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A hybrid attention model based on first-order statistical features for smoke recognition

Science China(Technological Sciences) 2024年第3期67卷 809-822页

作者： GUO Nan LIU JiaHui DI KeXin GU Ke QIAO JunFei Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Artificial Intelligence InstituteBeijing 100124China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China

Smoke and fire recognition are of great importance on foreseeing fire disasters and preventing environmental pollution by monitoring the burning process of objects(e.g., straw, fuels). However, since fire images suffer from problems like the variability of the features, complexity of scenarios, interference from background, changeable weather conditions as well as image quality problems, identifying smoke and fire accurately and promptly from a given image still remains a substantial challenge. Automatically learning the features of smoke images by CNNs has improved the target recognition ability compared to traditional approaches,nonetheless, convolutions and pooling operations in CNNs may cause severe information loss which may lead to *** tackle the above problems, this paper proposed a hybrid attention model based on the characteristics of smoke images. This model adopted multiple optimized attention mechanism in several stages to quickly and precisely capture the important features,achieving state-of-the-art performance on smoke and fire recognition in terms of accuracy and speed. Our proposed module mainly consists of two stages: pooling and attention. In the first stage, we conducted several newly proposed first-order pooling methods. Through traversing the data space in a larger scope, features are better reserved, thus constructing a more intact feature space of smoke and fire in an image. In the second stage, feature maps are aggregated together to perform channel and spatial attention. The channel and spatial dependencies allow us to quickly catch the important features presented in an image. By fully exploring the feature space and prominent salient features, characteristics of smoke and fire are better presented so as to obtain better smoke and fire detection results. Experiments have been conducted on public smoke detection dataset and new proposed fine-grained smoke and fire detection database. Experimental results revealed that the propose

关键词： hybrid attention first-order pooling smoke and fire detection deep convolutional neural networks

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Sparse convolutional model with semantic expression for waste electrical appliances recognition

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Science China(Technological Sciences) 2024年第9期67卷 2881-2893页

作者： HAN HongGui LIU YiMing LI FangYu DU YongPing Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of computational intelligence and Intelligent System Beijing 100124China Engineering research Center of Digital Community Ministry of EducationBeijing 100124China Beijing Artificial intelligence Institute and Beijing Laboratory for Intelligent Environmental Protection Beijing 100124China

Deep neural networks play an important role in the recognition of waste electrical appliances. However, deep neural network components still lack reliability in decision-making features. To address this problem, a sparse convolutional model with semantic expression(SCMSE) is proposed. First, a low-rank sparse semantic expression component, combining the benefits of residual networks and sparse representation, is adapted to enhance sparse feature extraction and semantic expression. Second, a reliable network architecture is obtained by iterating the optimal sparse solution, enhancing semantic expression. Finally, the results of visualization experiments on the waste electrical appliances dataset demonstrate that the proposed SCMSE can obtain excellent semantic performance.

关键词： sparse convolutional model deep neural network semantic expression visualization computer vision

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A Comprehensive Overview of CFN From a Commonsense Perspective

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Machine intelligence research 2024年第2期21卷 239-256页

作者： Ru Li Yunxiao Zhao Zhiqiang Wang Xuefeng Su Shaoru Guo Yong Guan Xiaoqi Han Hongyan Zhao Key Laboratory of computational intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information TechnologyShanxi UniversityTaiyuan 030006China

Chinese FrameNet(CFN)is a scenario commonsense knowledge base(CKB)that plays an important role in research on Chinese language *** is based on the theory of frame semantics and English FrameNet(FN).The CFN knowledge base contains a wealth of scenario commonsense knowledge,including frames,frame elements,and frame relations,as well as annotated instances with rich scenario-related labels on Chinese sentences and *** this paper,we conduct a comprehensive overview of CFN from a commonsense perspective,covering topics such as scenario commonsense representation,CFN resources,and its *** also summarize recent breakthroughs and identify future research ***,we introduce the concept of scenario commonsense,including its definitions,examples,and representation methods,with a focus on the relationship between scenario commonsense and the frame concept in *** addition,we provide a comprehensive overview of CFN resources and their applications,highlighting the newly proposed frame-based discourse representation and a human-machine collaboration framework for expanding the CFN ***,we explore emerging topics such as expanding the CFN resource,improving the interpretability of machine reading comprehension,and using scenario CKBs for text generation.

