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检索条件"任意字段=IEEE International Conference on Distributed Computing in Sensor Systems"

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Towards a One-Stop-Shop Solution for the Application of Data-Driven Value-Adding Services in Production

Towards a One-Stop-Shop Solution for the Application of Data...

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international conference on Emerging Technologies and Factory Automation (ETFA)

作者： Magnus Redeker Dennis Quirin Rafael Schroeder Tobias Klausmann Alexander Löwen Alexander Wollbrink Heiko Stichweh Simon Althoff Amelie Bender Walter Sextro Marc Hesse Fraunhofer IOSB IOSB-INA Lemgo Fraunhofer Institute of Optronics System Technologies and Image Exploitation Bielefeld University Cognitronics & Sensor Systems Friedrich Remmert GmbH R & D Systems Engineering - Lenze SE Faculty of Mechanical Engineering Chair of Dynamics and Mechatronics Paderborn University Paderborn Germany Weidmüller Group

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

The holistic application of data-driven value-adding services (DDSs) to shop-floor assets (SFAs; e.g. equipment, machines, components) is a major obstacle, particularly for small and medium-sized enterprises (SMEs): Due to the high complexity and required expertise in various disciplines. It is necessary to i) select suitable DDSs for a relevant component in production, ii) provide data for their training, iii) deploy the DDSs in the given IT/OT infrastructure, and iv) setup data-streams between SFAs, DDSs, and dashboards. Currently, many companies are implementing flagship projects that go through the necessary steps for individual components, but these isolated solutions are not scalable due to the need for manual intervention. This paper describes how to completely automatize the four steps and their consolidation in a scalable Industry 4.0 one-stop-shop solution. To achieve this goal, each asset is equipped with an Asset Administration Shell (AAS) that specifies, in particular, the represented asset's capabilities, the data required to apply such a capability and the data generated by a capability. Based on the AASs of SFAs, DDSs, and computing resources (CRs), a matching service suggests automatically which DDSs would add value to which SFAs and, for each SFA-DDS match, in which CR to deploy the DDS. A trade fair demonstration provides strong support for the statements made: The one-stop-shop solution including the automatic matching greatly simplifies the application of data-driven services and, in some cases, even makes it possible at all.

关键词： Training Space vehicles Production systems Remotely guided vehicles Semantics sensors Laser beam cutting Streams Space stations Monitoring

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Harnessing Blockchain and Deep Learning for Enhanced Energy Analytics in Photovoltaic systems

Harnessing Blockchain and Deep Learning for Enhanced Energy ...

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ieee international conference on System, Computation, Automation and Networking (ICSCAN)

作者： Subbulakshmi G Sujatha S Saraswathi V Ranganathan Rani Hemamlini Department of Artificial Intelligence and Data Science Engineering St Joseph's College of Engineering and Technology Chennai India Department of Computer Appication BIT Campus Anna University Tiruchirappalli India Department of Computer Science Engineering Chennai Institute of Technology Chennai India Department of Electrical And Elctronics Engineering St. Peter's Institute of Higher Education And Research Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

The rapid evolution of solar energy in industrial sectors has sparked a growing interest in renewable energy, particularly within the context of smart grids and systems. The effective detection and management of energies within photovoltaic (PV) systems stand as crucial tasks in this landscape. This abstract introduces a proposed model aimed at exploring the integration of blockchain networks within large-scale solar energy infrastructures. The primary objective is to establish a framework that enables the distribution of solar energy in a decentralized manner to end-users while ensuring secure energy transactions between producers and consumers. Blockchain technology serves as the cornerstone of this model, encompassing intelligent techniques to address energy mitigation challenges within the solar energy network. By leveraging blockchain, the detection of energies within solar power plants becomes shared knowledge accessible to all users connected to the solar power network. The research within this framework emphasizes the exploration and application of diverse Deep Learning (DL) architectures, including autoencoders, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Deep Stacking Networks (DSNs), and Deep Belief Networks (DBNs). Furthermore, this research extends beyond conventional machine learning approaches, incorporating knowledge-driven methodologies. It incorporates generative models such as adversarial networks and transfer learning, elevating the scope of machine learning in optimizing energy detection, distribution, and consumption within the block chain-based solar energy infrastructure. This interdisciplinary approach signifies a significant stride in fortifying renewable energy systems. By amalgamating blockchain's decentralized nature with sophisticated DL architectures and knowledge-driven models, it establishes a resilient and efficient renewable energy infrastructure capable of meeting escalating demands for sustainabl

关键词： Deep learning Photovoltaic systems Renewable energy sources Heuristic algorithms Solar energy sensor systems Blockchains sensors Peer-to-peer computing Long short term memory

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Optimizing 3D Object Detection for Embedded systems in Automated Vehicles Using sensor Data Fusion and CUDA computing

Optimizing 3D Object Detection for Embedded Systems in Autom...

