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检索条件"任意字段=Conference on Advanced Sensor Systems and Applications X"

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Structural and Electrical Properties of Ag/SnO2 for Photovoltaic applications 11

Structural and Electrical Properties of Ag/SnO2 for Photovol...

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11th IEEE International conference on systems and Control, ICSC 2023

作者： Djeffal, F. Ferhati, H. University of Batna Laboratory of Advanced Electronic Department of Electronics Lea 05000 Algeria

ISBN: (纸本)9798350304886

In this article, optical and electrical properties of ultra-thin Ag/SnO2 structure were experimentally studied for indium-free transparent conductive oxide (In-free TCO) applications. The DC sputtering technique was used for the development of different thin-films. The impact of the silver (Ag) ultrathin top film on the TCO multilayer film, transmittance, optical properties and resistivity of the deposited tin-dioxide thin-film is analyzed by performed optoelectronic characterizations using the appropriate experimental techniques such as UV-Visible spectroscopy, xRD, and Hall effect measurements. The proposed bilayer structure shows a good transparency of 62% over the visible spectral range. Through experimental analysis, it is found that the recorded optoelectronic haacke's Figure-of-Merit (FoM=Transmittance at (550nm) / sheet resistance) of the proposed Ag/SnO2 TCO layer is 6.7x10-4 Ω-1, while the FoM of the conventional ultrathin SnO2 is 2x10-6 Ω-1, with an increasing of more than 95% at almost the same layer thickness (50 nm), Consequently, this contribution can provide new insights to boost the optoelectronic properties of transparent electrodes for high-performance and eco-friendly solar cells for photovoltaic applications. © 2023 IEEE.

关键词： Transparent electrodes

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Development, Optimization, and Deployment of Thermal Forward Vision systems for Advance Vehicular applications on Edge Devices 15

Development, Optimization, and Deployment of Thermal Forward...

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15th International conference on Machine Vision (ICMV)

作者： Farooq, Muhammad Ali Shariff, Waseem Khan, Faisal Corcoran, Peter Univ Galway Galway Ireland

ISBN: (纸本)9781510666184;9781510666191

In this research work, we have proposed a thermal tiny-YOLO multi-class object detection (TTYMOD) system as a smart forward sensing system that should remain effective in all weather and harsh environmental conditions using an end-to-end YOLO deep learning framework. It provides enhanced safety and improved awareness features for driver assistance. The system is trained on large-scale thermal public datasets as well as newly gathered novel open-sourced dataset comprising of more than 35,000 distinct thermal frames. For optimal training and convergence of YOLO-v5 tiny network variant on thermal data, we have employed different optimizers which include stochastic decent gradient (SGD), Adam, and its variant AdamW which has an improved implementation of weight decay. The performance of thermally tuned tiny architecture is further evaluated on the public as well as locally gathered test data in diversified and challenging weather and environmental conditions. The efficacy of a thermally tuned nano network is quantified using various qualitative metrics which include mean average precision, frames per second rate, and average inference time. Experimental outcomes show that the network achieved the best mAP of 56.4% with an average inference time/ frame of 4 milliseconds. The study further incorporates the optimization of tiny network variant using the TensorFlow Lite quantization tool which is beneficial for the deployment of deep learning architectures on the edge and mobile devices. For this study, we have used a raspberry pi 4 computing board for evaluating the real-time feasibility performance of an optimized version of the thermal object detection network for the automotive sensor suite. The source code, trained and optimized models and complete validation/ testing results are publicly available at https://***/MAliFarooq/Thermal-YOLO-And-Model-Optimization-Using-TensorFlowLite.

关键词： Thermal Vison Object Detection Optimization LWIR Deep Learning advanced Driver Assistance systems

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Comparison between three scanning systems for laser cutting of airbag materials

Comparison between three scanning systems for laser cutting ...

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conference on Laser plus Photonics for advanced Manufacturing

作者： Dogaru, Nicolae Negrei, Pavel-Bogdan Duma, Virgil-Florin SC Autoliv Romania SRL Str Tapiei 61 Lugoj 305500 Romania Polytech Univ Timisoara Doctoral Sch 1 Mihai Viteazu Ave Timisoara 300222 Romania Aurel Vlaicu Univ Arad Optomechatron Grp 3OM 2 Elena Dragoi Str Arad 310177 Romania Polytech Univ Timisoara Fac Elect Telecommun & Informat Technol 2 Vasile Parvan Ave Timisoara 300223 Romania Natl Univ Sci & Technol POLITEHN Bucharest Ctr Res & Dev Mechatron Splaiul Independentei 313Sect 6 Bucharest Romania

