Article
Engineering, Mechanical
Pengfei Wei, Fuchao Liu, Marcos Valdebenito, Michael Beer
Summary: Efficient propagation of imprecise probability models is achieved through the development of a new methodology framework named NIPI, focusing on the distributional probability-box model and the estimation of probabilistic moments of model responses. By integrating spatial correlation information revealed by the GPR model, NIPI estimations with high accuracy are derived, and numerical errors are treated as epistemic uncertainty.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Engineering, Mechanical
Menghao Ping, Xinyu Jia, Costas Papadimitriou, Xu Han, Chao Jiang
Summary: A new Bayesian modeling framework is proposed to account for the uncertainties in model parameters arising from various factors. The framework incorporates uncertainty using a single level hierarchy with Normal distributions. The likelihood function is constructed based on the discrepancy between model predictions and measurements, and the posterior PDF of model parameters depends on the lower two moments of the respective PDFs.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Statistics & Probability
Wensheng Guo, Mengying You, Jialin Yi, Michel A. Pontari, J. Richard Landis
Summary: By clustering UCPPS patients into homogeneous subgroups and utilizing longitudinal data modeling, we are able to identify different trajectories of the disease and associate them with clinical outcomes and baseline predictors. The proposed method shows promising performance in simulation studies and identifies four distinct subgroups of UCPPS patients, each with varying disease progression patterns.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2022)
Article
Automation & Control Systems
Kamelia Daudel, Randal Douc, Francois Roueff
Summary: This paper introduces a novel family of iterative algorithms for alpha-divergence minimisation in a Variational Inference context. The algorithms ensure a systematic decrease in the alpha-divergence between the variational and the posterior distributions. The approach allows for simultaneous optimization of the weights and components parameters of the mixture model, and shows improved results on multimodal target distributions and real data examples.
JOURNAL OF MACHINE LEARNING RESEARCH
(2023)
Article
Chemistry, Analytical
Tulay Ercan, Costas Papadimitriou
Summary: A framework for optimal sensor placement for virtual sensing is proposed based on modal expansion technique and information theory. The framework maximizes a utility function to reduce uncertainty in predicted quantities of interest at virtual sensing locations, considering uncertainties in structural model and modeling error parameters. The Gaussian nature of the response is utilized to derive analytical expressions for the utility function, highlighting the importance of robustness to errors and uncertainties.
Article
Engineering, Mechanical
Jiaxin Zhang, Stephanie TerMaath, Michael D. Shields
Summary: This paper proposes a new framework to quantify uncertainties in probability model-form and model parameters resulting from small datasets, and integrates these uncertainties into Sobol' index estimates. Imprecise Sobol' indices are calculated from candidate probability models using an importance sampling reweighting method, providing a measure of confidence in sensitivity estimates and guiding data collection efforts. The approach is demonstrated through examples involving Timoshenko beam parameters and E-glass fiber composite material properties.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Engineering, Mechanical
Dimitrios G. Giovanis, Michael D. Shields
Summary: The objective of this study is to quantify the uncertainty in probability of failure estimates resulting from incomplete knowledge of the probability distributions for the input random variables. The study proposes a framework that combines Subset Simulation (SuS) with Bayesian/information theoretic multi-model inference, and through methods such as multi-model inference and importance sampling, empirical probability distributions of failure probabilities that provide direct estimates of the uncertainty in failure probability estimates are obtained.
PROBABILISTIC ENGINEERING MECHANICS
(2022)
Article
Physics, Multidisciplinary
Omid Kharazmi, Mostafa Tamandi, Narayanaswamy Balakrishnan
Summary: This paper investigates the information generating (IG) function and relative information generating (RIG) function measures associated with maximum and minimum ranked set sampling (RSS) schemes with unequal sizes. It also examines the IG measures for simple random sampling (SRS) and provides comparison results between SRS and RSS procedures in terms of dispersive stochastic ordering. Additionally, the paper discusses the RIG divergence measure between SRS and RSS frameworks.
Article
Mathematics
Teresa Aparicio, Inmaculada Villanua
Summary: This paper discusses the problem of selecting the best model from a set of overlapping binary models, and focuses on the case where neither of the competing models is correctly specified. The study concludes that, in general, all criteria perform well.
