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A novel surrogate model with deep learning for predicting spacial-temporal pressure in coalbed methane reservoirs

Natural Gas Industry B 2025年第2期12卷 219-233页

作者： Dong, Yukun Zhang, Xiaodong Zhang, Jiyuan Wu, Kuankuan Liu, Shuaiwei Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China College of Petroleum Engineering China University of Petroleum (East China) Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software China

Coalbed methane (CBM) is a vital unconventional energy resource, and predicting its spatiotemporal pressure dynamics is crucial for efficient development strategies. This paper proposes a novel deep learning–based data-driven surrogate model, AxialViT-ConvLSTM, which integrates AxialAttention Vision Transformer, ConvLSTM, and an enhanced loss function to predict pressure dynamics in CBM reservoirs. The results showed that the model achieves a mean square error of 0.003, a learned perceptual image patch similarity of 0.037, a structural similarity of 0.979, and an R² of 0.982 between predictions and actual pressures, indicating excellent performance. The model also demonstrates strong robustness and accuracy in capturing spatial–temporal pressure features. © 2025 Sichuan Petroleum Administration

关键词： AxialAttention Vision Transformer Coalbed methane ConvLSTM Deep learning Spatial-temporal pressure prediction Surrogate models

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A Deep Reinforcement Learning based VM Scheduling Strategy Decreasing Data Center Communication costs 24

A Deep Reinforcement Learning based VM Scheduling Strategy D...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Xiao, Zhijiao Liu, Xiao Ming, Zhong Software Engineering Research Center College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798350319934

With the rapid development of cloud computing, more and more complicated services are deployed on the cloud. The frequent communication between services places a great burden on the network of the data center and leads to a decline in the quality of service. Researches have shown that the costs of communication between services vary with the placements of virtual machines on which services are running. Therefore, the virtual machine strategy should be optimized by migrating virtual machines with high communication frequency closer. Based on fat-tree network topology, a Deep Q network based online virtual machines scheduling strategy(DQN-VMS) is proposed. The objective is to obtain an optimized virtual machine scheduling strategy that brings down communication costs while guaranteeing the quality of service. The calculation model for communication costs is built for the fat-tree network topology. A reward function that takes into account the communication costs and Service Level Agreements violation(SLAV) rate is designed. The experiment shows that the proposed strategy performs about 26.81% and 40.55% better in comparison with two virtual machine scheduling strategies based on graph partition algorithm and greedy algorithm, which are commonly used in existing data centers. © 2022 IEEE.

关键词： Virtual machine

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Towards Trustworthy Multi-Label Sewer Defect Classification via Evidential Deep Learning 48

Towards Trustworthy Multi-Label Sewer Defect Classification ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhao, Chenyang Hu, Chuanfei Shao, Hang Wang, Zhe Wang, Yongxiong Southeast University The College of Software Engineering Nanjing China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9781728163277

An automatic vision-based sewer inspection plays a key role of sewage system in a modern city. Recent advances focus on utilizing deep learning model to realize the sewer inspection system, benefiting from the capability of data-driven feature representation. However, the inherent uncertainty of sewer defects is ignored, resulting in the missed detection of serious unknown sewer defect categories. In this paper, we propose a trustworthy multi-label sewer defect classification (TMSDC) method, which can quantify the uncertainty of sewer defect prediction via evidential deep learning. Meanwhile, a novel expert base rate assignment (EBRA) is proposed to introduce the expert knowledge for describing reliable evidences in practical situations. Experimental results demonstrate the effectiveness of TMSDC and the superior capability of uncertainty estimation is achieved on the latest public benchmark. © 2023 IEEE.

关键词： Defects

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Predicting the Risks of Brain Stroke Using Machine Learning Models and Artificial Neural Network

Predicting the Risks of Brain Stroke Using Machine Learning ...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Nahid, Nafiz Hossain, Mehrab Rifat, Ariful Islam Khan Raisa, Meher Durdana Islam, Ashraful Sylhet Engineering College Department of Computer Science and Engineering Sylhet Bangladesh American International University Department of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Department of Software Engineering Dhaka Bangladesh University of Regina Department of Computer Science Regina Canada