ISBN: (纸本)9781510673298;9781510673281

The automotive industry has made significant advancements regarding the safety technology. Thus, airbags play a pivotal role in protecting passengers during accidents, therefore saving more lives and preventing serious injuries. The precise cutting of airbag textile materials is one of the important processes for ensuring the effectiveness and performance of such equipment. The aim of this work is to compare three scanning systems employed for CO2 laser cutting of airbag textile material in the automotive sector: (i) a plotter system (i.e., a traditional laser cutting setup with a fixed cutting bed);(ii) fixed 2D galvanometer scanner (GS), commonly used for laser cutting in more compact and automated environments;(iii) mobile 2D GS, with an increased portability and versatility in comparison to the previous solution. These three systems have been installed and utilized in an industrial facility for airbags manufacturing. The pecific parameters, advantages and drawbacks of each system for the targeted application have been compared. Examples of applications are presented - from the industrial site where we have implemented and utilized them. The choice between different such scanning systems for laser manufacturing are discussed in the trade-off between quality and productivity.

关键词： laser scanning CO2 laser airbag textiles automotive industry laser cutting industrial process laser manufacturing galvanometer scanner 2D scanning plotter system

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Mechanical modeling of a ring-type fingertip force sensor

Mechanical modeling of a ring-type fingertip force sensor

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2022 IEEE International conference on systems, Man, and Cybernetics, SMC 2022

作者： Okuyama, Takeshi Sugimoto, Tomoki Tanaka, Mami Tohoku University Department of Robotics Miyagi Japan Tohoku University Graduate School of Biomedical Engineering Miyagi Japan

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

In this paper, focusing on the relationship between tendon tension and force applied to the fingertip, we develop a ring-type sensor that estimate fingertip force from tendon tension. To establish the measurement principle, mechanical models were constructed in three steps for sensor structure, deformation of finger and sensor, and finger kinematics. By integrating those mechanical relationships, we derived a relation expression between ring-type sensor output and fingertip force. In addition, we conducted experiments in which the developed ring-type fingertip force sensor was attached to the index finger and force was applied to the fingertip. As the results, it was confirmed that the fingertip force could be estimated by using the derived relational expressions both when the finger was extended and when it was bent. The results showed the validity of the mechanical models and the effectiveness of the sensor. These results provide important insights that will be useful for future sensor design and applications. © 2022 IEEE.

关键词： Tendons

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Migrating Deep Learning Data and applications among Kubernetes Edge Nodes 23

Migrating Deep Learning Data and Applications among Kubernet...

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23rd IEEE International conference on High Performance Computing and Communications, 7th IEEE International conference on Data Science and systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Mangkhangcharoen, Suchanat Haga, Jason Rattanatamrong, Prapaporn Thammasat University Department of Computer Science Pathum Thani Thailand National Institute of Advanced Industrial Science and Technology Tsukuba Japan

ISBN: (纸本)9781665494571

Many current IoT applications deployed at the edge use deep learning (DL) in their real-time processing and analytics. Not only inference but also training is moving to edge devices. DL application and dataset migration among these devices are mandatory for scenarios like node failure, user mobility or when nodes need to collaborate (e.g., distributed training). Container technologies and Kubernetes (K8s) are being increasingly adopted to manage infrastructure at the edge. Unfortunately, there is no built-in mechanism in K8s to support migration of stateful containers between its cluster nodes. The K8s cluster's master node generally launches a new fresh container in another node to replace the failed one. While there is an existing mechanism for migrating a Pod between K8s nodes, there is no past work investigating the migration of DL datasets and containerized DL applications among K8s cluster nodes. In this paper, we present our 1) comprehensive study on the effectiveness and limitations of existing checkpointing mechanisms for containerized DL applications and 2) our comparative performance study of several approaches in migrating DL datasets and applications in a K8s cluster. Our results show that migrating states of DL applications and restoring them from their previous states enables faster recovery (reducing training time by 10 to 73 percent) than re-running these models from the beginning regardless of the percentage of epochs that have completed. Additionally, our experimental results show that transferring a dataset between K8s workers using the K8s persistent volume with kubectl cp is generally suitable and efficient. However, when network latency is high, using our customized middleware with a feedback controller to migrate data in parallel can speed up total migration time compared to the K8s's persistent volume approach alone. © 2021 IEEE.

关键词： Training Deep learning Checkpointing Computers Containers Real-time systems Internet of Things

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Gideon-TS: Efficient Exploration and Labeling of Multivariate Industrial sensor Data 24

Gideon-TS: Efficient Exploration and Labeling of Multivariat...

