chips

来源：评论

学校读者我要写书评

暂无评论

POstER: Feature Selection to Optimize DoS Detection in Wireless sensor Networks 1

POSTER: Feature Selection to Optimize DoS Detection in Wirel...

引用

1st international conference of Smart systems and Emerging Technologies, SMART-TECH 2020

作者： Al-Akhras, Mousa Al-Issa, Abdulaziz I. Alsahli, Mohammed S. Alawairdhi, Mohammed College of Computing and Informatics Saudi Electronic University Riyadh11673 Saudi Arabia

ISBN: (纸本)9781728174075

Denial of Service is the most common attack in Wireless sensor Networks (WSNs). Decision Trees (DT) and Artificial Neural Network (ANN) are used to detect attackers' signature. The most relevant features and the most dangerous attacks are selected from the WSN dataset. Feature selection reduces the time needed to learn the attackers' signature and improves the speed of detection as it relies on a reduced set of features. Results show that both DT and ANN achieved the best results when only the relevant features are used and the most dangerous attacks are considered. DT achieved 99.83% classification accuracy, 0.998 True Positive (TP) rate and 0.014 False Positive (FP) rate. The best DT has 463 nodes and 232 leaves. The best ANN model achieves 99.76% accuracy, 0.998 TP, 0.004 FP, and it has 28 neurons which is considerably smaller than the best DT. © 2020 ieee.

关键词： Wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Printed Temperature sensor Based on PEDOT: PSS-Graphene Oxide Composite

引用

ieee sensorS JOURNAL 2020年第14期20卷 7525-7531页

作者： Soni, Mahesh Bhattacharjee, Mitradip Ntagios, Markellos Dahiya, Ravinder Univ Glasgow Bendable Elect & Sensing Technol BEST Grp Glasgow G12 8QQ Lanark Scotland

Temperature sensing is an important parameter needed to be measured by the eSkin during the physical interaction of robots with real-world objects. Yet, most of the work on sensors in eSkin has focused on pressure sensing. Here we present a skin conformable printed temperature sensor with poly(3,4-ethylenedioxythiophene): poly (styr-enesulfonate) (PEDOT:PSS)-graphene oxide (GO) as a temperature sensitive layer and silver (Ag) as contact electrodes. The demonstration of PEDOT:PSS/GO as a highly temperature sensitive layer is the distinct feature of the work. The response of presented sensor observed over similar to 25 degrees C (room temperature (RT)) to 100 degrees C, by measuring the variation in resistance across the GO/PEDOT:PSS layer showed similar to 80% decrease in resistance. The sensitivity of the sensor was found to be 1.09% per degrees C. The sensor's response was also observed under static and dynamic bending (for 1000 cycles) conditions. The stable and repeatable response of sensor, in both cases, signifies strong adhesion of the layers with negligible delamination or debonding. In comparison to the commercial thermistor, the printed GO/PEDOT:PSS sensor is faster (similar to 73% superior) with response and recovery times of 18 s and 32 s respectively. Finally, the sensor was attached to a robotic hand to allow the robot to act by using temperature feedback.

关键词： Temperature sensors Temperature measurement Resistance Robot sensing systems sensor phenomena and characterization Temperature sensor eSkin printed electronics tactile skin robotics

来源：评论

学校读者我要写书评

暂无评论

IoT–based tire pressure monitoring system for air and temperature pressure using MPX5500D and LM35 sensor

IoT–based tire pressure monitoring system for air and tempe...

引用

international conference on computing, Engineering, and Design (ICCED)

作者： Agung Pangestu Iing Sodikin Muhammad Yusro Rum Sapundani Rosyid Ridlo Al Hakim Sinka Wilyanti Electrical Engineering Department Faculty of Engineering and Computer Science Jakarta Global University Depok Indonesia Department of Electronic Engineering Faculty of Engineering State University of Jakarta Jakarta Indonesia Electromedical Engineering Technology Faculty of Health Science University of Muhammadiyah Purwokerto Purwokerto Indonesia Graduate Program in Electrical Engineering Faculty of Engineering and Computer Science Jakarta Global University Depok Indonesia

ISBN: (纸本)9781665453905

Research has been carried out to monitor vehicle tires before they are used and can reduce damage, including overcoming vehicle fuel waste because air pressure is continuously monitored. This research aims to utilize the MPX5500DP sensor as an air pressure device, the LM35 sensor as a temperature reader, and a buzzer based on IoT to build a tire pressure monitoring system (TPMS). The MPX5500DP and LM35 sensor inputs to the Arduino Uno microcontroller are distributed by the NodeMCU, fitted with a Wi-Fi module. The Blynk application sends and displays the data on a smartphone using the IoT-based. Based on this research, data on the percentage of errors in monitoring air pressure and tire temperature on vehicles were obtained by comparing the data to the pressure gauge and thermometer: 1—the results of the average reading of the sensor error value. MPX5500DP air pressure against pressure gauge is 5.3%. 2—the average reading of the LM35 sensor error value on the temperature thermometer is 6.8%. With this research, the air pressure and temperature in the tires can be monitored in real-time via a smartphone using the IoT-based.

关键词： Temperature measurement Temperature distribution Thermometers Microcontrollers Tire pressure Real-time systems Pressure gauges

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：