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2024 international conference on intelligent computing and data Mining, ICDM 2024

作者： Qiu, Shuwei Humayoo, Mahammad Zhou, Zexun Huang, Wei Dong, Xiaoqing Hanshan Normal University Department of Computer Science Chaozhou521041 China Hanshan Normal University Department of Physics Chaozhou521041 China

ISBN: (纸本)9798331506261

In a Wireless Powered Sensor Network (WPSN) aided by Unmanned Aerial Vehicles (UAVs), UAVs wirelessly recharge nodes to support the network's operation. In WPSN, efficient broadcasting of IPv6 packets is crucial for various IoT applications. This paper presents a new IPv6 packet broadcasting scheme that leverages network coding to improve broadcast reliability. Additionally, we reformulate the IPv6 packet broadcasting problem as a unicast problem, deriving a throughput expression to create an optimization problem with the goal of maximizing throughput as the objective function. The variables to be optimized include the broadcast tree and the UAV locations. We then propose a two-stage optimization method to address this problem. Simulation results demonstrate that our approach significantly enhances the throughput of IPv6 packet broadcasting in WPSNs. © 2024 IEEE.

关键词： Network coding

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Keynote Talk 1 Cognition and Future

Keynote Talk 1 Cognition and Future

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data science and intelligent computing (icdsic), international conference on

作者： Sattar B. Sadkhan Wireless Digital Communication and Information Security Babylon University - IT College Iraq

The main topic of this lecture will be related to the Information security and the role of the Cognitive science in Enhancing this security. The main topics will be: *** of Future Parameters in Advance Technologies *** Features towards New Technologies *** and its main role in progress of the new science and technologies applications *** Cognitive Security *** Cryptography *** Important Parameters *** Scientific support

关键词： Information security Wireless communication IEEE Sections Digital communication Communication system security Radar data science

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

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

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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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Two-Stage Deep Energy Optimization in IRS-Assisted UAV-Based Edge computing Systems

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IEEE TRANSACTIONS ON MOBILE computing 2025年第1期24卷 449-465页

作者： Wu, Jianqiu Yu, Zhongyi Guo, Jianxiong Tang, Zhiqing Wang, Tian Jia, Weijia BNU HKBU United Int Coll Dept Comp Sci Guangdong Key Lab AI & Multimodal Data Proc Zhuhai 519087 Peoples R China Beijing Normal Univ Adv Inst Nat Sci Zhuhai 519087 Peoples R China

Integrating wireless-powered Mobile Edge computing (MEC) with Unmanned Aerial Vehicles (UAVs) leverages computation offloading services for mobile devices, significantly enhancing the mobility and control of MEC networks. However, current research has not focused on customizing system designs for Terahertz (THz) communication networks. When dealing with THz communication, one must account for blockage vulnerability due to severe THz wave propagation attenuation and insufficient diffraction. The intelligent Reflecting Surface (IRS) can effectively address these limitations in the model, enhancing spectrum efficiency and coverage capabilities while reducing blockage vulnerability in THz networks. In this paper, we introduce an upgraded MEC system that integrates IRS and UAVs into THz communication networks, focusing on a binary offloading policy for studying the computation offloading problem. Our primary objective is to optimize the energy consumption of both UAVs and User Electronic Devices, alongside refining the phase shift of the IRS reflector. The problem is a Mixed Integer Non-Linear Programming problem known as NP-hard. To tackle this challenge, we propose a two-stage deep learning-based optimization framework named Iterative Order-Preserving Policy Optimization (IOPO). Unlike exhaustive search methods, IOPO continually updates offloading decisions through an order-preserving quantization method, thereby accelerating convergence and reducing computational complexity, especially when handling complex problems with extensive solution spaces. The numerical results demonstrate that the proposed algorithm significantly improves energy efficiency and achieves near-optimal performance compared to benchmark methods.

关键词： Terahertz communications Autonomous aerial vehicles Optimization Resource management Servers Computational modeling Costs Mobile edge computing deep learning unmanned aerial vehicles intelligent reflective surface terahertz communications

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A Novel data-Driven Soft Sensor in Metaverse Provisioning Predictive Credibility Based on Uncertainty Quantification 2

A Novel Data-Driven Soft Sensor in Metaverse Provisioning Pr...

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2024 international conference on Metaverse computing, Networking, and Application

作者： Park, Seong-Ho Je-Gal, Hong Lee, Hyun-Suk Sejong Univ Dept IME Seoul South Korea

ISBN: (纸本)9798331516000;9798331515997

Soft sensors are used in various application domains to replace physical sensors. However, the soft sensor's predictions cannot be trusted as much as the physical sensor's measurements. Therefore, the evaluation of the credibility of soft sensor predictions is crucial to prepare for the issues caused by inaccurate soft sensor measurements. In this paper, we propose a novel soft sensor architecture that can provide not only predictions but also their predictive credibility via uncertainty quantification. The proposed architecture offers the confidence interval of each prediction that can be used to evaluate the credibility of the soft sensor's predictions. In our experiments, we build scenarios with different predictive uncertainties and demonstrate the proposed architecture provides credible uncertainty quantification. By using the proposed architecture, we can assess how trustworthy the soft sensor's predictions are, and thus, effectively manage and respond to varying levels of prediction accuracy.

关键词： deep learning soft sensor deep ensemble uncertainty quantification coverage rate

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Wind Speed Prediction Method Based on Combined Deep Learning Model 9

Wind Speed Prediction Method Based on Combined Deep Learning...

