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检索条件"机构=Software Engineering and Technology"

共 31298 条记录，以下是181-190 订阅

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Research on Real-Time Navigation of Mobile Robot Based on Vertical Monocular Surveillance Camera in Indoor Environment 3rd

Research on Real-Time Navigation of Mobile Robot Based on Ve...

引用

3rd World Conference on Intelligent and 3D Technologies, WCI3DT 2024

作者： Zhong, Zixuan Software and Systems Engineering Department Lappeenranta University of Technology Lahti City15210 Finland

ISBN: (纸本)9789819791279

To informatively plan optimal paths for autonomous mobile robots in indoor environment is essential in real life cases. In view of the shortcomings of the traditional path planning strategies based on the cameras mounted on robot body and considering surveillance cameras are always ready and available sensors in indoor environment, a real-time navigation system based on vertical surveillance camera is proposed. Based on the assumption that the surveillance camera is able to capture the full picture of the environment, for each frame of the video recorded by the camera, a traversable node map of the environment for the robot is firstly established. Background subtraction is then applied, to achieve real-time localization of the robot in the node map, by comparing an empty environment with each frame of the video feed where the robot exists. Secondly, two path planning algorithms are applied on the localized node map, and a comparison is made for informative path planning. To evaluate the robustness of the proposed system, three experiments are conducted on a preset map, a differential caster wheel robot and a suite of matched motion planning system. The time for the robot to navigate itself from entrance to exit and number of nodes traversed for each experiment are recorded and compared. The final result demonstrates the robustness and efficiency of the proposed path planning method based on a vertical surveillance camera. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robot programming

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Lightning Nowcasting Based on Depthwise Separable Residual Convolution with Attention Mechanism by Using Multisource Data 6

Lightning Nowcasting Based on Depthwise Separable Residual C...

引用

6th International Conference on Electronics and Communication, Network and Computer technology, ECNCT 2024

作者： Xie, Dan Luo, Fei School of Software Engineering Chengdu University of Information Technology Chengdu610225 China

ISBN: (纸本)9798350366143

Lightning is a common natural phenomenon with great destructive power, capable of damaging buildings, igniting fires, and causing casualties. Timely prediction of lightning occurrences and the implementation of appropriate precautions are crucial for maintaining human productivity and safety. As the traditional method of lightning prediction, the extrapolation method of thunderstorm monomer is unable to grasp the generation and disappearance of lightning accurately. Meanwhile, the lightning forecast relies on numerical simulations and parameterizations have problems in the spatiotemporal deviations. In this paper, we propose a depth separable residual convolution block(ResCDBlock) with an attention mechanism and construct a ResCD-3DUnet model for lightning short-term prediction. The utilization of multi-source data, including satellite, radar, and lightning location data with high spatiotemporal resolution, enables the model to learn the lightning characteristics more effectively. The results show that the constructed ResCD-3DUnet scores higher than other baseline models for lightning prediction. The proposed ResCDBlock and the fusing of multi-source data are demonstrated more effectively in the ablation studies. © 2024 IEEE.

关键词： Deep neural networks

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Automatic Chart Decoding System Based on Deep Learning and Image Processing 4

Automatic Chart Decoding System Based on Deep Learning and I...

引用

4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Liang, Chaofan Xue, Fenghao Li, Zhangwei College of Software Engineering Chengdu University of Information Technology Chengdu610000 China

ISBN: (纸本)9798331541729

Statistical Charts contain a wealth of information. As an important way to visualize data presentation, statistical charts allow viewers to obtain a complete and intuitive understanding of the content shown in a very short time. At present, the research on automatic extraction and understanding of a large amount of text information has been relatively mature. However, even the latest big artificial intelligence models cannot accurately extract statistical graphs, which are personalized and contain a large amount of information. We propose an automatic bar chart data extraction process by combining deep learning and image processing technology, and construct an intelligent bar chart decoding system. The system is divided into three parts: the classification of statistical chart types, the text detection in the image, the classification of text roles and the image extraction. The original data used to create the chart in the pan-bar graph image is extracted for downstream applications. We evaluate and compare our system on public datasets. The results show that our system has better accuracy. © 2024 IEEE.

