Innovation of Matching Structures Through Clustering and Reconstructing Basic Operation Actions in the Form Layer

2017 ◽  
Author(s):  
Li Yu-Tong ◽  
Wang Yuxin
Keyword(s):  
Basic Mechanism ◽  
Maximum Matching ◽  
Basic Operation ◽  
Function Structure ◽  
Matching Problem ◽  
Direct Function ◽  
Matching Process ◽  
Order Relations ◽  
Functional Reasoning ◽  
Essential Knowledge

Due to a lack of essential knowledge to support functional reasoning from the design requirements of the kinematic compound mechanisms to their constituent mechanisms, the creative conceptual design of kinematic compound mechanisms based on functional synthesis approach is still a challenging task. Through introducing the dynamic partition-matching process between the function layer and the form layer to substitute for the direct function-structure matching in the FBS model, the function-structure matching problem corresponding to deficient functional reasoning knowledge for kinematic compound mechanisms is solved by the authors. The following challenge is how to cluster the divided subset of basic operation actions generated in the form layer during the partition-matching process into a well-organized and complete kinematic behavior that can be matched by the sub-function in the function layer and implemented by a structure in the database. The adopted strategies in this paper are: through defining the correlation indexes between basic operation actions, the basic operation action and its realized function behavior, and its embodied structure, as well as its dynamic behavior characteristics, the clustering possibility for a group of basic operation actions is determined. With the aid of the compatibility conditions between basic operation actions in the form layer and the consistency of the order relations between basic operation actions in the function layer and the form layer respectively, the consistency of the order relations among basic operation actions between the sub-functions in the function layer and the sub-behaviors in the form layer are guaranteed. Then, the optimal matching structures corresponding to the sub-functions in the function layer are determined based on the maximum matching coefficients of basic operation actions. In this way, the subsets of basic operation actions in the form layer are clustered into a number of complete behaviors that can be realized by mechanisms in the structure database and matched by the sub-functions in the function layer. Since multiple functional behaviors of each constituent basic mechanism take part in matching, some novel schemes of compound mechanisms with fewer and simpler constituent mechanisms to implement the overall function may be dug out.

scholarly journals Deterministic Dynamic Matching in O(1) Update Time

Algorithmica ◽  
2019 ◽  
Vol 82 (4) ◽  
pp. 1057-1080 ◽  
Author(s):  
Sayan Bhattacharya ◽  
Deeparnab Chakrabarty ◽  
Monika Henzinger
Keyword(s):  
Computer Science ◽  
Vertex Cover ◽  
Approximation Ratio ◽  
Approximate Algorithm ◽  
Maximum Matching ◽  
Matching Problem ◽  
Dynamic Algorithm ◽  
Dynamic Matching ◽  
Deterministic Dynamic ◽  
Oblivious Adversary

Abstract We consider the problems of maintaining an approximate maximum matching and an approximate minimum vertex cover in a dynamic graph undergoing a sequence of edge insertions/deletions. Starting with the seminal work of Onak and Rubinfeld (in: Proceedings of the ACM symposium on theory of computing (STOC), 2010), this problem has received significant attention in recent years. Very recently, extending the framework of Baswana et al. (in: Proceedings of the IEEE symposium on foundations of computer science (FOCS), 2011) , Solomon (in: Proceedings of the IEEE symposium on foundations of computer science (FOCS), 2016) gave a randomized dynamic algorithm for this problem that has an approximation ratio of 2 and an amortized update time of O(1) with high probability. This algorithm requires the assumption of an oblivious adversary, meaning that the future sequence of edge insertions/deletions in the graph cannot depend in any way on the algorithm’s past output. A natural way to remove the assumption on oblivious adversary is to give a deterministic dynamic algorithm for the same problem in O(1) update time. In this paper, we resolve this question. We present a new deterministic fully dynamic algorithm that maintains a O(1)-approximate minimum vertex cover and maximum fractional matching, with an amortized update time of O(1). Previously, the best deterministic algorithm for this problem was due to Bhattacharya et al. (in: Proceedings of the ACM-SIAM symposium on discrete algorithms (SODA), 2015); it had an approximation ratio of $$(2+\varepsilon )$$(2+ε) and an amortized update time of $$O(\log n/\varepsilon ^2)$$O(logn/ε2). Our result can be generalized to give a fully dynamic $$O(f^3)$$O(f3)-approximate algorithm with $$O(f^2)$$O(f2) amortized update time for the hypergraph vertex cover and fractional hypergraph matching problem, where every hyperedge has at most f vertices.

