This paper proposes a new data-driven method for power outage detection. By capturing the changes in data distribution of smart meters (SM), it can detect power outages in partially visible ...distributed systems. First, a mechanism based on breadth-first search (BFS) is proposed, which decomposes the network into a set of regions to find the location information where power outages are most likely to occur. Then, the SM data for each region, generating a generative adversarial network (GAN), is used in order to extract unsupervised manner implicit temporal behavior under normal conditions. After network training, anomaly scoring technology is used to determine whether the real-time measurement data is the data of a power outage event. Finally, in order to infer the location of a power outage in a multi-area network, a regional coordination process with interdependence be-tween cross-regions is used. At the same time, the concept of entropy is used to provide performance analysis for the algorithm in this paper. This method has been verified on the distribution feeder model with actual SM data. Experimental results show that the algorithm is effective and feasible.
This paper deals with the algorithms for state feedback stabilization of Boolean control networks (BCNs). By resorting to the semi-tensor product (STP) technique, the labelled digraph that can be ...used to completely characterize the dynamics of BCNs is derived, which leads to an equivalent graphical description for the stabilization of BCNs. What is more interesting is the fact that the existence of a state feedback control law stabilizing the BCN to some given equilibrium point can be characterized in terms of its spanning in-tree. Consequently, two in-tree search algorithms, namely, the breadth-first search and the depth-first search, are proposed to design the state feedback stabilizing law when global stabilization is feasible. Besides, some basic properties about the tree-search algorithms are addressed. A biological example is employed to illustrate the applicability and usefulness of the developed algorithms.
Graphs are the most complex data structure that involves heavy mathematical computations. Various real-time applications use the graph at storage, processing, object characterization, and behavior ...specification stages. The connectivity analysis, coverage building, and routing are the critical applications of graph evaluation. Various associated applications need the exploration of a graph with a practical and real-time outcome. The processing of larger graphs is a big challenge for the researcher. In this paper, a hybrid graph-based computation optimization method is designed to optimize the performance of graph processing in multi-core processors. The BFS algorithm is applied over the multiple processors to optimize the functional response. The multithreaded-BFS is implemented to reduce the time of graph processing and computation. The proposed algorithm is implemented in the OpenMP environment. The experimentation is done on two-core and four-core processors. The experiment is conducted on a 100 to 1000-node network. The traversing and graph coloring algorithms are implemented for the analysis. The comparative analysis is conducted on single-core and multi-core processors with DFS and BFS-based algorithms. The analysis results identified that the proposed algorithm reduced the processing time effectively.
Local community detection is of great value to network analysis, because it can efficiently find the community in which the given node located. However, the seed-dependent, core-criteria and ...termination problems are still difficulties for the major detection frames, which explore the core community and extend the community based on evaluation functions. In this paper, we proposed a local community detection algorithm (TSB) based on Breadth First Search (BFS), the proposed node transfer similarity and Local Clustering Coefficient (LCC). In our modeling, the initial clustering ability is strengthened by clustering the direct neighbor node with high clustering coefficient, so the seed-dependent problem could be avoided. We gave a new core criteria to evaluate the candidate core nodes, which is combined of LCC, the node transfer similarity and BFS depth. The dynamic termination thresholds for each candidate node in the core and extension stages are separately relying on its father node and the average node transfer similarity. In the experimental work, we compared our method with several previous methods through real-world and artificial synthetic networks. The results show that our method has better performance on accuracy with various community structure.
•Accuracy. Accurate community detection and accurate community number.•Robustness. Adaptive and accurate dynamic thresholds for different network structure.•Node transfer similarity. Extension of traditional node similarity to measure the relationship between non-adjacent nodes.•Innovative evaluation function. Proposed evaluation function for candidate nodes considering node transfer similarity, LCC and BFS depth.
