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检索条件"任意字段=19th Data Compression Conference"

共 11762 条记录，以下是61-70 订阅

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Enhancing GPR-based tunnel lining delamination detection: An unsupervised domain-adaptive YOLO network 20

Enhancing GPR-based tunnel lining delamination detection: An...

引用

20th International conference on Ground Penetrating Radar, GPR, 2024

作者： Wang, Yuanzheng Qin, Hui Liu, Jinbo Chen, Youjian Wang, Lipeng Ren, Hongyong Meng, Xianbiao Luo, Wenjiang Gao, Lei School of Infrastructure Engineering Dalian University of Technology Dalian116024 China China Railway 19th Bureau Group Co. Ltd. Beijing10855 China China Railway 19th Bureau Group 5th Engineering Co. Ltd. Dalian116199 China China Railway 19th Bureau Group Rail Transit Engineering Co. Ltd. Beijing101300 China

the ground penetrating radar (GPR) is one of the most recommended methods for tunnel lining inspection, yet the interpreting the GPR data requires significant time and expertise. Recent attempts to automate this procedure using deep learning techniques have made significant progress, yet is still hindered by the need of extensive and accurately annotated data, which is challenging to gather in real scenarios. this paper introduces a domain-adaptive YOLO network, evolving from YOLOv7, which trains on FDTD data for source domain detection and real-world data for target domain adaption. the proposed network enhances the network's ability to generate generalized features across domains and hence improves the detection performance on real GPR data. © Published under licence by IOP Publishing Ltd.

关键词： Tunnel linings

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Manual Frequency Restoration Reserve Markets: What Does the data Tell Us? 19

Manual Frequency Restoration Reserve Markets: What Does the ...

引用

19th International conference on the European Energy Market (EEM)

作者： Miletic, Marija Capuder, Tomislav Pandzic, Hrvoje Pavic, Ivan Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust Luxembourg Luxembourg

ISBN: (纸本)9798350312584

European energy and ancillary service markets have recently undergone many changes. Some were structural, such as the go-live of balancing energy trading platforms MARI and PICASSO, while others, like price spikes, were consequences of world events. these changes may negatively impact tools that rely on data stationarity, especially machine learning-based models. We use the energy and ancillary service market data for Croatia, France and Germany to determine prerequisites for predicting manual frequency restoration reserve (mFRR) capacity and balancing energy prices. We present a statistical analysis of the data, and draw conclusions about the steps necessary for training machine learning models on this data.

关键词： manual frequency restoration reserve balancing capacity market balancing energy market data analysis

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Robot-Mounted Ultrasonic Micro-Wire Bonding Tool for Structural Electronics and Sensors 19

Robot-Mounted Ultrasonic Micro-Wire Bonding Tool for Structu...

引用

ASME 19th International Manufacturing Science and Engineering conference (MSEC) held jointly with the 52nd North American Research conference (NAMRC)

作者： Tofangchi, Alireza Caid, Cassidy Wei, Danming Hsu, Keng Popa, Dan O. Univ Louisville Speed Sch Engn LARRI Louisville KY 40292 USA Arizona State Univ Sch Mfg Syst & Networks Tempe AZ 85287 USA

ISBN: (纸本)9780791888117

this research describes the development of a cost-effective ultrasonic wire bonding system, which was later integrated into an industrial robot to enhance welding automation. Initially, the team developed an innovative 3D printed adapter to attach the ultrasonic transducer to the tool tip, along with a 3D Gantry platform for manual wire bonding to determine the optimal process conditions. the study examined the influence of different variables, such as wire type and size, ultrasonic power and duration, and mechanical pressure. In the follow-up phase, the system was further enhanced by integrating the ultrasonic tool with a robotic arm having six degrees of freedom. A control law was developed using the calculated arm stiffness. through experimental testing, which varied compression strength and pulse duration, the team attempted to identify effective operating conditions via destructive bond strength analysis. this integration significantly improved the system's functionality, allowing for more precise automated wire bonding and better data collection for process control. Practical applications demonstrated include automated wire bonding and cutting on aluminum plates and printed circuit boards with small features, using aluminum and gold wires of various diameters. the system's ability to cut wires in-process allows continuous bonding at different locations without halting the process. Future objectives include implementing real-time monitoring of bonding failures using data-driven methods.

