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检索条件"任意字段=Conference on Advances in Optical Data Storage Technology"

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A radar data compression method based on autoencoder neural network and range encoding 5

A radar data compression method based on autoencoder neural ...

引用

2023 5th International conference on Information Science, Electrical, and Automation Engineering, ISEAE 2023

作者： Hu, Zelong Yang, Feng Qiao, Xu Li, Fanruo School of Mechanical Electronic and Information Engineering China University of Mining and Technology Beijing China

ISBN: (纸本)9781510667440

Ground Penetrating Radar (GPR) data requires a significant amount of network bandwidth and storage space for transmission and storage due to the large number of channels and vast amount of data. In this paper, we propose an improved method for compressing GPR data. Firstly, we analyze and preprocess the features of the data to enhance its compression potential. Secondly, we introduce convolutional layers into the AutoEncoder to improve its generalization ability. We then use multiple-level compression to further compress the data based on the radar data's features. Finally, we introduce range encoding for secondary compression. Simulation experiments demonstrate that our proposed algorithm can effectively compress radar data while maintaining high compression ratios and speed. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： data compression

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Study on the Multi-parameter Monitoring System in the Old VLCC for Offshore Oil storage 3

Study on the Multi-parameter Monitoring System in the Old VL...

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3rd International conference on Testing technology and Automation Engineering, TTAE 2023

作者： Wang, Yining Li, Yuan Chen, Wenxiang Jin, Tiangang Zhu, Faxin Li, Jinyuan School of Naval architecture Maritime of ZheJiang Ocean University ZhouShan316022 China Jiangsu Canal Ocean Shipping Co. Ltd. Huaian223001 China

ISBN: (纸本)9781510674769

In recent years, the global oil price fluctuated greatly, offshore oil storage in the VLCC has risen gradually in foreign countries, the case of VLCC offshore oil storage has not appeared in China. In order to improve China's oil reserve system, improve effectively the profit of the old VLCC, the actual VLCC was taken as the research object, relevant research were carried out in the paper. In consideration of the actual situation of the aging equipment in the old VLCC, high potential risk of offshore oil storage and the importance of the multi-parameter monitoring so on, the design requirements of the multi-parameter monitoring system in the old VLCC for offshore oil storage were analyzed, the multi-parameter monitoring system in the old VLCC was structured by use of the software Python, the error of the monitoring system data was tested. The results shown that the errors of oxygen content and gas pressure in the multi-parameter monitoring system in the old VLCC developed were less than 2%, and the accuracy rate was more than 98%. So the multi-parameter monitoring system in the old VLCC for offshore oil storage is feasible and can provide a good guarantee for the offshore oil storage in the old VLCC. © 2024 SPIE.

关键词： Python

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Automated layout planning of walkway plates in climbing scaffolding platform using contour data

Automated layout planning of walkway plates in climbing scaf...

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2024 International conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2024

作者： An, Di Zhong, Hui Fu, Yangyi Li, Jiajia Qiao, Feng School of Intelligence and Engineering Shenyang City University Shenyang110112 China School of Computer Science and Engineering Shenyang Jianzhu University Shenyang110168 China

ISBN: (纸本)9781510689176

The rapid advancement of information technology has made computer-aided design a primary focus in the construction industry. This paper addresses the challenge of automating the layout of walkway plates in climbing scaffolding platforms in high-rise buildings. This paper presents a comprehensive approach that uses building contour data for one-click layout planning of perimeter walkway plates. The approach processes building contour data to fit the dimensions and storage forms of walkway plates. It generates a partition diagram based on point relationships and extracts individual walkway plate data needed for modeling. This data is then integrated with Revit to expedite the planning process. © 2025 SPIE.

关键词： Tall buildings

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Design and Implementation of a Big data Platform for Production and Business Management: A Case Study from Vietnam National Coal and Mineral Industries Group 3rd

Design and Implementation of a Big Data Platform for Product...

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3rd International conference on advances in Information and Communication technology, ICTA 2024

作者： Do, Thi-Thu-Trang Tran, Van-Dung Ngo, Manh-Hung Dang, Dinh-Dao Dam, Quang-Thinh Mai-Hoang, Thai-Bao Nguyen, Van-Quyet Huynh, Quyet-Thang Faculty of Information Technology Hung Yen University of Technology and Education Hungyen160000 Viet Nam Vinacomin-Mining Chemical Industry Holding Corporation Limited Hanoi100000 Viet Nam AI Academy Viet Nam Hanoi100000 Viet Nam School of Information and Communication Technology Hanoi University of Science and Technology Hanoi100000 Viet Nam