关键词： Data assimilation

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Short-term Ground Wind Field Forecasting Based on DGCN-2TS 5

Short-term Ground Wind Field Forecasting Based on DGCN-2TS

引用

5th International Conference on Computer engineering and Intelligent Control, ICCEIC 2024

作者： Yang, Xiao Luo, Fei School of Software Engineering Chengdu University of Information Technology Chengdu610225 China

ISBN: (纸本)9798331507992

Wind forecasting plays a crucial role in renewable energy dispatch and extreme weather warnings. However, due to the highly dynamic spatiotemporal characteristics of wind and its rapid variability, accurate wind forecasting has always been a challenging task. In recent years, spatiotemporal graph neural networks have shown potential in integrating temporal information and spatial correlations among stations, providing a new solution for wind forecasting. Nevertheless, existing methods still face two main challenges: a) Predefining the adjacency relationships between stations using a static graph structure fails to effectively capture the dynamic meteorological features that change over time, resulting in the forecasting model's lack of adaptability to complex weather variations. b) The rapid and frequent changes in wind speed and direction make it difficult for traditional models to fit these nonlinear and high-frequency variations, affecting the prediction accuracy. To address these issues, we propose a new spatiotemporal forecasting framework that dynamically combines the spatial correlation structure among stations with the temporal correlations, adaptively generating the adjacency matrix based on meteorological features at each time step to capture the spatiotemporal dynamics of weather conditions. Furthermore, we design a multi-level forecasting model that first predicts meteorological variables with smaller changes and stronger trends, providing foresight information for subsequent wind forecasting and enhancing the model's adaptability to complex meteorological conditions. Experiments conducted on a real dataset from Southwest China demonstrate that the proposed method outperforms existing models in the wind forecasting task in this region. © 2024 IEEE.

关键词： Wind forecasting

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A Secure Encryption Method for Computer Network Communication Data based on Improved Ant Colony Optimization Algorithm 2

A Secure Encryption Method for Computer Network Communicatio...

引用

2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Xiao, Dongsheng Department of Network Technology Software Engineering Institute of Guangzhou Guangdong Guangzhou China

ISBN: (纸本)9798331504960

At present, ant colony algorithm is widely used in computer network communication data security encryption methods, but traditional ant colony algorithm is limited by update mechanism and heuristic function, resulting in insufficient encryption security. Heuristic functions optimize network data features by using packet size and sensitivity as parameters to improve the targeting and efficiency of search strategies. Then, the improved ant colony algorithm is integrated with the traditional AES (Advanced Encryption Standard) encryption technology. The keys generated through algorithms not only have higher randomness, but also adjust the length and complexity of the keys according to changes in the network environment, thereby enhancing the security and adaptability of the encryption process. The randomness of the key gradually increases as the number of iterations increases, from 0.82 in 50 iterations to 0.93 in 200 iterations. The computer network communication data security encryption method based on improved ant colony optimization algorithm effectively improves the efficiency and security of the encryption algorithm, and is suitable for use in high security network communication. © 2024 IEEE.

关键词： Ant colony optimization

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SIDN-NAS: Scalable Iterative Dense Network with Neural Architecture Search Optimization for Medical Image Segmentation

SIDN-NAS: Scalable Iterative Dense Network with Neural Archi...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhou, Jianjun Chen, Junying School of Software Engineering South China University of Technology Guangzhou510006 China

ISBN: (纸本)9781665410205

Medical image segmentation faces problems such as the unbalanced foreground and background of medical images and the limited dataset size. Meanwhile, Neural Architecture Search (NAS) methods have shown the great application potential and value in medical image segmentation tasks. Compared to the manually-designed medical image segmentation models, the NAS methods can improve the model accuracy, but the complexity and the computing cost of the searched medical image segmentation model is relatively high. In this work, we propose a high-precision but low-complexity scalable medical image segmentation model (Scalable Iterative Dense Network, SIDN) with NAS optimization. The proposed SIDN model consists of scalable iterative U-shape encoder/decoder stacks, uses fewer convolutional kernels in each encoder or decoder and performs dense multi-scale residual semantic connections between encoders and decoders, which can make up for the semantic differences between different levels. It then uses a gradient-based NAS algorithm to further reduce the model complexity and improve the segmentation accuracy. The SIDN model training and NAS optimization only require 1 GPU hour, and the segmentation accuracy has been improved by 7% to 10% on multiple medical image datasets. More importantly, the number of model parameters has been reduced by 31% to 88%, and the amount of calculation has been reduced by 32% to 44 %, greatly improving the model inference speed. Our source code is available at: https://***/Bob5090/SIDN-NAS. © 2024 IEEE.

