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Logging-while-drilling formation dip interpretation based on long short-term memory

Petroleum Exploration and Development 2021年第4期48卷 978-986页

作者： SUN Qifeng LI Na DUAN Youxiang LI Hongqiang TANG Haiquan College of Computer Science and Technology in China University of Petroleum Qingdao 266580China Drilling Technology Research Institute of Shengli Petroleum Engineering Corporation Limited SinopecDongying 257000China Measurement and Control Technology Research Institute of Shengli Petroleum Engineering Corporation Limited.Dongying 257000 China

Azimuth gamma logging while drilling(LWD)is one of the important technologies of geosteering but the information of real-time data transmission is limited and the interpretation is *** study proposes a method of applying artificial intelligence in the LWD data interpretation to enhance the accuracy and efficiency of real-time data *** examining formation response characteristics of azimuth gamma ray(GR)curve,the preliminary formation change position is detected based on wavelet transform modulus maxima(WTMM)method,then the dynamic threshold is determined,and a set of contour points describing the formation boundary is *** classification recognition model based on the long short-term memory(LSTM)is designed to judge the true or false of stratum information described by the contour point set to enhance the accuracy of formation ***,relative dip angle is calculated by nonlinear least square *** of azimuth gamma data and application of real-time data processing while drilling show that the method proposed can effectively and accurately determine the formation changes,improve the accuracy of formation dip interpretation,and meet the needs of real-time LWD geosteering.

关键词： logging while drilling azimuth gamma stratigraphic identification artificial intelligence long short-term memory wavelet transform

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An Energy Efficient Dynamic Scheduling Algorithm for Reliability Constraints in Distributed Heterogeneous Systems

An Energy Efficient Dynamic Scheduling Algorithm for Reliabi...

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IEEE Joint International Information technology and Artificial Intelligence Conference (ITAIC)

作者： Yifan Liu Chenglie Du Jinchao Chen Xiaoyan Du Wenquan Yu School of Software Northwestern Polytechnical University Xi'an China School of Computer Science Northwestern Poly technical University Xi'an China Department of Land Unmanned System Research and Development North Automatic Control Technology Research Institute Taiyuan China

Scheduling algorithms in distributed heterogeneous systems have been the subject of extensive research. However, with the advancement of technology, addressing real-time, reliability, and energy consumption issues in dynamic environments has become increasingly critical. Existing studies mostly focus on static approaches, which are challenging to achieve scheduling objectives rapidly and adaptively. Therefore, we propose a novel dynamic energy-aware scheduling algorithm for deadlines and reliability constrained applications. Firstly, we introduce a prioritization method for multitasking applications to ensure more applications meet their deadlines. Subsequently, we present specific strategies for task allocation to achieve a better trade-off between energy consumption and reliability. Experimental results on randomly generated applications validate the superiority of our proposed method in terms of performance compared to other algorithms.

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Non-Convex Potential Games for Finding Global Solutions to Sensor Network Localization

Non-Convex Potential Games for Finding Global Solutions to S...

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European control Conference (ECC)

作者： Gehui Xu Guanpu Chen Yiguang Hong Baris Fidan Thomas Parisini Karl H. Johansson Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering Tongji University Shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Electrical and Electronic Engineering Imperial College London London UK Department of Engineering and Architecture University of Trieste Trieste Italy

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and the available measurements between non-anchor and anchor nodes. Attributed to the intrinsic non-convexity, obtaining a globally optimal solution to SNL is challenging, as well as implementing corresponding algorithms. In this paper, we formulate a non-convex multi-player potential game for a generic SNL problem to investigate the identification condition of the global Nash equilibrium (NE) therein, where the global NE represents the global solution of SNL. We employ canonical duality theory to transform the non-convex game into a complementary dual problem. Then we develop a conjugation-based algorithm to compute the stationary points of the complementary dual problem. On this basis, we show an identification condition of the global NE: the stationary point of the proposed algorithm satisfies a duality relation. Finally, simulation results are provided to validate the effectiveness of the theoretical results.

关键词： Location awareness Coordinate measuring machines Simulation Current measurement Noise Europe Games

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Predictive control based on Transformer as Surrogate Model for Cooling System Optimization in Data Center

Predictive Control based on Transformer as Surrogate Model f...