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ieee international conference on Intelligent Computer Communication and Processing (ICCP)

作者： Topi Miekkala Matti Kutila Mathias Schneider Alfred Höß VTT Techincal Research Centre of Finland Ltd. Tampere Finland Technical University of Applied Sciences Amberg-Weiden Germany

ISBN: (纸本)9781665464383

This article explores the utilization of the processing power of GPUs using CUDA computation for real-time aggregation of multi-sensor data and detection of 3D objects using parallel clustering algorithms. The purpose is to implement an algorithm that fuses raw lidar point cloud data and 2D camera image object detections to produce 3D object clusters in a lidar point cloud. Most of the computation has been implemented using CUDA parallelism to investigate the capability of GPU devices in this task, which is a common challenge in automated driving. The results indicate that processing times can be optimized within the algorithm, which is crucial when considering the large amounts of data provided by lidar and camera-based systems. The algorithm can perform inference on the Jetson Xavier AGX at rates of $\sim$20 to $\sim$220 ms depending on the number of objects and their corresponding point amounts in the KITTI dataset.

关键词： Point cloud compression Performance evaluation Three-dimensional displays Laser radar Graphics processing units Clustering algorithms Object detection

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Exploring Model Compression Techniques for Efficient 1D CNN-Based Hand Gesture Recognition on Resource-Constrained Edge Devices

Exploring Model Compression Techniques for Efficient 1D CNN-...

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international conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Mahdi Mnif Salwa Sahnoun Mahdi Djemaa Ahmed Fakhfakh Olfa Kanoun National School of Electronics and Telecommunications of Sfax University of Sfax Tunisia Laboratory of Signals Systems Artificial Intelligence and Networks (SM@RTS) Digital Research Center of Sfax (CRNS) Tunisia Measurements and Sensor Technology Faculty of Electrical Engineering and Information Technology Chemnitz University of Technology Germany

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Tiny Machine Learning is undergoing rapid evolution in the context of edge computing and intelligent Internet of Things (IoT) devices. This paper investigates the potential of model compression techniques for enhancing energy efficiency within IoT edge devices, as an application, hand gesture recognition based on Electrical Impedance Tomography data was used. To achieve efficient and accurate machine learning on battery-powered devices, three model compression techniques: Global pruning, Knowledge Distillation (KD), and quantization were investigated and applied on a 1D convolutional neural network. The global pruning technique resulted in a slight improvement in test data accuracy while maintaining the model size at 284.45 kB, though it increased the model’s sparsity. With KD, the model size significantly decreased to 75.042 kB, with negligible impact on accuracy. In contrast, 8-bit quantization reduced the model size to 78.434 kB, but this came at the cost of a substantial 13.75% decrease in accuracy. Each model compression technique contributes to reducing model size or increasing sparsity. This is particularly beneficial for deploying deep learning models on resource-constrained IoT devices.

关键词： Electrical impedance tomography Accuracy Image coding Quantization (signal) Computational modeling Tiny machine learning Image edge detection

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Improvement of Voltage Profile and Loss Reduction Based on Optimal Placement and Sizing of Renewable distributed Generations Using 4-Rule Harmony Search Algorithm 6th

Improvement of Voltage Profile and Loss Reduction Based on O...

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6th international conference on Harmony Search, Soft computing and Applications, ICHSA 2020

作者： Sadollah, Ali Nasir, Mohammad Al-Shamiri, Abobakr Khalil Kim, Joong Hoon Department of Mechanical Engineering University of Science and Culture Tehran Iran Materials and Energy Research Center Dezful Branch Islamic Azad University Dezful Iran Research Institute for Mega Construction Korea University Seoul Korea Republic of School of Civil Environmental and Architectural Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9789811586026

distributed generation (DG) resources based on solar and wind energies are among the DGs that can optimize the voltage profile and reduce network losses by optimal locating and sizing in distribution systems. In this paper, 4-Rule Harmony Search (4-RHS) algorithm is presented for optimal placement and sizing of the renewable DG resources. In this regard, installation of the DG is performed by loss sensitivity factors first in the most candidate buses. Then, the 4-RHS is employed for placement and sizing of DGs from the selected buses. In 4-RHS, an extra rule is added to standard HS. The 4-RHS is studied on ieee 69-bus distribution system. The results comparison of the 4-RHS algorithm with the standard HS algorithm and other optimization methods in the literature shows the efficiency and effectiveness of the 4-RHS in finding the optimal location and size of renewable DG resources and subsequently reducing losses and improving voltage profiles. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： distributed power generation

