Article
Computer Science, Artificial Intelligence
Hongfeng Long, Zhenming Peng, Yong Deng
Summary: The application of geometry in the analysis and interpretation of basic probability assignment (BPA) is a unique research direction in evidence theory. By visualizing BPA, the geometric properties and characteristics of BPA can be intuitively analyzed, and the potential features of BPA can be observed directly. Therefore, the proposed vector-based BPA visualization method has important research value.
Article
Computer Science, Artificial Intelligence
Junwei Li, Baolin Xie, Yong Jin, Lin Zhou
Summary: In this study, a method based on an interval number distance model and reliability is proposed to transform objective data into a basic probability assignment. By constructing an interval number model, calculating the interval number distance, and discounting the initial basic probability assignment using static and dynamic reliability, the final basic probability assignment is obtained. Experimental results show that this method outperforms other methods in terms of classification accuracy and remains effective in an incomplete information environment.
Article
Multidisciplinary Sciences
Shuning Wang, Yongchuan Tang
Summary: Dempster-Shafer evidence theory is commonly used for reasoning uncertain information, with generating BPA functions as the first step. A new BPA generation method based on Gaussian distribution is proposed in this paper, which involves constructing the distribution, calculating function values, data fusion, and decision-making processes. The method's feasibility and effectiveness are verified in classification problems using UCI datasets.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Dingbin Li, Yong Deng, Kang Hao Cheong
Summary: This study discusses the application of information quality in the frameworks of probability theory and possibility theory, introducing a method for fusing multisource information and proposing an approach applicable to evidence theory. Through a numerical example, the effectiveness of the method in pattern recognition is verified.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Zezheng Yan, Hanping Zhao, Xiaowen Mei
Summary: The study proposes an improved combination method for conflicting evidence based on the redistribution of the basic probability assignment. The method involves calculating conflict intensity and evidence unreliability, constructing a redistribution equation for the basic probability assignment, and using information entropy to modify the basic probability assignment for more accurate results.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Yuanpeng He, Fuyuan Xiao
Summary: To address highly conflicting evidence combinations, a new base function is proposed to alleviate conflicts and assign values to propositions based on importance. Single subset propositions are considered more crucial than multiple ones to reduce uncertainties and achieve intuitive combination results. Additionally, an averaging operation is carried out twice to prevent significant deviations between modified and original masses.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Yuanpeng He, Fuyuan Xiao
Summary: An improved method is proposed for addressing the conflicting management issue in the Dempster combination rule, which is crucial in multisource data fusion for applications like group decision making and target recognition. The new combination method presented in this study can handle highly conflicting environments without requiring normalization, offering convenience in computation and higher accuracy in predicting potential possibilities, especially in extreme circumstances. The validity and rationality of the proposed method are confirmed through numerical examples and real benchmark data from the UCI database.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Ming Jing, Yongchuan Tang
Summary: Dempster-Shafer evidence theory is applied to process uncertain information, but traditional methods may produce counterintuitive results. A new bBPA method is proposed to handle highly conflicting data, consistent with classical probability theory. Experimental results show the superiority of the new method in dealing with highly conflicting data.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Hanwen Li, Rui Cai
Summary: The paper proposes a new method to measure information quality in basic probability assignment, taking into account the influence of intersection among statements on uncertainty, and illustrates the effectiveness of this method with numerical examples. Additionally, an application in target recognition is used to demonstrate the validity of the proposed form of information quality in combining conflicting evidences.
Article
Physics, Multidisciplinary
Jingyu Liu, Yongchuan Tang
Summary: The paper proposes a conflict data fusion method based on bBPA and evidence distance for multi-agent systems, aiming to improve the accuracy of the identification process of the MAIF system.
Article
Computer Science, Information Systems
Moitrayee Chatterjee, Akbar Siami Namin
Summary: The paper presents a method for identifying web spams using fuzzy evidence and the Dempster-Shafer theory, improving classification accuracy. By combining fuzzy reasoning and DS theory, conflicts among evidence are reduced, providing a more reliable spam detection solution.
JOURNAL OF INFORMATION SECURITY AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Ramisetty Kavya, Jabez Christopher, Subhrakanta Panda
Summary: Dempster Shafer (DS) theory is used for modeling uncertainty in information and is based on basic probability assignment. This work proposes an uncertainty measure that satisfies most of the mathematical properties and a generic set of behavioral requirements. The uncertainty value depends on the length and plausibility of the belief interval.
