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A Convolutional Recurrent Mixer Network For Radar Meteorological Image Super-Resolution

A Convolutional Recurrent Mixer Network For Radar Meteorolog...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Pires, Rafael Gonçalves Santos, Daniel F.S. Calheiros, Roberto V. Papa, João Paulo Lee, Ik Hyun Bakshi, Sambit Muhammad, Khan Department of Computing São Paulo State University Bauru Brazil Meteorological Research Institute São Paulo State University Bauru Brazil Dept. of Mechatronics Engineering Tech University of Korea IKLAB Inc. Korea Republic of Dept. of CS&E National Institute of Technology Rourkela India School of Convergence Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9798350368741

Image super-resolution (SR) focuses on reconstructing high-resolution images from their low-resolution counterparts, often affected by sensor limitations or environmental factors. Convolutional Neural Networks (CNNs) are state-of-the-art for SR tasks but computationally heavy. This paper introduces a novel CRMN (Convolutional Recurrent Mixer Network), a hybrid deep learning-based SR technique designed to address the complexity of CNNs, which is validated in the context of meteorological radar images. Experiments on public benchmark datasets (Berkley432 and T291) and our newly manually collected precipitation dataset from the Meteorological Research Institute (IPMET) show that our CRMN model provides competitive results compared to leading SR methods with significantly fewer parameters, making it a promising and practical solution for SR applications, particularly radar meteorology. © 2025 IEEE.

关键词： Convolutional Neural Networks Deep Learning Meteorology Recurrent Mixer Network Super Resolution

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Enhancing Contextualized GNNs for Multimodal Emotion Recognition: Improving Accuracy and Robustness 7

Enhancing Contextualized GNNs for Multimodal Emotion Recogni...

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7th international Multi-Topic ICT conference: IMTIC 2023

作者： Shahbaz, Muhammad Huzaifa Zain-Ul-Abidin Mahboob, Khalid Ali, Fayyaz Sir Syed University of Engineering & Technology Software Engineering Department Karachi Pakistan

ISBN: (纸本)9798350338461

Emotion recognition from facial expressions is an important research area in the field of artificial intelligence. In this study, a novel deep-learning model is proposed for emotion recognition from facial expressions. The model uses a combination of modifications in COGMEN (Contextualized GNN-based Multimodal Emotion Recognition) to extract features from facial images and other modalities and capture temporal dependencies between frames. The model was evaluated on the IEMOCAP dataset, achieving an accuracy of 87.8%, outperforming (mostly) existing state-of-the-art methods. Furthermore, the model has a lower computational complexity compared to other methods, making it more practical for real-time applications. A high F1 score was reported for each emotion category, indicating good performance across all classes. The results demonstrate the potential method for improving emotion recognition from multiple modalities (i.e.: Audio, Visual & Text) and have implications for a wide range of applications such as affective computing, human-computer interaction, and mental health monitoring. © 2023 IEEE.

关键词： Emotion Recognition

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Research on Computer Cloud Intelligent System Based on Intelligent Virtualization technology

Research on Computer Cloud Intelligent System Based on Intel...

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Electrical Engineering, Big Data and Algorithms (EEBDA), IEEE international conference on

作者： Qingfeng Shi Fei Zhao College of Physical Education Taishan University Tai'an China Zibo High-sports Industry Development Zone Education and Sports Center Zibo City China

This paper is mainly the smart home system and some other intelligent devices and robot technology organic integration together, so that it can be more comprehensive for the robot service. At the same time, computer cloud computing technology is also integrated with the API of other third-party cloud computing platforms, so that it has more business capabilities. The goal of computer cloud computing architecture is to improve the computing and storage performance of robots with the help of massive data. The integration of third-party cloud computing services with intelligent hardware technology expands the resources and business functions of robots. By using cloud computing simulation technology, the inconsistency between observer and predictor is corrected, and a new cloud computing predictive control algorithm is designed to effectively suppress complex network attacks. Secondly, the D cloud computing system with predictive control mechanism is modeled, and the state convergence criterion in D cloud computing is established by using Lyapunov stability theory and LMI theory. Through remote operation of mobile terminals, the collaboration between mobile terminals based on cloud computing and third-party clouds is realized.

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Numerical Simulation of Three-Dimensional Simple Floating Body in Numerical Wave Tank 11th

Numerical Simulation of Three-Dimensional Simple Floating Bo...

引用

11th international conference on Asian and Pacific Coasts, APAC 2023

作者： Chang, Xiaoqin Diao, Feng Xu, Gang Jiangsu University of Science and Technology Zhenjiang212100 China China Ship Scientific Research Center Wuxi214082 China Zhuhai519000 China

ISBN: (纸本)9789819974085

In the field of ship and ocean engineering, numerical simulation of wave environments is an important method. This paper establishes a three-dimensional numerical wave tank using STAR-CCM + software based on the N-S equations and VOF method. The left side of the numerical wave tank is used velocity inlet wave-making method to simulate the generation of fifth-order Stokes wave and irregular wave in finite water depth. The convergence of the grid size, time step, and length of the wave-absorbing zone is analyzed, and the optimal scheme is selected to improve the accuracy of the numerical simulation and save computing cost. Finally, the interaction between waves and simple three-dimensional floating body is presented by numerical simulation. The results showed good agreement between experimental and calculated values, proving that the numerical wave tank and simulation method established in this paper are correct. This lays an important foundation for studying the interaction between waves and ocean structures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Navier Stokes equations

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Building a Time-Series Forecast Model with Automated Machine Learning for Heart Rate Forecasting Problem 13

Building a Time-Series Forecast Model with Automated Machine...

