The proceedings contain 35 papers. The topics discussed include: research on a parallel algorithm for video image compression of transmission line inspection;implementation of differential compression algorithm for su...
ISBN:
(纸本)9798400709166
The proceedings contain 35 papers. The topics discussed include: research on a parallel algorithm for video image compression of transmission line inspection;implementation of differential compression algorithm for substation inspection video images based on wavelet transform;a key frame extraction method for intelligent patrol video of large-scale substation based on sequence segmentation;advantages of unmanned aerial vehicle multi-view images in intelligent matching and defect hidden danger detection of substation inspection;automatic quality correction algorithm design for transmission line images based on the saliency model;research on video image transmission system of power equipment inspection based on deep learning;and polarization image processing technology based on machine vision detection.
The proceedings contain 50 papers. The topics discussed include: semi-automatic method of searching for the control points in two facial images;a novel edge-preserving filter for medical image enhancement;a steganogra...
ISBN:
(纸本)9789898533661
The proceedings contain 50 papers. The topics discussed include: semi-automatic method of searching for the control points in two facial images;a novel edge-preserving filter for medical image enhancement;a steganographic image-based approach for protection of PDF files;an android application to visualize point clouds and meshes in VR;keypoint-based object tracking and localization using networks of low-power embedded smart cameras;intensity normalization in brain MR images using spatially varying distribution matching;and deep learning in medical image analysis: recent advances and future trends.
The proceedings contain 35 papers. The topics discussed include: data-driven approach for generating colormaps of scientific simulation data;a 3D-shockwave volume rendering algorithm based on feature boundary detectio...
ISBN:
(纸本)9789898704214
The proceedings contain 35 papers. The topics discussed include: data-driven approach for generating colormaps of scientific simulation data;a 3D-shockwave volume rendering algorithm based on feature boundary detection;using reorderable matrices to compare risk curves of representative models in oil reservoir development and management activities;laser spot detection and characteristic analysis in plasma interaction simulation;hybrid sort a pattern-focused matrix reordering approach based on classification;data interpolation based on contextual analysis for generating tomographic images in concrete specimen;graphical user interface personalization: user study of image frequency preferences;evaluation of color spaces for unsupervised and deep learning skin lesion segmentation;and repeated pattern extraction with knowledge-based attention and semantic embeddings.
The proliferation of data produced by sensors and IoT technologies drives a new level of user data exploitation. Analyzing those data enables an intelligent understanding of user behavior and context to predict user i...
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ISBN:
(纸本)9783031804373;9783031804380
The proliferation of data produced by sensors and IoT technologies drives a new level of user data exploitation. Analyzing those data enables an intelligent understanding of user behavior and context to predict user intention correctly and provide relevant service recommendations. Many studies have exploited data from different sources for process mining purposes, but a few covered intention mining and its relation to context data. In this paper, we propose an architecture that uses information from data emissions to extract the user's situation, recognize his intention and context and then propose relevant services to the user according to his intention. In addition, our approach includes lightweight ontology to script the semantic annotations of the data stream.
With the rapid development of bigdata technology, there is an increasing demand for underlying computing architectures. RISC-V, as an open-source instruction set architecture, has shown tremendous potential and advan...
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This paper intends to use cloud computing technology combined with multi-source heterogeneous data to study extensive data analysis and modeling methods for dense environments. This paper aims to improve the mining an...
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ISBN:
(纸本)9783031705069;9783031705076
This paper intends to use cloud computing technology combined with multi-source heterogeneous data to study extensive data analysis and modeling methods for dense environments. This paper aims to improve the mining and detection of massive Internet of Things (IoT) bigdata in the cloud environment. Firstly, relevant statistical characteristics and correlation rules are extracted from massive IoT bigdata. Secondly, a multi-source heterogeneous network model based on block is proposed. This paper uses a multi-source isomer model to process the collected data, and it proposes a semantic ontology decomposition method for bigdata in dense IoT scenarios in a cloud environment, and establishes its association rule knowledge base. Meanwhile, the multi-source heterogeneous information transmission mechanism, and then the dense IoT in the cloud environment is analyzed and mined with bigdata. Experiments show the proposed algorithm performs better anti-jamming when applied in dense IoT environments. This method has better mining accuracy and less time cost.
The computation of the skyline provides a mechanism for utilizing multiple location-based criteria to identify optimal data points. However, the efficiency of these computations diminishes and becomes more challenging...
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The computation of the skyline provides a mechanism for utilizing multiple location-based criteria to identify optimal data points. However, the efficiency of these computations diminishes and becomes more challenging as the input data expands. This study presents a novel algorithm aimed at mitigating this challenge by harnessing the capabilities of Apache Spark, a distributed processing platform, for conducting area skyline computations. The proposed algorithm enhances processing speed and scalability. In particular, our algorithm encompasses three key phases: the computation of distances between data points, the generation of distance tuples, and the execution of the skyline operators. Notably, the second phase employs a local partial skyline extraction technique to minimize the volume of data transmitted from each executor (a parallel processing procedure) to the driver (a central processing procedure). Afterwards, the driver processes the received data to determine the final skyline and creates filters to exclude irrelevant points. Extensive experimentation on eight datasets reveals that our algorithm significantly reduces both data size and computation time required for area skyline computation.
When it comes to bigdata, the smartest thing is often allocating resources through cheapest channel. The latter involves assessing the costs and selecting the cheapest alternatives so as to make informed resource all...
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Through the exploration of the principle of behavioral decision-making method, we propose an intelligent decision-making framework and design a personalized driving strategy generation model. Combined with bigdata dr...
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The proceedings contain 35 papers. The topics discussed include: research on the pricing of the double-competition closed-loop supply chain with uncertain recycling quantity;a prediction model of buses passenger flow ...
The proceedings contain 35 papers. The topics discussed include: research on the pricing of the double-competition closed-loop supply chain with uncertain recycling quantity;a prediction model of buses passenger flow based on neural networks;prediction of learning behavior based on improved random forest algorithm;one target classification method based on target feature distribution fusion;an improved priority heuristic for the fixed guillotine rectangular packing problem;a new construction algorithm of the digital economy innovation system;single-phase ground fault identification method for distribution network based on inception model and sample expansion;and multi-structure non-linear semi-parametric optimal weighting parameters estimation algorithm based on adaptive sparse representation deconstruction model and integrative COD comprehensive model.
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