关键词： Semantic Segmentation

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Multi-source data-driven visibility prediction: integrating channel attention mechanism with multi-scale temporal convolution 4

Multi-source data-driven visibility prediction: integrating ...

引用

4th International Conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Zhang, Ziyu Luo, Fei School of Software Engineering Chengdu University of Information Technology Chengdu610225 China

ISBN: (纸本)9781510686106

Visibility prediction is crucial for traffic safety and aerospace, especially under conditions of low visibility caused by fog, smoke, rain, and snow, which significantly affect visual range and driving safety. Accurate visibility prediction helps prevent accidents. However, traditional numerical weather forecasts exhibit low accuracy at small scales and are highly sensitive to initial conditions. Existing deep learning methods primarily focus on temporal changes. To address these challenges, this paper proposes the MSC-CGCRN model for visibility prediction, a graph neural network that integrates multi-scale temporal convolution and channel attention mechanisms. By integrating satellite data and ground meteorological station data, this model utilizes multi-source data for predictions. Experimental results demonstrate that the MSC-CGCRN model outperforms other benchmark models, and ablation experiments confirm the effectiveness of its components. © 2024 SPIE.

关键词： Graph neural networks

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Towards Enhancing Aviation Safety Through Advanced Incident Analysis Using Large Language Models 34

Towards Enhancing Aviation Safety Through Advanced Incident ...

引用

34th Annual International Conference on Collaborative Advances in software and Computing, CASCON 2024

作者： Siddeshwar, Vaishali Azim, Akramul Alwidian, Sanaa Makrehchi, Masoud University of Ontario Institute of Technology Department of Electrical Computer and Software Engineering Canada

ISBN: (纸本)9798331504830

Aerospace incident reports, crucial for identifying abnormal aircraft occurrences, are submitted to regulatory authorities like FAA or EASA. These reports, often in natural language, offer clarity and context. Existing literature proposes incident-analysis tools utilizing manual rules and NLP techniques like Part-of-Speech Tagging and dependency parsers to extract entities from logs. However, these approaches may miss nuances, leading to incomplete or inaccurate extractions and potential mis-interpretations. This study aims to automate initial extraction using Large Language Models, extracting Aircraft Number/Model, contributing and human factors, primary root cause, and incident summaries. Automating this phase can expedite investigations and improve expert efficiency during review processes. © 2024 IEEE.

关键词： Aircraft accidents

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Automatic Patch Generation System for Smart Contract 6

Automatic Patch Generation System for Smart Contract

引用

6th IEEE Eurasian Conference on Educational Innovation, ECEI 2023

作者： Guo, Shengran Software Engineering Chengdu University of Technology Chengdu China

ISBN: (纸本)9798350334289

Smart contract is a computer program running on the blockchain to automatically execute the contract terms without supervision. However, the vulnerability of smart contracts often causes massive property losses to users. SolSaviour is a framework for repairing and recovering vulnerabilities from deployed smart contracts. Thus, we design a set of automatic patch generation system APG within the SolSaviour framework, which solves the security problem of patches applied by SolSaviour and enhances the protection function of SolSaviour for smart contracts. APG consists of two existing frameworks: Slither and EVMPatch. Through comparative analysis, the improved SolSaviour by APG has higher security. © 2023 IEEE.

关键词： Smart contract

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A Novel Weakness of SM2 Algorithm 14

A Novel Weakness of SM2 Algorithm

引用

14th International Conference on Information technology in Medicine and Education, ITME 2024

作者： Hu, An Wu, Huiwen Liu, Chenglian Software Engineering Institute of Guangzhou Department of Network Technology Guangzhou510900 China

ISBN: (纸本)9798350356670

In 2023, Fan et al. proposed an article "Weakness of Enhanced DNA and ElGamal cryptosystem for secure data storage and retrieval in cloud"which it found a leakage by Thangavel and Varlakshmi's scheme. In this paper, the authors would like to use this method to check SM2 algorithm, and claim their viewpoint is correctness. We also demonstrate that this can be unsafe when the XOR operation is used in a special environment. Mathematical proof and experimental proof must be demonstrated in this article, which is our method. The results showed that the SM2 algorithm not safe. © 2024 IEEE.

关键词： Information leakage

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