INFORMATION SCIENCES
(2023)
Article
Statistics & Probability
Ran Yu, Yong Deng
Summary: In this paper, a generalized Renyi entropy is proposed to measure the uncertainty of basic probability assignments, and some desirable properties are explored. Numerical examples demonstrate the feasibility and effectiveness of the proposed method. The entropy is shown to be more efficient compared to other existing measures.
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2023)
Article
Nuclear Science & Technology
Xianghao Gao, Xiaoyan Su, Hong Qian, Xiaolei Pan
Summary: Human reliability analysis (HRA) is important in fields like nuclear engineering, and assessing dependence among human tasks is a key part of HRA. However, existing methods rely on subjective and uncertain expert opinions and cannot handle dynamic influencing factors. In this paper, a new model based on Dempster-Shafer evidence theory and fuzzy numbers is proposed to address these challenges.
NUCLEAR ENGINEERING AND TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Ildar Batyrshin, Luis Alfonso Villa-Vargas, Marco Antonio Ramirez-Salinas, Moises Salinas-Rosales, Nailya Kubysheva
Summary: The paper introduces the notion of negation of a probability distribution, emphasizing the need for such a negation in knowledge-based systems. The study focuses on transforming probability distributions point by point using decreasing functions defined on [0,1]. The characterization of linear negators is presented as a convex combination of Yager's and uniform negators.
Article
Engineering, Industrial
Xiaoge Zhang, Sankaran Mahadevan
Summary: This paper analyzes historical passenger airline accidents to construct a Bayesian network capturing causal relationships between accidents, estimating prior and conditional probabilities for accident analysis, and developing a computer program for automated network generation.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Engineering, Mechanical
Yixuan Liu, Chen Jiang, Xiaoge Zhang, Zissimos P. Mourelatos, Dakota Barthlow, David Gorsich, Amandeep Singh
Summary: This article introduces a bio-inspired approach for multivehicle mission planning of off-road autonomous ground vehicles in dynamic environments. It analyzes vehicle reliability using physics-based simulations and identifies optimal paths using a combination of Physarum algorithm and navigation mesh.
JOURNAL OF MECHANICAL DESIGN
(2022)
Article
Computer Science, Artificial Intelligence
Xiaoge Zhang, Sankaran Mahadevan, Felix T. S. Chan
Summary: This paper presents a framework based on uncertainty quantification to enhance the interpretability of deep learning models. By propagating uncertainty to model predictions and optimizing pixels using entropy and differential evolution, the model interpretability is effectively improved.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xiaoge Zhang, Felix T. S. Chan, Chao Yan, Indranil Bose
Summary: This paper provides a systematic and comprehensive overview of the various risks that may arise in AI/ML systems, and emphasizes the need for research on developing a risk management framework.
DECISION SUPPORT SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Xiaoge Zhang, Zhen Hu, Sankaran Mahadevan
Summary: This article investigates a resilience-based design optimization approach for configuring logistics service centers. The approach considers the impact of potential disruptive events and aims to enhance the system's ability to withstand such events through optimized decision variables. An adaptive importance sampling method is used to tackle the complex optimization problem, and the effectiveness of the proposed approach is demonstrated through a numerical example.
IEEE TRANSACTIONS ON RELIABILITY
(2022)
Article
Computer Science, Artificial Intelligence
Cho Yin Yiu, Kam K. H. Ng, Xinyu Li, Xiaoge Zhang, Qinbiao Li, Hok Sam Lam, Man Ho Chong
Summary: Teams composed of aviation professionals are crucial for maintaining a safe and efficient airport environment. This research evaluates the impact of an enhanced communication protocol on cognitive workload under adverse weather conditions and develops a human-centered classification model for identifying hazardous meteorological conditions. The findings indicate that reduced visibility significantly increases subjective workload, but inclusion of turning direction information in communications does not intensify cognitive workload. The proposed Bayesian neural network-based classification model outperforms other algorithms in identifying potentially hazardous weather conditions.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Engineering, Civil
Yingxiao Kong, Xiaoge Zhang, Sankaran Mahadevan
Summary: This paper focuses on the hard landing problem, which is one of the riskiest phases of a flight. A probabilistic predictive model is built using machine learning and Bayesian neural network approach to forecast the aircraft's vertical speed at touchdown. The model is validated using test flights and shows satisfactory performance.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Transportation Science & Technology
Xiaoge Zhang, Sanqiang Zhong, Sankaran Mahadevanb
Summary: This paper introduces a machine learning model for predicting the trajectories of ground objects on the airport surface, aiming to reduce collision events. The model utilizes a spatial-temporal graph convolutional neural network (STG-CNN) and other techniques to forecast the movement of objects and defines a separation distance-based metric for assessing safety. The model's performance is validated at two airports, demonstrating its superiority over an alternative approach.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Engineering, Industrial