关键词： Additive manufacturing Bonding Electronics Sensors Degrees of freedom Process control Ultrasonic transducers Welding Pressure Robotics Microwire Compressive strength Cutting Robots Bond strength Aluminum plate data collection Failure Teams Testing Wire Wire bonding Aluminum Stiffness Printed circuit boards

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Predicting State of Health of Lithium Ion Batteries 19

Predicting State of Health of Lithium Ion Batteries

引用

IEEE 19th conference on Industrial Electronics and Applications (ICIEA)

作者： Anand, Aiswarya Pai, Manohara M. M. Pai, Radhika M. MAHE Manipal Inst Technol Dept Informat & Commun Technol Manipal India MAHE Manipal Inst Technol Dept Data Sci & Comp Applicat Manipal India

ISBN: (纸本)9798350360875;9798350360868

the paper focuses on predicting the stateof health of Lithium Ion batteries used in Electric Vehicles to make predictive analytics on Remaining Useful Life as well as understand the battery dynamics for recycling enabling sustainable development. the work makes use of three different datasets, namely the Calce dataset, the lithium-ion battery model dataset, and the Oxford Battery Degradation dataset. Multiple machine learning and deep learning algorithms are adopted to predict the state of the health of the batteries in the datasets and are compared with respect to various metrics.

关键词： State of Health Remaining Useful Life Constant Current Charge Time Constant Voltage Charge Time Convolutional Neural Network Lithium Ion

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Quality Control of Body Measurement data Using Linear Regression Methods 19

Quality Control of Body Measurement Data Using Linear Regres...

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19th conference on Computer Science and Intelligence Systems

作者： Bicevskis, Janis Bicevska, Zane Diebelis, Edgars Purina, Liva Univ Latvia Raina Blvd 19 LV-1586 Riga Latvia Div Grupa Ltd Fridriha Candera Str 1 LV-1046 Riga Latvia

ISBN: (纸本)9798350359718;9788396960160

Body measurement data are inherently inaccurate and quite error-prone due to manual measurement and data collection. In this study, professionally collected and self-collected body measurement data were used to investigate to what extent potentially erroneous data can be identified during collection by utilizing the anthropologically given correlation of body measurements. the study specifically uses a dataset created within the framework of a project for made-to-measure pattern creation, consisting of data from 2053 female individuals with up to 52 recorded body measurements. Using linear regression, a method for validating the collected data is defined, wherein potentially inconsistent data are identified based on tolerance intervals. the tolerance intervals calculated within the study are specific to the particular application and the personal data used in the study. the outlined method is applicable to almost any set of manually collected body data in at least the triple-digit range, enabling the identification of probable data errors already during their collection.

关键词： linear regression body measurement assessment data quality in pattern generation

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Plant-traits: how citizen science and artificial intelligence can impact natural science 19

Plant-traits: how citizen science and artificial intelligenc...

引用

19th conference on Computer Science and Intelligence Systems

作者： Ignesti, Giacomo Moroni, Davide Martinelli, Massimo CNR ISTI Via Giuseppe Moruzzi 1 I-56124 Pisa Italy Univ Pisa Second FloorLargo Bruno Pontecorvo 3 I-56127 Pisa Italy

ISBN: (纸本)9798350359718;9788396960160

Citizen science has emerged as a valuable resource for scientific research, providing large volumes of data for training deep learning models. However, the quality and accuracy of crowd-sourced data pose significant challenges for supervised learning tasks such as plant trait detection. this study investigates the application of AI techniques to address these issues within natural science. We explore the potential of multi-modal data analysis and ensemble methods to improve the accuracy of plant trait classification using citizen science data. Additionally, we examine the effectiveness of transfer learning from authoritative datasets like PlantVillage to enhance model performance on open-access platforms such as iNaturalist. By analysing the strengths and limitations of AI-driven approaches in this context, we aim to contribute to developing robust and reliable methods for utilising citizen science data in natural science.

关键词： data accuracy

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Empowering Smart Mobility with a Component-based data Acquisition System for Multi-sensor Readout 19

Empowering Smart Mobility with a Component-based Data Acquis...

