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检索条件"任意字段=International Conference on Database Systems for Advanced Applications"

共 20859 条记录，以下是101-110 订阅

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Enhanced Automatic Number Plate Recognition for High-Speed Vehicles: Leveraging YOLO and Haar Cascade 4

Enhanced Automatic Number Plate Recognition for High-Speed V...

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4th international conference on Pervasive Computing and Social Networking (ICPCSN)

作者： Kumaran, K. Tharani, R. Saranya, G. Prashanthi, V Kumar, G. Manoj Sagar, K. Manoj Easwari Engn Coll Dept Informat Technol Chennai Tamil Nadu India Sri Sairam Coll Engn Dept Artificial Intelligence & Machine Learning Bengaluru Karnataka India SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India SRM Inst Sci & Technol Dept Comp Technol Chennai Tamil Nadu India Malla Reddy Univ Dept Articial Intelligence & Machine Learning Hyderabad India

ISBN: (纸本)9798350386356;9798350386349

The integration of Artificial Intelligence (AI) into advanced Automatic Number Plate Recognition (ANPR) systems offers a state-of-the-art approach for precise identification of license plates on vehicles. This system employs sophisticated techniques including template matching and connected component analysis to efficiently extract characters from input images. Plate extraction, character segmentation, and template matching are integral parts of the process, ensuring reliable operation across diverse outdoor environments with a focus on rapid identification during daylight hours. ANPR systems find wide applications in automated toll collection, parking access control, traffic law enforcement, and road traffic monitoring. Leveraging AI, the approach utilizes multiple templates and character identification methods to enhance accuracy and efficiency. Following character recognition, the identified characters are validated against a license plate database, achieving an accuracy of about 91.8%. Renowned for its simplicity and rapidity in character recognition and plate segmentation across various weather conditions, this model represents a significant advancement in high-speed ANPR technology enabled by AI.

关键词： database Feature Extraction High-speed Segmentation Template matching Practical applications Character recognition Environmental conditions Artificial Intelligence (AI) Automatic Number Plate Recognition (ANPR)

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Fault Diagnosis Based on database-Driven Techniques: Application to the Electric Vehicle Case

Fault Diagnosis Based on Database-Driven Techniques: Applica...

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international conference on advanced systems and Emergent Technologies (ICASET)

作者： Souissi, Zouhour Messaoud, Anis Univ Gabes Natl Engn Sch Gabes ENIG Lab Numer Control Ind Proc Omar Ibn Khattab St Gabes 6029 Tunisia

ISBN: (纸本)9798350384901;9798350384895

The progress of technology within the automotive sector, particularly the advent of electric vehicles, hinges on the reliability and efficacy of safety mechanisms like the anti-lock braking system (ABS). This study introduces a data-centric approach to identify and rectify shortcomings within the ABS. Through a comparative analysis of classification algorithms, this research evaluates their proficiency in detecting faults within vehicle and wheel speed sensors. The findings underscore the superiority of decision tree (DT) algorithms in terms of accuracy and effectiveness. This investigation implies that employing such algorithms could significantly bolster vehicle safety by promptly identifying potential ABS-related issues.

关键词： Electric car ABS Fault diagnosis Classification algorithms

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Conceptual modeling of a document-oriented NoSQL database 20

Conceptual modeling of a document-oriented NoSQL database

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20th ACS/IEEE international conference on Computer systems and applications (AICCSA)

作者： Abdelhedi, Fatma Rajhi, Hela Zurfluh, Gilles CBI 2 TRIMANE Puteaux La Defense France Toulouse Capitole Univ IRIT Toulouse France

ISBN: (纸本)9798350319439

Massive databases or Big Data are generally managed by NoSQL systems. Most of these systems are schemaless, meaning that the schema is not predefined;it is provided as the database (DB) is fed. This property increases data flexibility. However, the absence of a schema is a major obstacle for expressing complex queries and for the semantic analysis of data. In our previous work [1], we proposed a process for extracting the logical schema of a document-oriented NoSQL DB;this schema allows to describe the structure of the data stored in the DB and thus facilitates query writing. But to understand the meaning of the data and its structure, a conceptual schema is the most appropriate tool, since it ignores the technical aspects linked to implementation. In this paper, we propose a process for transforming the logical schema of a DB into a UML-type conceptual schema. It shows the object classes and semantic links (association, composition and inheritance) contained in the DB. Based on Model Driven Architecture (MDA), our process relies on metamodels and a set of transformation rules to automatically produce the conceptual schema from the logical schema. We experimented our process using a medical application.

