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2022 7th International Conference on Electronic Technology and Information science, ICETIS 2022

作者： Li, Xiaolong Xu, Junwu Liao, Jiayin Wang, Chaoqun Chen, Xiaohui Li, Fang School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China Product Development Department Shanghai Academy of Spaceflight Technology Shanghai China Gas Intelligent Application Technology R&D Center Shanghai Aerospace Energy Co. Ltd Shanghai China

ISBN: (纸本)9783800757794

The accurate measurement of gas flow is directly related to the economic benefits and social interests of gas enterprises. Moreover, as a kind of time-series data, there is a serious imbalance in the distribution of positive and negative samples in the data, which leads to low accuracy and difficulty in detecting gas anomaly data. To address these problems, a cost-sensitive hybrid network model is proposed in this paper. The model combines the stronger local feature learning ability of CNN network and the better sequence feature learning ability of GRU network, and then introduces a cost-sensitive loss function to solve the problem of inaccurate classification accuracy due to skewed data distribution. The experimental results show that using this model can find the outliers in the measurement data more accurately, which has some reference significance for the accurate measurement of traffic. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Flow of gases

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UWB Positioning Analysis and Algorithm Research

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Procedia computer science 2022年 198卷 466-471页

作者： Yuekui Zhang Yunxia Chu Yunfang Fu Zhixiang Li Yufei Song School of Computer Science and Engineering Shijiazhuang University Shijiazhuang 050035 China Hebei Province Internet of Things Intelligent Perception and Application Technology Innovation Center Shijiazhuang 050035 China

In order to improve the accuracy of indoor positioning, this paper studied the three-dimensional positioning method based on TDOA and designed the ranging experiment with or without occlusion. The error data were analyzed by many experiments, and the advantages of strong penetration and good anti-interference ability of UWB signal were verified. At the same time, the UWB positioning direct matrix algorithm is optimized, and the least square matrix method is used to process the positioning data to obtain more accurate position coordinates. The experimental results show that the accuracy of the positioning data can be improved by about 25% by using the least square matrix method.

关键词： TDOA Three-dimensional positioning UWB Least squares matrix method

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Exploring Dimensionality Reduction Techniques for On-line Analytical Processing

Exploring Dimensionality Reduction Techniques for On-line An...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Anup Kumar Inzimam Srinivasan Patel Department of Mechnical Engineering ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Management School of Management - PG JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Exploring Dimensionality discount strategies for Analytical Processing (OLAP) is an area of research that makes a specialty of the use of diverse techniques to reduce the variety of dimensions needed to investigate a records set. That is high-quality because it reduces processing time, garage area requirements, and simplifies the statistics and outcomes acquired. Diverse strategies are to be had for reducing the wide variety of dimensions, and those are based totally upon the application and the associated information set. The strategies include central factor assessment (PCA), characteristic desire, weighted length reduction, and Locality Sensitive Hashing (LSH). This paper provides a comparative evaluation of those strategies and discusses their strengths and obstacles. The outcomes of this observation will help researchers and practitioners understand each method’s talents and limitations while using it in OLAP packages.

关键词： Dimensionality reduction Principal component analysis Singular value decomposition

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MyWAL:performance optimization by removing redundant input/output stack in key-value store

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Frontiers of Information Technology & Electronic engineering 2023年第7期24卷 980-993页

作者： Xiao ZHANG Mengyu LI Michael NGULUBE Yonghao CHEN Yiping ZHAO School of Computer Science Northwestern Polytechnical UniversityXi'an 710072China MIIT Key Laboratory of Big Data Storage and Management Northwestern Polytechnical UniversityXi'an 710072China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical UniversityXi'an 710072China School of Software Northwestern Polytechnical UniversityXi'an 710072China

Based on a log-structured merge(LSM)tree,the key-value(KV)storage system can provide high reading performance and optimize random writing *** is widely used in modern data storage systems like e-commerce,online analytics,and real-time *** LSM tree stores new KV data in the memory and flushes to disk in *** prevent data loss in memory if there is an unexpected crash,RocksDB appends updating data in the write-ahead log(WAL)before updating the ***,synchronous WAL significantly reduces writing *** this paper,we present a new WAL mechanism named *** directly manages raw devices(or partitions)instead of saving data on a traditional file *** can avoid useless metadata updating and write data sequentially on *** results show that MyWAL can significantly improve the data writing performance of RocksDB compared to the traditional WAL for small KV data on solid-state disks(SSDs),as much as five to eight times *** non-volatile memory express soild-state drives(NVMe SSDs)and non-volatile memory(NVM),MyWAL can improve data writing performance by 10%–30%.Furthermore,the results of YCSB(Yahoo!Cloud Serving Benchmark)show that the latency decreased by 50%compared with SpanDB.