Article
Engineering, Civil
Marc Fina, Celine Lauff, Matthias G. R. Faes, Marcos A. Valdebenito, Werner Wagner, Steffen Freitag
Summary: This paper proposes a framework to calculate the bounds on failure probability of linear structural systems affected by random and interval variables. The framework uses the maximum standard deviation of the structural response as a proxy for detecting the crisp values of interval parameters and obtaining failure probability bounds. The proposed approach is applicable to linear structural systems with aleatoric and epistemic uncertainty and Gaussian loading.
Article
Computer Science, Artificial Intelligence
Ahmad Esfandiari, Hamid Khaloozadeh, Faezeh Farivar
Summary: This paper introduces a multivariate filter feature selection method called interaction-based feature clustering (IFC), which is cost-effective in terms of computational cost while achieving high classification accuracy. The proposed method ranks features based on the symmetric uncertainty criterion and performs feature clustering by calculating their interactive weight as a similarity measure. Experimental results show that the IFC algorithm is more efficient than comparable methods in terms of classification accuracy and computational time.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Statistics & Probability
Kamelia Daudel, Randal Douc, Francois Portier
Summary: This paper introduces an (alpha, Gamma)-descent algorithm for alpha-divergence minimisation in a Bayesian framework, extending the variational approximation method and systematically decreasing the alpha-divergence at each step. The algorithm recovers the Entropic Mirror Descent and offers the Power Descent as an alternative, while also being able to optimize mixture model weights without information on the underlying distribution of the variational parameters. Empirical results show the benefits of the Power Descent over the Entropic Mirror Descent as dimensions grow.
ANNALS OF STATISTICS
(2021)
Article
Computer Science, Artificial Intelligence
Derek S. Prijatelj, Mel McCurrie, Samuel E. Anthony, Walter J. Scheirer
Summary: An interesting development in automatic visual recognition is the emergence of tasks where objective labels cannot be assigned to images, but human judgements can still be collected. This study proposes a Bayesian framework for evaluating black box predictors in this scenario, providing a method for estimating the epistemic uncertainty of the predictors. The framework is successfully applied to four image classification tasks that use subjective human judgements.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Taisuke Kobayashi
Summary: This paper presents a new interpretation of the traditional optimization method in reinforcement learning, using KL divergence, and introduces a new optimization method using forward KL divergence. The study demonstrates that moderate optimism can accelerate learning and lead to higher rewards.
Article
Engineering, Environmental
Xiaogang Deng, Xuepeng Zhang, Xiaoyue Liu, Yuping Cao
Summary: This paper proposes an improved SFA method, called probability-related randomized SFA (PRSFA), to enhance the detection of incipient faults. The proposed method combines random Fourier mapping and SFA to capture the nonlinear slow features, and introduces Kullback Leibler divergence (KLD) to measure the changing of the slow features' probability distributions. Multiple randomized SFA sub-models are developed and integrated through Bayesian inference mechanism to construct the global statistics for the whole system monitoring. Simulation results demonstrate that the proposed method outperforms traditional SFA and kernel SFA methods in incipient fault detection performance.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2023)
Letter
Surgery
Simin Yuan, Zhou Luo, Jiaxin Zhang
ASIAN JOURNAL OF SURGERY
(2023)
Article
Engineering, Multidisciplinary
Congjie Wei, Jiaxin Zhang, Kenneth M. Liechti, Chenglin Wu
Summary: A thermodynamically consistent neural network approach is proposed to model the constitutive behavior of interfaces. It overcomes the issue of sparse training data and achieves good results by comparing with physical constraints and optimizing weight factors.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Plant Sciences
Jiaxin Zhang, Wenxin Zhang, Li Yang, Wenjing Zhao, Zuojia Liu, Erkang Wang, Jin Wang
Summary: This study finds that the phytochemical gallic acid (GA) has a positive effect in the treatment of nonalcoholic fatty liver disease (NAFLD), exerting hepatoprotective effects by controlling the transition from simple hepatosteatosis to steatohepatitis.
Review
Materials Science, Multidisciplinary
Xianglin Liu, Jiaxin Zhang, Zongrui Pei
Summary: This review discusses the use of machine learning in overcoming challenges in the field of high-entropy alloys (HEAs). It introduces the basics of machine learning algorithms and application scenarios, summarizes the latest machine learning models for describing atomic interactions, thermodynamic and mechanical properties, and provides examples of machine-learned phase-formation rules and order parameters. The article also discusses the remaining challenges and future research directions, including uncertainty quantification and machine learning-guided inverse materials design.
