Algorithms for Bayesian network modeling and reliability inference of complex multistate systems: Part II-Dependent systems

• URL: http://arxiv.org/abs/2204.01327v1
• Date: Mon, 4 Apr 2022 08:57:55 GMT
• Title: Algorithms for Bayesian network modeling and reliability inference of complex multistate systems: Part II-Dependent systems
• Authors: Xiaohu Zheng, Wen Yao, Xiaoqian Chen
• Abstract summary: This Part II proposes a novel method for BN reliability modeling and analysis to apply the compression idea to the complex multistate dependent system. The results show that both Part I and Part II's proposed algorithms make the reliability modeling and analysis of the complex multistate system feasible.
• Score: 1.9856044678176497
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: In using the Bayesian network (BN) to construct the complex multistate system's reliability model as described in Part I, the memory storage requirements of the node probability table (NPT) will exceed the random access memory (RAM) of the computer. However, the proposed inference algorithm of Part I is not suitable for the dependent system. This Part II proposes a novel method for BN reliability modeling and analysis to apply the compression idea to the complex multistate dependent system. In this Part II, the dependent nodes and their parent nodes are equivalent to a block, based on which the multistate joint probability inference algorithm is proposed to calculate the joint probability distribution of a block's all nodes. Then, based on the proposed multistate compression algorithm of Part I, the dependent multistate inference algorithm is proposed for the complex multistate dependent system. The use and accuracy of the proposed algorithms are demonstrated in case 1. Finally, the proposed algorithms are applied to the reliability modeling and analysis of the satellite attitude control system. The results show that both Part I and Part II's proposed algorithms make the reliability modeling and analysis of the complex multistate system feasible.

Related papers

• Quantum Algorithms for Inverse Participation Ratio Estimation in multi-qubit and multi-qudit systems [0.24466725954625895]
  Inverse Participation Ratios quantify the spread of a quantum state over a selected basis of the Hilbert space. We propose three quantum algorithms to estimate IPRs on multi-qubit and multi-qudit quantum devices.
  arXiv  Detail & Related papers  (2024-05-06T10:39:03Z)
• Distributionally Robust Model-based Reinforcement Learning with Large State Spaces [55.14361269378122]
  Three major challenges in reinforcement learning are the complex dynamical systems with large state spaces, the costly data acquisition processes, and the deviation of real-world dynamics from the training environment deployment. We study distributionally robust Markov decision processes with continuous state spaces under the widely used Kullback-Leibler, chi-square, and total variation uncertainty sets. We propose a model-based approach that utilizes Gaussian Processes and the maximum variance reduction algorithm to efficiently learn multi-output nominal transition dynamics.
  arXiv  Detail & Related papers  (2023-09-05T13:42:11Z)
• Hybrid Ground-State Quantum Algorithms based on Neural Schrödinger Forging [0.0]
  Entanglement forging based variational algorithms leverage the bi- partition of quantum systems. We propose a new method for entanglement forging employing generative neural networks to identify the most pertinent bitstrings. We show that the proposed algorithm achieves comparable or superior performance compared to the existing standard implementation of entanglement forging.
  arXiv  Detail & Related papers  (2023-07-05T20:06:17Z)
• Multi-Resource Allocation for On-Device Distributed Federated Learning Systems [79.02994855744848]
  This work poses a distributed multi-resource allocation scheme for minimizing the weighted sum of latency and energy consumption in the on-device distributed federated learning (FL) system. Each mobile device in the system engages the model training process within the specified area and allocates its computation and communication resources for deriving and uploading parameters, respectively.
  arXiv  Detail & Related papers  (2022-11-01T14:16:05Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• Fidelity-Guarantee Entanglement Routing in Quantum Networks [64.49733801962198]
  Entanglement routing establishes remote entanglement connection between two arbitrary nodes. We propose purification-enabled entanglement routing designs to provide fidelity guarantee for multiple Source-Destination (SD) pairs in quantum networks.
  arXiv  Detail & Related papers  (2021-11-15T14:07:22Z)
• Quantum anonymous veto: A set of new protocols [0.41998444721319217]
  We propose protocols for quantum anonymous veto (QAV) based on different types of quantum resources. The proposed schemes are analyzed for all the requirements of a valid QAV scheme. A trade-off between correctness and robustness of the probabilistic QAV schemes is observed.
  arXiv  Detail & Related papers  (2021-09-13T18:56:09Z)
• Novel General Active Reliability Redundancy Allocation Problems and Algorithm [1.5990720051907859]
  The reliability redundancy allocation problem (RRAP) is used to maximize system reliability. A novel RRAP, called the general RRAP (GRRAP), is proposed to extend the series-parallel structure or bridge network to a more general network structure. To solve the proposed novel GRRAP, a new algorithm, called the BAT-SSOA3, used the simplified swarm optimization (SSO) to update solutions.
  arXiv  Detail & Related papers  (2021-08-18T11:54:42Z)
• Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications [119.81868223344173]
  The paper investigates the general problem of resource allocation for mitigating channel fading effects in Free Space Optical (FSO) communications. Under this framework, we propose two algorithms that solve FSO resource allocation problems.
  arXiv  Detail & Related papers  (2020-07-27T17:38:51Z)
• Type-2 fuzzy reliability redundancy allocation problem and its solution using particle swarm optimization algorithm [9.760638545828497]
  The fuzzy multi-objective reliability redundancy allocation problem (FMORRAP) is proposed. FMORRAP is proposed, which maximizes the system reliability while simultaneously minimizing the system cost.
  arXiv  Detail & Related papers  (2020-05-02T15:39:54Z)
• RIS Enhanced Massive Non-orthogonal Multiple Access Networks: Deployment and Passive Beamforming Design [116.88396201197533]
  A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS) The problem of joint deployment, phase shift design, as well as power allocation is formulated for maximizing the energy efficiency. A novel long short-term memory (LSTM) based echo state network (ESN) algorithm is proposed to predict users' tele-traffic demand by leveraging a real dataset. A decaying double deep Q-network (D3QN) based position-acquisition and phase-control algorithm is proposed to solve the joint problem of deployment and design of the RIS.
  arXiv  Detail & Related papers  (2020-01-28T14:37:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.