关键词： NoSQL DB Schemaless conceptual schema semantic links model transformation

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A GRAPH database SCHEMA FOR METAL ADDITIVE INFORMATICS

A GRAPH DATABASE SCHEMA FOR METAL ADDITIVE INFORMATICS

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ASME 2024 international Design Engineering Technical conferences and Computers and Information in Engineering conference, IDETC-CIE 2024

作者： Allen, Marshall D. Arróyave, Raymundo Malak, Richard Texas A&M University College StationTX United States Texas A&M University College StationTX United States

ISBN: (纸本)9780791888353

Metal additive manufacturing (AM) enables the creation of metal structures with complex materials and geometry for advanced performance capabilities. Metal AM continues to grow in popularity for use cases from alloy design to advanced manufacturing. In this work, we identify the requirements for a novel metal AM informatics schema based on the benefits and shortcomings of existing methods. After investigating different types of databases as the basis of our schema, including relational database management systems (RDBMS) and graph databases (GDBs), we identify the labeled property graph (LPG) configuration of GDBs as a solution due to its capabilities for dimensional scalability, data reusability & adaptability, topological representation, graph traversal queries, and property/label metadata storage. Then, we propose an LPG database schema that addresses the identified requirements for metal AM informatics for materials design. This approach closes the loop from materials development to manufacturing & characterization, enabling accelerated high-fidelity alloy design for metal AM applications. We discuss our implementation of the LPG database schema in Python and demonstrate the schema’s capabilities for visualization and exploration of the alloy design space. We then apply our LPG database schema by designing a compositionally graded alloy (CGA) part with real materials. Lastly, we discuss the possibilities for future work to expand upon the proposed schema. Copyright © 2024 by ASME.

关键词： Reusability

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Hybrid Renewable Energy systems and Their Optimization for Remote Community applications 13

Hybrid Renewable Energy Systems and Their Optimization for R...

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13th international conference on Renewable Energy Research and applications

作者： Qiu, Kuanrong Entchev, Evgueniy Nat Resources Canada CanmetENERGY Technol Ctr Ottawa ON Canada

ISBN: (纸本)9798350375596;9798350375589

There is a growing interest in implementing hybrid renewable energy systems (HRES) in remote communities where people are using diesel power. In the HRES, two or more renewable energy sources are combined, allowing the communities to counteract the weaknesses of one renewable energy source with the strengths of another. This study aims to design, simulate and optimize the HRES consisting of photovoltaic panels, wind turbines, and a bio-based generator for applications in remote and northern communities in Canada. A methodology is developed to optimize HRESs so that net present cost (NPC) and levelized cost of electricity (LCOE) of the energy systems can be minimized. The optimization is performed using a genetic algorithm-based model. A sample remote northern community in Canada is selected for a case study. The HRES being investigated targets to supply an average load demand of 2205 kWh/day and the peak load of 236.06kW for the sample community. Results show that the LCOE for the top optimal HRES configuration is $0.308/kWh, and its NPC is $4.29M. Through the present study, the HRES is shown to be an effective approach to replacing diesel power in remote communities.

关键词： hybrid renewable energy systems optimization net present cost levelized cost of electricity remote community

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Multimodal Knowledge Graph for database Alarm based on Enhanced Fusion Strategy 6

Multimodal Knowledge Graph for Database Alarm based on Enhan...

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6th international conference on Next Generation Data-Driven Networks, NGDN 2024

作者： Gao, Dequan Li, Jiwei Feng, Bao Wang, Zhifan Ding, Xuewei Zhang, Linfeng State Grid Information & Telecommunication Brach Beijing China Nanjing Nari Information & Communication Technology Co. Nanjing China

ISBN: (纸本)9798350388374

database alarms are a pivotal monitoring mechanism, designed to notify system administrators of potential database performance issues, security risks, or failures in a real-time context. Traditional database alarm systems typically depend on pre-established rules and thresholds, which curtails their efficacy when confronted with unknown or intricate anomalies. Moreover, these systems often neglect the multimodal characteristics of alarm data, such as textual logs and visual charts, further impairing the precision and promptness of the alarm. We put forward an innovative database alarm management model, ImageKnowledgeRetriever and the advanced Textual Knowledge Retriever module are designed to extract the image and text information and design a fusion strategy to merge multiple modal characteristics. Our model is adept at comprehending and processing textual logs, as well as analyzing and interpreting image data, including performance charts and architectural diagrams. This method enables our model to conduct a more exhaustive analysis of alarms, thereby pinpointing and diagnosing database issues with heightened accuracy. Our model demonstrates superiority over existing database alarm systems regarding accuracy and F1 score. Specifically, our model achieves an average accuracy enhancement of 8.1% and surpasses the current leading model by 0.5% in the F1 score metric. These findings affirm the advanced capabilities and effectiveness of our model in managing database alarms. © 2024 IEEE.

关键词： Alarm systems

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Method of Data Processing System Synthesis for Heterogeneous Distributed databases Based on Network-Centric Control 12

Method of Data Processing System Synthesis for Heterogeneous...

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12th IEEE international conference on Intelligent Data Acquisition and advanced Computing systems: Technology and applications, IDAACS 2023

作者： Mukhin, Vadym Kornaga, Yaroslav Zavgorodnii, Valerii Bazaka, Yurii Zavgorodnya, Anna Mukhin, Oleg National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of System Design Kiev Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Information Systems and Technologies Kiev Ukraine Igor Sikorsky Kyiv Polytechnic Institute Department of the Artificial Intellect National Technical University of Ukraine Kiev Ukraine

ISBN: (纸本)9798350358056

The methodology for the synthesis of a distributed computer system that includes the database was developed. There are suggested the new models for the data processing parameters evaluation and along with the analysis and forecasting of the functional parameters. The proposed methods for the number of data processing nodes determining in the systems with dynamic structure based on a network-centric control mechanism allows increase the efficiency of the heterogeneous distributed databases. The implementation of the proposed methodology allows synthesizing a distributed data processing system for processing requests to heterogeneous databases, taking into account the parameters of the nodes and the bandwidth of the channels between the nodes. © 2023 IEEE.