PROGRESS IN MATERIALS SCIENCE
(2023)
Review
Chemistry, Multidisciplinary
Jiaxin Zhang, Leaf Huang, Guangbo Ge, Kaili Hu
Summary: Dysregulated epigenetic modifications drive tumor progression, drug resistance, and metastasis by affecting the tumor microenvironment. Therapies targeting epigenetic dysregulation show promise, but clinical results of combination therapies are disappointing due to toxicities and limited effectiveness. This article discusses the role of epigenetic processes, the regulation of the tumor microenvironment, and the use of advanced drug delivery system for targeted therapy.
Article
Computer Science, Interdisciplinary Applications
Katiana Kontolati, Somdatta Goswami, Michael D. Shields, George Em Karniadakis
Summary: Constructing accurate surrogate models for complex physico-chemical processes is challenging. This study proposes manifold-based polynomial chaos expansion (m-PCE) and deep neural operator (DeepONet) as two promising approaches. The results show that DeepONet outperforms m-PCE in capturing highly non-smooth dynamics, and over-parameterization improves the generalization of both models. However, m-PCE achieves good accuracy at low training cost, while highly over-parameterized DeepONet provides better accuracy and robustness at a higher cost.
JOURNAL OF COMPUTATIONAL PHYSICS
(2023)
Article
Chemistry, Multidisciplinary
Pengfei Zhao, Shuang Wang, Jizong Jiang, Yanrong Gao, Yuewei Wang, Yuge Zhao, Jiaxin Zhang, Meng Zhang, Yongzhuo Huang
Summary: Lactate, produced by active glycolysis, plays a role in promoting breast cancer progression through crosstalk with the immune microenvironment. Quercetin, a monocarboxylate transporter inhibitor, can reduce lactate production. Doxorubicin induces immunogenic cell death, stimulating tumor-specific immune activation. Therefore, a combination therapy of quercetin and doxorubicin is proposed to inhibit lactate metabolism and stimulate anti-tumor immunity.
JOURNAL OF CONTROLLED RELEASE
(2023)
Review
Pharmacology & Pharmacy
Ante Ou, Yuewei Wang, Jiaxin Zhang, Yongzhuo Huang
Summary: Brain diseases are a significant global healthcare burden. The blood-brain barrier poses challenges for delivering therapeutics into the brain parenchyma. To overcome this, cell and cell derivative-based drug delivery systems have gained interest due to their biocompatibility, low immunogenicity, and ability to penetrate the blood-brain barrier. This review provides an overview of recent advancements in these delivery systems for brain disease diagnosis and treatment, as well as the challenges and potential solutions for clinical translation.
Article
Chemistry, Multidisciplinary
Mengyuan Li, Zhuoting Lu, Jiaxin Zhang, Liying Chen, Xialian Tang, Quanheng Jiang, Qinglian Hu, Lin Li, Jie Liu, Wei Huang
Summary: This study reports the use of an organic fluorescent substance AS1, which has fluorescence, photothermal, and photodynamic functions in the near-infrared II window. The encapsulation of AS1 into nanostructures results in high-performance phototheranostics (AS1R) with superior brightness, photothermal effect, and photodynamic performance compared to other types of organic phototheranostics.
ADVANCED MATERIALS
(2023)
Correction
Engineering, Biomedical
Xing Huang, Jiaxin Zhang, Yijie Song, Tong Zhang, Bing Wang
BIOMEDICAL MATERIALS
(2023)
Review
Materials Science, Multidisciplinary
Beilin Zhang, Ruijie Xie, Jiamin Jiang, Shiping Hao, Bin Fang, Jiaxin Zhang, Hua Bai, Bo Peng, Lin Li, Zhiyuan Liu, Li Fu
Summary: Implantable neural electrodes play a crucial role in interfacing with the neural system for recording and stimulation. However, the mismatch between rigid electrodes and nerve tissue can cause inflammation and signal degradation during long-term implantation. To address this issue, researchers have been improving material, structure, and implantation methods to develop flexible neural electrodes with better compatibility. This review highlights the progress in implantable neural electrodes from five aspects: materials, structures, stretchability, multifunctionality, and applications in nerve interfaces, and discusses the prospects for these electrodes.
