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A machine learning approach for the prediction of pore pressure using well log data of Hikurangi Tuaheni Zone of IODP Expedition 372,New Zealand

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Energy Geoscience 2024年第2期5卷 225-231页

作者： Goutami Das Saumen Maiti machine learning and Geocomputing Laboratory Department of Applied GeophysicsIIT(ISM)Dhanbad826004JharkhandIndia

Pore pressure(PP)information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field *** prediction is an essential requirement to ensure safe drilling operations and it is a fundamental input for well design,and mud weight estimation for wellbore ***,the pore pressure trend prediction in complex geological provinces is challenging particularly at oceanic slope setting,where sedimentation rate is relatively high and PP can be driven by various complex *** overcome these difficulties,an advanced machine learning(ML)tool is implemented in combination with empirical *** empirical method for PP prediction is comprised of data pre-processing and model establishment ***'s method and Porosity method have been used for PP calculation of the well U1517A located at Tuaheni Landslide Complex of Hikurangi Subduction zone of IODP expedition ***-ray,sonic travel time,bulk density and sonic derived porosity are extracted from well log data for the theoretical framework *** normal compaction trend(NCT)curve analysis is used to check the optimum fitting of the low permeable zone *** statistical analysis is done using the histogram analysis and Pearson correlation coefficient matrix with PP data series to identify potential input combinations for ML-based predictive model *** dataset is prepared and divided into two parts:Training and *** PP data and well log of borehole U1517A is pre-processed to scale in between[-1,+1]to fit into the input range of the non-linear activation/transfer function of the decision tree regression *** Decision Tree Regression(DTR)algorithm is built and compared to the model performance to predict the PP and identify the overpressure zone in Hikurangi Tuaheni Zone of IODP Expedition 372.

关键词： Well log Pore pressure machine learning(ML) IODP Hikurangi Tuaheni Zone IODP Expedition 372

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Understanding adversarial attacks on observations in deep reinforcement learning

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Science China(Information Sciences) 2024年第5期67卷 69-83页

作者： You QIAOBEN Chengyang YING Xinning ZHOU Hang SU Jun ZHU Bo ZHANG department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua-Bosch Joint Center for Machine Learning Institute for Artificial Intelligence Tsinghua University Peng Cheng Laboratory

Deep reinforcement learning models are vulnerable to adversarial attacks that can decrease the cumulative expected reward of a victim by manipulating its observations. Despite the efficiency of previous optimization-based methods for generating adversarial noise in supervised learning, such methods might not achieve the lowest cumulative reward since they do not generally explore the environmental ***, a framework is provided to better understand the existing methods by reformulating the problem of adversarial attacks on reinforcement learning in the function space. The reformulation approach adopted herein generates an optimal adversary in the function space of targeted attacks, repelling them via a generic two-stage framework. In the first stage, a deceptive policy is trained by hacking the environment and discovering a set of trajectories routing to the lowest reward or the worst-case performance. Next, the adversary misleads the victim to imitate the deceptive policy by perturbing the observations. Compared to existing approaches, it is theoretically shown that our adversary is strong under an appropriate noise level. Extensive experiments demonstrate the superiority of the proposed method in terms of efficiency and effectiveness, achieving state-of-the-art performance in both Atari and MuJoCo environments.

关键词： deep learning reinforcement learning adversarial robustness adversarial attack

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EPR-Net: constructing a non-equilibrium potential landscape via a variational force projection formulation

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National Science Review 2024年第7期11卷 135-147页

作者： Yue Zhao Wei Zhang Tiejun Li Center for Data Science Peking University Zuse Institute Berlin department of Mathematics and Computer Science Freie Universit?t Berlin Laboratory of Mathematics and Applied Mathematics(LMAM)and School of Mathematical Sciences Peking University Center for machine learning Research Peking University

We present EPR-Net, a novel and effective deep learning approach that tackles a crucial challenge in biophysics: constructing potential landscapes for high-dimensional non-equilibrium steady-state ***-Net leverages a nice mathematical fact that the desired negative potential gradient is simply the orthogonal projection of the driving force of the underlying dynamics in a weighted inner-product ***, our loss function has an intimate connection with the steady entropy production rate(EPR),enabling simultaneous landscape construction and EPR estimation. We introduce an enhanced learning strategy for systems with small noise, and extend our framework to include dimensionality reduction and the state-dependent diffusion coefficient case in a unified fashion. Comparative evaluations on benchmark problems demonstrate the superior accuracy, effectiveness and robustness of EPR-Net compared to existing methods. We apply our approach to challenging biophysical problems, such as an eight-dimensional(8D)limit cycle and a 52D multi-stability problem, which provide accurate solutions and interesting insights on constructed landscapes. With its versatility and power, EPR-Net offers a promising solution for diverse landscape construction problems in biophysics.