ISBN: (纸本)9798350303452

The primary cause of death and disability worldwide is stroke. Accurate prediction models and identification of stroke risk factors can aid in early intervention and preventive measures. In this study, an approach based on machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), AdaBoost (AdB), Gradient Boosting (GB), Decision Tree (DT), and a unique Artificial Neural Network (ANN) model, is proposed to predict stroke risk at four different levels. To address the issue of imbalanced dataset, Synthetic Minority Over-sampling Technique coupled with Edited Nearest Neighbors (SMOTEENN) was used. 11 features were selected for training the models out of the initial 12 features, and the data-set was divided into an 80-20 train-test ratio for model assessment. The Decision Tree (DT) model achieved an accuracy of 98.53%. Additionally, a 1000 epoch Artificial Neural Network (ANN) model with various batch sizes was created;128 produced the best results. Stochastic Gradient Descent (SGD), Adam, and RMSprop were examined along with other optimizers, however SGD produced the highest accuracy rate of 99.21%. The results suggest that utilizing the selected features and the balanced data-set, machine learning algorithms such as the unique CNN model can accurately predict stroke risk at various levels. These models have the potential to aid in early detection of stroke risk and guide preventive interventions, thus improving patient care and outcomes. Further testing and implementation of these models in real clinical settings are warranted. © 2023 IEEE.

关键词： Decision trees

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VCApather: A Network as a Service Solution for Video Conference Applications 34

VCApather: A Network as a Service Solution for Video Confere...

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34th Workshop on Network and Operating System Support for Digital Audio and Video, NOSSDAV 2024

作者： He, Dongbiao Lin, Canshu Yu, Xian Westphal, Cedric Ming, Zhongxing Cui, Laizhong Zhou, Xu Garcia-Luna-Aceves, J.J. Sangfor Technologies Inc China College of Computer Science and Software Engineering Shenzhen University China Computer Network Information Center Chinese Academy of Sciences China China Electrical and Computer Engineering Department University of Toronto Canada Futurewei

ISBN: (纸本)9798400706134

We propose a network service as a solution for video conference applications by constructing network layer routing strategies. Our approach takes into account the characteristics of conferencing flows, addresses various self-customized metrics, and proactively ensures a positive user experience by preventing contention. The performance of VCApather is evaluated using a fully-meshed topology with five nodes and real-world video conference traffic. The results show that VCApather is capable of achieving high link utilization and balance, while also meeting predefined user metrics. Compared to other schemes, VCApather was found to satisfy 69.8% more QoE requirement and to yield an average bitrate improvement of 1.74×. © 2024 ACM.

关键词： Network layers

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Improving Breast Cancer Detection Accuracy Through Random Forest Machine Learning Algorithm

Improving Breast Cancer Detection Accuracy Through Random Fo...

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International Conference on Advanced engineering, Technology and Applications, ICAETA 2024

作者： Agrawal, Atul Khanday, Akib MohiUd Din Alazzawi, Esraa Mohammed Bhushan, Bharat Baniya, Pashupati Jamil, Akhtar Department of Computer Science and Engineering ITS Engineering College Greater Noida India Department of Computer Science and Engineering Sharda University Greater Noida India Department of Computer Science and Software Engineering United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Department of Computer Engineering University of Baghdad Al-Jadriya Baghdad10071 Iraq Department of Computer Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

ISBN: (纸本)9783031709234

Breast cancer continues to be a significant global health issue that greatly affects the well-being of people worldwide. Detecting breast cancer early is vital for improving the outcomes of patients. One promising method for breast cancer detection is the use of the Random Forest machine learning algorithm. In a recent research paper, we investigated how Random Forest can be used to predict breast cancer by employing explainable AI techniques. We analyzed the specific features that the algorithm relies on to classify breast cancer and highlighted the advantages of Random Forest compared to other machine learning algorithms in diagnosing breast cancer. Our model offers a well-thought-out, efficient, and easily understandable approach to predicting breast cancer using explainable machine learning techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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BFT Consensus Algorithms 10

BFT Consensus Algorithms

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhang, Xiaojun Zhong, Weiyu Yang, Ce Chen, Lin Liao, Jing Xiong, Naixue QuanZhou University of Information Engineering Software College QuanZhou362000 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350312461

With the advent of Bitcoin, the blockchain as the underlying technology of Bitcoin has entered people's field of vision. In recent years, the blockchain and its related technologies have developed rapidly. The consensus algorithm is the key to the realization of blockchain technology. There will be Byzantine nodes in the process of reaching consensus among the nodes in the blockchain. Byzantine nodes are tolerated in the blockchain to achieve consensus. Consensus is an essential performance of consensus algorithms. This paper first introduces blockchain technology and the application of consensus algorithms in it;secondly, introduces the classic PBFT algorithm;then, introduces some existing consensus algorithms;finally summarizes the research status and challenges of consensus algorithms, and analyzes its Prospects. © 2023 IEEE.