JOURNAL OF MATERIALS CHEMISTRY C
(2023)
Article
Mechanics
Wenchao Hu, Xueliang Zhang, Chen Chen, Ziqian Li, Jiaxin Zhang, Bangchun Wen
Summary: This paper studies a new generalized dynamical model driven by multiple eccentric rotors, and reveals the stable states and motion characteristics of the system in different resonant regions through qualitative and quantitative analysis.
MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES
(2023)
Article
Computer Science, Software Engineering
Dimitrios Tsapetis, Michael D. Shields, Dimitris G. Giovanis, Audrey Olivier, Lukas Novak, Promit Chakroborty, Himanshu Sharma, Mohit Chauhan, Katiana Kontolati, Lohit Vandanapu, Dimitrios Loukrezis, Michael Gardner
Summary: This paper introduces the latest improvements in the Version 4 of UQpy. The code was restructured to conform with Python coding conventions, simplified tightly coupled features, and improved extensibility and modularity. The adoption of software engineering best practices, collaboration workflow, continuous integration, and automated testing improved the robustness and reliability of UQpy. Continuous deployment and Docker image enabled automated packaging and distribution in system agnostic format.
Article
Engineering, Mechanical
Promit Chakroborty, Somayajulu L. N. Dhulipala, Yifeng Che, Wen Jiang, Benjamin W. Spencer, Jason D. Hales, Michael D. Shields
Summary: This paper presents a robust multifidelity surrogate modeling strategy that combines low-fidelity and high-fidelity models to estimate the probability of failure for complex systems. Through an active-learning strategy, the multifidelity surrogate is assembled using an on-the-fly model adequacy assessment set within a subset simulation framework. The algorithm is highly accurate and significantly reduces computational cost.
JOURNAL OF ENGINEERING MECHANICS
(2023)
Meeting Abstract
Gastroenterology & Hepatology
Yuhao Yao, Jiaxin Zhang, Xu Cao, Xiaoke Li, Xiaobin Zao, Yong'an Ye
JOURNAL OF HEPATOLOGY
(2023)
Article
Engineering, Mechanical
Xuanen Kan, Yanjun Lu, Fan Zhang, Weipeng Hu
Summary: A blade disk system is crucial for the energy conversion efficiency of turbomachinery, but differences between blades can result in localized vibration. This study develops an approximate symplectic method to simulate vibration localization in a mistuned bladed disk system and reveals the influences of initial positive pressure, contact angle, and surface roughness on the strength of vibration localization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Zimeng Liu, Cheng Chang, Haodong Hu, Hui Ma, Kaigang Yuan, Xin Li, Xiaojian Zhao, Zhike Peng
Summary: Considering the calculation efficiency and accuracy of meshing characteristics of gear pair with tooth root crack fault, a parametric model of cracked spur gear is established by simplifying the crack propagation path. The LTCA method is used to calculate the time-varying meshing stiffness and transmission error, and the results are verified by finite element method. The study also proposes a crack area share index to measure the degree of crack fault and determines the application range of simplified crack propagation path.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Rongjian Sun, Conggan Ma, Nic Zhang, Chuyo Kaku, Yu Zhang, Qirui Hou
Summary: This paper proposes a novel forward calculation method (FCM) for calculating anisotropic material parameters (AMPs) of the motor stator assembly, considering structural discontinuities and composite material properties. The method is based on multi-scale theory and decouples the multi-scale equations to describe the equivalence and equivalence preconditions of AMPs of two scale models. The effectiveness of this method is verified by modal experiments.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Hao Zhang, Jiangcen Ke
Summary: This research introduces an intelligent scheduling system framework to optimize the ship lock schedule of the Three Gorges Hub. By analyzing navigational rules, operational characteristics, and existing problems, a mixed-integer nonlinear programming model is formulated with multiple objectives and constraints, and a hybrid intelligent algorithm is constructed for optimization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Jingjing He, Xizhong Wu, Xuefei Guan
Summary: A sensitivity and reliability enhanced ultrasonic method has been developed in this study to monitor and predict stress loss in pre-stressed multi-layer structures. The method leverages the potential breathing effect of porous cushion materials in the structures to increase the sensitivity of the signal feature to stress loss. Experimental investigations show that the proposed method offers improved accuracy, reliability, and sensitivity to stress change.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Benyamin Hosseiny, Jalal Amini, Hossein Aghababaei
