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Rockburst Intensity Grade Prediction Model Based on Batch Gradient Descent and Multi-Scale Residual Deep Neural Network

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Computer Systems Science & Engineering 2023年第11期47卷 1987-2006页

作者： Yu Zhang Mingkui Zhang Jitao Li Guangshu Chen School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for GeoMechanics and Deep Underground Engineering China University of Mining&TechnologyBeijing100083China

Rockburst is a phenomenon in which free surfaces are formed during excavation,which subsequently causes the sudden release of energy in the construction of mines and *** rockburst only peels off rock slices without ejection,while severe rockburst causes casualties and property *** frequency and degree of rockburst damage increases with the excavation ***,rockburst is the leading engineering geological hazard in the excavation process,and thus the prediction of its intensity grade is of great significance to the development of geotechnical ***,the prediction of rockburst intensity grade is one problem that needs to be solved *** comprehensively considering the occurrence mechanism of rockburst,this paper selects the stress index(σθ/σc),brittleness index(σ_(c)/σ_(t)),and rock elastic energy index(Wet)as the rockburst evaluation indexes through the Spearman coefficient *** overcomes the low accuracy problem of a single evaluation index prediction *** this,the BGD-MSR-DNN rockburst intensity grade prediction model based on batch gradient descent and a multi-scale residual deep neural network is *** batch gradient descent(BGD)module is used to replace the gradient descent algorithm,which effectively improves the efficiency of the network and reduces the model training ***,the multi-scale residual(MSR)module solves the problem of network degradation when there are too many hidden layers of the deep neural network(DNN),thus improving the model prediction *** experimental results reveal the BGDMSR-DNN model accuracy to reach 97.1%,outperforming other comparable ***,actual projects such as Qinling Tunnel and Daxiangling Tunnel,reached an accuracy of 100%.The model can be applied in mines and tunnel engineering to realize the accurate and rapid prediction of rockburst intensity grade.

关键词： Rockburst prediction rockburst intensity grade deep neural network batch gradient descent multi-scale residual

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Carbon emission reduction enabled by informatization construction: an analysis of spatial effects based on China’s experience

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Environmental Science and Pollution Research 2024年第24期31卷 35595-35608页

作者： He, Zhuo Ma, Shaojuan Deng, Zixin Meng, Ying College of Business Administration Nanchang Institute of Technology Jiangxi Nanchang330032 China School of Mathematics and Information Science Northern Minzu University Ningxia Yinchuan750021 China Ningxia Key Laboratory of Intelligent Information and Big Data Processing Ningxia Yinchuan750021 China School of Statistics Capital University of Economics and Business Beijing100071 China

The "dual-carbon" objective presents a huge challenge for China and the world, with profound implications for the advancement of China’s eco-friendly economy. Additionally, informatization development has a significant impact on the level of carbon emissions in both local and neighbouring regions. Therefore, we employ panel data from 30 provinces in China spanning the years 2012 to 2021, and use the Kernel density estimate and Moran’s index to explore informatization level and carbon emissions space agglomeration characteristics. We elucidate the nonlinear relationship and heterogeneity between informatization improvement and carbon emissions based on the spatial Durbin model. The primary findings are as follows. Firstly, we discover a distinct spatial clustering phenomenon which the informatization level is high in coastal areas and low in inland areas, whereas carbon emissions are low in the south and high in the north. Secondly, the effect of the informatization level on carbon emissions is shown as a U-shaped and non-linear correlation, signifying inhibitory and subsequently promoting phases. Thirdly, we reveal the negative influence on carbon emissions caused by spatial lag terms of the informatization level, and find that a higher local informatization level will have an inhibitory effect on carbon emissions in neighbouring areas. Finally, there is a spatial heterogeneity in the impact of the informatization level on carbon emissions, which presents the U-shaped relation between informatization level and carbon emissions varies across the North-South subregion and the three major economic subregion of China. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Informatization

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Protein Structure Prediction:Challenges,Advances,and the Shift of Research Paradigms

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Genomics, Proteomics & Bioinformatics 2023年第5期21卷 913-925页

作者： Bin Huang Lupeng Kong Chao Wang Fusong Ju Qi Zhang Jianwei Zhu Tiansu Gong Haicang Zhang Chungong Yu Wei-Mou Zheng Dongbo Bu Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Changping Laboratory Beijing 102206China Microsoft Research AI4Science Beijing 100080China Huawei Noah’s Ark Lab Wuhan 430206China Zhongke Big Data Academy Zhengzhou 450046China Institute of Theoretical Physics Chinese Academy of SciencesBeijing 100190China

Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields,including biochemistry,medicine,physics,mathematics,and computer *** researchers adopt various research paradigms to attack the same structure prediction problem:biochemists and physicists attempt to reveal the principles governing protein folding;mathematicians,especially statisticians,usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure,while computer scientists formulate protein structure prediction as an optimization problem-finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native *** research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman,namely,data modeling and algorithmic ***,we have also witnessed the great success of deep learning in protein structure *** this review,we present a survey of the efforts for protein structure *** compare the research paradigms adopted by researchers from different fields,with an emphasis on the shift of research paradigms in the era of deep *** short,the algorithmic modeling techniques,especially deep neural networks,have considerably improved the accuracy of protein structure prediction;however,theories interpreting the neural networks and knowledge on protein folding are still highly desired.

