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检索条件"任意字段=IEEE International Conference on Data Science and Data Intensive Systems"

共 86302 条记录，以下是221-230 订阅

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data Interface for an Interactable Ship Bridge towards Maritime Autonomous Surface Ships at Human-in-the-loop Levels 4

Data Interface for an Interactable Ship Bridge towards Marit...

引用

4th ieee international conference on Human-Machine systems, ICHMS 2024

作者： Wu, Baiheng Hatledal, Lars Ivar Madsen, Andreas Wang, Tongtong Brandsater, Andreas Osen, Ottar Laurits Norwegian University Of Science And Technology Department Of Ocean Operations And Civil Engineering Larsgårdsvegen 2 Ålesund6009 Norway Norwegian University Of Science And Technology Department Of Ict And Natural Science Larsgårdsvegen 2 Ålesund6009 Norway Dnv Veritasveien 1 Oslo1363 Norway

ISBN: (纸本)9798350315790

This article presents a data interface for maritime simulators to facilitate the development of ship Automatic Navigational systems (ANS) at different autonomy levels. The core architecture of this data interface consists of the simulator, Sim-PC, and downstream clients. Two data transmission channels are established between the simulator and SimPC using UDP and Modbus TCP/IP protocols, while data interaction between Sim-PC and clients is achieved through websockets. The article provides technical details of each data channel and demonstrates their application through relevant examples. These examples include supporting Decision Support systems (DSS) development through UDP-based NMEA data publishing, external control of the simulator using Modbus, and remote control or decision support of the simulator through websockets from downstream clients. This data interface offers opportunities for ANS testing on simulators and provides the necessary conditions for the development and testing of all Maritime autonomous surface ships (MASS) autonomy levels defined by the international Maritime Organization (IMO). © 2024 ieee.

关键词： Decision support systems

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Enhancing Security and Privacy in Cloud-Based Medical data systems using AES Cryptography and Digital Envelopes

Enhancing Security and Privacy in Cloud-Based Medical Data S...

引用

2024 ieee international Students' conference on Electrical, Electronics and Computer science, SCEECS 2024

作者： Pendam, Triveni Baig, Mirza Moiz Sonekar, Shrikant Sawwashere, Supriya S. Jd College of Engineering and Management Department of Computer Science & Engineering Maharashtra Nagpur India Jd College of Engineering and Management Department of Information Technology Maharashtra Nagpur India

ISBN: (纸本)9798350348460

The integration of healthcare data storage and management within cloud systems has revolutionized accessibility and administration in the medical field. This paper, proposes a security framework for medical data over the cloud. The medical data can be encrypted using AES algorithm. Preprocessing and data aggregation are pivotal in creating protected training files sent to the cloud. Cloud processing utilizes an SVM algorithm for data analysis, enabling informed medical decisions. The study findings affirm the system's robustness and security in handling diverse security and privacy concerns linked with cloud-based medical data. By employing digital envelopes and encryption standards, this system offers a dependable and efficient approach to accessing and processing healthcare information. © 2024 ieee.

关键词： Big data Cloud Computing data Security Healthcare Information System Internet of Things (Iot) Machine Learning Privacy-Preserving data Sharing Secure Medical Support System

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data Dimensionality Reduction using Principal Component Analysis: A Case Study 1

Data Dimensionality Reduction using Principal Component Anal...

引用

1st ieee international conference on Communications and Computer science, InCCCS 2024

作者： Ramasubramanian, Srivaths Sowmyarani, C.N. Athreya, Amogh J. Devarajan, Anjali Shankar, Adithya Udaya Kumar, Ramakanth P. Dept. of ECE RV College of Engineering Bangalore India Dept. of CSE RV College of Engineering Bangalore India Dept. of CSE RV College of Engineering Bangalore India

ISBN: (纸本)9798350358858

Training of Machine Learning (ML) models requires huge amounts of data. Usually, in the training of sophisticated models, data-sets can be very computationally intensive on resources. In order to reduce the computational burden of large training data-sets on ML models, the work describes Principal Component Analysis (PCA), a popular dimensionality reduction technique used in ML to reduce the size of training data-sets. This method aims to transform the given set of variables into a new set of components on a lower dimensional subspace. After dimensionality reduction, these components retain the prominent features of data, which leads to reduction in volume and efficient analysis. Inferences on the choice of number of components are presented by analyzing the results obtained from variance contribution plots. © 2024 ieee.

关键词： Principal component analysis

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FinD: Fine-grained Dynamic Task Scheduling with Lightweight Threads on Many-core Processors 21

FinD: Fine-grained Dynamic Task Scheduling with Lightweight ...