关键词： Chinese FrameNet(CFN) commonsense scenario commonsense frame knowledge

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A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

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Frontiers of Structural and Civil Engineering 2024年第3期18卷 393-410页

作者： Tram BUI-NGOC Duy-Khuong LY Tam T TRUONG Chanachai THONGCHOM T.NGUYEN-THOI Laboratory for computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Civil Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Department of Computer Science Aarhus UniversityAarhus 8000Denmark Thammasat University research unit in structural and foundation engineering Department of Civil EngineeringThammasat UniversityPathumthani 12120Thailand Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Thammasat School of Engineering Thammasat UniversityPathumthani 12120Thailand

The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming *** surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement *** proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential *** achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training *** approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical *** study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured ***,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally *** approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection deep neural network full-scale structures finite element model updating noisy incomplete modal data

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Transfer Learning in Motor Imagery Brain Computer Interface: A Review

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Journal of Shanghai Jiaotong university(Science) 2024年第1期29卷 37-59页

作者：李明爱许冬芹 Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of computational intelligence and Intelligent System Beijing 100124China Engineering research Center of Digital Community Ministry of EducationBeijing 100124China

Transfer learning,as a new machine learning methodology,may solve problems in related but different domains by using existing knowledge,and it is often applied to transfer training data from another domain for model training in the case of insuficient training *** recent years,an increasing number of researchers who engage in brain-computer interface(BCI),have focused on using transfer learning to make most of the available electroencephalogram data from different subjects,effectively reducing the cost of expensive data acquisition and labeling as well as greatly improving the learning performance of the *** paper surveys the development of transfer learning and reviews the transfer learning approaches in *** addition,according to the"what to transfer"question in transfer learning,this review is organized into three contexts:instance-based transfer learning,parameter-based transfer learning,and feature-based transfer ***,the current transfer learning applications in BCI research are summarized in terms of the transfer learning methods,datasets,evaluation performance,*** the end of the paper,the questions to be solved in future research are put forward,laying the foundation for the popularization and in-depth research of transfer learning in BCI.

关键词： transfer learning brain-computer interface(BCI) electroencephalogram machine learning

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Leveraging Uncertainty for Depth-Aware Hierarchical Text Classification

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Computers, Materials & Continua 2024年第9期80卷 4111-4127页

作者： Zixuan Wu Ye Wang Lifeng Shen Feng Hu Hong Yu Chongqing Key Laboratory of computational intelligence Chongqing University of Posts and TelecommunicationsChongqing400000China Division of Emerging Interdisciplinary Areas Hong Kong University of Science and TechnologyHong Kong999077China

Hierarchical Text Classification(HTC)aims to match text to hierarchical *** methods overlook two critical issues:first,some texts cannot be fully matched to leaf node labels and need to be classified to the correct parent node instead of treating leaf nodes as the final classification ***,error propagation occurs when a misclassification at a parent node propagates down the hierarchy,ultimately leading to inaccurate predictions at the leaf *** address these limitations,we propose an uncertainty-guided HTC depth-aware model called ***,we design an early stopping strategy with uncertainty to identify incomplete matching between text and labels,classifying them into the corresponding parent node *** approach allows us to dynamically determine the classification depth by leveraging evidence to quantify and accumulate *** results show that the proposed DepthMatch outperforms recent strong baselines on four commonly used public datasets:WOS(Web of Science),RCV1-V2(Reuters Corpus Volume I),AAPD(Arxiv Academic Paper Dataset),and ***,on the BGC dataset,it improvesMicro-F1 andMacro-F1 scores by at least 1.09%and 1.74%,respectively.