引用

19th conference on Ph.D Research in Microelectronics and Electronics (PRIME)

作者： Verzeroli, Matteo Galliani, Andrea Ghislotti, Luca Gaioni, Luigi Lazzaroni, Paolo Re, Valerio Univ Bergamo Dipartimento Ingn & Sci Applicate Bergamo Italy

ISBN: (纸本)9798350386301;9798350386318

this work presents a component-based data acquisition system for reading multi-sensor modules in the context of active mobility and light vehicles. Leveraging technologies coming from electric mobility in a light vehicle scenario means dealing with low power requirements and limited computational resources. To collect data simultaneously from different sensor platforms, an ad-hoc multi-process application has been developed for a single-board computer. this data acquisition module supports multi-protocol communication interfaces and it can aggregate data about the environment, the status of the vehicle and the user. Moreover, it can support the integration with an IoT communication infrastructure. A prototype of the system has been implemented to prove the feasibility of the architecture.

关键词： active mobility data acquisition system IoT light vehicles V2X

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A Multi-layer Approach through threat Modelling and Attack Simulation for Enhanced Cyber Security Assessment 24

A Multi-layer Approach through Threat Modelling and Attack S...

引用

19th International conference on Availability, Reliability, and Security (ARES)

作者： Iturbe, Eider Arcas, Javier Rios, Erkuden Toledo, Nerea TECNALIA Basque Res & Technol Alliance BRTA Derio Bizkaia Spain Univ Basque Country Fac Engn Bilbao Spain

ISBN: (纸本)9798400717185

there is a growing concern about the dynamic landscape of cyber security threats escalating, and the need for improvement in defence capabilities against emerging sophisticated incidents. In response, this paper presents a solution called the Cyber Incident Simulation System, which enables system security engineers to simulate cyber-physical attacks and incidents without the requirement to affect or disrupt the ongoing business operation of the system. Leveraging graph-based threat modelling and AI-generated incident data, the system empowers professionals to predict the effect of the incident within the system under study. the synthetic data is used by anomaly-based Intrusion Detection Systems (IDSs) and other additional security controls to improve their detection algorithms to enhance their accuracy and effectiveness. the Cyber Incident Simulation System is designed to enhance the cyber security measures through the simulation of various incident scenarios.

关键词： cyber security cyber security assessment threat modelling simulation synthetic data generation attack graph

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On Implementing Linear Regression on Homomorphically Encrypted data: A Case-Study 24

On Implementing Linear Regression on Homomorphically Encrypt...

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19th International conference on Availability, Reliability, and Security (ARES)

作者： Dini, Gianluca Univ Pisa Dept Informat Engn Pisa Italy

ISBN: (纸本)9798400717185

Fully Homomorphic Encryption (FHE) is a key technological enabler for secure computations as it allows a third-party to perform arbitrary computations on encrypted data learning neither the input nor the results of a computation. Notwithstanding the recent theoretical breakthroughs in FHE, building a secure and efficient FHE-based application is still a challenging engineering task where optimal choices are heavily application-dependent. Taking linear regression as a case-study, we investigate the programming and configuration solutions to implement FHE-based applications. We show that, although obviously slower than the non-homomorphic version, the implementation of linear regression on homomorphically encrypted data is viable provided the programmer adopts appropriate programming expedients and parameters selection.

关键词： Privacy-Enhancing Technologies Homomorphic Encryption Machine Learning Linear Regression

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19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International conference on Big data and Cloud Computing, 14th IEEE International conference on Social Computing and Networking and 11th IEEE International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

19th IEEE International Symposium on Parallel and Distribute...

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19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International conference on Big data and Cloud Computing, 14th IEEE International conference on Social Computing and Networking and 11th IEEE International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

ISBN: (纸本)9781665435741

the proceedings contain 222 papers. the topics discussed include: DRL-deploy: adaptive service function chains deployment with deep reinforcement learning;accuracy vs. efficiency: achieving both through hardware-aware quantization and reconfigurable architecture with mixed precision;cmss: collaborative modeling of safety and security requirements for network protocols;FGPA: fine-grained pipelined acceleration for depthwise separable CNN in resource constraint scenarios;Dyacon: JointCloud dynamic access control model of data security based on verifiable credentials;understanding the runtime overheads of deep learning inference on edge devices;and alleviating imbalance in synchronous distributed training of deep neural networks.

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