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Mobile Internet, Cloud Computing and Information Security (MICCIS), International Conference on

作者： Jiezhou Ji Donghui Yu Dewang Yang Xiao Shen Haoran Liu Linqiang Zhao Tao Wang Yu Zeng Qiuhui Pan School of Mathematical Sciences Dalian University of Technology Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China Quarkdata Technology Beijing China China Telecom Research Institute Beijing China China Telecom Corporation Limited Beijing China School of Innovation and Entrepreneurship Dalian University of Technology Dalian China

This paper develops a kind of Model Predictive control (MPC) based on the Transformer proxy model. Traditional MPC usually makes predictions via linear models which are unable to understand the interaction between various input variables. In this paper, we utilize the Transformer to approximate the nonlinear model, and capture the law of time series data. Specifically, we exploit the idea of dynamic programming to optimize the model to predict the cost of the trajectory, and recommend the control operation at each time point to determine the optimal strategy, and finally achieve the purpose of optimization. Experiments conducted on the data room energy-saving control problem demonstrated that, our Transformer based MPC model can predict the temperature change more accurately and is more robust to the field environment change. Moreover, compared with the previous Proportion Integration (PI) control, the MPC developed by us can make the temperature in the machine room is stable around 22°C, and the power consumption is reduced by 37.40%.

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Plate Shape Prediction Based on Data-Driven in Roll Quenching Process

Plate Shape Prediction Based on Data-Driven in Roll Quenchin...

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Chinese Automation Congress (CAC)

作者： Liu Hu Luefeng Chen Jie Hu Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and the Engineering Research Center of Intelligent Technology for Geo-Exploration School of Automation Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan The Tokyo Institute of Technology Tokyo Japan

In the process of steel plate production, predicting the plate shape is of great significance for producing high-quality and consistently stable plate shapes. This paper presents a model that predicts both the defect types and flatness of the plate, providing theoretical support for setting process parameters in roller quenching production. First, the parameters of the quenching process are analyzed to identify their characteristics. Then, the K-Means clustering algorithm and correlation analysis are employed to process the quenching process parameters. A gradient boosting decision tree (GBDT) model is used to predict the defect types and flatness of the steel plates. Finally, industrial production data is utilized for experimental validation. The obtained experimental results verify the reliability of the proposed method.

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Di-Graphgan: An Enhanced Adversarial Learning Framework for Accurate Spatial-Temporal Traffic Forecasting Under Data Missing Scenarios

SSRN

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SSRN 2023年

作者： Li, Lincan Bi, Jichao Yang, Kaixiang Luo, Fengji School of Control Science and Engineering Zhejiang University Hangzhou310012 China Zhejiang Institute of Industry and Information Technology Hangzhou310012 China School of Computer Science and Engineering South China University of Technology Guangzhou510641 China School of Civil Engineering The University of Sydney Sydney2006 Australia

Nowadays, various disturbances in urban transportation data acquisition/processing/storage lead to the inevitable data missing problem, which undermines the valuable traffic information and greatly threats the reliability of existing benchmark traffic prediction models. Inspired from the powerful generative learning ability of GANs, we propose an integrated spatiotemporal Data imputation Graph Attention Generative Adversarial Networks (Di-GraphGAN) for accurate and efficient spatial-temporal traffic forecasting under data missing scenarios. Specifically, we first propose a traffic data imputation module named DI-LSTM, which adopts the architecture of LSTM Network with an extra Time Damping unit to accurately estimating the missing values. Then, we facilitate Di-GraphGAN with an original developed Task-Efficient Graph Attention Networks (TE-GAT) for better graph representation learning and a Temporal Contextual Attention (TCA) mechanism to capture the dynamic spatiotemporal traffic patterns. Finally, extensive evaluations are conducted on two real-world traffic speed datasets from China, demonstrating that Di-GraphGAN achieves state-of-the-art performance in both traffic forecasting and spatiotemporal data imputation tasks. © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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scIAC: clustering scATAC-seq data based on Student’s t-distribution similarity imputation and denoising autoencoder