引用

21st ieee international Symposium on Parallel and Distributed Processing with Applications, 13th ieee international conference on Big data and Cloud Computing, 16th ieee international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Hu, Nan Yan, Chenglu Chen, Zhiguang Lu, Yutong Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350329223

The emergence of many-core processors presents significant opportunities for large-scale multithreading. Exploiting these intensive computing resources poses an urgent challenge for data processing systems. Although current big data frameworks offer automatic parallel computing capabilities, their coarse-grained parallelism hinders multi-threaded scalability on many-core processors. The NUMA architecture exacerbates load imbalance and resource wastage during the processing of skewed datasets. This paper introduces FinD, a fine-grained data processing framework that maximizes the utilization of many-core resources with lightweight threads and effectively addresses data skew through dynamic task scheduling. FinD incorporates bilateral NUMA-aware work stealing to achieve load balancing and reduce remote memory access across NUMA nodes. Experimental results demonstrate that FinD achieves a speedup of up to 9.7 × compared to popular frameworks on big data benchmarks and HPC workloads while reducing overall execution time by 40% on skewed datasets. © 2023 ieee.

关键词： Big data

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Anomaly Detection System for Terminal-Level data Access Behavior

Anomaly Detection System for Terminal-Level Data Access Beha...

引用

9th international conference on Signal and Image Processing (ICSIP)

作者： Gao, Dongying Guo, Caiwei Ji, Wen Chen, Shaoqin Ji, Meiying Li, Zheng Lin, Sichen State Grid Fujian Informat & Telecommun Co Fuzhou Fujian Peoples R China

ISBN: (纸本)9798350350920

Terminal devices are critical in establishing efficient, reliable, and secure computer network systems. Anomaly detection, a key technique in computer security, is widely researched for its adaptability and ability to recognize novel attacks, despite facing issues such as low efficiency. To address the monitoring and identification of anomalous behaviors by users during data access at the terminal level, a comprehensive assessment incorporating data content, methods of operation, and user behavior is essential. Therefore, a terminal-level data access anomaly detection system utilizing the TL-IDA algorithm is proposed. This system employs a sliding window technique to enhance its performance. The primary aim is to increase data security and prevent unauthorized access, providing guidance for the prompt adoption of appropriate security measures to ensure the protection of data.

关键词： computer security anomaly detection terminal-level intrusion detection

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data-Driven Design for Parity Space-Based Replay Attack Detection 22

Data-Driven Design for Parity Space-Based Replay Attack Dete...

引用

22nd ieee international conference on Industrial Informatics, INDIN 2024

作者： Song, Tianyi Sun, Tieliang Lv, Feng Zhao, Dong Li, Yafeng School of Cyber Science and Technology Beihang University Beijing China Oil and Gas Control Center PipeChina Beijing China

ISBN: (纸本)9798331527471

This article studies the data-driven design of parity space based replay attack detection method for cyber-physical systems. By utilizing the input and output data, the proposed data-driven detection can uncover stealthy replay attacks. First, the replay attack detector is formulated by following the parity space method. Subsequently, data-driven parity space identification, residual quantification, and parity matrix optimization are presented to support data-driven residual generation and evaluation for the detector. To enhance detection performance, an integration of attack defense model information and process data is presented, and the data-driven detector design is proposed. Finally, simulations are conducted to testify the performance of our schemes. © 2024 ieee.

关键词： Embedded systems

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Learning from Spatio-temporal Correlation for Semi-Supervised LiDAR Semantic Segmentation

Learning from Spatio-temporal Correlation for Semi-Supervise...

引用

2024 international conference on Intelligent Robots and systems

作者： Lee, Seungho Lee, Hwijeong Shim, Hyunjung Yonsei Univ Seoul South Korea Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798350377712;9798350377705

We address the challenges of the semi-supervised LiDAR segmentation (SSLS) problem, particularly in low-budget scenarios. The two main issues in low-budget SSLS are the poor-quality pseudo-labels for unlabeled data, and the performance drops due to the significant imbalance between ground-truth and pseudo-labels. This imbalance leads to a vicious training cycle. To overcome these challenges, we leverage the spatio-temporal prior by recognizing the substantial overlap between temporally adjacent LiDAR scans. We propose a proximity-based label estimation, which generates highly accurate pseudo-labels for unlabeled data by utilizing semantic consistency with adjacent labeled data. Additionally, we enhance this method by progressively expanding the pseudo-labels from the nearest unlabeled scans, which helps significantly reduce errors linked to dynamic classes. Additionally, we employ a dual-branch structure to mitigate performance degradation caused by data imbalance. Experimental results demonstrate remarkable performance in low-budget settings (i.e., <= 5%) and meaningful improvements in normal budget settings (i.e., 5 - 50%). Finally, our method has achieved new state-of-the-art results on SemanticKITTI and nuScenes in semi-supervised LiDAR segmentation. With only 5% labeled data, it offers competitive results against fully-supervised counterparts. Moreover, it surpasses the performance of the previous state-of-the-art at 100% labeled data (75.2%) using only 20% of labeled data (76.0%) on nuScenes. The code is available on https://***/halbielee/PLE.