关键词： high-dimensional potential landscape non-equilibrium system entropy production rate dimensionality reduction deep learning

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Tracking Nonstationary Streaming Data via Exponentially Weighted Moving Average Stochastic Gradient Descent

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Journal of Systems Science & Complexity 2024年

作者： QIAN Chengde JIANG Haiyan LIANG Decai School of Statistics and Data Science Nankai University department of machine learning Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)

In many applications involving data streams, the sequences of data arise from highly dynamic and often unstable real-life processes, rendering untenable the standard assumption that current and future data come from the same distribution. In response, new methodologies, such as dynamic online learning, have been proposed in order to account for the nonstationary features in the data-generating process. Motivated by the stability and statistical efficiency of the notable stochastic approximation method, average stochastic gradient descent(ASGD) in time-invariant systems, the authors propose an exponentially weighted moving average(EWMA)-based stochastic gradient descent(SGD) which accommodates the dynamic structure by introducing a forgetting factor and replacing the simple averaging step in ASGD with an EWMA step. Provided that the dynamic drift is Lipschitz continuous, the mean squared tracking error rate of the proposed method achieves the optimal rate in the nonparametric statistical paradigm. The proposed framework also allows us to derive the dynamic regret bound and asymptotic normality with a path variation constraint in a natural manner. Numerical analysis has been conducted to verify the performance of the proposed method. In particular, the proposed method is much more robust to the selection of learning rates compared with the ordinary SGD method.

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An explainable AI framework for robust and transparent data-driven wind turbine power curve models

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Energy and AI 2024年第1期15卷 312-327页

作者： Simon Letzgus Klaus-Robert Müller machine learning Group Technische Universitβe BerlinStraβe des 17.Juni 135Berlin10623Germany Berlin Institute for the Foundations of learning and Data Berlin10587Germany department of Artificial Intelligence Korea UniversitySeoul136-713South Korea Max Planck Institute for Informatics Saarbrücken66123Germany

In recent years,increasingly complex machine learning methods have become state-of-the-art in modelling wind turbine power curves based on operational *** these methods often exhibit superior performance on test sets,they face criticism due to a perceived lack of transparency and concerns about their robustness in dynamic,non-stationary environments encountered by wind *** this work,we address these issues and present a framework that leverages explainable artificial intelligence methods to gain systematic insights into data-driven power curve *** its core,we propose a metric to quantify how well a learned model strategy aligns with the underlying physical principles of the *** novel tool enables model validation beyond the conventional error metrics in an automated *** demonstrate,for instance,its capacity as an indicator for model generalization even when limited data is ***,it facilitates understanding how decisions made during the machine learning development process,such as data selection,pre-processing,or training parameters,affect learned *** a result,we obtain physically more reasonable models,a prerequisite not only for robustness but also for meaningful insights into turbine operation by domain *** latter,we illustrate in the context of wind turbine performance *** summary,the framework aims to guide researchers and practitioners alike toward a more informed selection and utilization of data-driven wind turbine power curve models.

关键词： Explainable AI(XAI) machine learning Wind energy Wind turbine power curve SCADA Condition monitoring

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Investigation on FBG based optical sensor for pressure and temperature measurement in civil application

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Optoelectronics Letters 2024年第9期20卷 531-536页

作者： Somesh Nandi Chethana K T Srinivas Preeta Sharan department of Artificial Intelligence and machine learning Rashtreeya Vidyalaya College of EngineeringBangalore560059India department of Electronics and Communications Jyothy Institute of TechnologyBangalore 560082India department of Electrical Communication Engineering Indian Institute of ScienceBangalore 560012India department of Electronics and Communications the Oxford College of EngineeringBangaloreIndia

Optical fiber Bragg grating(FBG)sensors have advanced significantly in the last several *** use of innovative FBG in temperature and pressure measurement is examined in this *** benefits of FBGs,such as their compact size,low weight,resilience to corrosion,immunity to electromagnetic interference,distributed sensing,and remote monitoring,have brought attention to the growing research in this field of structural health monitoring of civil infrastructures.

关键词： immunity civil application

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Design of Protograph LDPC-Coded MIMO-VLC Systems with Generalized Spatial Modulation

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China Communications 2024年第3期21卷 118-136页

作者： Dai Lin Fang Yi Guan Yongliang Mohsen Guizani School of Information Engineering Guangdong University of TechnologyGuangzhou 51006China The State Key Laboratory of Integrated Services Networks Xidian UniversityXi’an 710126China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore department of machine learning Mohamed Bin Zayed University of Artificial Intelligence(MBZUAI)Abu DhabiUnited Arab Emirates