scIAC: clustering scATAC-seq data based on Student’s t-dist...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wei Lan Jin Ye Xun Sun Xiaoshu Zhu Qingfeng Chen Yi Pan School of computer electronic and information and Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China School of computer electronic and information Guangxi University Nanning China School of Computer Science and Engineering Yulin Normal University Yulin China School of Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Shenzhen China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Assay of single cell transposase-accessible chromatin with high-throughput sequencing (scATAC-seq) have enabled massively profiling of the chromatin accessibility landscape at the single-cell level. The essential step in analyzing scATAC-seq data is to cluster the cells into different clusters and utilize the clustering information in the subsequent downstream analysis. However, there are some challenges in the clustering analysis of scATAC-seq data. For example, scATAC-seq data are often high-dimensional and extremely sparse, as well as featuring high loss rate or noise. In this study, we proposed the scIAC to address these challenges of scATACseq data. In particular, scIAC combines the Student’s t-distribution similarity imputation and the denoising autoencoder based on the Zero-inflated Negative Binomial (ZINB) distribution. The Student’s t-distribution similarity imputation is used to solve the problem of high sparsity and high loss rate. The denoising autoencoder is employ to extract features which are useful for clustering and to reduce data noises. In addition, the self-training soft K-means and pairwise constraints are utilized in the clustering phase to enhance clustering performance. The experimental validation on several datasets shows that the proposed method performed better than other state-of-the-art methods. In conclusion, scIAC is an effective method to accurately cluster and identify cell types in scATAC-seq data.

关键词： Sequential analysis Runtime Clustering methods Noise reduction Neural networks Feature extraction Data models

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Graph Attention Network for Word Embeddings 7th

Graph Attention Network for Word Embeddings

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Long, Yunfei Xu, Huosheng Qi, Pengyuan Zhang, Liguo Li, Jun College of Computer Science and Technology Harbin Engineering University Harbin150001 China Wuhan Digital Engineering Institute Wuhan430000 China China Industrial Control Systems Cyber Emergency Research Team Beijing100000 China

ISBN: (纸本)9783030786113

The word embeddings approaches have attracted extensive attention and widely used in many natural language processing (NLP) tasks. Relatedness between words can be reflected in vector space by word embeddings. However, the current word embeddings approaches commonly do not explore the context-specific information of word deeply in the overall corpus. In this paper, we propose to use graph attention network for word embeddings. We build a large single word graph for a corpus based on word order, then learn a word embeddings graph attention network (WEGAT) for the corpus. Our WEGAT is initialized with one-hot representation for word. We propose to use masked language model (MLM) as supervised task. In addition, through the text classification experiment, it is showed that accuracy of the word embeddings represented by WEGAT is higher than the current method for the same classification method. © 2021, Springer Nature Switzerland AG.

关键词： Embeddings

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A Formation control for Multi-Agent Systems for Event-Trigger-Based Variable Gain controllers

A Formation Control for Multi-Agent Systems for Event-Trigge...

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Annual Conference of Industrial Electronics Society

作者： Daiki Asada Takuya Nakagawa Yoshikatsu Hoshi Hidetoshi Oya Shunya Nagai Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan The Department of Computer Science Tokyo City University Tokyo Japan Department of Electronics and Control Engineering National Institute of Technology (KOSEN) Niihama College Ehime Japan

This paper deals with a design problem for a new event-trigger-based variable gain controller which achieves consensus for multi-agent systems (MASs) with the leader-follower structure. The proposed variable gain controller consists of stabilizing state feedback laws, state feedback inputs for consensus, and compensation inputs with adjustable parameters, and is designed by taking relative positions into account explicitly. In this paper, we show that sufficient conditions for the existence of the proposed formation control system are given in terms of Linear Matrix Inequalities (LMIs).

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Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement

Distributed Optimization with Finite Bit Adaptive Quantizati...

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IEEE Conference on Decision and control

作者： Apostolos I. Rikos Wei Jiang Themistoklis Charalambous Karl H. Johansson Artificial Intelligence Thrust of the Information Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong China Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia Cyprus Division of Decision and Control Systems KTH Royal Institute of Technology SE-100 44 Stockholm Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we addresses the challenge of unconstrained distributed optimization. In our scenario each node’s local function exhibits strong convexity with Lipschitz continuous gradients. The exchange of information between nodes occurs through 3-bit bandwidth-limited channels (i.e., nodes exchange messages represented by a only 3 -bits). Our proposed algorithm respects the network’s bandwidth constraints by leveraging zoom-in and zoom-out operations to adjust quantizer parameters dynamically. We show that during our algorithm’s operation nodes are able to converge to the exact optimal solution. Furthermore, we show that our algorithm achieves a linear convergence rate to the optimal solution. We conclude the paper with simulations that highlight our algorithm’s unique characteristics.

关键词： Adaptation models Quantization (signal) Heuristic algorithms Bandwidth Distributed algorithms Optimization Convergence

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