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24th International conference on Enterprise Information systems (ICEIS)

作者： Langer, Tristan Welbers, Viktor Meisen, Tobias Univ Wuppertal Chair Technol & Management Digital Transformat Lise Meitner Str 27-31 D-42119 Wuppertal Germany

ISBN: (纸本)9789897585692

Modern digitization in industrial production requires the acquisition of process data that is subsequently used in analysis and optimization scenarios. For this purpose, the use of machine learning methods has become more and more established in recent years. However, training advanced machine learning models from scratch requires a lot of labeled data. The creation of such labeled data is a major challenge for many companies, as the generation process cannot be fully automated and is therefore very time-consuming and expensive. Thus, the need for corresponding software tools to label complex data streams, such as sensor data, is steadily increasing. Existing contributions are not designed for handling large datasets and forms common for industrial applications, and offer little support for the labeling of large data volumes. For this reason, we introduce Gideon-TS - an interactive labeling tool for sensor data that is tailored to the needs of industrial use. Gideon-TS can integrate time series datasets in multiple modalities (univariate, multivariate, samples, with and without timestamp) and remains performant even with large datasets. We also present an approach to semi-automatic labeling that reduces the time needed to label large volumes of data. We evaluated Gideon-TS on an industrial exemplary use case by conducting performance tests and a user study to show that it is suitable for labeling large datasets and significantly reduces labeling time compared to traditional labeling methods.

关键词： Labeling Time Series Analysis sensor Data Visual Analytics

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TiO2-Coated Optical Fibers for Distributed pH Monitoring at High Pressures and Temperatures 2

TiO2-Coated Optical Fibers for Distributed pH Monitoring at ...

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conference on Optical Waveguide and Laser sensors II

作者： Shumski, Alexander Diemler, Nathan Wuenschell, Jeffrey Ohodnicki, Paul Wright, Ruishu Natl Energy Technol Lab 626 Cochran Mill Rd Pittsburgh PA 15236 USA NETL Support Contractor 626 Cochran Mill Rd Pittsburgh PA 15236 USA Univ Pittsburgh Dept Mech Engn & Mat Sci Pittsburgh PA 15261 USA Univ Pittsburgh Dept Elect & Comp Engn Pittsburgh PA 15261 USA Univ Pittsburgh Dept Phys & Astron Pittsburgh PA 15261 USA

ISBN: (纸本)9781510661783;9781510661790

In the oil and gas, CO2 sequestration, H-2 subsurface storage, and geothermal energy sectors, subsurface pH measurements are critical for monitoring the geochemical conditions and structural integrity of wellbore systems. Real-time pH measurements in these conditions are vital for detecting and predicting corrosion deterioration of wellbore components that may jeopardize the safety and continued operation of wellbore systems. The viability of TiO2-coated optical fibers has previously been demonstrated as an effective sensor design for continuous distributed pH monitoring at elevated temperatures and ambient pressures. However, real wellbore conditions contain high pressures and the effects of high pressures on sensor results and the sensing layer have not been well studied. As TiO2 has been established in the literature as being stable at temperatures and pressures substantially higher than those expected in typical wellbore conditions, it makes for a promising sensing material for applications requiring high-pressure, high-temperature (HPHT) conditions. In this study, a sol-gel deposition method is used to coat the optical fiber sensors with TiO2 sensing layer. The sensor performance was measured using optical transmission measurements at various pH and using optical backscatter reflectometry for distributed pH sensing demonstration in wellbore-relevant pressures (up to 1000 psi) and temperatures (similar to 80 degrees C). The TiO2 sensing layer was characterized using scanning electron microscopy (SEM) and full spectrum UVVis-NIR transmission data for a planar substrate. The TiO2-coated optical fiber sensor is tested for any pressure-derived effects and the viability of this sensor design for real-time in-situ wellbore pH monitoring is discussed.

关键词： High pressure high temperature TiO2 thin film pH sensor optical fiber sensor sol-gel coating wellbore monitoring distributed sensing

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Enabling the next generation of Keck AO with the real time controller upgrade 9

Enabling the next generation of Keck AO with the real time c...

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conference on Adaptive Optics systems Ix

作者： Marin, Eduardo Chin, Jason C. Y. Cetre, Sylvain Wizinowich, Peter Ragland, Sam Wetherell, Ed Surendran, Avinash Bouchez, Antonin Delorme, Jacques-Robert Lilley, Scott Lyke, James Service, Maxwell Tsubota, Kevin Correia, Carlos van Dam, Marcos Biasi, Roberto Pataunar, Christian Pescoller, Dietrich Glazebrook, Karl Jameson, Andrew Gauvin, Will Rigaut, Francois Gratadour, Damien Bernard, Julien WM Keck Observ Waimea HI 96743 USA Wakea Consulting Grenoble France Space Odt Porto Portugal Flat Wavefronts Christchurch New Zealand Microgate Srl Bolzano Italy Swinburne Univ Technol Melbourne Vic Australia Australian Natl Univ Canberra ACT Australia Univ PSL Sorbonne Univ Univ Paris Cite LESIAObervatorie Paris Paris France