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splitDyn: Federated Split Neural Network for distributed Edge AI Applications

splitDyn: Federated Split Neural Network for Distributed Edg...

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ieee international conference on Big Data

作者： Tran Anh Khoa Do-Van Nguyen Minh-Son Dao Koji Zettsu Big Data Integration Research Center National Institute of Information and Communications Technology Tokyo Japan

ISBN: (纸本)9781665480468

Split learning (SL) is a popular distributed machine learning (ML) method used to enable ML. It divides a neural network based model into subnetworks. Then, it separately trains the subnetworks on distributed parties (e.g., client and server). In distributed ML, data are generated and collected on the client-side. In contrast, the collected data are processed using an application deployed on the server side. However, when applied in practice using Internet of things systems and clients, numerous obstacles occur because of limited configuration and resources. Dividing neural networks in the SL is the biggest problem and an open question in numerous studies. This study introduces splitDyn, which is a new dynamic SL solution to solve the aforementioned problems. This method provides a solution for eliminating their inherent drawbacks. The main idea is to apply a Round-Robin schedule to select the client for the training process. Then, the next idea is to use the Hungarian optimization algorithm to assign a layer to a client and enhance the accuracy. The proposed method reasonably achieved better accuracy and reduced processing time than the other learning models. Furthermore, it applies the incident datasets to predict the incident event and in edge computing for edge artificial intelligence (AI) applications.

关键词： Training Temperature distribution Schedules Neural networks Big Data Prediction algorithms Servers

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distributed-To-Centralized Data Management through Blockchain Technologies in Large-Scale IoT Networks of Multicampus University 22

Distributed-To-Centralized Data Management through Blockchai...

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22nd ieee international conference on High Performance computing and Communications, 18th ieee international conference on Smart City and 6th ieee international conference on Data Science and systems, HPCC-SmartCity-DSS 2020

作者： Sinaeepourfard, Amir Dorri, Ali Department of Computer Science Trondheim Norway School of Computer Science Brisbane Australia

ISBN: (纸本)9781728176499

Traditionally, blockchain technologies focus on data security and privacy issues in different business domains of smart cities. However, we believe that blockchain technologies can be beneficial for data and resource management in largescale internet of things (IoT) networks of smart cities. Blockchain technologies and their related consensus algorithms and miners may apply through multilevel information communication technology (ICT) architecture in large-scale IoT networks of smart cities to organize the city-data and ICT resources. This position paper addresses an initial idea about the contribution of different smart cities and ICT concepts, including 'blockchain technologies,' 'distributed-To-centralized ICT architecture (D2C-ICT),' 'distributed-To-centralized data management (D2CDM),' and 'smart university.' We first present our D2C-ICT architecture in the smart university context using different distributed and centralized technologies, including cloudlet and cloud technologies. Finally, we show that the facilities of blockchain technologies for data and resource management through D2C-ICT architecture in large-scale IoT networks of smart cities. © 2020 ieee.

关键词： Information management

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Cost-optimal V2X Service Placement in distributed Cloud/Edge Environment 16

Cost-optimal V2X Service Placement in Distributed Cloud/Edge...

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16th international conference on Wireless and Mobile computing, Networking and Communications (ieee WiMob)

作者： Moubayed, Abdallah Shami, Abdallah Heidari, Parisa Larabi, Adel Brunner, Richard Western Univ London ON Canada Ericsson Montreal PQ Canada Edge Grav Ericsson Montreal PQ Canada

ISBN: (纸本)9781728197227

Deploying V2X services has become a challenging task. This is mainly due to the fact that such services have strict latency requirements. To meet these requirements, one potential solution is adopting mobile edge computing (MEC). However, this presents new challenges including how to find a cost efficient placement that meets other requirements such as latency. In this work, the problem of cost-optimal V2X service placement (CO-VSP) in a distributed cloud/edge environment is formulated. Additionally, a cost-focused delay-aware V2X service placement (DA-VSP) heuristic algorithm is proposed. Simulation results show that both CO-VSP model and DA-VSP algorithm guarantee the QoS requirements of all such services and illustrates the tradeoff between latency and deployment cost.

