Article
Engineering, Environmental
Pezhman Kazemi, Christophe Bengoa, Jean-Philippe Steyer, Jaume Giralt
Summary: This study proposes a practical data-driven framework for fault detection in anaerobic digestion process, based on predicting VFA concentration and validated using advanced techniques. The results demonstrate the good performance and feasibility of the framework in terms of fault detection.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2021)
Article
Computer Science, Information Systems
Jihye Ryu, Juhyeok Kwon, Jeong-Dong Ryoo, Taesik Cheung, Jinoo Joung
Summary: In this study, a timeslot scheduling algorithm for traffic with similar requirements but different priorities is designed using a double deep q-network (DDQN), a reinforcement learning algorithm. The behavior of the DDQN agent is evaluated by defining a reward function based on the difference between estimated delay and packet deadline, as well as packet priority. The simulation shows that the designed algorithm outperforms existing algorithms in terms of more packets arrived within the deadline. The proposed DDQN-based scheduler can be implemented in upcoming frameworks for autonomous network scheduling.
Article
Multidisciplinary Sciences
Muhammad Arslan, Syed Masroor Anwar, Showkat Ahmad Lone, Zahid Rasheed, Majid Khan, Saddam Akbar Abbasi
Summary: The AEWMA control charts are an advanced form of classical memory control charts used for efficiently monitoring shifts in process parameters. This study presents a new AEWMA control chart that estimates location shifts using HEWMA statistic and adaptively updates the smoothing constant, improving the performance of the control chart.
Article
Computer Science, Information Systems
Yasmin A. Badr, Khaled T. Wassif, Mahmoud Othman
Summary: A new soft computing metaheuristic framework is introduced for automatic clustering to generate the optimal cluster formation and to determine the best estimate for the number of clusters. Experimental results show that the proposed hybrid algorithm outperforms standard genetic algorithm and bat algorithm, and is compared with other algorithms through a literature review. The clusters obtained are statistically validated using Mann-Whitney-Wilcoxon rank-sum test.
Article
Computer Science, Theory & Methods
Eva Papadogiannaki, Sotiris Ioannidis
Summary: The adoption of network traffic encryption is on the rise, providing privacy protection for users but also increasing the potential for malicious activities to be hidden through encryption. Research and review are needed to address the adaptability challenges between emerging encryption technologies and traditional traffic processing systems.
ACM COMPUTING SURVEYS
(2021)
Article
Computer Science, Information Systems
Jiawei Zhu, Anqiang Wang, Wei Wu, Zhijin Zhao, Yuting Xu, Rong Lei, Keqiang Yue
Summary: In this paper, an intelligent receiving scheme of frequency-hopping sequences is proposed, which combines time-frequency analysis with deep learning to achieve intelligent estimation of frequency-hopping sequences. A hybrid network module is designed by combining a convolutional neural network (CNN) with a gated recurrent unit (GRU). Simulation results show that the proposed method has strong generalization ability and robustness.
Article
Computer Science, Artificial Intelligence
Shams Forruque Ahmed, Md. Sakib Bin Alam, Maruf Hassan, Mahtabin Rodela Rozbu, Taoseef Ishtiak, Nazifa Rafa, M. Mofijur, A. B. M. Shawkat Ali, Amir H. Gandomi
Summary: Deep learning is revolutionizing evidence-based decision-making techniques and has the ability to overcome limitations posed by large datasets. However, as a multidisciplinary field that is still in its nascent phase, there is a limited number of articles that comprehensively review DL architectures. This paper aims to provide insights into state-of-the-art DL modelling techniques and their challenges and advantages.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Yaozu Wu, Yankai Chen, Zhishuai Yin, Weiping Ding, Irwin King
Summary: Due to advancements in biomedical technologies, large amounts of relational data have been collected for biomedical research. Biomedical graphs, a popular representation of this data, can capture complex biomedical systems. However, traditional graph analysis methods face difficulties when handling high-dimensional and sparsely interconnected biomedical data. To address these issues, graph embedding methods have gained attention. These methods convert graph-based data into low-dimensional vector space, which is used for downstream biomedical tasks. This article focuses on graph embedding techniques in the biomedical domain, introducing recent developments, methodologies, tasks, datasets, and implementations, while discussing limitations and potential solutions.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Christopher W. Hays, Troy Henderson
Summary: Fusing information from separate sensors is a common problem in various scientific and engineering fields, with multiple potential solutions available in literature. This study presents a fusion methodology that cooperatively combines the information from two sources while maintaining both consistency and tightness. Analytically derived upper and lower bounds for a scaling parameter, Omega, are also provided to ensure the consistency and tightness of the fusion solution. The results show that the proposed solution outperforms the current state-of-the-art solution by providing tighter approximations of the optimal solution while maintaining the optimal solution as the lower bound.
INFORMATION FUSION
(2023)
Article
Geosciences, Multidisciplinary
Hao Ding, Xinyu Xu, Yuanjin Pan, Mengkui Li
Summary: This study comprehensively analyzes seven spherical harmonic-based array processing techniques, applied to four global observation networks, and makes important findings such as restoring cleaner mode sequences and estimating Love numbers.