关键词： Protein folding Protein structure prediction Deep learning Transformer Language model

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Classifying Galaxy Morphologies with Few-shot Learning

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Research in Astronomy and Astrophysics 2022年第5期22卷 9-20页

作者： Zhirui Zhang Zhiqiang Zou Nan Li Yanli Chen College of Computer Nanjing University of Posts and Telecommunications Nanjing 210023.China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing 210023China Key Laboratory of Optical Astronomy National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China

The taxonomy of galaxy morphology is critical in astrophysics as the morphological properties are powerful tracers of galaxy *** the upcoming Large-scale Imaging Surveys,billions of galaxy images challenge astronomers to accomplish the classification task by applying traditional methods or human ***,machine learning,in particular supervised deep learning,has been widely employed to classify galaxy morphologies recently due to its exceptional automation,efficiency,and ***,supervised deep learning requires extensive training sets,which causes considerable workloads;also,the results are strongly dependent on the characteristics of training sets,which leads to biased outcomes *** this study,we attempt Few-shot Learning to bypass the two *** research adopts the data set from the Galaxy Zoo Challenge Project on Kaggle,and we divide it into five categories according to the corresponding truth *** classifying the above data set utilizing few-shot learning based on Siamese Networks and supervised deep learning based on AlexNet,VGG_16,and ResNet_50 trained with different volumes of training sets separately,we find that few-shot learning achieves the highest accuracy in most cases,and the most significant improvement is 21%compared to AlexNet when the training sets contain 1000 *** addition,to guarantee the accuracy is no less than 90%,few-shot learning needs~6300 images for training,while ResNet_50 requires~13,000 *** the advantages stated above,foreseeably,few-shot learning is suitable for the taxonomy of galaxy morphology and even for identifying rare astrophysical objects,despite limited training sets consisting of observational data only.

关键词： Galaxies-Galaxy morphological classification-Method neural networks

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Rockburst prediction using artificial intelligence techniques:A review

Rock Mechanics Bulletin

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Rock Mechanics Bulletin 2024年第3期3卷 1-13页

作者： Yu Zhang Kongyi Fang Manchao He Dongqiao Liu Junchao Wang Zhengjia Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China Thomas Lord Department of Computer Science University of Southern CaliforniaLos Angeles90007CAUSA

Rockburst is a phenomenon where sudden,catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation *** disasters endanger the safety of people's lives and property,national energy security,and social interests,so it is very important to accurately predict *** rockburst prediction has not been able to find an effective prediction method,and the study of the rockburst mechanism is facing a *** the development of artificial intelligence(AI)techniques in recent years,more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst *** previous research,several scholars have attempted to summarize the application of AI techniques in rockburst ***,these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction,or they do not provide a comprehensive *** on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques,this paper conducts a comprehensive review of rockburst prediction methods leveraging AI ***,pertinent definitions of rockburst and its associated hazards are ***,the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized,with emphasis placed on the respective advantages and disadvantages of each ***,the strengths and weaknesses of prediction methods leveraging AI are summarized,alongside forecasting future research trends to address existing challenges,while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.

关键词： Rockburst Rockburst prediction Artificial intelligence techniques

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Brain-inspired artificial intelligence research: A review

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Science China(Technological Sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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RECC: A Relationship-Enhanced Content Caching Algorithm Using Deep Reinforcement Learning 6

RECC: A Relationship-Enhanced Content Caching Algorithm Usin...