关键词： Multi-Access Edge computing (MEC) Cloud computing Intelligent Transportation systems (ITS) V2X Services V2X Service Placement

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10th international conference on New Trends in Model and Data Engineering, MEDI 2021 held with 4th international Workshop on Modeling, Verification and Testing of Dependable Critical systems, DETECT 2021, Symposium on Intelligent and Autonomous systems, SIAS 2021, 1st Workshop on Control Software: Methods, Models, and Languages, CSMML 2021, Workshop on Blockchain for Inter-Organizational Collaboration, BIOC 2021 and 1st international Health Data Workshop, HEDA 2021

10th International Conference on New Trends in Model and Dat...

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10th international conference on New Trends in Model and Data Engineering, MEDI 2021 held with 4th international Workshop on Modeling, Verification and Testing of Dependable Critical systems, DETECT 2021, Symposium on Intelligent and Autonomous systems, SIAS 2021, 1st Workshop on Control Software: Methods, Models, and Languages, CSMML 2021, Workshop on Blockchain for Inter-Organizational Collaboration, BIOC 2021 and 1st international Health Data Workshop, HEDA 2021

ISBN: (纸本)9783030876562

The proceedings contain 24 papers. The special focus in this conference is on New Trends in Model and Data Engineering. The topics include: Record Linkage for Auto-tuning of High Performance computing systems;protecting Sensitive Data in Web of Data;COVID-DETECT: A Deep Learning Based Approach to Accelerate COVID-19 Detection;time Insertion Functions;static Checking Consistency of Temporal Requirements for Control Software;visual Language for Device Management in Telecommunication Product Line;Using Process-Oriented Structured Text for IEC 61499 Function Block Specification;the DibiChain Protocol: Privacy-Preserving Discovery and Exchange of Supply Chain Information;towards a Resource-Aware Formal Modelling Language for Workflow Planning;Development of Critical systems with UML/OCL and FoCaLiZe;systematic Literature Review of Methods for Maintaining Data Integrity;medical Data Engineering – Theory and Practice;querying Medical Imaging Datasets Using Spatial Logics (Position Paper);evaluation of Anonymization Tools for Health Data;usages of the ContSys Standard: A Position Paper;systematic Assessment of Formal Methods Based Models Quality Criteria;deriving Interaction Scenarios for Timed distributed systems by Symbolic Execution;energy Efficient Real-Time Calibration of Wireless sensor Networks for Smart Buildings;edge-to-Fog Collaborative computing in a Swarm of Drones;Coverage Maximization in WSN Deployment Using Particle Swarm Optimization with Voronoi Diagram;EPSAAV: An Extensible Platform for Safety Analysis of Autonomous Vehicles;bridging Trust in Runtime Open Evaluation Scenarios.

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Cloud Resource Provisioning and Bottleneck Eliminating for Meshed Web systems 13

Cloud Resource Provisioning and Bottleneck Eliminating for M...

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ieee 13th international conference on Cloud computing (CLOUD)

作者： Lei, Yamin Cai, Zhicheng Wu, Hang Buyya, Rajkumar Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Univ Melbourne Sch Comp & Informat Syst Cloud Comp & Distributed Syst CLOUDS Lab Melbourne Vic Australia

ISBN: (纸本)9781728187808

Most of existing resource provisioning methods are designed for traditional Web applications with linear structures. However, Web systems with the meshed topology are becoming widespread. Meshed connections among different tiers make Virtual Machine (VM) provisioning and bottleneck elimination complex. In this paper, a Jackson network based Proactive and Reactive VM auto-scaling Method (JPRM) is proposed. In JPRM, request transition behaviors among tiers are modeled as a finite-state Markov stochastic process. A transition probability matrix is studied on-line to predict resource requirements based on M/M/N queuing models as proactive control. For reactive provisioning, the final increased request rate of each tier is determined based on stable state checking and Jackson equilibrium equation solving to eliminate bottleneck tiers and avoid bottleneck shifting. The JPRM is evaluated in a simulation environment established using CloudSim. Experimental results show that JPRM avoids bottleneck shifting with reasonable additional VM rental costs compared with existing methods.

关键词： Resource provisioning Bottleneck eliminating Meshed Web systems Jackson network Cloud computing

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