EARTH-SCIENCE REVIEWS
(2021)
Article
Automation & Control Systems
Silvia Maria Zanoli, Crescenzo Pepe, Giacomo Astolfi, Lorenzo Orlietti
Summary: This article presents the application of advanced process control techniques to an Italian water distribution network. In-depth hydraulic studies were conducted prior to hardware modifications through sectorization procedures. The performance was further improved through pressure management and optimization of pump scheduling. The developed advanced process control system successfully reduced water losses and operational costs.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2022)
Article
Mathematics
F. Albiac, J. L. Ansorena, S. J. Dilworth, Denka Kutzarova
Summary: The article contributes to the study of the structure of subsymmetric basic sequences in Banach spaces by introducing the concept of positionings and developing new tools, leading to a dichotomy theorem for general spaces with subsymmetric bases. It demonstrates the classification of all subsymmetric sequences in certain types of spaces, showing that Garling sequence spaces have a unique symmetric basic sequence but no symmetric basis, and have a continuum of subsymmetric basic sequences.
TRANSACTIONS OF THE AMERICAN MATHEMATICAL SOCIETY
(2021)
Article
Operations Research & Management Science
Satit Saejung, Pongsakorn Yotkaew
Summary: This paper derives Delta-convergence and strong convergence theorems for asymptotically quasi-nonexpansive sequences in Hadamard spaces, extending and improving recent results in the literature. Some of the results are even new in Hilbert spaces. Applications to convex minimization and common fixed point problems are discussed.
Article
Physics, Multidisciplinary
Won-Tak Hong, Eunju Hwang
Summary: This paper introduces a multivariate time series model for stock prices in the stock market. The model adopts a multivariate heterogeneous autoregressive (HAR) model with exponentially decaying coefficients, which is suitable for handling multivariate data with strong cross-correlation and long memory and represents the common structure of the joint data in terms of decay rates. Tests are proposed to identify the existence of decay rates in the multivariate HAR model, and simulation studies and empirical analysis with joint datasets of U.S. stock prices show that the proposed model outperforms conventional HAR models.
Article
Computer Science, Information Systems
F. Durante, J. Fernandez Sanchez, C. Ignazzi
Summary: In this study, operators defined on bounded functions on the power set of an infinite set X under finitely additive measures are reconsidered with an extended use of the concept of filter, offering new insights into the problem. The obtained results are then applied to the study of the aggregation of infinite sequences.
INFORMATION SCIENCES
(2021)
Article
Statistics & Probability
Wolfgang Bischoff, Andreas Gegg
JOURNAL OF THEORETICAL PROBABILITY
(2016)
Article
Statistics & Probability
Wolfgang Bischoff, Andreas Gegg
STATISTICS & PROBABILITY LETTERS
(2016)
Article
Pharmacology & Pharmacy
Wolfgang Bischoff, Frank Miller
JOURNAL OF BIOPHARMACEUTICAL STATISTICS
(2009)
Article
Statistics & Probability
Wolfgang Bischoff, Wayan Somayasa
JOURNAL OF MULTIVARIATE ANALYSIS
(2009)
Article
Statistics & Probability
W. Bischoff, A. Gegg
JOURNAL OF MULTIVARIATE ANALYSIS
(2011)
Proceedings Paper
Statistics & Probability
Wolfgang Bischoff
MODA 11 - ADVANCES IN MODEL-ORIENTED DESIGN AND ANALYSIS
(2016)
Article
Statistics & Probability
Wolfgang Bischoff, Enkelejd Hashorva, Juerg Huesler
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2007)
Article
Environmental Sciences
Wolfgang Bischoff, Mong-Na Lo Huang, Lei Yang
Article
Statistics & Probability
Wolfgang Bischoff, Frank Miller
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
(2006)
Article
Statistics & Probability
Wolfgang Bischoff, Frank Miller
STATISTICS & PROBABILITY LETTERS
(2006)
Article
Statistics & Probability
Wolfgang Bischoff, Frank Miller
ANNALS OF STATISTICS
(2006)
Article
Geochemistry & Geophysics
W Bischoff, B Heck, J Howind, A Teusch
JOURNAL OF GEODESY
(2006)
Article
Biology
W Bischoff, F Miller
Article
Statistics & Probability
Wolfgang Bischoff
AUSTRIAN JOURNAL OF STATISTICS
(2008)
Article
Computer Science, Interdisciplinary Applications
Blair Robertson, Chris Price
Summary: Spatial sampling designs are crucial for accurate estimation of population parameters. This study proposes a new design method that generates samples with good spatial spread and performs favorably compared to existing designs.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Hiroya Yamazoe, Kanta Naito
Summary: This paper focuses on the simultaneous confidence region of a one-dimensional curve embedded in multi-dimensional space. An estimator of the curve is obtained through local linear regression on each variable in multi-dimensional data. A method to construct a simultaneous confidence region based on this estimator is proposed, and theoretical results for the estimator and the region are developed. The effectiveness of the region is demonstrated through simulation studies and applications to artificial and real datasets.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Cheng Peng, Drew P. Kouri, Stan Uryasev
Summary: This paper introduces a novel optimal experimental design method for quantifying the distribution tails of uncertain system responses. The method minimizes the variance or conditional value-at-risk of the upper bound of the predicted quantile, and estimates the data uncertainty using quantile regression. The optimal design problems are solved as linear programming problems, making the proposed methods efficient even for large datasets.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Xiaofei Wu, Hao Ming, Zhimin Zhang, Zhenyu Cui
Summary: This paper proposes a model that combines quantile regression and fused LASSO penalty, and introduces an iterative algorithm based on ADMM to solve high-dimensional datasets. The paper proves the global convergence and comparable convergence rates of the algorithm, and analyzes the theoretical properties of the model. Numerical experimental results support the superior performance of the model.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Xin He, Xiaojun Mao, Zhonglei Wang