Summary: This paper presents a method for monitoring sub-second or sub-minute displacements using GBSAR signals, which employs spectral estimation to achieve multi-dimensional target detection. It improves the processing of MIMO radar data and enables high-resolution fast displacement monitoring from GBSAR signals.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xianze Li, Hao Su, Ling Xiang, Qingtao Yao, Aijun Hu
Summary: This paper proposes a novel method for bearing fault identification, which can accurately identify faults with few samples under complex working conditions. The method is based on a Transformer meta-learning model, and the final result is determined by the weighted voting of multiple models.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xiaomeng Li, Yi Wang, Guangyao Zhang, Baoping Tang, Yi Qin
Summary: Inspired by chaos fractal theory and slowly varying damage dynamics theory, this paper proposes a new health monitoring indicator for vibration signals of rotating machinery, which can effectively monitor the mechanical condition under both cyclo-stationary and variable operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Hao Wang, Songye Zhu
Summary: This paper extends the latching mechanism to vibration control to improve energy dissipation efficiency. An innovative semi-active latched mass damper (LMD) is proposed, and different latching control strategies are tested and evaluated. The latching control can optimize the phase lag between control force and structural response, and provide an innovative solution to improve damper effectiveness and develop adaptive semi-active dampers.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Menghao Ping, Xinyu Jia, Costas Papadimitriou, Xu Han, Chao Jiang, Wang-Ji Yan
Summary: Identification of non-Gaussian processes is a challenging task in engineering problems. This article presents an improved orthogonal series expansion method to convert the identification of non-Gaussian processes into a finite number of non-Gaussian coefficients. The uncertainty of these coefficients is quantified using polynomial chaos expansion. The proposed method is applicable to both stationary and nonstationary non-Gaussian processes and has been validated through simulated data and real-world applications.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Lei Li, Wei Yang, Dongfa Li, Jianxin Han, Wenming Zhang
Summary: The frequency locking phenomenon induced by modal coupling can effectively overcome the dependence of peak frequency on driving strength in nonlinear resonant systems and improve the stability of peak frequency. This study proposes the double frequencies locking phenomenon in a three degrees of freedom (3-DOF) magnetic coupled resonant system driven by piezoelectricity. Experimental and theoretical investigations confirm the occurrence of first frequency locking and the subsequent switching to second frequency locking with the increase of driving force. Furthermore, a mass sensing scheme for double analytes is proposed based on the double frequencies locking phenomenon.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Kai Ma, Jingtao Du, Yang Liu, Ximing Chen
Summary: This study explores the feasibility of using nonlinear energy sinks (NES) as replacements for traditional linear tuned mass dampers (TMD) in practical engineering applications, specifically in diesel engine crankshafts. The results show that NES provides better vibration attenuation for the crankshaft compared to TMD under different operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Wentao Xu, Li Cheng, Shuaihao Lei, Lei Yu, Weixuan Jiao
Summary: In this study, a high-precision hydraulic mechanical stand and a vertical mixed-flow pumping station device were used to conduct research on cavitation signals of mixed-flow pumps. By analyzing the water pressure pulsation signal, it was found that the power spectrum density method is more sensitive and capable of extracting characteristics compared to traditional time-frequency domain analysis. This has significant implications for the identification and prevention of cavitation in mixed-flow pump machinery.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xiaodong Chen, Kang Tai, Huifeng Tan, Zhimin Xie
Summary: This paper addresses the issue of parasitic motion in microgripper jaws and its impact on clamping accuracy, and proposes a symmetrically stressed parallelogram mechanism as a solution. Through mechanical modeling and experimental validation, the effectiveness of this method is demonstrated.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Zhifeng Shi, Gang Zhang, Jing Liu, Xinbin Li, Yajun Xu, Changfeng Yan
Summary: This study provides useful guidance for early bearing fault detection and diagnosis by investigating the effects of crack inclination and propagation direction on the vibration characteristics of bearings.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)