scholarly journals Decision Diagram Based Symbolic Algorithm for Evaluating the Reliability of a Multistate Flow Network

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Rongsheng Dong ◽  
Yangyang Zhu ◽  
Zhoubo Xu ◽  
Fengying Li
Keyword(s):  
Large Scale ◽  
Complexity Analysis ◽  
Decision Diagrams ◽  
Binary Decision ◽  
Flow Network ◽  
Memory Space ◽  
Efficient Data ◽  
Np Hard Problem ◽  
Efficient Data Structures ◽  
Symbolic Algorithms

Evaluating the reliability of Multistate Flow Network (MFN) is an NP-hard problem. Ordered binary decision diagram (OBDD) or variants thereof, such as multivalued decision diagram (MDD), are compact and efficient data structures suitable for dealing with large-scale problems. Two symbolic algorithms for evaluating the reliability of MFN, MFN_OBDD and MFN_MDD, are proposed in this paper. In the algorithms, several operating functions are defined to prune the generated decision diagrams. Thereby the state space of capacity combinations is further compressed and the operational complexity of the decision diagrams is further reduced. Meanwhile, the related theoretical proofs and complexity analysis are carried out. Experimental results show the following: (1) compared to the existing decomposition algorithm, the proposed algorithms take less memory space and fewer loops. (2) The number of nodes and the number of variables of MDD generated in MFN_MDD algorithm are much smaller than those of OBDD built in the MFN_OBDD algorithm. (3) In two cases with the same number of arcs, the proposed algorithms are more suitable for calculating the reliability of sparse networks.

A Variable Ordering Heuristic Based on Zero-Suppressed Binary Decision Diagrams

2010 ◽  
Author(s):  
Ya-zhou Li ◽  
Jin Wang ◽  
Li-qin Hu ◽  
Yi-can Wu
Keyword(s):  
Nuclear Power ◽  
Safety Assessment ◽  
Large Scale ◽  
Binary Decision Diagrams ◽  
Decision Diagrams ◽  
Binary Decision ◽  
Minimal Cut Sets ◽  
Variable Ordering ◽  
Safety And Reliability ◽  
Event Trees

Two approaches have been proposed to solve the large-scale fault trees or event trees for Probabilistic Safety Assessment in a nuclear power plant. The first one consists in MCS/ZBDD, which uses ZBDDs (Zero-suppressed Binary Decision Diagrams) to implement classical MCS (Minimal Cut Sets) algorithm. The second consists in designing heuristics and strategies to reduce the complexity of the BDDs (Binary Decision Diagrams) construction. This paper was motivated to combine the MCS/ZBDD and designing heuristics for ZBDDs together. A heuristic, which took the failure rate of basic event into account and utilized that truncation could be implemented on ZBDDs during the calculating process, was proposed. This heuristic accelerated the analysis progress by bringing forward the truncation and reducing the complexity of the intermediate ZBDDs. RiskA, a Zero-suppressed Binary Decision Diagram package extended to safety and reliability analysis, has adopted this heuristic. RiskA’s truncation strategies, which had some relations with the ordering scheme, were also introduced. The correctness and efficiency of this new heuristic were verified by some practical models’ analyses.

scholarly journals Enhancing the Performance of Large-scale Profitable Itemset Mining using Efficient Data Structures

2019 ◽  
Vol 8 (9) ◽  
pp. 1768-1772
Keyword(s):  
Data Structures ◽  
Large Scale ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Second Phase ◽  
Itemset Mining ◽  
Efficient Data ◽  
Profit Value ◽  
Efficient Data Structures

The process of extracting the most frequently bought items from a transactional database is termed as frequent itemset mining. Although it provides us with an idea of the best-selling itemsets, the method fails to identify the most profitable items from the database. It is not uncommon to have minimal intersection between frequent itemsets and profitable itemsets, and the process of extracting the most profitable itemsets is termed as Greater Profitable Itemset (GPI) mining. There have been various approaches to mine GPI in which [7] proposed a two-phased algorithm to optimize regeneration of GPI when the profit value of any item changes. This constituted of keeping track of the pruned items in the first phase and using it to efficiently regenerate GPI in the second phase. This paper proposes an enhancement to the way these changes are tracked by storing the pruned itemsets according to their constituent items, unlike the earlier algorithm that stored records iteration wise. By storing the itemsets according to their constituent items, we make sure that only the required items are being retrieved. In contrast, the earlier algorithm would fetch all the items pruned in any iteration, regardless of its relevance. By fetching only relevant itemset, the proposed method would significantly bring down the computational requirements.

scholarly journals Selecting Optimal Combination of Data Channels for Semantic Segmentation in City Information Modelling (CIM)

2021 ◽  
Vol 13 (7) ◽  
pp. 1367
Author(s):  
Yuanzhi Cai ◽  
Hong Huang ◽  
Kaiyang Wang ◽  
Cheng Zhang ◽  
Lei Fan ◽  
...  
Keyword(s):  
Large Scale ◽  
Semantic Segmentation ◽  
Optimal Combination ◽  
Reconstruction Technique ◽  
Process Data ◽  
Redundant Data ◽  
Multiple Data ◽  
Information Models ◽  
Efficient Data

Over the last decade, a 3D reconstruction technique has been developed to present the latest as-is information for various objects and build the city information models. Meanwhile, deep learning based approaches are employed to add semantic information to the models. Studies have proved that the accuracy of the model could be improved by combining multiple data channels (e.g., XYZ, Intensity, D, and RGB). Nevertheless, the redundant data channels in large-scale datasets may cause high computation cost and time during data processing. Few researchers have addressed the question of which combination of channels is optimal in terms of overall accuracy (OA) and mean intersection over union (mIoU). Therefore, a framework is proposed to explore an efficient data fusion approach for semantic segmentation by selecting an optimal combination of data channels. In the framework, a total of 13 channel combinations are investigated to pre-process data and the encoder-to-decoder structure is utilized for network permutations. A case study is carried out to investigate the efficiency of the proposed approach by adopting a city-level benchmark dataset and applying nine networks. It is found that the combination of IRGB channels provide the best OA performance, while IRGBD channels provide the best mIoU performance.

A Scalable Redefined Stochastic Blockmodel

2021 ◽  
Vol 15 (3) ◽  
pp. 1-28
Author(s):  
Xueyan Liu ◽  
Bo Yang ◽  
Hechang Chen ◽  
Katarzyna Musial ◽  
Hongxu Chen ◽  
...  
Keyword(s):  
Large Scale ◽  
Network Science ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Real World Data ◽  
Computational Overhead ◽  
Stochastic Blockmodel ◽  
Np Hard Problem ◽  
Large Scale Networks ◽  
The Cost

Stochastic blockmodel (SBM) is a widely used statistical network representation model, with good interpretability, expressiveness, generalization, and flexibility, which has become prevalent and important in the field of network science over the last years. However, learning an optimal SBM for a given network is an NP-hard problem. This results in significant limitations when it comes to applications of SBMs in large-scale networks, because of the significant computational overhead of existing SBM models, as well as their learning methods. Reducing the cost of SBM learning and making it scalable for handling large-scale networks, while maintaining the good theoretical properties of SBM, remains an unresolved problem. In this work, we address this challenging task from a novel perspective of model redefinition. We propose a novel redefined SBM with Poisson distribution and its block-wise learning algorithm that can efficiently analyse large-scale networks. Extensive validation conducted on both artificial and real-world data shows that our proposed method significantly outperforms the state-of-the-art methods in terms of a reasonable trade-off between accuracy and scalability. 1

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