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Analysis of the characteristics and causes of city gas explosion accidents based on DT-AR algorithm

Journal of intelligent and Fuzzy Systems 2024年第4期46卷 9057-9068页

作者： Qu, Ying Wang, Xuming Data Science and Intelligent Computing Research Center Hebei University of Science and Technology Hebei Shijiazhuang China School of Economics and Management Hebei University of Science and Technology Hebei Shijiazhuang China

In order to effectively prevent and control accidents, it is essential to trace back the causes of gas explosions in cities. The DT-AR(decision tree-association rule) algorithm is proposed as a quantitative analysis of gas accident features and causality. First, 210 gas explosion accident investigation reports were taken as samples. The gas accident causation system is divided into three aspects, including environmental factors, management factors and physical factors. management factors were sorted into organizational-level and individual-level factors from the investigation reports. Second, the CART decision tree model was used to compare location features, organizational causality features, and individual causality features of the piped and bottled gas accidents, and a decision tree model with the gas system fault site as the root node was built to filter the key feature variables. In order to reveal factor correlations and deep-level causation, the Apriori algorithm is used to mine accident association rules. The combinations on the branches of the decision tree are used as constraints to filter the critical causality rule, which improves the efficiency of association rule screening and enhances prediction accuracy. The results demonstrate that the DT-AR algorithm can evaluate the importance of variables, quickly locate effective combinations of factors, and mine the complete causal chain. The association rule is screened based on the constraint of the key element combination of the decision tree, which compensates for the low efficiency of the Apriori algorithm for association rule mining. In addition, the accident-caused excavation results provide an effective path for gas companies, outsourced service companies and administrative departments to implement gas safety chain supervision, which can address the problem of gas accident safety management failures and provide decision support for accident prevention. © 2024 – IOS Press.

关键词： Association rules

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Judgment of blast furnace iron-tapping status based on data differential processing and dynamic window analysis algorithm

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Progress in Natural Science:Materials International 2023年第4期33卷 450-457页

作者： Youcheng Zong Zixian Wang Xiang Liu Yi Nian Jianian Pan Chaojie Zhang Yingxue Wang Jianhua Chu Liqiang Zhang The Intelligent Metallurgy Research Institute School of Metallurgical Engineering Anhui University of Technology Department of Technical Quality Manufacturing Management Division(Technical Center) Anhui Changjiang Steel Co. Ltd. National Engineering Research Center for Public Safety Risk Perception and Control by Big Data(RPP) China Academic of Electronics and Information Technology

Ironmaking plants hold a core position in steel production. Efficient level detection and iron-tapping status judgment can accurately measure the liquid level and predict the amount of molten iron in the ladle, thereby ensuring production safety and improving production efficiency. At present, the judgment of the start and end statuses of blast furnace iron-tapping mainly relies on manual visual inspection. However, due to the high-temperature environment on-site and interference from iron powder, gunning mud, and other dust, operators find it challenging to make accurate judgments. To address these issues, this study proposes a robust and accurate method for judging the iron-tapping status of blast furnaces. This study collected four million on-site liquid level data, underwent interpolation, noise reduction, normalization, and other preprocessing steps, and then used differential processing to calculate the iron-tapping rate. Combined with dynamic window feature analysis, it effectively captured the change characteristics of the liquid level in the ladle under different iron-tapping *** simulated on-site experiments, the method achieved a 96.6 % accuracy rate for iron-tapping status *** real-time iron-tapping status judgment method based on differential processing of liquid level data and dynamic window feature analysis proposed in this study demonstrates high precision and robustness in the complex environment of ironmaking plants. This technology not only improves the production efficiency of the iron and steel link but also frees on-site operators from the harsh working environment, eliminating human errors in judgments and omissions, further promoting the intelligent process of ironmaking plants.

关键词： Blast furnace iron-tapping Molten iron level Radar level gauge Iron-tapping status judgment data differential processing Sliding window

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MCT-Net: a multi-branch hybrid CNN-transformer model for medical image segmentation

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Pattern Analysis and Applications 2025年第2期28卷 1-21页

作者： Shen, Longfeng Diao, Liangjin Peng, Rui Chen, Jiacong Lu, Zhengtian Ge, Fangzhen Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB) College of Computer Science and Technology Huaibei Normal University Anhui Huaibei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui Big-Data Research Center on University Management Huaibei Normal University Anhui Huaibei China

