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An experimental study on fire characteristics of urban complex underground space based on BIM

CAAI Transactions on Intelligence Technology 2023年第4期8卷 1403-1414页

作者： Jinrong Cui Hao Mei Haosen Sun Ye Jingqi Wei Hu Yong Xu College of Mathematics and Informatics(College of Software Engineering) South China Agricultural UniversityGuangzhouChina The University of Manchester ManchesterUK Harbin Institute of Technology(Shenzhen) ShenzhenChina

Compared with the space on the ground,if there is a fire in the urban complex underground space,the loss will be greatly *** addition,the complex underground space is usually connected with other large space areas and densely *** a fire occurs in the complex underground space,it will cause huge property losses and *** order to reduce the risk of fire,it is necessary to deeply understand the development rules and characteristics of fire in the complex underground space of the *** article has mainly carried on the following work:(I)A particularly complex model of the multi‐storey subway station was *** this basis,three groups of comparative experiments were conducted to study the effects of fire power,fire location and smoke control system on fire development,and the conclusion that fire location is the most important factor for fire development was obtained;(II)In order to explore the entire space fire and the local space fire,CFD(Computational Fluid Dynamics)is used to build a large‐size fire model and a small‐size fire model respectively;(III)Multiple detector data as temperature slices were built,and it is expected to make full use of the simulation data to deduce the important index of fire location in the early stage of *** of the works in this paper will provide reference experimental data for the prevention and firefighting of a sudden fire in the complex underground space.

关键词： fire simulation underground multi‐storey subway station urban complex underground space

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Bayesian estimation-based sentiment word embedding model for sentiment analysis

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CAAI Transactions on Intelligence Technology 2022年第2期7卷 144-155页

作者： Jingyao Tang Yun Xue Ziwen Wang Shaoyang Hu Tao Gong Yinong Chen Haoliang Zhao Luwei Xiao Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials School of Physics and Telecommunication EngineeringSouth China Normal UniversityGuangzhouChina College of Mathematics and Informatics&College of Software Engineering South China Agricultural UniversityGuangzhouChina School of Foreign Languages Zhejiang University of Finance&EconomicsHangzhouZhejiangChina Educational Testing Service PrincetonNew JerseyUSA School of Computing Informatics and Decision Systems EngineeringArizona State UniversityTempeUSA

Sentiment word embedding has been extensively studied and used in sentiment analysis ***,most existing models have failed to differentiate high-frequency and low-frequency ***,the sentiment information of low-frequency words is insufficiently captured,thus resulting in inaccurate sentiment word embedding and degradation of overall performance of sentiment analysis.A Bayesian estimation-based sentiment word embedding(BESWE)model,which aims to precisely extract the sentiment information of low-frequency words,has been *** the model,a Bayesian estimator is constructed based on the co-occurrence probabilities and sentiment proba-bilities of words,and a novel loss function is defined for sentiment word embedding *** experimental results based on the sentiment lexicons and Movie Review dataset show that BESWE outperforms many state-of-the-art methods,for example,C&W,CBOW,GloVe,SE-HyRank and DLJT1,in sentiment analysis tasks,which demonstrate that Bayesian estimation can effectively capture the sentiment information of low-frequency words and integrate the sentiment information into the word embedding through the loss *** addition,replacing the embedding of low-frequency words in the state-of-the-art methods with BESWE can significantly improve the performance of those methods in sentiment analysis tasks.

关键词： function embedding estimation

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SLAH: A Siamese Network Vision Tracking Algorithm Using LSTM Adaptive Head 13

SLAH: A Siamese Network Vision Tracking Algorithm Using LSTM...

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13th International Conference on Information Science and Technology, ICIST 2023

作者： Liu, Zhitao Wang, Dong Luo, Xiaonan Li, Fang School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China College of Mathematics and Informatics College of Software Engineering South China Agricultural Univerrsity Guangzhou China

ISBN: (纸本)9798350313925

In the field of single object tracking, conventional methods often rely on correlation filters or visual image processing. However, these approaches typically focus solely on extracting target object features and consider only the position information in the current frame. They lack integration of contextual and spatial-temporal information, which can limit tracking *** method takes inspiration from single object detection and tracking techniques. It combines efficient model-based object detection with the spatial position of the final target in the image. This approach offers significant advantages in reducing tracking drift (deviation from the target object) and improving tracking accuracy (predicting bounding boxes that closely follow the target).To extract spatial information, we utilize a three-layer ResNet and Convolutional Block Attention Module (CBAM) in conjunction with a siamese network. This combination effectively captures the spatial characteristics of the target object. Additionally, we employ an adaptive processing head with an internal structure of Long Short Term Memory (LSTM) to capture temporal information. By integrating spatial and temporal cues, our method achieves more robust and accurate *** comprehensive comparison and ablation experiments across multiple datasets, we have demonstrated notable improvements with our method. It tightly connects the predicted bounding box with the tracking target and effectively combines spatial and temporal information for precise object tracking. © 2023 IEEE.