关键词： Key-value(KV)store Log-structured merge(LSM)tree Non-volatile memory(NVM) Non-volatile memory express soild-state drive(NVMe SSD) Write-ahead log(WAL)

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Iterative Equalization of CPM with Unitary Approximate Message Passing

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Zilong Song, Yi Guo, Qinghua Sun, Peng Gong, Kexian Wang, Zhongyong Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Henan Zhengzhou450001 China Henan University of Technology Henan Engineering Research Center of Grain Condition Intelligent Detection and Application Henan Zhengzhou450001 China Henan University of Technology College of Science and Engineering Henan Zhengzhou450001 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia

Continuous phase modulation (CPM) has extensive applications in wireless communications due to its high spectral and power efficiency. However, its nonlinear characteristics pose significant challenges for detection in frequency selective fading channels. This paper proposes an iterative receiver tailored for the detection of CPM signals over frequency selective fading channels. This design leverages the factor graph framework to integrate equalization, demodulation, and decoding functions. The equalizer employs the unitary approximate message passing (UAMP) algorithm, while the unitary transformation is implemented using the fast Fourier transform (FFT) with the aid of a cyclic prefix (CP), thereby achieving low computational complexity while with high performance. For CPM demodulation and channel decoding, with belief propagation (BP), we design a message passing-based maximum a posteriori (MAP) algorithm, and the message exchange between the demodulator, decoder and equalizer is elaborated. With proper message passing schedules, the receiver can achieve fast convergence. Simulation results show that compared with existing turbo receivers, the proposed receiver delivers significant performance enhancement with low computational complexity. © 1967-2012 IEEE.

关键词： Demodulation

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Profiling Industrial 3D Parts with Complex Shapes Using 2D Imagery

Profiling Industrial 3D Parts with Complex Shapes Using 2D I...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Nivedan Mahato Harshita Kaushik Uma C Swadimath Department of Mechnical Engineering ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Management School of Mangement - PG JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Commercial 3-D element profiling via second imagery is a modern technique to acquire the 3-D geometric characteristics of quite complicated shapes frequently encountered in industrial additives. This approach makes use of a set of pix taken from multiple angles to derive the corresponding 3-D profile of the component with the use of photogrammetry strategies. The resulting three-D statistics can be similarly characterized to evaluate the part’s dimensional accuracy, floor texture, and different attributes. The novel technique described in this work augments conventional measuring methods and opens new possibilities for automated bodily asset evaluation, reverse engineering, and automated inspection. The method defined has potential programs in an extensive type of fields in which correct geometrical characterization of complex parts is important.

关键词： Measurement Accuracy Three-dimensional displays Portable computers Shape Computational modeling Machine vision Reverse engineering Observers Photogrammetry

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Accessible Authentication Using Keyword Search through Homomorphic Encryption 1

Accessible Authentication Using Keyword Search through Homom...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Vekariya, Vipul Manjunath, R. Gill, Jasmeen Gopianand, M. Kumar, Avneesh Jha, Amaresh Parul University Department of CSE PIET Gujarat Vadodara India Department of Data Science & Mathamatics Bangalore India RIMT University Department of CSE Godindgarh Mandi India PSNA College of Engineering and Technology Department of Computer Applications Dindigul India Galgotias University Department of Computer Application Uttar Pradesh Greater Noida India School of Modern Media UPES Dehradun India

ISBN: (纸本)9781665456876

Data storage presents substantial issues due to concerns about data security and privacy. In this study, we provide a search-based, interoperable, verified public key cryptography system. The searchability is significantly improved by our recommended method, which allows the cloud servers to create invert crypto indexes while needing a query trap and leveraging asymmetric cryptography developed by van Vries, Miller, Showing different, and Vaikuntanathan. Contrarily, the recommended method offers a brand-new permitted dataset built on the inversion decryption scheme and shows how it may be used to evaluate the thoroughness and correctness of serps. Additionally, using the provided method, many users may do encrypted searches on cryptography. Finally, it is shown that the offered technique is secure utilizing the approximate-GCD problem. The experiment's results showed that the proposed technique had less computational complexity than the methods currently in use. © 2022 IEEE.

关键词： Public key cryptography

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A Hybrid Blockchain Scheme for Tracing Manufacturing Processes and Trading Transactions 22

A Hybrid Blockchain Scheme for Tracing Manufacturing Process...