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6th Asian Conference on Artificial Intelligence Technology, ACAIT 2022

作者： Ren, Jiarui Zhang, Haiyan Zhou, Xiaoping Zhu, Menghan Ningxia University School of Information Engineering Yinchuan China Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing China

ISBN: (纸本)9781665453110

Mobile edge caching (MEC) is a promising technology to alleviate traffic congestion in the network. Current studies explored deep reinforcement learning (DRL)-based MEC methods. These methods consider the dynamics of the request size to maximize the cache hit rate. However, they usually ignored the potential request relationships among contents. Two contents with a strong relationship are usually requested sequentially. Inspired by this assumption, this paper proposes a relationship-enhanced content caching algorithm using DRL, named RECC. Our RECC infers user preferences by mining the request relationships among contents. In this work, the relationships are modeled as request sequences, and the request features are learned by using graph embedding. These features will be used as input of state in our DRL-based algorithm. We utilize the Wolpertinger architecture to solve the limitation of large discrete action space. The simulation results indicate that our RECC outperformed the traditional cache policies and state-of-the-art DRL-based method in cache hit rate. Furthermore, the proposed RECC has advantages in long-term stability in the environment where content popularity changes dynamically, and also has a higher cache hit rate when handling the requests with number changes dynamically. © 2022 IEEE.

关键词： Reinforcement learning

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Integrated Building Information Modelling and 3D Printing for Prefabricated Building

Integrated Building Information Modelling and 3D Printing fo...

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第35届中国控制与决策会议

作者： Yukang Wang Yu Zhou Xiaoping Zhou Junjun Han Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture

The prefabricated components can be connected to form buildings in prefabricated building based on the design *** production of components in the factory is not standardized and accurate,which leads to difficulty in connecting prefabricated components and low efficiency in *** construction materials are piled up on the construction site and these wastes have caused a lot of pollution to the *** proposes to integrating Building Information Modelling(BIM) and 3 D printing for prefabricated *** of BIM models is *** components information is extracted by Industry Foundation Class(IFC) files so that BIM can provide prefabricated component ***,the conversion relationship between BIM and 3 D printing is ***,the conversion rules and methods for converting BIM data into STL files are developed so that the 3 D printer can print the prefabricated components according to the corresponding ***,testing the data format conversion on BIM models shows that our presented approach enables us to convert a BIM model into a data format recognizable for 3 D *** BIM and 3 D print in prefabricated buildings will boost the fineness of components and the efficiency of construction and have good performance in decreasing engineering waste.

关键词：

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Identifying Outliers in Astronomical Images with Unsupervised Machine Learning

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Research in Astronomy and Astrophysics 2022年第8期22卷 74-84页

作者： Yang Han Zhiqiang Zou Nan Li Yanli Chen School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing 210023China Key Laboratory of Optical Astronomy National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China

Astronomical outliers,such as unusual,rare or unknown types of astronomical objects or phenomena,constantly lead to the discovery of genuinely unforeseen knowledge in *** unpredictable outliers will be uncovered in principle with the increment of the coverage and quality of upcoming survey ***,it is a severe challenge to mine rare and unexpected targets from enormous data with human inspection due to a significant *** learning is also unsuitable for this purpose because designing proper training sets for unanticipated signals is *** by these challenges,we adopt unsupervised machine learning approaches to identify outliers in the data of galaxy images to explore the paths for detecting astronomical *** comparison,we construct three methods,which are built upon the k-nearest neighbors(KNN),Convolutional Auto-Encoder(CAE)+KNN,and CAE+KNN+Attention Mechanism(att CAE_KNN)*** sets are created based on the Galaxy Zoo image data published online to evaluate the performance of the above *** show that att CAE_KNN achieves the best recall(78%),which is 53%higher than the classical KNN method and 22%higher than CAE+*** efficiency of att CAE_KNN(10 minutes)is also superior to KNN(4 h)and equal to CAE+KNN(10 minutes)for accomplishing the same ***,we believe that it is feasible to detect astronomical outliers in the data of galaxy images in an unsupervised ***,we will apply att CAE_KNN to available survey data sets to assess its applicability and reliability.

关键词： Galaxy Physical data and Processes Galaxy:fundamental parameters

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ReconBoost: Boosting Can Achieve Modality Reconcilement 41

ReconBoost: Boosting Can Achieve Modality Reconcilement

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41st International Conference on Machine Learning, ICML 2024

作者： Hua, Cong Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits further exploitation of the features in the weak modality, leading to modality competition, where the dominant modality overpowers the learning process. To address this issue, we study the modality-alternating learning paradigm to achieve reconcilement. Specifically, we propose a new method called ReconBoost to update a fixed modality each time. Herein, the learning objective is dynamically adjusted with a reconcilement regularization against competition with the historical models. By choosing a KL-based reconcilement, we show that the proposed method resembles Friedman's Gradient-Boosting (GB) algorithm, where the updated learner can correct errors made by others and help enhance the overall performance. The major difference with the classic GB is that we only preserve the newest model for each modality to avoid overfitting caused by ensembling strong learners. Furthermore, we propose a memory consolidation scheme and a global rectification scheme to make this strategy more effective. Experiments over six multi-modal benchmarks speak to the efficacy of the method. We release the code at https://***/huacong/ReconBoost. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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