关键词： Long short-term memory

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MobileVitV2-Based Fusion of Vision Transformers and Convolutional Neural Networks for Underwater Image Enhancement 13

MobileVitV2-Based Fusion of Vision Transformers and Convolut...

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13th International Conference on Information Science and Technology, ICIST 2023

作者： Liu, Jiaji Li, Fang Luo, Xiaonan Li, Ji Wang, Dong Lan, Rushi School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China College of Mathematics and Informatics College of Software Engineering South China Agricultural University Guangzhou China

ISBN: (纸本)9798350313925

Acquisition of underwater images presents challenges stemming from inherent distortions and the degradation of color and contrast within the aquatic environment. These challenges, characterized by image quality issues, pose significant obstacles to the training of supervised deep learning models on extensive and diverse datasets, potentially constraining the overall performance of the model. High-quality underwater images play an important role in the development of Autonomous Underwater Vehicles(AUVs). In this work, we present MobileVitV2 Block Fusion Augmentor and Global-Local Feature Fusion Block with Attent-UNet, which are offered as blocks of generator. Through extensive experiments on different datasets, our proposed method achieves better visual quality, and the method can obtain better quality images in poorer underwater environments. © 2023 IEEE.

关键词： Image enhancement

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Robot Global Relocation Algorithm Based on Deep Neural Network and 3D Point Cloud 1

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14th International Symposium on Intelligence Computation and Applications, ISICA 2023

作者： Chen, Yan Li, Zhengying Qiu, Wenbin College of Mathematics and Informatics College of Software Engineering South China Agricultural University GuangZhou China

ISBN: (数字)9789819743933

ISBN: (纸本)9789819743926

When a mobile robot is restarted or "hijacked" due to an emergency situation, it cannot determine its position and orientation in a known map using its own sensors in places without a GPS signal, such as indoors or outdoors with tall buildings, trees, and other dense areas. In both of these cases, the robot will be unable to find its position in the known map, resulting in a relocation failure. To address the relocation problem of mobile robots, this paper proposes a mobile robot global relocation method that combines deep neural networks and 3D point clouds, based on SLAM (Simultaneous Localization and Mapping) technology. The main idea is to use spherical projection to process the point cloud data, then feed it into a neural network to extract descriptors for similarity measurement, and perform pose calculation to accomplish relocalization. The application of SLAM technology enables the robot to achieve simultaneous localization and map building based on sensor data, accurately determining its own position even in the absence of GPS signals. Extensive experiments have been conducted to validate the proposed method, demonstrating its effectiveness in reducing relocation time and memory costs, as well as achieving relatively high relocation accuracy. Therefore, this method not only enhances the robustness of the system but also improves the feasibility of the overall system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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Object pest detection method based on lightweight SSD-RA algorithm 11

Object pest detection method based on lightweight SSD-RA alg...

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11th International Conference on Networks, Communication and Computing, ICNCC 2022

作者： Li, Shixuan Peng, Hongxing Yuan, Jingqi Software Engineering Institute of Guangzhou China College of Mathematics and Informatics South China Agricultural University China

ISBN: (纸本)9781450398039

In order to detect the types and quantities of pests in rice fields quickly and accurately, a lightweight target pest detection method SSD-RA based on SSD algorithm is proposed. In order to deal with the problems of high missed detection rate, inaccurate positioning, slow detection speed, large number of model parameters and low accuracy of the detection model, the ResNet feature extraction network was introduced and optimized. The first prediction feature layer of SSD was connected to the Conv3-x module of the ResNet network, and all network layers after the Conv3-x module were dropped. The number of parameters of the model is reduced, so that the model is more lightweight, the detection speed is improved, and the redundant features are reduced to ensure the accuracy of the model. In addition, aiming at the characteristics of small target, the structure of prediction feature layer of SSD algorithm is improved, the number of prediction feature layers is adjusted, and the output of underlying feature Conv2-x is connected to the prediction feature layer. The candidate box of each cell in the new prediction feature layer is 6, which accurately divides the boundaries of large, medium and small target boxes. The experimental results show that the mAP of SSD-RA of the improved algorithm in this paper is 84.1%, which is 23.4 percentage points higher than that of the original SSD model. The reasoning time in CPU and GPU environment is 0.056s and 0.009s, which is 0.101s and 0.005s faster than that of the original SSD model, and the model size is 51.9MB. It is reduced to about 7/100 of the original SSD model. Compared with other models, the mAP of SSD-RA is 7.8 and 4.2 percentage points higher than that of EfficientDet and RFCN, respectively. The SSD-RA model is more effective and faster to detecting insect pests and reduces the missed detection rate. © 2022 ACM.

关键词： Forecasting

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A Formal Approach for Traceability Preservation in software Development Process 41st

A Formal Approach for Traceability Preservation in Softwar...