In recent years, neural networks have demonstrated substantial progress in medical image segmentation. However, accurately segmenting objects in medical images is often restricted by edge blurring, which complicates the delineation of structures of interest. The purpose of this study is to develop a multi-branch hybrid CNN-transformer model to address the issue of edge blurring in medical image segmentation. This approach enables more precise edge detection and segmentation. Integrating hybrid CNN and transformer decoders facilitates detailed recovery while preserving global structures, thus minimizing errors in regions with blurred edges. Additionally, we incorporate a hybrid attention mechanism at the model’s bottleneck to enhance feature transfer between the encoder and decoder. The model was trained using the ACDC, Synapse, and polyp datasets, with a focus on improving segmentation accuracy in regions with blurry edges. Experimental results reveal that the proposed model surpasses existing state-of-the-art methods on the ACDC, Synapse, and polyp datasets, demonstrating its superior performance and effectiveness. These results suggest that the proposed model offers a significant improvement in handling edge blurring, with potential applications in enhancing medical image analysis and diagnostics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Attention mechanism Convolutional neural network Edge information Medical image segmentation Transformer

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Hierarchical Multiview Top-k Pooling with Deep-Q-Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2985-2996页

作者： Li, Zhi-Peng Su, Hai-Long Wu, Yong- Zhang, Qin-Hu Yuan, Chang-An Gribova, Valeriya Filaretov, Vladimir Fedorovich Huang, De-Shuang Eastern Institute of Technology Zhejiang Ningbo315201 China University of Science and Technology of China School of Life Sciences Anhui Hefei230026 China Tongji University Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Shanghai201804 China Guangxi Academy of Sciences Institute of Big Data and Intelligent Computing Research Center Nanning530007 China Far Eastern Branch of the Russian Academy of Sciences Institute of Automation and Control Processes Vladivostok690041 Russia

Graph neural networks (GNNs) are extensions of deep neural networks to graph-structured data. It has already attracted widespread attention for various tasks such as node classification and link prediction. Existing research focuses more on graph convolutional neural networks (GCNs). However, it is usually overlooked that graph pooling can obtain graph representations by summarizing and down-sampling node information. Meanwhile, existing graph pooling methods mainly use top-k for node selection, but most of them consider only single view information when scoring nodes, and the k values in top-k are usually selected empirically. This work proposes the hierarchical multiview top-k pooling (HMTPool) with deep-Q-networks, which scores nodes taking into account multiview information (considering graph structure and features) and does not rely on the empirical adaptive selection of the best k value. HMTPool is a two-stage process. It first uses a variant GCN and multilayer perceptron to score the nodes from structural and feature perspectives, respectively and then performs fusion operations on the multiview information scores of the nodes. In addition, to select the optimal pooling ratio of top-k, we propose a deep-Q-network-based top-k (DTop-k) node selection method, which can adaptively select the best pooling ratio without prior knowledge. Experimental results on six TUdatasets and two Benchmarking GNNs datasets demonstrate the effectiveness of our proposed approach. © 2020 IEEE.

关键词： Convolution

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A Model-Agnostic Hierarchical Framework Towards Trajectory Prediction

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Journal of Computer Science & technology 2025年第2期40卷 322-339页

作者： Tang-Wen Qian Yuan Wang Yong-Jun Xu Zhao Zhang Lin Wu Qiang Qiu Fei Wang Domain-Oriented Intelligent System Research Center Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100190China CAS Key Laboratory of Network Data Science and Technology Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China

Predicting the future trajectories of multiple agents is essential for various applications in real life,such as surveillance systems,autonomous driving,and social *** trajectory prediction task is influenced by many factors,including the individual historical trajectory,interactions between agents,and the fuzzy nature of the observed agents’*** existing methods have made great progress on the topic of trajectory prediction,they treat all the information uniformly,which limits the effectiveness of information *** this end,in this paper,we propose and utilize a model-agnostic framework to regard all the information in a two-level hierarchical ***,the first-level view is the inter-trajectory *** this level,we observe that the difficulty in predicting different trajectory samples *** define trajectory difficulty and train the proposed framework in an“easy-to-hard”*** second-level view is the intra-trajectory *** find the influencing factors for a particular trajectory can be divided into two *** first part is global features,which keep stable within a trajectory,i.e.,the expected *** second part is local features,which change over time,i.e.,the current *** believe that the two types of information should be handled in different *** hierarchical view is beneficial to take full advantage of the information in a fine-grained *** results validate the effectiveness of the proposed model-agnostic framework.