关键词： Hierarchical text classification incomplete text-label matching uncertainty depth-aware early stopping strategy

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Toric Parameterization Based Isogeometric Collocation Method for Planar Multi-Sided Physical Domains

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Journal of Systems Science & Complexity 2024年

作者： ZHOU Pei ZHU Chungang School of Mathematical Sciences Dalian University of Technology Key Laboratory for computational Mathematics and Data intelligence of Liaoning Province

Isogeometric collocation method(IGC) shows high computational efficiency compared with isogeometric Galerkin method(IGG) when solving partial differential equations(PDEs). However, few studies about IGC have focused on multi-sided physical domains. In this paper, the authors propose a new IGC method based on toric parameterization(IGCT) for the multi-sided planar physical *** to the high order continuity of toric basis functions, the IGCT method shows more accurate numerical approximation. Moreover, the authors generalize the adaptive w-refinement method into IGCT(IGCT-w), in which the weights of basis functions in physical domains are optimized independently for geometry representation. The numerical accuracy of IGCT-w is significantly improved by an order of magnitude in comparison with IGCT method. To save the computational cost of IGCT-w, the authors devise a selection of weights scheme according to relative residuals. Finally, several numerical examples demonstrate the effectiveness and robustness of the proposed method.

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Spectroscopy and molecule opacity investigation on excited states of SiS

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Chinese Physics B 2024年第5期33卷 381-387页

作者：李瑞吕浩男桑纪群刘晓华梁桂颖吴勇 College of Teacher Education Qiqihar UniversityQiqihar 161006China National Key Laboratory of computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China Department of Physics College of ScienceQiqihar UniversityQiqihar 161006China School of Data Science and Artificial intelligence Jilin Engineering Normal UniversityChangchun 130052China HEDPS Center for Applied Physics and TechnologyPeking UniversityBeijing 100084China

The SiS molecule,which plays a significant role in space,has attracted a great deal of attention for many *** to complex interactions among its low-lying electronic states,precise information regarding the molecular structure of SiS is *** obtain accurate information about the structure of its excited states,the high-precision multireference configuration interaction(MRCI)method has been *** method is used to calculate the potential energy curves(PECs)of the 18Λ–S states corresponding to the lowest dissociation limit of *** core–valence correlation effect,Davidson’s correction and the scalar relativistic effect are also included to guarantee the precision of the MRCI *** on the calculated PECs,the spectroscopic constants of quasi-bound and bound electronic states are calculated and they are in accordance with previous experimental *** transition dipole moments(TDMs)and dipole moments(DMs)are determined by the MRCI *** addition,the abrupt variations of the DMs for the 1^(5)Σ^(+)and 2^(5)Σ^(+)states at the avoided crossing point are attributed to the variation of the electronic *** opacity of SiS at a pressure of 100 atms is presented across a series of *** increasing temperature,the expanding population of excited states blurs the band boundaries.

关键词： SiS opacity excited state spectroscopic constant configuration interaction

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PHUI-GA: GPU-based efficiency evolutionary algorithm for mining high utility itemsets

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Journal of Systems Engineering and Electronics 2024年第4期35卷 965-975页

作者： JIANG Haipeng WU Guoqing SUN Mengdan LI Feng SUN Yunfei FANG Wei Jiangsu Provincial Engineering Laboratory of Pattern Recognition and computational intelligence Department of Computer Science and TechnologyJiangnan UniversityWuxi 214122China China Ship Scientific research Center Wuxi 214082China Taihu Laboratory of Deepsea Technological Science Wuxi 214082China Department of Mathematics Nanjing UniversityNanjing 210023China

Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search *** have good running and mining performance,but they still require huge computational resource and may miss many *** to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded *** show that the mining performance of PHUI-GA outperforms the existing *** mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.

关键词： high utility itemset mining(HUIM) graphics process-ing unit(GPU)parallel genetic algorithm(GA) mining perfor-mance

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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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