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41st National Conference of Theoretical Computer Science, NCTCS 2023

作者： Wen, Hao Wu, Jinzhao Jiang, Jianmin Li, Jianqing Hong, Zhong Chengdu Institute of Computer Applications Chinese Academy of Sciences Chengdu China University of Chinese Academy of Sciences Beijing China College of Software Engineering Chengdu University of Information Technology Chengdu China College of Mathematics and Informatics Fujian Normal University Fuzhou China

ISBN: (纸本)9789819977420

Traceability is the ability to trace the usage of artifacts during the software lifecycle process. Though the benefits of establishing a traceability software system have been widely recognized, it is difficult to be applied well in actual development. In this paper, we propose a new method for traceability preservation which may be used in the practical software development process. A formal model for the traceability of software artifacts is first presented, which consists of variable traceability relations, classification and version number controls. We then present the composition, restriction and refinement operations in the software development process. Next, the preservation of traceability under these three operations is discussed respectively. To demonstrate the effectiveness of our approach, we finally develop a prototype tool named Formalized software Management System (FSMS). © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Formal methods

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Semantic information based solution for visual SLAM in dynamic environment

Semantic information based solution for visual SLAM in dynam...

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作者： Chuqi Shao College of Mathematics and informatics College of Software Engineering South China Agricultural University

In recent years,the utilization of visual SLAM with a camera as a sensor has become increasingly widespread,particularly in the context of rapidly developing artificial intelligence such as mobile robots,VR,and *** approach is favored due to its affordability,lightweight design,ability to capture comprehensive information,and other *** traditional slam technology has achieved a very mature effect with the static scene as the assumption condition,and the classic representatives are LSD-SLAM and ORB-SLAM which will be introduced in the ***,since dynamic scenarios are unavoidable in the real world,overcoming the influence of dynamic objects becomes a challenge if researchers want to move forward with more *** present,under the dynamic environment of the visual slam algorithm faces positioning problems of low accuracy and poor *** address the challenges posed by dynamic objects in a scene,many researchers are incorporating deep learning techniques and exploring the use of reference semantic information to collaboratively resolve the *** paper reviews this and summarizes the development process and important algorithms.

关键词： visual SLAM semantic segmentation dynamic environment

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Probabilistic Graph Modeling based Safety Classifier Algorithm for Smart Transportation

Probabilistic Graph Modeling based Safety Classifier Algorit...

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2023 International Symposium on Green Technologies and Applications, ISGTA 2023

作者： Karkouri, Najib El Tigani, Smail Saadane, Rachid Chehri, Abdelah Pierre, Samuel Neya, Noureddine Mathematics and Informatics Lab Faculty of Sciences University Ibno Toufial Kenitra Morocco Research and Development Unit Accsellium LLC Fez Morocco SIRC-LaGeS Hassania School of Public Labors Casablanca20250 Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada Department of Computer and Software Engineering Polytechnique Montreal Canada

This research introduces a novel Probabilistic Graph Modeling-based Safety Classifier Algorithm designed for the purpose of classifying road safety in smart transportation systems. Leveraging a combination of numerical integration methods and Gaussian kernel density estimation models, the proposed algorithm offers an effective approach to assess and categorizing the safety levels of roads. The integration of these techniques enables a comprehensive analysis, allowing for a more nuanced understanding of the complex interactions within transportation networks. The algorithm's efficacy in road safety classification holds promising implications for enhancing traffic management and promoting safer urban environments. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Accident prevention

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Distributed quantum architecture search

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Physical Review A 2024年第2期110卷 022403-022403页

作者： Haozhen Situ Zhimin He Shenggen Zheng Lvzhou Li College of Mathematics and Informatics School of Electronic and Information Engineering Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area (Guangdong) Shenzhen 518045 China Institute of Quantum Computing and Software School of Computer Science and Engineering

Variational quantum algorithms, inspired by neural networks, have become a novel approach in quantum computing. However, designing efficient parameterized quantum circuits remains a challenge. Quantum architecture search tackles this by adjusting circuit structures along with gate parameters to automatically discover high-performance circuit structures. In this study, we propose an end-to-end distributed quantum architecture search framework, where we aim to automatically design distributed quantum circuit structures for interconnected quantum processing units with specific qubit connectivity. We devise a circuit generation algorithm which incorporates TeleGate and TeleData methods to enable nonlocal gate implementation across quantum processing units. While taking into account qubit connectivity, we also incorporate qubit assignment from logical to physical qubits within our quantum architecture search framework. A two-stage progressive training-free strategy is employed to evaluate extensive circuit structures without circuit training costs. Through numerical experiments on three VQE tasks, the efficacy and efficiency of our scheme is demonstrated. Our research into discovering efficient structures for distributed quantum circuits is crucial for near-term quantum computing where a single quantum processing unit has a limited number of qubits. Distributed quantum circuits allow for breaking down complex computations into manageable parts that can be processed across multiple quantum processing units.

关键词： Machine learning Quantum circuits

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