关键词： spatial-temporal data mining trajectory prediction hierarchical framework model-agnostic

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AlphaFold2 and its applications in the fields of biology and medicine

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Signal Transduction and Targeted Therapy 2023年第4期8卷 1630-1643页

作者： Zhenyu Yang Xiaoxi Zeng Yi Zhao Runsheng Chen West China Biomedical Big Data Center West China HospitalSichuan UniversityChengdu 610041China Key Laboratory of Intelligent Information Processing Advanced Computer Research CenterInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Key Laboratory of RNA Biology Center for Big Data Research in HealthInstitute of BiophysicsChinese Academy of SciencesBeijing 100101China Pingshan Translational Medicine Center Shenzhen Bay LaboratoryShenzhen 518118China

AlphaFold2(AF2)is an artificial intelligence(AI)system developed by DeepMind that can predict three-dimensional(3D)structures of proteins from amino acid sequences with atomic-level *** structure prediction is one of the most challenging problems in computational biology and chemistry,and has puzzled scientists for 50 *** advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much *** release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community,especially in the fields of biology and ***2 is thought to have a significant impact on structural biology and research areas that need protein structure information,such as drug discovery,protein design,prediction of protein function,et *** the time is not long since AF2 was developed,there are already quite a few application studies of AF2 in the fields of biology and medicine,with many of them having preliminarily proved the potential of *** better understand AF2 and promote its applications,we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success,and particularly focus on reviewing its applications in the fields of biology and *** of current AF2 prediction will also be discussed.

关键词： system fields Fold

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data Processing Model of Students' Evaluation of Teaching Based on Outlier Detection 5

Data Processing Model of Students' Evaluation of Teaching Ba...

引用

5th IEEE Eurasian Conference on Educational Innovation, ECEI 2022

作者： Qu, Ying Guo, Fei Hebei University of Science and Technology School of Economics and Management Data Science and Intelligent Computing Research Center Hebei China

ISBN: (纸本)9781665433181

The problem of low credibility of students in teaching evaluation is caused by ignoring non-teaching factors such as students' emotions, class culture, and course nature in the existing data of university students' teaching evaluation. A data processing model of students' teaching evaluation is constructed based on outlier detection. The outliers of the original data are screened and optimized based on students' emotional differences. Based on the analysis of non-teaching factors such as class culture and curriculum characteristics, the abnormal data of students' evaluation of teaching are processed. This improves the credibility of students' evaluation of teaching data to a certain extent. For the same sample data, the optimized data processing model of students' evaluation of teaching shows realistic evaluation results. The guidance of students' evaluation of teaching needs to be strengthened based on the original intention of teaching quality evaluation. It is suggested that evaluation models are scientifically selected, and the evaluation results are rationally used. © 2022 IEEE.

关键词： Students

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Air Pollution Monitoring By Integrating Local and Global Information in Self-Adaptive Multiscale Transform Domain

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IEEE Transactions on Multimedia 2025年 27卷 3716-3728页

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： Information leakage

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What makes a successful rebuttal in computer science conferences?: A perspective on social interaction

What makes a successful rebuttal in computer science confere...

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作者： Huang, Junjie Huang, Win-bin Bu, Yi Cao, Qi Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Information Management Peking University Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

With an exponential increase in submissionsto top-tier Computer Science (CS) conferences, more and more conferences have introduced a rebuttal stage to the conference peer review process. The rebuttal stage can be modeled as social interactions between authors and reviewers. A successful rebuttal often results in an increased review score after the rebuttal stage. In this paper, we conduct an empirical study to determine the factors contributing to a successful rebuttal using over 3000 papers and 13,000 reviews from ICLR2022, one of the most prestigious computer science conferences. First, we observe a significant difference in review scores before and after the rebuttal stage, which is crucial for paper acceptance. Furthermore, we investigate factors from the reviewer's perspective using signed social network analysis. A notable finding is the increase in balanced network structure after the rebuttal stage. Subsequently, we evaluate several quantifiable author rebuttal strategies and their effects on review scores. These strategies can help authors in improving their review scores. Finally, we used machine learning models to predict rebuttal success and validated the impact of potential factors analyzed in this paper. Our experiments demonstrate that the utilization of all features proposed in this study can aid in predicting the success of the rebuttal. In summary, this work presents a study on the impact factors of successful rebuttals from both reviewers’ and authors’ perspectives and lays the foundation for analyzing rebuttals with social network analysis. © 2023 Elsevier Ltd

关键词： Forecasting

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A Meta-learning System for Automatic Objects Tracking 4

A Meta-learning System for Automatic Objects Tracking

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4th International Conference on Applied Machine Learning, ICAML 2022

作者： Li, Qiaolin Wang, Wenfeng Zhang, Jingjing Research Institute of Intelligent Engineering and Data Applications Shanghai Institute of Technology Shanghai China Research Center of Artificial Intelligence and Robotics Interscience Institute of Management and Technology Bhubaneswar India

ISBN: (纸本)9781665462655

Recent years, reinforcement learning has been developed in many area and achieve high performance in many application. However, for huge amount data and training will make hard leaning for the agent of reinforcement learning, in the project, the agent of DDQN Network was integrated with LSTM meta learner for learning the game called Cartpole and LunarLander. It get a good performance for large train. © 2022 IEEE.

关键词： Reinforcement learning

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