Taotao Zhou, Xiaoge Zhang, Enrique Lopez Droguett, Ali Mosleh
Summary: In this paper, a PINN-based framework is proposed to assess the reliability of multi-state systems. The framework uses machine learning to convert the reliability assessment into a problem and solves the issue of gradient imbalance and establishes a continuous latent function. Experimental results show that the framework performs well in MSS reliability assessment.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Correction
Computer Science, Interdisciplinary Applications
Adam Thelen, Xiaoge Zhang, Olga Fink, Yan Lu, Sayan Ghosh, Byeng D. Youn, Michael D. Todd, Sankaran Mahadevan, Chao Hu, Zhen Hu
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2023)
Review
Computer Science, Interdisciplinary Applications
Adam Thelen, Xiaoge Zhang, Olga Fink, Yan Lu, Sayan Ghosh, Byeng D. Youn, Michael D. Todd, Sankaran Mahadevan, Chao Hu, Zhen Hu
Summary: Digital twin, an emerging technology in the era of Industry 4.0, is comprehensively modeling the physical world as interconnected digital models. This paper provides a literature review of digital twin trends, analyzes digital twin modeling and enabling technologies, and offers perspectives on the future trajectory of digital twin technology.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2022)
Review
Computer Science, Interdisciplinary Applications
Adam Thelen, Xiaoge Zhang, Olga Fink, Yan Lu, Sayan Ghosh, Byeng D. Youn, Michael D. Todd, Sankaran Mahadevan, Chao Hu, Zhen Hu
Summary: Digital twin, as an emerging technology in the industry 4.0 era, is drawing unprecedented attention due to its potential in optimizing various processes. In this second part of the paper, the focus is on reviewing the key enabling technologies of digital twins, including uncertainty quantification, optimization methods, open-source datasets and tools. A case study of a battery digital twin is presented to illustrate the modeling and twinning methods discussed in the review. The code and preprocessed data for generating the case study results are available on Github.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2023)
Article
Engineering, Manufacturing
Jingchang Li, Xiaoge Zhang, Qi Zhou, Felix T. S. Chan, Zhen Hu
Summary: This paper proposes a feature-level multi-sensor fusion approach that combines acoustic emission signals with photodiode signals to achieve quality monitoring in selective laser melting (SLM) technology. By developing an off-axial monitoring system to capture process signatures and using a convolutional neural network to extract and fuse features from two sensors, the proposed approach outperforms several baseline models in quality monitoring.
JOURNAL OF MANUFACTURING PROCESSES
(2022)
Article
Engineering, Multidisciplinary
Yiming Zhang, Dingyang Zhang, Xiaoge Zhang, Lemiao Qiu, Felix T. S. Chan, Zili Wang, Shuyou Zhang
Summary: This paper proposes a Guided Probabilistic Reinforcement Learning (Guided-PRL) model to minimize the life-cycle cost of multi-component systems maintenance. The Guided-PRL model improves upon traditional Actor-Critic models by introducing a guided sampling scheme and Bayesian models for uncertainty quantification.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Computer Science, Hardware & Architecture
Wenting Yi, Wai Kit Chan, Hiu Hung Lee, Steven T. Boles, Xiaoge Zhang
Summary: This article introduces the importance of uncertainty quantification in mission-critical engineering applications and presents a methodology that seamlessly integrates a spectral-normalized neural Gaussian process (SNGP) module into GoogLeNet for accurately detecting defects in steel wire ropes. The methodology consists of three steps, including collecting raw magnetic flux leakage (MFL) signals, transforming the signals into 2-D images using Gramian angular field, and integrating SNGP into GoogLeNet with spectral normalization (SN) and Gaussian process (GP) layers. Comparative evaluations demonstrate the advantages of the developed methodology in classifying SWR defects and identifying out-of-distribution (OOD) SWR instances.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Engineering, Industrial
Mateusz Oszczypala, Jakub Konwerski, Jaroslaw Ziolkowski, Jerzy Malachowski
Summary: This article discusses the issues related to the redundancy of k-out-of-n structures and proposes a probabilistic and simulation-based optimization method. The method was applied to real transport systems, demonstrating its effectiveness in reducing costs and improving system availability and performance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Wencheng Huang, Haoran Li, Yanhui Yin, Zhi Zhang, Anhao Xie, Yin Zhang, Guo Cheng
Summary: Inspired by the theory of degree entropy, this study proposes a new node identification approach called Adjacency Information Entropy (AIE) to identify the importance of nodes in urban rail transit networks (URTN). Through numerical and real-world case studies, it is found that AIE can effectively identify important nodes and facilitate connections among non-adjacent nodes.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yaohui Lu, Liwei Chen