ISBN: (纸本)9783031809422

This paper introduces a modern Big data platform for production and business management, utilizing a hybrid architecture that integrates distributed and cloud-based technologies to enhance processing efficiency. The system employs advanced big data processing technologies, including Hadoop, Spark, Kafka, and AI techniques such as YOLO and CNN, to effectively manage unstructured data. This platform improves system performance, facilitates detailed business queries, and enhances storage and query capabilities. We apply this system to the Vietnam National Coal and Mineral Industry Group (VINACOMIN), resolving inefficiencies in their existing software and achieving a tenfold improvement in speed and report generation efficiency. Experiments show that the system can analyze video data under various conditions using advanced AI techniques, particularly the DeepSearch algorithm. The results demonstrate that the platform optimizes report generation and provides a scalable framework for future Big data applications in similar industrial settings. This research underscores the transformative potential of innovative Big data solutions in improving data processing, enabling timely decision-making, and maintaining a competitive advantage in the current business environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Competition

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Decformer: A Multi-model Fusion Method Based on Decomposition and Transformer for Industrial Energy Consumption Prediction 16

Decformer: A Multi-model Fusion Method Based on Decompositio...

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16th International conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Li, Jun Li, Dagang Xia, Pengcheng Ni, Yiyang Qian, Yuwen School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Engineering Jiangsu Second Normal University Nanjing210013 China

ISBN: (纸本)9798350390643

As the industrial Internet of Things (IIoT) advances, the need for precise and effective long-term energy consumption forecasting grows more critical. Addressing this challenge relies on the technology of Time Sequence Forecast (TSF). Some ad-vanced deep learning methods have been used for TSF. However, existing work suffers from overly complex model structures and a lack of consideration for characteristics of the specific data. Therefore, we propose a time series prediction method called Decformer based on sequence decomposition and multi-model fusion. For components exhibiting strong periodicity, we use a simplified Transformer model with channel-independence processing to efficiently extract time-dimension feature information. Experimental results on multiple datasets demonstrate that the proposed method outperforms state-of-the-art time series prediction methods. © 2024 IEEE.

关键词： Prediction models

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A segment method of querying shortest distance approximately on encrypted large-scale graph 3

A segment method of querying shortest distance approximately...

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3rd International conference on Electronic Information Engineering, Big data, and Computer technology, EIBDCT 2024

作者： Dong, Xiaotong Li, Bo Li, Yong Zhu, Xiaojie Wang, Weiping Institute of Information Engineering Chinese Academy of Sciences Beijing100089 China School of Cyber Security University of Chinese Academy of Sciences Beijing China King Abdullah University of Science and Technology Saudi Arabia Saudi Jeddah Saudi Arabia

ISBN: (纸本)9781510680449

As one of the fundamental primitive operations, querying shortest distance between node pairs in a graph has gradually attracted great attention in both academia and industry. In recent years, with the rapid development of cloud computing, it allows clients to outsource their data to cloud servers. In this way, users can outsource large volumes of data which were difficult to store and compute on their own clients. However, it worth noting that for privacy and security reasons, user data needs to be encrypted before being transmission to the server. Meanwhile, it is crucial for the encrypted data transmitted to server to maintain availability in order to enable users' operations searching and querying. To preserve data privacy while enabling querying, clients must construct and transmit a secure index to the server for subsequent querying. When performing shortest distance queries on encrypted graph data outsourced in external storage, a significant challenge is computing the shortest distance in an efficient, accurate and secure manner. Furthermore, the challenge becomes more pronounced as the scale of the graph increases. In this paper, we propose a novel approach to address this issue by splitting the graph data into multiple segments and constructing 2HCL index LID within each subgraphs, as well as establishing an index LBD among these subgraphs. We also provide a comprehensive analysis of complexity and security of SSDAQ and present conclusion at the end of this paper. Compared to 2HCL, which is a classical and widely used algorithm, we reduce the time and space complexity from O(N2) time and O(kn|Lmax(sN)|) space to O(kn2) time and O(kn|Lmax(sn)|) space in setup phase. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Digital storage

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ByteZip: Efficient Lossless Compression for Structured Byte Streams Using DNNs

ByteZip: Efficient Lossless Compression for Structured Byte ...