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13th international conference on Information and Communication technology convergence, ICTC 2022

作者： Cap, Huu-Anh-Duc Do, Trong-Hop Lakew, Demeke Shumeye Cho, Sungrae University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam School of Computer Science and Engineering Chung-Ang University Seoul Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Time series forecasting is currently a very popular field of study. Easily find a variety of time series data in medicine, weather forecasting, biology, supply chain management, stock price forecasting, and more. With the proliferation of data and computing power in recent years, deep learning has become the first choice for building time series predictive models. While traditional Machine Learning models - such as autoregression (AR), Exponential smoothing, or autoregressive integrated moving average (ARIMA) - perform manual conversion of the original raw data set into a set of attributes, and the optimization of the parameter must also be based on feature selection, the Deep Learning model only learns the features directly from the data alone. As a result, it speeds up the data preparation process and can fully learn more complex data patterns. In this paper, we designed LSTM deep learning network using Automated Machine Learning (AutoML) method to predict time series data which is the heart rate data. The results of this model can be applied to the field of medicine and health care. © 2022 IEEE.

关键词： Weather forecasting

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Sumformer: Recursive Positional Encoding for Transformer in Short Text Classification 3

Sumformer: Recursive Positional Encoding for Transformer in ...

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3rd international conference on Artificial Intelligence, Automation, and High-Performance computing, AIAHPC 2023

作者： Zhan, Peilin Lu, Liren Huang, Weishuang Zheng, Manna Lin, Qingwen Zuo, Yayao School of Computer Science and Technology Guangdong University of Technology Guangzhou515006 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

In various transformers, positional encoding is used to compensate for the inability of the attention mechanism to capture positional information between words. Previous research on transformers' temporal modeling has utilized recursive and relative positional encoding based on the recurrent neural network (RNN). Recursive positional encoding captures linear text structure but lacks parallelization, hindering speed. In contrast, relative positional encoding ignores linear text structure, leading to weaker performance in short text classification compared to recursive positional encoding. To address the issues, we propose a model, sumformer, which mainly includes two parts different from the other transformers: cumsum calculation and summer initialization. Cumsum calculation simplifies the feature extraction part of RNN by a substitution method, replacing the dynamic rate function of RNNs with static trainable position parameters, and preserves the recursive structure, which enables the model to capture the linear structure information of the text through cumsum calculation method and maintains a low time overhead compared to RNNs. In addition, the summer initialization method, which limits the highest standard deviation of the positional parameter, enables the model to pay attention to the multi-level information of the text during initialization, with the richer optimization space, thereby improving the convergence ability of the model. The experimental results show the sumformer achieves roughly a 3% improvement in performance and a 58% improvement in speed compared to existing transformers based on recursive positional encoding. It achieves better short text classification faster, and summer initialization also can improve the performance without increasing training and inference time. © 2023 SPIE.

关键词： Recurrent neural networks

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Efficient Method for computing Critical Path Delay using Longest Path Matrix Algorithm 2

Efficient Method for Computing Critical Path Delay using Lon...

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2nd international conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Prasad, Aparna Banuchitira, S.K. Srividhya, G. Mariammal, K. Department of Electronics Madras Institute of Technology Chennai India

ISBN: (纸本)9798350340235

Iterative algorithms are a fundamental component of numerous computational tasks, from numerical simulations to optimization problems. To ensure the efficiency and reliability of these iterative processes, it is crucial to determine suitable iteration bounds that balance computational cost and accuracy. Recent techniques to compute critical path delay like Machine Learning-based approaches. Path S ensitization techniques heavily rely on historical data. Hence changes in the initial conditions and inaccuracies in data often lead to unreliable predictions and result in significant deviation. Canonicity is important in multipliers to reduce complexity and DS P filters like IIR and FIR filters, whose stability decides the performance of the system. In this research work, a novel approach utilizing the Longest Path Matrix Algorithm(LPM) is presented, commonly known as the Critical Path Method (CPM). It is the ability to identify the longest path in a Directed Acyclic Graph (DAG) involving delays between nodes for complex networks. Multi-rate DAGs can be used but it contains many redundant nodes and edges. It provides a deterministic approach and involves matrix-based operations to compute critical paths, thus achieving precise, efficient and predictable results. CPM involves constructing, manipulating matrices and computing iteration bounds, which can be efficiently handled in MATLAB. Thus, it offers a robust and versatile solution for a wide range of iterative problems thereby optimizing the convergence of iterative algorithms. Minimum cycle mean algorithm is a better method than LPM with a lower polynomial time complexity. The DAG is implemented in Xilinx Vivado software to obtain the resource and power utilization for the various matrices obtained. The DAG used for LPM is applied to various filters like pipelined FIR filter, clustered lookahead pipelined filter and all pole lattice filter and their resource utilization is tabulated. © 2023 IEEE.