关键词： Blockchain

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Prior Information Guided Coarse-to-fine Dual-branch Encoding Network for Fovea Localization and Optic Disc/Cup Segmentation

Prior Information Guided Coarse-to-fine Dual-branch Encoding...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lei, Haijun Xie, Hai Zhao, Jia Zhao, Danrui Huang, Limin Lei, Baiying Shenzhen University College of Computer Science & Software Engineering Shenzhen China Shenzhen University School of Biomedical Engineering Shenzhen China Southwest University School of Computer Information & Science Shenzhen China

ISBN: (纸本)9798350337488

Fundus images are commonly used to document the presence and severity of various retinal degenerative diseases, where the fovea, optic disc (OD), and optic cup (OC) serve as important anatomical landmarks. Locating and segmenting these landmarks are crucial for clinical diagnosis and treatment. Many existing methods treat the recognition of the fovea, OD, and OC as separate tasks without incorporating any clinical prior knowledge related to various anatomical structures. In this paper, we propose a prior information guided coarse-to-fine dual-branch encoding network, which enables fovea localization and OD/OC segmentation. In coarse stage, we employ a dual-branch network consisting of convolutional neural network (CNN) and Transformer to encode local and global features, and then utilize multi-scale feature fusion techniques to merge the extracted semantic features, aiming to enhance the localization accuracy. In addition, we effectively use the distance information from each pixel to the landmark of interest, and output the results of distance map and heat map regression as prior information to further guide the network to learn the positional relationship between fovea and OD. In fine stage, we refine the region of interest (ROI) of the OD, balance the distribution of the OD and OC using polar coordinate transformation (PCT), extract critical boundary features using the boundary attention module (BAM), and improve the generalization performance of our method through model ensemble strategy. Extensive experimental results demonstrate that our proposed method outperforms existing state-of-the-art (SOTA) methods on the publicly available GAMMA and REFUGE datasets. © 2023 IEEE.

关键词： coarse-to-fine dual-branch fovea localization optic disc and cup segmentation Prior information

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Incorporating metapath interaction on heterogeneous information network for social recommendation

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Frontiers of computer science 2024年第1期18卷 33-48页

作者： Yanbin JIANG Huifang MA Xiaohui ZHANG Zhixin LI Liang CHANG College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal UniversityGuilin 541001China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China

Heterogeneous information network(HIN)has recently been widely adopted to describe complex graph structure in recommendation systems,proving its effectiveness in modeling complex graph *** existing HIN-based recommendation studies have achieved great success by performing message propagation between connected nodes on the defined metapaths,they have the following major *** works mainly convert heterogeneous graphs into homogeneous graphs via defining metapaths,which are not expressive enough to capture more complicated dependency relationships involved on the ***,the heterogeneous information is more likely to be provided by item attributes while social relations between users are not adequately *** tackle these limitations,we propose a novel social recommendation model MPISR,which models MetaPath Interaction for Social Recommendation on heterogeneous information ***,our model first learns the initial node representation through a pretraining module,and then identifies potential social friends and item relations based on their similarity to construct a unified *** then develop the two-way encoder module with similarity encoder and instance encoder to capture the similarity collaborative signals and relational dependency on different *** experiments on five real datasets demonstrate the effectiveness of our method.

关键词： heterogeneous information network social reco-mmender system metapath interaction attention mechanism

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Automotive Lightweight Design Modeling And Intelligent Optimization Learn Key Technologies 10

Automotive Lightweight Design Modeling And Intelligent Optim...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Xu, Gejing Liang, Wei Cai, Jiahong Xiao, Jiahong Chen, Xingyu Gong, Yinyan QuanZhou University Information Engineering Software College QuanZhou362000 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350312461

The automotive industry has always been seeking innovative solutions to improve car performance, safety, and cost savings. Lightweight design technology has become one of the solutions. This article summarizes the modeling and optimization methods of automotive lightweight design, as well as key technologies based on intelligent optimization learning. First, this article outlines the basic concepts of automotive lightweight design, as well as the needs and challenges of the industry for lightweight design. Then, the modeling methods of lightweight design are introduced in detail, including geometric modeling, topology optimization, structural optimization, and multidisciplinary optimization. At the same time, commonly used materials, manufacturing processes, and testing methods in lightweight design are introduced, as well as relevant design guidelines and standards. This article also introduces some algorithms and their applicable scenarios. Additionally, this article summarizes the application prospects and future development directions of key technologies for automotive lightweight design modeling and intelligent optimization learning. We emphasize the opportunities and challenges in this field and propose how to continue promoting the development of lightweight design technology and responding to increasingly complex market demands. This article provides a systematic review of key technologies for automotive lightweight design modeling and intelligent optimization learning, which helps researchers and practitioners to deepen their understanding of the technical development and application trends in this field. © 2023 IEEE.

关键词： Automotive industry

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