ISBN: (纸本)9781510675186;9781510675179

The Real Time Controllers (RTCs) for the W. M. Keck Observatory Adaptive Optics (AO) systems have been upgraded from a Field Programmable Gate Array (FPGA) to a Graphics Processing Unit (GPU) based solution. The previous RTCs, operating since 2007, had reached their limitations after upgrades to support new hardware including an Infra-Red (IR) Tip/Tilt (TT) Wave Front sensor (WFS) on Keck I and a Pyramid WFS on Keck II. The new RTC, fabricated by a Microgate-led consortium with SUT leading the computation engine development, provides a flexible platform that improves processing bandwidth and allows for easier integration with new hardware and control algorithms. Along with the new GPU-based RTC, the upgrade includes a new hardware Interface Module (IM), new OCAM2K EMCCD cameras, and a new Telemetry Recording Server (TRS). The first system upgrade to take advantage of the new RTC is the Keck I All-sky Precision Adaptive Optics (KAPA) Laser Tomography AO (LTAO) system, which uses the larger and more sensitive OCAM2K EMCCD camera, tomographic reconstruction from four Laser Guide Stars (LGS), and improvements to the IR TT WFS. On Keck II the new RTC will enable a new higher-order Deformable Mirror (DM) as part of the HAKA (High order advanced Keck Adaptive optics) project, which will also use an EMCCD camera. In the future, the new RTC will allow the possibility for new developments such as the proposed 'IWA (Infrared Wavefront sensor Adaptive optics) system. The new RTC saw first light in 2021. The Keck I system was released for science observations in late 2023, with the Keck II system released for science in early 2024.

关键词： Adaptive Optics Real Time Controller Wavefront sensor Keck

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IoT-Based Safety Monitoring and Alerting System for Coal Mines

IoT-Based Safety Monitoring and Alerting System for Coal Min...

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Intelligent systems and advanced applications (ICISAA), International conference on

作者： Rajashree Bhokare Anshu Sharma Pankaj Kumar Akanksha Tayade Dipali Wani Rupali Chaudhari Department of Electrical Engineering DIT Pimpri Pune

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

Coal mining is the process of extracting coal from the surface of the earth. Many skilled workers and labourers have died because of there is no early warning system in traditional coal mines and which causes more coal mine accidents. Therefore, in present generation the major challenge is the safety of the mining workers. Thus, the mine safety system should be in such a way that it can provide an immediate alert before any disaster happens. This paper proposes a system which enhance the safety of workers in underground mines by interfacing the different sensors with the help of IOT module. After collecting all the sensor values, they are transmitted to the Arduino, which generates an alert based on the threshold value. This helps the workers recognize that there is a potential problem ahead. For this purpose, a cost-effective IOT-based coal mining and alerting system is immensely helpful for coal mine employees and anyone who is working the coal mine industry.

关键词： Wireless sensor networks Wireless networks Surveillance Robot sensing systems Software sensor systems Coal mining Fuel processing industries Servers Surface treatment

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Internet of Things-Based Drone Case Study for Atmospheric Data Collection

Internet of Things-Based Drone Case Study for Atmospheric Da...

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International conference on advanced Engineering, Technology and applications, ICAETA 2024

作者： Özer, Muhammed Mirac Unmanned/Intelligent Systems Lab Faculty of Aeronautics and Astronautics Tarsus University Mersin33400 Turkey

ISBN: (纸本)9783031709234

Although ground-based monitoring, manned aircrafts and satellites are used for atmospheric measurements, rapid and comprehensive data collection is not always possible near pollution sources due to the complexity of the areas to be measured, moving sources or physical obstacles. Therefore, drone solutions equipped with different sensors offer new approaches and research opportunities in air pollution and emissions monitoring, as well as on-site air quality monitoring to study atmospheric trends such as climate change. While the potential of unmanned aerial vehicles for air quality research has been identified, several challenges still need to be addressed, including flight durability, payload capacity, sensor consistency/accuracy and sensitivity. In this study, a rotary-wing drone architecture that utilizes artificial intelligence (AI), computer vision algorithms, as well as 4G-LTE IoT-based monitoring to collect atmospheric data, is presented. Designed as a new scalable platform to accommodate sensors suitable for flight control and communication requirements, the system has the ability to take off, fly and land with full autonomy. In case of possible communication interruption, it can switch to the fail- safe mode set in the ground control station. In addition to being able to make atmospheric measurements, a design has been developed in such a way that wide variety of parameters such as urban planning and development of smart cities, monitoring of industrial emissions, determination of pollution points in the city, redirection and management of traffic can be monitored in real time. The designed drone has a small, lightweight and low- cost multi-sensor system capable of measuring temperature, humidity, pressure, air velocity, noise, amount of UV, light intensity, PM2.5, PM10. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Drones

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