引用

International Joint conference on Neural Networks (IJCNN)

作者： Ramakrishnan, Parvathy P. Das, Satyajit Indian Inst Technol Palakkad Dept Data Sci Palakkad Kerala India

ISBN: (纸本)9798350359329;9798350359312

data compression plays a key role in efficient data storage, transmission, and processing. With the fast development of deep learning techniques, deep neural networks have been used in this field to achieve a higher compression rate. Deep learning-based general-purpose lossless compression techniques are formulated as an autoregressive sequential prediction problem. These methods are state-of-the-art in terms of compression ratio but not practical due to runtime and resource constraints. Recent advances in lossless image compression using non-autoregressive methods for probability modeling prove to be a faster and more practical approach. In this paper, we propose ByteZip, a lossless compression method based on the non-autoregressive approach for known or defined structured byte streams. ByteZip involves hierarchical probabilistic modeling using autoencoders and density mixture models. This approach reduces the overhead of sequential processing. The goal is to design a practical lossless compressor with faster compression and decompression along with a competitive compression ratio. Experiments show that the proposed approach achieves a 64x higher compression speed than the state-of-the-art transformer-based model TRACE with an overhead of only 5% less size reduction on average. Our approach outperforms general-purpose compressors such as Gzip (23% more size reduction on average) and 7z (16% more size reduction on average).

关键词： lossless compression neural networks autoencoders mixture density networks

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Utilizing Big data Algorithms and Cloud Computing to Establish Centralized Electronic Health Records 5th

Utilizing Big Data Algorithms and Cloud Computing to Establi...

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5th International conference on data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Sharma, Khushboo Saxena, Kumud Bargavi, Manju Chaudhary, Chetan Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Computer Science Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

This paper aims to leverage massive statistics algorithms and cloud computing to create centralized electronic fitness statistics (EHRs) that are quickly and easily handy for all authorized healthcare professionals. The paper will evaluate the critical principles and technological advances that might be essential to setting up a possible EHR gadget. It will start with a dialogue about what an EHR is and how it has grown to be an increasing number of critical these days. The discussion will also include an overview of the different styles of EHRs and their benefits and disadvantages. The paper will then study the benefits of big statistics algorithms and cloud computing for EHRs. It will discuss how cloud computing permits efficient records retrieval, storage, and analysis competencies, in addition to how big facts analytics may be used for meaningful evaluation of large datasets. Eventually, the paper will consider the demanding situations related to using significant facts algorithms and cloud computing and recommend a few possible solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Reversible data Hiding in Shared Aerial Remote Sensing Images for Hierarchical Management 24

Reversible Data Hiding in Shared Aerial Remote Sensing Image...

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8th International conference on advances in Image Processing, ICAIP 2024

作者： Wang, Haina Meng, Lili Li, Jing Zhang, Kai Ren, Yannan Wan, Wenbo School of Information Science and Engineering Shandong Normal University Jinan China School of Journalism and Communication Shandong Normal University Jinan China School of Information Science and Electrical Engineering Shandong Jiao Tong University Jinan China

ISBN: (纸本)9798400717505

In the fields of Vision and Remote Sensing (VIR), aerial remote sensing images often contain privacy-related data, such as the types of detected objects and the geo-graphical information of the image content’s location. The misuse of these images and data can pose a threat to people’s privacy. This paper proposes a hierarchical secret sharing scheme for the secure storage of aerial remote sensing images. The scheme utilizes the correlation between image pixels, combined with the image sharing operation process, to achieve large-capacity reversible data embedding. Different regions of aerial remote sensing images and secret information are shared at multiple levels, embedding corresponding levels of secret data. When recovering the secret image and data, the set of authorized parties corresponding to the grade can reconstruct the same grade image and extract the secret data. Different roles extract different image regions and data to protect privacy, thus constructing an efficient security protection scheme for images and data. Experiments show that the scheme has a high data embedding capacity, and the secret images and data can be reconstructed without loss. © 2024 Copyright held by the owner/author(s).

关键词： Differential privacy

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A Novel UPQC Architecture based on optical storage Cooperation and its DC Bus Voltage Stabilization Control Strategy 3

A Novel UPQC Architecture based on Optical Storage Cooperati...

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3rd IEEE International conference on Information technology, Big data and Artificial Intelligence, ICIBA 2023

作者： Zhu, Minjie Li, Haibo Zou, Hongliang Chen, Mingxu Technology Branch of Taizhou Hongchuang Power Group Co. Ltd. Zhejiang Taizhou China

ISBN: (纸本)9781665490788

With the large-scale penetration of renewable new energy, it brings great challenges to the power quality management in the micro-grid. Aiming at the problem that the energy coupling between series and parallel filters reduces the stability of DC bus voltage stabilization control in the current micro-grid oriented power quality comprehensive treatment, this paper proposes a new type of power quality regulator UPQC architecture based on light storage cooperation. The RW-PSO algorithm based on random weighted particle swarm optimization is proposed to realize the dynamic on-line optimization of the PI parameters of the controller, which solves the defects of the new UPQC architecture that the DC voltage parameters fluctuate greatly and there are many disturbances. The simulation verifies the feasibility of the DC side bus voltage control strategy and the compensation effect of UPQC. © 2023 IEEE.

关键词： Power quality

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