关键词： characteristic heterogeneous distributed database method methodology for the synthesis node parameter

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Countering Deepfakes using an Improved advanced CNN and its Ensemble with Pretrained Models 3

Countering Deepfakes using an Improved Advanced CNN and its ...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Mathur, Abeer Tejpal, Moulik Bhargava, Kshitiz Natarajan, Krishnaraj Singh, Manvendra Vellore Institute of Technology School of Computer Science & Engineering Department of Software Systems Vellore India Vellore Institute of Technology School of Computer Science & Engineering Department of Database Systems Vellore India

ISBN: (纸本)9798350369083

The extensive spread of DeepFake images on the internet has emerged as a significant challenge, with applications ranging from harmless entertainment to harmful acts like blackmail, misinformation, and spreading false propaganda. To tackle this issue, this paper introduces a sophisticated DeepFake detection model designed to identify and mitigate the increase of these deceptive images. The model architecture integrates an ensemble approach, combining the strengths of two pre-trained Convolutional Neural Network (CNN) models - MobileNet and Xception - with a novel CNN architecture, the advanced CNN (ACNN). This rigorous validation process enabled the model to achieve a high accuracy rate of 97.89% in detecting DeepFakes. The successful implementation of this ensemble CNN approach demonstrates its effectiveness in distinguishing between real and fabricated imagery with high precision. This research makes a substantial contribution to the field of digital image forensics, offering a reliable tool for stakeholders across various sectors to identify and counteract the spread of DeepFake images online. © 2024 IEEE.

关键词： Generative adversarial networks

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Exploring the Feasibility of Event-Related Potentials in Visual Surveillance applications 12

Exploring the Feasibility of Event-Related Potentials in Vis...

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12th international Winter conference on Brain-Computer Interface (BCI)

作者： Kwon, Byoung-Hee Han, Dong-Kyun Ahn, Hyung-Ju Korea Univ Dept Brain & Cognit Engn Seoul South Korea Korea Univ Dept Artificial Intelligence Seoul South Korea

ISBN: (纸本)9798350309430

This study investigates the feasibility of leveraging Event-Related Potentials (ERP) within the realm of military surveillance, utilizing non-invasive brain-computer interface (BCI) technologies. Focused on enhancing static image target recognition capabilities, especially in the domain of satellite imagery, our research involved the creation of an extensive EEG database to foster advanced ERP classification through deep learning methodologies. ERPs, known for their association with sensory, cognitive, or motor event processing, are pivotal in the effective deployment of BCI for practical applications. Our approach entailed an in-depth analysis of EEG data, derived from personnel engaged in simulated surveillance tasks, to discern nuanced ERP classifications crucial for military surveillance operations. The cornerstone of our study was the real-time analysis of single-trial ERP data, employing sophisticated deep learning algorithms, with the objective of validating the practical applicability of EEG-based systems in military surveillance contexts. The outcomes of this research have the potential to inaugurate a new frontier in military surveillance, synergizing human cognitive prowess with advanced computational techniques. This integration of human cognitive processes with machine learning algorithms in military surveillance and target recognition is a significant contribution to the burgeoning field of BCI, offering novel solutions for bolstering national security and defense mechanisms.

关键词： event related potential surveillance deep learning electroencephalogram

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Unified Monitoring and Telemetry Platform for Future Intelligent Optical Networks 24

Unified Monitoring and Telemetry Platform for Future Intelli...

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24th international conference on Transparent Optical Networks (ICTON)

作者： Shen, Sen Han, Jing Li, Haiyuan Teng, Yiran Yan, Shuangyi Simeonidou, Dimitra Univ Bristol Smart Internet Lab High Performance Networks Grp Bristol Avon England

ISBN: (纸本)9798350377330;9798350377323

With the advancement of machine learning (ML) in network automation, the importance of telemetry and monitoring across entire optical networks is paramount. Traditional, localized monitoring methods are not sufficient to provide the data required to support modern ML-driven network applications that are essential for planning, optimizing and managing large dynamic optical networks. In this paper, a unified monitoring and telemetry scheme is developed that leverages a Kafka-based telemetry pipeline, advanced ML applications and time-series databases implemented on InfluxDB with multiple functional plugins. The developed platform offers a robust and dynamic analytical platform, enabling proactive management of network operations and informed decision-making in real-time. It is shown that the proposed telemetry pipeline facilitates real-time data collection, processing and streaming with a total latency of less than 0.05s, ensuring high levels of performance, reliability, and operational efficiency through advanced telemetry and ML technologies.

关键词： machine learning optical telemetry monitoring time-series database

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