Summary: This paper discusses the four phases of the system life cycle and the different costs associated with each phase. It proposes an improvement importance method to optimize system reliability and analyzes the process of failure risk under limited resources.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Xian Zhao, Chen Wang, Siqi Wang
Summary: This paper proposes a new rebalancing strategy for balanced systems by switching standby components. Different switching rules are provided based on different balance conditions. The system reliability is derived using the finite Markov chain imbedding approach, and numerical examples and sensitivity analysis are presented for validation.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Fengyuan Jiang, Sheng Dong
Summary: Corrosion defects are the primary causes of pipeline burst failures. The traditional methodologies ignore the effects of random morphologies on failure behaviors, leading to deviations in remaining strength estimation and reliability analysis. To address this issue, an integrated methodology combining random field, non-linear finite element analysis, and Monte-Carlo Simulation was developed to describe the failure behaviors of pipelines with random defects.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Guoqing Cheng, Jiayi Shen, Fang Wang, Ling Li, Nan Yang
Summary: This paper investigates the optimal joint inspection and mission abort policies for a multi-component system with failure interaction. The proportional hazards model is used to characterize the effect of one component's deterioration on other components' hazard rates. The optimal policy is studied to minimize the expected total cost, and some structural properties of the optimal policy are obtained.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yaohui Lu, Shaomin Wu
Summary: A new resilience model is proposed in this paper for systems under competing risks, and related indices are introduced for evaluating the system's resilience. The model takes into account the degradation process, external shocks, and maintenance interactions of the system, and its effectiveness is demonstrated through a case study.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yang Li, Jun Xu
Summary: This paper proposes a translation model based on neural network for simulating non-Gaussian stochastic processes. By converting the target non-Gaussian power spectrum to the underlying Gaussian power spectrum, non-Gaussian samples can be generated.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yanyan Liu, Keping Li, Dongyang Yan
Summary: This paper proposes a new random walk method, CBDRWR, to analyze the potential risk of railway accidents. By combining accident causation network, we assign different restart probabilities to each node and improve the transition probabilities. In the case study, the proposed method effectively quantifies the potential risk and identifies key risk sources.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Nan Hai, Daqing Gong, Zixuan Dai
Summary: The current risk management of utility tunnel operation and maintenance is of low quality and efficiency. This study proposes a theoretical model and platform that offer effective decision support and improve the safety of utility tunnel operation and maintenance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Tomoaki Nishino, Takuya Miyashita, Nobuhito Mori
Summary: A novel modeling methodology is proposed to simulate cascading disasters triggered by tsunamis considering uncertainties. The methodology focuses on tsunami-triggered oil spills and subsequent fires and quantitatively measures the fire hazard. It can help assess and improve risk reduction plans.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Mingjiang Xie, Yifei Wang, Jianli Zhao, Xianjun Pei, Tairui Zhang
Summary: This study investigates the effect of rockfall impact on the health management of pipelines with fatigue cracks and proposes a crack propagation prediction algorithm based on rockfall impact. Dynamic SIF values are obtained through finite element modeling and a method combining multilayer perceptron with Paris' law is used for accurate crack growth prediction. The method is valuable for decision making in pipeline reliability assessment and integrity management.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Saeed Jamalzadeh, Lily Mettenbrink, Kash Barker, Andres D. Gonzalez, Sridhar Radhakrishnan, Jonas Johansson, Elena Bessarabova
Summary: This study proposes an integrated epidemiological-optimization model to quantify the impacts of weaponized disinformation on transportation infrastructure and supply chains. Results show that disinformation targeted at transportation infrastructure can have wide-ranging impacts across different commodities.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Jiaxi Wang
Summary: This paper investigates the depot maintenance packet assignment and crew scheduling problem for high-speed trains. A mixed integer linear programming model is proposed, and computational experiments show the effectiveness and efficiency of the improved model compared to the baseline one.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yuxuan Tian, Xiaoshu Guan, Huabin Sun, Yuequan Bao
Summary: This paper proposes a DFMs searching algorithm based on the graph neural network (GNN) to improve computational efficiency and adaptively identify DFMs. The algorithm terminates prematurely when unable to identify new DFMs.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)