The Maximum Matching Problem Based on Self-Assembly Model of Molecular Beacon

2018 ◽  
Vol 10 (2) ◽  
pp. 213-218 ◽  
Author(s):  
Jing Yang ◽  
Zhixiang Yin ◽  
Kaifeng Huang ◽  
Jianzhong Cui
Keyword(s):  
Self Assembly ◽  
Molecular Beacon ◽  
Maximum Matching ◽  
Assembly Model ◽  
Matching Problem

Optimizing Ontology Alignment Through Compact MOEA/D

2017 ◽  
Vol 31 (04) ◽  
pp. 1759004 ◽  
Author(s):  
Xingsi Xue ◽  
Jianhua Liu
Keyword(s):  
Evolutionary Algorithm ◽  
Similarity Measures ◽  
Main Memory ◽  
Ontology Matching ◽  
Ontology Alignment ◽  
Matching Problem ◽  
Multi Objective ◽  
Matching Process ◽  
Self Tuning ◽  
Optimal Alignments

In order to support semantic inter-operability in many domains through disparate ontologies, we need to identify correspondences between the entities across different ontologies, which is commonly known as ontology matching. One of the challenges in ontology matching domain is how to select weights and thresholds in the ontology aligning process to aggregate the various similarity measures to obtain a satisfactory alignment, so called ontology meta-matching problem. Nowadays, the most suitable methodology to address the ontology meta-matching problem is through Evolutionary Algorithm (EA), and the Multi-Objective Evolutionary Algorithms (MOEA) based approaches are emerging as a new efficient methodology to face the meta-matching problem. Moreover, for dynamic applications, it is necessary to perform the system self-tuning process at runtime, and thus, efficiency of the configuration search strategies becomes critical. To this end, in this paper, we propose a problem-specific compact Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), in the whole ontology matching process of ontology meta-matching system, to optimize the ontology alignment. The experimental results show that our proposal is able to highly reduce the execution time and main memory consumption of determining the optimal alignments through MOEA/D based approach by 58.96% and 67.60% on average, respectively, and the quality of the alignments obtained is better than the state of the art ontology matching systems.

scholarly journals Two-Sided Matching on Comprehensive Transportation Network Emergency Vehicles’ Allocation

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Kunwei Xie ◽  
Heying Xu ◽  
Hongxia Lv
Keyword(s):  
Simulation Analysis ◽  
Transportation Network ◽  
Matching Problem ◽  
Evaluation Systems ◽  
Emergency Rescue ◽  
Matching Process ◽  
Emergency Vehicles ◽  
Material Transportation ◽  
Satisfaction Evaluation ◽  
The One

In emergency rescue, the allocation of comprehensive transportation network emergency vehicles often affects the efficiency of the whole rescue process. In the context of disasters, this paper researches the one-to-many two-sided matching problem between the emergency vehicles and the materials to be transported. Firstly, based on the needs of both parties involved in the matching, the satisfaction evaluation systems are constructed; with the goal of maximizing the weighted satisfaction of the affected areas and vehicles, the optimization model of the materials and emergency vehicles matching is established; then, an improved National Intern Matching Program (NIMP) algorithm is designed to solve the model, which is based on the k: 1 experimental pairing and updating ideas, and can take into account the capacity and destination constraints of vehicles in the matching process. Finally, through the calculation of an example, the matching scheme can make the satisfaction of material transportation reach 0.7392, and the simulation analysis proves that the scheme keeps certain stability in risky conditions.

scholarly journals Evaluation of two heuristic approaches to solve the ontology meta-matching problem

2017 ◽  
Author(s):  
Jorge Martinez-Gil ◽  
José F. Aldana-Montes
Keyword(s):  
Genetic Algorithm ◽  
Similarity Measure ◽  
Ontology Matching ◽  
Complex Nature ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Greedy Strategy ◽  
Heuristic Approaches ◽  
Matching Process ◽  
Maximum Similarity

Nowadays many techniques and tools are available for addressing the ontology matching problem, however, the complex nature of this problem causes existing solutions to be unsatisfactory. This work aims to shed some light on a more flexible way of matching ontologies. Ontology meta-matching, which is a set of techniques to configure optimum ontology matching functions. In this sense, we propose two approaches to automatically solve the ontology meta-matching problem. The first one is called maximum similarity measure, which is based on a greedy strategy to compute efficiently the parameters which configure a composite matching algorithm. The second approach is called genetics for ontology alignments and is based on a genetic algorithm which scales better for a large number of atomic matching algorithms in the composite algorithm and is able to optimize the results of the matching process.

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