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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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Knowledge-Enhanced Interactive Matching Network for Multi-turn Response Selection in Medical Dialogue systems 27th

Knowledge-Enhanced Interactive Matching Network for Multi-tu...

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27th International conference on Database systems for advanced applications (DASFAA)

作者： Zhu, Ying Feng, Shi Wang, Daling Zhang, Yifei Han, Donghong Northeastern Univ Sch Comp Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9783031001291;9783031001284

Recently, the response selection for retrieval-based dialogue systems has gained enormous attention from both academic and industrial communities. Although the previous methods achieve promising results for intelligent customer service systems and open-domain chatbots, the response selection in medical dialogues suffers from lower performance because of the strong dependency on the domain knowledge. In this paper, we construct two specialized medical knowledge bases and propose a Knowledge-enhanced Interactive Matching Network (KIMN) for multi-turn response selection in medical dialogue systems. Compared with previous response selection approaches, the KIMN adopts pre-trained language model to alleviate the limited training data problem, and incorporates internal and external medical domain knowledge to perform interactive matching between responses and contexts. The experiments on a real-world medical dialogue dataset show that our proposed model consistently outperforms the strong baseline methods by large margins, which shows that our proposed model can retrieve more accurate responses for medical dialogue systems.

关键词： Multi-turn conversation Knowledge-enhanced matching Medical dialogue systems

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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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Research advanced in the visual SLAM methods under indoor environment 2

Research advanced in the visual SLAM methods under indoor en...

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2nd International conference on Computing Innovation and Applied Physics, CONF-CIAP 2023

作者： Lu, Zhengsheng Wang, Jinchang Zhang, Zelin Zhu, Conghua Virginia Polytechnic Institute and State University 24060 United States Shangqiu Normal University 476000 China Tongji University 201804 China University of Nottingham NG7 2QL United Kingdom

Simultaneous localization and mapping (SLAM) have been a hotspot study topic in the computer vision community these days, which aims to locate the agent's position and perceive the surrounding environment to build a map. In Visual SLAM, the camera is the external sensor used, creating an environment map while locating itself. Compared with radar and other rangefinders, the visual sensor is usually tiny and has low power consumption, which has been the mainstream framework in SLAM research task. This paper starts from the system overview of visual SLAM, introduces the landmark achievements and research hotspots of visual SLAM, and discusses the key issues and three research methods based on visual SLAM in indoor environment. Meanwhile, the applications of visual SLAM in dynamic scenes and large indoor environments are listed. Finally, the trend of its future development is prospected. © Published under licence by IOP Publishing Ltd.

关键词： Indoor positioning systems

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In vivo brain tumor classification using fiber endoscopy 22

In vivo brain tumor classification using fiber endoscopy

引用

conference on advanced Biomedical and Clinical Diagnostic and Surgical Guidance systems XXII

作者： Dremel, J. Wang, T. Richter, S. Uckermann, O. Polanski, W. Eyuepoglu, I. Czarske, J. Kuschmierz, R. Tech Univ Dresden Lab Measurement & Sensor Syst Tech Dresden Germany Tech Univ Dresden BIOLAS Biomed Computat Laser Syst Dresden Germany Tech Univ Dresden EKFZ Else Kroner Fresenius Zentrum Digital Hlth Dresden Germany Tech Univ Dresden Univ Hosp Carl Gustav Carus Dept Neurosurg Dresden Germany Tech Univ Dresden Fac Med Div Med Biol Dept Psychiat Dresden Germany Tech Univ Dresden Univ Hosp Carl Gustav Carus Dresden Germany

ISBN: (纸本)9781510669222;9781510669215

The classification of brain tissue plays a key role in brain tumor diagnosis and treatment. It revolves around post-surgical histochemical staining, which is often time consuming and delays follow up treatment. Identifying tumor boarders during tumor resection is essential for an efficient therapy minimizing removed healthy tissue and maximizing removed tumor tissue. The different approaches in use are either expensive and time consuming or limited to certain tumor types. We propose a real-time in vivo label free classification approach, applicable for both demands. Based on autofluorescence properties, a label-free differentiation between tissue types is possible. Therefore, a multicore fiber (MCF) based endoscope is designed to fit into biopsy needles used during diagnosis and to be used as a handheld probe during tumor resection. It allows illuminating and imaging through the same MCF, minimizing the endoscope to a submillimeter diameter. Currently, autofluorescence images are not used in pathology. Thus, medical doctors cannot interpret them. We use a neural network for diagnosis, bridging this gap. One problem with neural networks in medical applications is data availability for training. Different techniques are investigated to maximize the classification performance with a limited training dataset. Cascaded neural networks in combination with digital twins improve the results while lowering the needed training dataset size. The preliminary data indicates that our technology might lead to a paradigm shift in brain tumor diagnosis and therapy due to the accurate result, the versatile design, and being low-cost.