关键词： Spatio-temporal data

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Research and Application of Power Grid data Integration Technology Based on Middleware Framework 3

Research and Application of Power Grid Data Integration Tech...

引用

3rd international conference on Energy and Electrical Power systems (ICEEPS)

作者： Huang, Guanbiao Tang, Yingqi Xu, Yuan Su, Yinsheng Zhou, Baorong Zhao, Ligang Lin, Jianfeng Ma, Zhigao CSG Elect Power Res Inst State Key Lab HVDC Technol Guangzhou Peoples R China CSG Power Dispatching & Control Ctr Guangzhou Peoples R China SCUT Sch Automat Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9798350375145;9798350375138

With the acceleration of the digital transformation process of the power grid, various business systems of the power grid generate diverse types of data, inconsistent data formats, and serious data silos. In order to solve the problem of data interoperability, improve data sharing and application efficiency, this article designs a middleware solution that can customize data format conversion and data integration for various business data in the power grid. The underlying layer of this middleware uses a specially designed rule parent table data structure, and combines the GORM framework and GEN tool in development, which not only reduces code redundancy but also improves program scalability, providing a new approach for work similar to business requirements.

关键词： data Migration data Integration Middleware Design ORM

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A Decision Support System to Compile Environmental Mitigations from Hydropower Licensing Documents

A Decision Support System to Compile Environmental Mitigatio...

引用

2024 ieee international conference on Big data, Bigdata 2024

作者： Yoon, Hong-Jun Ruggles, Tom Zhao, Huanhuan Singh, Debjani Oak Ridge National Laboratory Computational Sciences and Engineering Division Oak RidgeTN37830 United States Oak Ridge National Laboratory Environmental Sciences Division Oak RidgeTN37830 United States University of Tennessee Knoxville Data Science and Engineering KnoxvilleTN37996 United States

ISBN: (纸本)9798350362480

The process of deciphering, extracting, and compiling information from texts dense with domain-specific terminology and technical jargon is a challenging endeavor. It demands considerable expertise and deep knowledge in the respective field, resulting in a labor-intensive process when executed by humans. Furthermore, the task of identifying multiple class labels in extensive texts presents a challenge due to intra- and inter-reader variability, making the process time-consuming and ***'re introducing a user-friendly graphical interface, fortified with a BERT model-powered decision support system. This advanced system aims to augment efficiency, curtail data collection time, and sustain high precision in data acquisition. It is instrumental in deciphering and synthesizing intricate texts teeming with a spectrum of expressions, even within similar mitigation categories. Such tasks traditionally demand substantial human effort and specialized knowledge in the *** system is specifically engineered for the task of extracting environmental mitigation information to promote sustainable hydropower development from licenses issued by the Federal Energy Regulatory Commission (FERC). These license documents are comprehensive, each containing over 15,000 words and requiring the identification of 135 different class labels. We anticipate that our system will boost reading speed, improve the consistency of classification outputs among readers, and contribute to the development of a robust scientific database of environmental mitigations associated with the 2,000+ non-federal hydropower facilities licensed by FERC in the United States. © 2024 ieee.

关键词： Natural language processing systems

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Design and Implementation of a Transceiver for Near- End Communication System using Verilog HDL

Design and Implementation of a Transceiver for Near- End Com...

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2024 ieee international Students' conference on Electrical, Electronics and Computer science, SCEECS 2024

作者： Erla, Nithin Reddy Kistapati, Sravani Reddy Dandetikar, Vidyasagar Maheshwari, Vikas Guru Nanak Institutions Technical Campus Dept of Ece Telangana Hyderabad India

ISBN: (纸本)9798350348460

This paper describes a fast and reliable technique for communication between two short-range devices over wired or wireless networks, using a self-designed transmitter and receiver system modules. A good and lossless Device to device communication (D2DC) is crucial in the development of different electronic systems. In the present day, data transmission techniques are inefficient as they require an adequately large amount of time for transmission and reception. This paper presents a simple lossless technique for transmission and reception of data across wired systems or low range wireless systems. The proposed system consumes less power and reduces the amount of time taken in data transmission and acquisition. © 2024 ieee.

关键词： BPSK data communication device to device (D2D) communication QPSK Verilog HDL VLSI

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