Summary: This paper proposes a nonparametric imputation method with sparsity to estimate the finite population mean, using an efficient kernel method and sparse learning for estimation. An augmented inverse probability weighting framework is adopted to achieve a central limit theorem for the proposed estimator under regularity conditions.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Christian H. Weiss, Fukang Zhu
Summary: This study introduces a multiplicative error model (CMEMs) for discrete-valued count time series, which is closely related to the integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH) models. It derives the stochastic properties and estimation approaches of different types of INGARCH-CMEMs, and demonstrates their performance and application through simulations and real-world data examples.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Ming-Hung Kao, Ping-Han Huang
Summary: Optimal designs for sparse functional data under the functional empirical component (FEC) settings are investigated. New computational methods and theoretical results are developed to efficiently obtain optimal exact and approximate designs. A hybrid exact-approximate design approach is proposed and demonstrated to be efficient through simulation studies and a real example.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Mateus Maia, Keefe Murphy, Andrew C. Parnell
Summary: The Bayesian additive regression trees (BART) model is a powerful ensemble method for regression tasks, but its lack of smoothness and explicit covariance structure can limit its performance. The Gaussian processes Bayesian additive regression trees (GP-BART) model addresses this limitation by incorporating Gaussian process priors, resulting in superior performance in various scenarios.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Xichen Mou, Dewei Wang
Summary: Human biomonitoring is a method of monitoring human health by measuring the accumulation of harmful chemicals in the body. To reduce the high cost of chemical analysis, researchers have adopted a cost-effective approach that combines specimens and analyzes the concentration of toxic substances in the pooled samples. To effectively interpret these aggregated measurements, a new regression framework is proposed by extending the additive partially linear model (APLM). The APLM is versatile in capturing the complex association between outcomes and covariates, making it valuable in assessing the complex interplay between chemical bioaccumulation and potential risk factors.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Lili Yu, Yichuan Zhao
Summary: The classical accelerated failure time model is a linear model commonly used for right censored survival data, but it cannot handle heteroscedastic survival data. This paper proposes a Laplace approximated quasi-likelihood method with a continuous estimating equation to address this issue, and provides estimation bias and confidence interval estimation formulas.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Shaobo Jin, Youngjo Lee
Summary: Hierarchical generalized linear models are widely used for fitting random effects models, but the standard error estimators receive less attention. Current standard error estimation methods are not necessarily accurate, and a sandwich estimator is proposed to improve the accuracy of standard error estimation.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Rebeca Pelaez, Ingrid Van Keilegom, Ricardo Cao, Juan M. Vilar
Summary: This article proposes an estimator for the probability of default (PD) in credit risk, derived from a nonparametric conditional survival function estimator based on cure models. The asymptotic expressions for bias, variance, and normality of the estimator are presented. Through simulation and empirical studies, the performance and practical behavior of the nonparametric estimator are compared with other methods.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
L. M. Andre, J. L. Wadsworth, A. O'Hagan
Summary: This paper proposes a dependence model that captures the entire data range in multi-variable cases. By blending two copulas with different characteristics and using a dynamic weighting function for smooth transition, the model is able to flexibly capture various dependence structures.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Niwen Zhou, Xu Guo, Lixing Zhu
Summary: The paper investigates hypothesis testing regarding the potential additional contributions of other covariates to the structural function, given the known covariates. The proposed distance-based test, based on Neyman's orthogonality condition, effectively detects local alternatives and is robust to the influence of nuisance functions. Numerical studies and real data analysis demonstrate the importance of this test in exploring covariates associated with AIDS treatment effects.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Blake Moya, Stephen G. Walker
Summary: A full posterior analysis method for nonparametric mixture models using Gibbs-type prior distributions, including the well known Dirichlet process mixture (DPM) model, is presented. The method removes the random mixing distribution and enables a simple-to-implement Markov chain Monte Carlo (MCMC) algorithm. The removal procedure reduces some of the posterior uncertainty and introduces a novel replacement approach. The method only requires the probabilities of a new or an old value associated with the corresponding Gibbs-type exchangeable sequence, without the need for explicit representations of the prior or posterior distributions. This allows the implementation of mixture models with full posterior uncertainty, including one introduced by Gnedin. The paper also provides numerous illustrations and introduces an R-package called CopRe that implements the methodology.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)