关键词： critical path critical path delay(CPD) digital signal processing(DSP) Direct Acyclic Graph (DAG) Iteration Bound Longest Path Matrix (LPM) MATLAB

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Research on lightweight detection algorithm of Infrared Image based on YOLOv5 4

Research on lightweight detection algorithm of Infrared Imag...

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4th international conference on Advanced Algorithms and Signal Image Processing, AASIP 2024

作者： Zhang, Wenyi Mo, Hongwei Wu, Songling Li, Xiaomin Harbin Engineering University School of Intelligent Science and Engineering Harbin150000 China Beijing Aerospace Automatic Control Institute Beijing100854 China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing100854 China

ISBN: (数字)9781510682818

ISBN: (纸本)9781510682801

At present, infrared image can show good detection effect in complex environment because of its unique imaging characteristics, which brings a lot of convenience to people's production and life. However, because the infrared image contains a great lot of unnecessary information, it has great requirements for computing power and memory capacity, so it is challenging to satisfy the demands of real-time detection on resource-constrained portable mobile devices. To solve this problem, this paper proposes a refined lightweight algorithm built upon the YOLOv5, which changes the backbone network of the model to GhostNet network, introduces CA attention mechanism into the model to improve the detection effect of small targets, and changes the loss function of the model to Focal-EIoU loss function to improve the location effect of the boundary box and quicken the model's rate of convergence. Then replace the conventional convolution used in the sampling process of the model with the space-to-depth convolution module, in order to enhance the model's detection performance on low-resolution images and enable the extraction of more feature information. According to the experimental results, the enhanced detection algorithm is just 1% less than the initial YOLOv5 algorithm, and the model volume and floating point calculation are reduced by 31.9% and 36% respectively. This is better suited for deployments that are lightweight. © 2024 SPIE.

关键词： Convolution

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A Blockchain System for Clustered Federated Learning with Peer-to-Peer Knowledge Transfer 50th

A Blockchain System for Clustered Federated Learning with Pe...

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50th international conference on Very Large Data Bases, VLDB 2024

作者： Wu, Honghu Zhu, Xiangrong Hu, Wei State Key Laboratory for Novel Software Technology Nanjing University China

Federated Learning (FL) is a novel distributed, privacy-preserving machine learning paradigm. Conventional FL suffers from drawbacks such as single point of failure and client drift. Blockchain is a distributed computing architecture famous for decentralization, transparency, and traceability. Incorporating blockchain as the underlying basis for FL decentralizes the FL process and brings opportunities to resolve the drawbacks. However, there still remain challenges to fulfilling FL with blockchain, regarding effectiveness, efficiency, and security. In this paper, we propose a new blockchain system for FL, called FedChain. To mitigate client drift and accelerate training, we present a clustered semi-asynchronous method for model aggregation. To optimize the local training in FL, we introduce a knowledge transfer method using other clients on the peer-to-peer network of blockchain. Moreover, we implement an access control mechanism to store and transmit models safely and efficiently. Extensive experiments on various benchmark datasets show that FedChain achieves superior results in accuracy, convergence, throughput, and latency. © 2024, VLDB Endowment. All rights reserved.

关键词： Blockchain

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Research on cloud computing resource scheduling based on improved ant colony optimization algorithm 4

Research on cloud computing resource scheduling based on imp...

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4th international conference on Frontiers technology of Information and Computer, ICFTIC 2022

作者： Xia, Chenyue Wang, Rui Deng, Zhuofu Zheng, Yingnan Software College of Northeastern University Liaoning Province Shenyang City China

ISBN: (纸本)9798350321951

With the rise of cloud computing services around the world, how to reduce the load balancing degree of cloud computing resource scheduling and improve the utilization rate of cloud computing resources has gradually attracted the attention of the academic community. In order to meet these needs, I proposed to use ant colony optimization algorithm scheduling to deal with large-scale cloud computing resource data sets. Meanwhile, in order to prevent the ant colony optimization algorithm I proposed from falling into the local optimal solution, I proposed to improve the probability and heuristic factor of selecting the next node of ant colony optimization algorithm as well as the update of pheromone, so as to give full play to the guiding role of pheromone to achieve the optimal cloud computing resource scheduling scheme. Examples have proved that the algorithm proposed by me has low relative standard deviation and load balancing degree. Meanwhile, the proposed scheduling scheme can evenly allocate resources, and the total utility value of scheduling is high, which can improve the scheduling ability of cloud computing resources to the greatest extent. At the same time, the fewer iterations required to schedule resources, the better the convergence of the algorithm. It can be seen that this algorithm can significantly improve the potential of cloud computing resource scheduling. © 2022 IEEE.

关键词： Cloud computing

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