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22nd IEEE International Symposium on Parallel and Distributed Processing with applications, ISPA 2024

作者： Yuan, Ye Yang, Mingxing Xiong, Ruoting Sun, Yani Zhang, Xianchao Ren, Wei HuBei Land Resources Vocational College China China University of Geosciences Gemological Institute Wuhan China University of East Anglia School of Computing Science United Kingdom Yunnan Innovation Institute Beihang University Yunnan Key Laboratory of Blockchain Application Technology Kunming China Jiaxing University Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing China State Key Laboratory of Geo-Information Engineering Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology Mnr Beijing China Ministry of Education China China University of Geosciences School of Computer Science Wuhan China

ISBN: (纸本)9798331509712

Blockchain can be envisioned as an enabling framework in terms of cryptography and distributed computing to construct trusted data records among un-trusted users. It can be applied in two typical scenarios - historical process provenance and transaction tracing, by immutably sequentialized blocks. In the former there exists no tokens;but in the latter token handover among accounts should be cryptographically guaranteed. In some situations, above two applications may be both required. E.g., in the manufacture of a jewel, processing flow by different operators should be recorded for quality guaranteeing, and in its trading stage, the ownership handover should be recorded for value confirmation. To tackle above two requirements, two chains are usually thus demanded. In this paper, we propose a single chain solution to avoid cross-chain burden, by a hybrid block structure. The process data can be imported from the legacy (non-anonymous) information processing system, and transaction data stems from anonymous payment demand for ownership handover. We formally describe and prove token handover model with cryptographical authenticity. We also propose several security enhancements in the design for shortening the length of the chain, and protecting the data confidentiality in the chain. Our design is general and not limited to single application. The experimental results and extensive analysis justified that the performance and security of our proposed scheme is efficient. © 2024 IEEE.

关键词： Data Provenance Data Tracing Hybrid Blockchain Token Handover

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Exploring Sentiment Analysis Algorithms for Brand-Product Reputation Enhancement

Exploring Sentiment Analysis Algorithms for Brand-Product Re...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Ashwini Kumar Sunil Sharma Sireesha Nanduri Department of Mechnical Engineering ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Management School of Mangement - PG JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper affords an overview of sentiment analysis algorithms for boosting brand-product reputation. The point of interest is on supervised and unsupervised systems getting to know strategies, which might be usually used for predicting sentiment. Specifically, the authors examine 4 forms of algorithms - Naïve Bayes classifiers, support vector machines, recurrent neural networks, and sentiment analysis with deep mastering - and how they can be used for boosting emblem-product popularity. Moreover, the look evaluates the constraints of the algorithms, in addition to capacity destiny studies instructions. The authors finish that while sentiment analysis with deep getting to know can also be the maximum accurate choice, cautious consideration must be made while choosing an algorithm as specific algorithms may also produce distinctive outcomes. This paper presents an informative and comprehensive assessment of sentiment analysis algorithms and their usefulness for boosting logo-product reputation.

关键词： Support vector machines Sentiment analysis Accuracy Recurrent neural networks Semantics Customer satisfaction Prediction algorithms Boosting Vectors Classification algorithms

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DHAKR: Learning Deep Hierarchical Attention-Based Kernelized Representations for Graph Classification 39

DHAKR: Learning Deep Hierarchical Attention-Based Kernelized...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Qian, Feifei Bai, Lu Cui, Lixin Li, Ming Lyu, Ziyu Du, Hangyuan Hancock, Edwin School of Artificial Intelligence Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education Beijing Normal University Beijing China School of Information Central University of Finance and Economics Beijing China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Zhejiang Institute of Optoelectronics Jinhua China School of Cyber Science and Technology Sun Yat-Sen University Shenzhen China School of Computer and Information Technology Shanxi University Taiyuan China Department of Computer Science University of York York United Kingdom

ISBN: (纸本)157735897X

Graph-based representations are powerful tools for analyzing structured data. In this paper, we propose a novel model to learn Deep Hierarchical Attention-Based Kernelized Representations (DHAKR) for graph classification. To this end, we commence by learning an assignment matrix to hierarchically map the substructure invariants into a set of composite invariants, resulting in hierarchical kernelized representations for graphs. Moreover, we introduce the feature-channel attention mechanism to capture the interdependencies between different substructure invariants that will be converged into the composite invariants, addressing the shortcoming of discarding the importance of different substructures arising in most existing R-convolution graph kernels. We show that the proposed DHAKR model can adaptively compute the kernel-based similarity between graphs, identifying the common structural patterns over all graphs. Experiments demonstrate the effectiveness of the proposed DHAKR model. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

关键词： Deep learning

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