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9th international conference on intelligent computing and Signal Processing, ICSP 2024

作者： Zeng, Chuxi Xia, Zhipnig Zhou, Yihuan Li, Xi Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350376548

The necessity of precise wind speed forecasts is crucial for integrating wind turbines into microgrids. This study introduces a hybrid forecast approach that initially optimizes the key factors of variational modal decomposition (VMD) through the whale optimization algorithm (WOA). Then decomposes the initial wind speed data into intrinsic mode functions (IMFs) using the WOA-VMD method. Secondly, the Pearson correlation coefficient is conducted between IMFs and the initial wind speed data, classifying the IMFs into two groups according to their correlation coefficients. IMFs closely correlated are forecasted utilizing a temporal convolutional network (TCN), while those less correlated are forecasted via a gate recurrent unit (GRU). Finally, the forecasting outcomes from each category are then aggregated to formulate the ultimate forecast results. The hybrid WOA-VMD-GRU-TCN model is tested on actual data from six wind farms across China, the results demonstrate that the proposed model markedly enhances forecast precision, minimizes wind speed prediction errors, and exhibits superior robustness compared to alternative methods. © 2024 IEEE.

关键词： Wind forecasting

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The Performance Analysis of Model Parallelism in Deep Residual Network 3

The Performance Analysis of Model Parallelism in Deep Residu...

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3rd international conference on intelligent computing and Human-Computer Interaction, ICHCI 2022

作者： Feng, Yuqin Major in Computer Science Department of Engineering Virginia Tech BlacksburgVA24060 United States

ISBN: (纸本)9781510661301

With the rapid information and communications technology growth and continuous invention, the concept of parallel computing has become the core of computer science, and its capabilities are continually documented in providing high processing solutions. The graphic processing unit (GPU) plays a crucial role in deep learning and parallel computing. The primary purpose of this paper is to discuss two concepts of deep computing: data parallelism and model parallelism. Additionally, the discussion will delve into their importance and associated challenges in parallel processing. Furthermore, the paper summarises the Residual Network (ResNet) system with the basic principles and formulas. Establishing efficient parallel algorithms for GPU processing is necessary. This paper evaluates the performance and speed between single-core and multi-core working environments for two kinds of convolutional neural network structures with different types of GPUs. The first one is LeNet, and the other one is VGG16. There are two final results of this experiment. By analyzing the data of performance, VGG16 has better performance than LeNet. By analyzing the data of speed, the speed of LeNet is faster than that of VGG16. © 2023 SPIE.

关键词： Graphics processing unit

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Analysis and Visualization of Stock data Based on BP Neural Network 24

Analysis and Visualization of Stock Data Based on BP Neural ...

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2024 international conference on intelligent Education and Computer Technology, IECT 2024

作者： Deng, Chao Peng, Jiao College of Computer Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9798400709920

In the field of finance, the analysis and prediction of stock data have always been a hot topic of research. Back Propagation (BP) neural networks have shown great potential in handling complex financial time series data due to their excellent nonlinear mapping capabilities. In this paper, a model for analyzing and visualizing stock data based on BP neural network is constructed to predict the closing price of the stock market. A multi-layer feedforward neural network is designed, with the cross-entropy loss function selected and the stochastic gradient descent (SGD) algorithm used to update parameters. By computing the gradient of the loss function with respect to each parameter, the direction and magnitude of parameter updates can be determined. After multiple rounds of iterative training, the model gradually approaches the optimal solution. Detailed preprocessing and analysis of historical stock data are conducted, and the trained BP model is used to predict the closing price. The results show that the model can effectively learn and predict stock price trends, with certain practical value. © 2024 ACM.

关键词： Prediction models

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Optimization and Prediction of intelligent Tourism data 8

Optimization and Prediction of Intelligent Tourism Data

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8th IEEE international conference on Big data Security on Cloud, 8th IEEE international conference on High Performance and Smart computing, and 8th IEEE international conference on intelligent data and Security, BigdataSecurity/HPSC/IDS 2022

作者： Zhong, Luoyifan QILU Institute of Technology School of Computing Science and Information Engineering Jinan China

ISBN: (纸本)9781665480697

Tourism is one of the main sources of income in Australia. The number of tourists will affect airlines, hotels and other stakeholders. Predicting the arrival of tourists can make full preparations for welcoming tourists. This paper selects Queensland Tourism data as intelligent data. Carry out data visualization around the intelligent data, establish seasonal ARIMA model, find out the characteristics and predict. In order to improve the accuracy of prediction. Based on the tourism data around Queensland, build a 10 layer Back Propagation neural network model. It is proved that the network shows good performance for the data prediction of this paper. © 2022 IEEE.

关键词： Forecasting

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Towards a Hierarchical Exascale Framework for Iterative Parallel data Analysis Algorithms 32

Towards a Hierarchical Exascale Framework for Iterative Para...

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32nd Euromicro international conference on Parallel, Distributed and Network-Based Processing (PDP)

作者： Cesario, Eugenio Lindia, Paolo Univ Calabria Arcavacata Di Rende CS Italy

ISBN: (纸本)9798350363074;9798350363081

Several parallel and distributed data mining algorithms have been proposed in literature to perform large scale data analysis, overcoming the bottleneck of traditional methods on a single machine. However, although the master-worker approach greatly simplifies the synchronization of all nodes since only the master is in charge to do that, it also presents several problematic issues for large-scale data analysis tasks (involving thousands or millions of nodes). This paper presents a hierarchical (or multi-level) master-worker framework for iterative parallel data analysis algorithms, to overcome the scalability issues affecting classic master-worker solutions. Specifically, the framework is composed of (more than one) merger and worker nodes organized in a k-tree structure, in which the workers are on the leaves and the mergers are on the root and the internal nodes in the tree.

关键词： distributed data mining exascale computing high performance computing

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