This paper investigates the bit-interleaved coded generalized spatial modulation(BICGSM) with iterative decoding(BICGSM-ID) for multiple-input multiple-output(MIMO) visible light communications(VLC). In the BICGSM-ID scheme, the information bits conveyed by the signal-domain(SiD) symbols and the spatial-domain(SpD) light emitting diode(LED)-index patterns are coded by a protograph low-density parity-check(P-LDPC) code. Specifically, we propose a signal-domain symbol expanding and re-allocating(SSER) method for constructing a type of novel generalized spatial modulation(GSM) constellations, referred to as SSERGSM constellations, so as to boost the performance of the BICGSM-ID MIMO-VLC ***, by applying a modified PEXIT(MPEXIT) algorithm, we further design a family of rate-compatible P-LDPC codes, referred to as enhanced accumulate-repeat-accumulate(EARA) codes,which possess both excellent decoding thresholds and linear-minimum-distance-growth property. Both analysis and simulation results illustrate that the proposed SSERGSM constellations and P-LDPC codes can remarkably improve the convergence and decoding performance of MIMO-VLC systems. Therefore, the proposed P-LDPC-coded SSERGSM-mapped BICGSMID configuration is envisioned as a promising transmission solution to satisfy the high-throughput requirement of MIMO-VLC applications.

关键词： bit-interleaved coded modulation generalized spatial modulation multiple-input multipleoutput protograph LDPC codes visible light communication

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New complex physiological findings evolve hypothesized mechanisms of Dravet syndrome

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Neural Regeneration Research 2024年第9期19卷 1867-1868页

作者： MacKenzie A.Howard department of Neurology Dell Medical SchoolAustinTXUSA Center for learning and Memory and department of Neuroscience University of Texas at AustinAustinTXUSA

Developmental and epileptic encephalopathies(DEEs)are neurological disorders generally involving medically intractable seizures and a diverse array of comorbid neuropsychiatric delays and deficits that may severely affect cognition,mood,sleep,social behavior,movement control,learning,and/or *** syndrome(DS),also known as Severe Myoclonic Epilepsy of Infancy.

关键词： sized finding seizures

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A NONLOCAL KRONECKER-BASIS-REPRESENTATION METHOD FOR LOW-DOSE CT SINOGRAM RECOVERY

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Journal of Computational Mathematics 2024年第4期42卷 1080-1108页

作者： Jian Lu Huaxuan Hu Yuru Zou Zhaosong Lu Xiaoxia Liu Keke Zu Lin Li Shenzhen Key Laboratory of Advanced machine learning and Applications College of Mathematics and StatisticsShenzhen UniversityShenzhen 518060China National Center for Applied Mathematics Shenzhen(NCAMS) Shenzhen 518055China department of Industrial and Systems Engineering University of Minnesota Twin CitiesMinneapolisMN55455USA department of Applied Mathematics The Hong Kong Polytechnic UniversityHong Kong SARChina School of Electronic Engineering Xidian UniversityXi'an 710071China

Low-dose computed tomography(LDCT)contains the mixed noise of Poisson and Gaus-sian,which makes the image reconstruction a challenging *** order to describe the statistical characteristics of the mixed noise,we adopt the sinogram preprocessing as a stan-dard maximum a posteriori(MAP).Based on the fact that the sinogram of LDCT has non-local self-similarity property,it exhibits low-rank *** conventional way of solving the low-rank problem is implemented in matrix forms,and ignores the correlations among similar patch *** avoid this issue,we make use of a nonlocal Kronecker-Basis-Representation(KBR)method to depict the low-rank problem.A new denoising model,which consists of the sinogram preprocessing for data fidelity and the nonlocal KBR term,is developed in this *** proposed denoising model can better illustrate the generative mechanism of the mixed noise and the prior knowledge of the ***-merical results show that the proposed denoising model outperforms the state-of-the-art algorithms in terms of peak-signal-to-noise ratio(PSNR),feature similarity(FSIM),and normalized mean square error(NMSE).

关键词： Low-dose computed tomography Kronecker-basis-representation Low-rank imation.Noise-generating-mechanism

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Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector machine

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Computers, Materials & Continua 2024年第1期78卷 1311-1328页

作者： Arslan Akram Imran Khan Javed Rashid Mubbashar Saddique Muhammad Idrees Yazeed Yasin Ghadi Abdulmohsen Algarni Faculty of Computer Science and Information Technology Department of Computer ScienceSuperior UniversityLahore54000Pakistan machine learning Code(MLC)Lab Meharban HouseHouse Number 209Zafar ColonyOkara56300Pakistan Information Technology Services University of OkaraOkara56310Pakistan department of Computer Science&Engineering University of Engineering&Technology LahoreNarowal CampusNarowal51601Pakistan department of Computer Science and Software Engineering Al Ain UniversityAl Ain11671Abu Dhabi department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Algorithms for steganography are methods of hiding data transfers in media *** machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image *** with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for *** address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of *** Vector machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or *** Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the *** WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

关键词： Curvelets fast fourier transformation support vector machine high pass filters steganography

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