关键词： tissue classification lensless endoscopy fiber bundle imaging deep learning

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ROS Powered Autonomous Mobile Robot for Indoor applications

ROS Powered Autonomous Mobile Robot for Indoor Applications

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Intelligent Algorithms for Computational Intelligence systems (IACIS), International conference on

作者： Madan H T Mahesh L Mohammed Sajjad Department of EC (Advanced Communication Technology) NMAM Institute of Technology (NMAMIT) Nitte (Deemed to be University) Nitte India School of Electronics and Communication Engineering REVA University Bangalore India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

In the realm of indoor robotics, navigation poses a critical challenge across service robots, humanoids, and warehouse automation. Existing techniques like line following, RFID tracking, and Aruco marker-based systems exhibit limitations in autonomy and adaptability. This has created a need for the development of an advanced and sophisticated autonomous navigation system that encompasses self-reliant perception, planning, and control capabilities. The proposed system makes use of LiDAR technology for mapping and Robot operating system for planning and control which takes place on Jetson Nano GPU platform, providing real-time processing power for environmental data. LiDAR sensors offer accurate environmental perception, obstacle detection, and adaptability to dynamic indoor settings. The system employs BLDC wheels and an aluminium base for versatility, supporting loads up to 120 kg. Additionally, it incorporates an IMU sensor with EKF sensor fusion for accurately estimating the robot's position. This flexible design has applications in various indoor robotics scenarios, ranging from complex service tasks to efficient warehouse operations. This versatile design finds applications in various indoor robotics scenarios, from complex service tasks to efficient warehouse operations.

关键词： Laser radar Navigation sensor fusion Robot sensing systems sensor systems Planning Indoor environment Mobile robots Robots Autonomous robots

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sensor fusion for functional safety of autonomous mobile robots in urban and industrial environments 27

Sensor fusion for functional safety of autonomous mobile rob...

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IEEE 27th International conference on Emerging Technologies and Factory Automation (ETFA)

作者： Wunderle, Yannick Lyczkowski, Eike SEW EURODRIVE GmbH & Co KG Res Technol Bruchsal Germany

ISBN: (数字)9781665499965

ISBN: (纸本)9781665499965

To enhance productivity, assist workers and to have a more flexible material flow, autonomous mobile robots (AMRs) are used in industrial applications. To guarantee the safety of workers, most AMRs are equipped with safety laser scanners and stop if a person enters the protective field. To leverage the benefits of AMRs outdoors and in urban logistics, several new challenges arise. Severe weather conditions like rain, fog, snow, low lighting and direct sunlight restrict the capabilities of cameras in the visible spectrum, thermal cameras, laser scanners and time of flight cameras. Besides the safe detection of persons, the distance from the robot to the person has to be measured or estimated to provide a classification if the person intrudes the protective field of the AMR. The state of the art pedestrian detectors are based on neural networks and designed for advanced driver assistance systems. Therefore, most datasets for training the networks are acquired from the rooftop of cars and do not reflect the perspective and environment of AMRs. Our study reviews the state of the art sensors and pedestrian detection methods and shows the benefit of sensor fusion technologies based on artificial intelligence. Further, we discuss the limitations of these methods for industrial outdoor and urban AMR safety applications and propose methods to overcome these drawbacks.

关键词： functional safety sensor fusion AI for safety AMRs pedestrian detection

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Proceedings - 3rd International conference on Smart Technologies, Communication and Robotics 2023, STCR 2023

Proceedings - 3rd International Conference on Smart Technolo...

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3rd International conference on Smart Technologies, Communication and Robotics, STCR 2023

ISBN: (纸本)9798350370867

The proceedings contain 82 papers. The topics discussed include: advanced malware analysis and prevention;design and analysis of Vivaldi antenna by an iterative method;EGFD-AROMA-based pattern forecasting with crime rate level identification;early detection and prognosis of brain tumor from micro array gene data using machine learning classifiers;handwritten digit recognition using fine-tuned convolutional neural network model;design of high gain 5G millimeter wave micro strip patch antenna for wireless applications;using machine learning regression model to predict the optimum election algorithm for parallel and distributed computing systems;a design thinking approach of metaheuristic empowerment for energy-efficient and optimized routing protocol in IoT-enabled wireless sensor networks;and an overview on IoT architecture application layer and security threats.

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