A quality-guided phase unwrapping algorithm is proposed based on the breadth-first-search and multi-level segmentation of the phase quality interval. In the proposed method, the pixels having phase ...quality values below a threshold associated with a given segment are unwrapped based on the breadth-first-search strategy implemented in a recursive manner. The pixel selection based on the multi-level phase quality interval segmentation allows to perform noise-robust phase unwrapping and the recursive breadth-first-search technique offers computational efficiency in the implementation of proposed algorithm. Three segmentation strategies are investigated in reference to the phase unwrapping accuracy. Simulation and experimental results indicate that the proposed algorithm offers desirable trade-off between the phase unwrapping accuracy and computation time.
•Three heuristic algorithms for reliability estimation.•Reducing redundant samplings using breadth-first search of a grid tree.•Securing a prescribed accuracy of reliability.•Detecting large ...curvatures on the limit state surface.•Fast computation of the reliability index via a deleting process.
A complete search of the input space is crucial for securing the accuracy of reliability estimation, but conventional search algorithm-based methods require a large number of samples to visit the entire input space. To this end, this paper presents three heuristic algorithms for reliability estimation based on breadth-first search (BFS) of a grid tree (GT), namely the reliability accuracy supervised search algorithm (RASSA), the limit state surface resolution supervised search algorithm (LSSRSSA), and the reliability index precision supervised search algorithm (RIPSSA). All the proposed algorithms are characterized by traversing the entire input space through a GT while simultaneously reducing redundant samplings through BFS, and each one has its own special advantage as follows: RASSA can guarantee a prescribed accuracy of reliability estimation; LSSRSSA is able to probe large curvatures on limit-state surfaces; and RIPSSA quickly computes the reliability index. The computational costs and limitations of the proposed algorithms are analyzed. In addition, the accuracy, efficiency, and practicality of the proposed algorithm are validated through comparisons with other methods and an engineering application.
•BFS algorithm is firstly used to solve global optimization of the energy management strategy.•With the same result as DP, the calculation time is reduced about 40–50% with BFS.•An A-ECMS is proposed ...by combining BFS results with particle swarm optimization.
Global optimization plays an important role in the energy management strategies (EMS) of the hybrid electric vehicles (HEV). The fuel consumption of HEV could be reduced significantly with an acceleration of global optimization and application of global result in real-time control. In this paper, a new algorithm called breadth first search (BFS) was first used to realize the global optimization in a parallel mild HEV, which transforms the energy management problem of HEV into optimal path searching. Through simulation and calculation, it was found that the totally identical control strategies and fuel consumption could be obtained with BFS and dynamic programming (DP) respectively, while the calculation time for BFS was just about 50%-60% of that. With BFS results as reference, particle swarm optimization was used to adjust the equivalent factor in real-time and an adaptive equivalent consumption minimization strategy (A-ECMS) based on BFS was proposed. The fuel consumption could be decreased with the proposed A-ECMS by 8–15% in different driving cycles compared with that using rule-based strategies. It is believed that BFS has great potential in the future research on EMS of the HEVs.
•A retrosynthetic analysis framework is established, on which retrosynthetic software tool “RetroSynX” is developed.•A hybrid database which consists of partial atom-mapping and full atom-mapping ...reaction templates is constructed.•GC-based thermodynamics models are developed to validate the virtual reactions generated by reaction templates.•Breadth-First Search algorithm is used while the concept of optimal synthesis molecule is introduced to evaluate candidates.•Three case studies involving the retrosynthetic pathways design of Aspirin, Ibuprofen and Zatosetron are presented.
Organic synthesis plays an essential role in the pharmaceutical industry. The drug synthesis route design is a critical decision step to convert raw materials to drug products. Traditionally, knowledge-based methods are commonly used for the design of the synthesis route. However, this type of method is expensive and time-consuming, which hinders the high-throughput design of the synthesis route. In this article, a retrosynthetic analysis framework is established based on hybird reaction templates and Group Contribution (GC)-based thermodynamic models. First, a hybrid database consisting of partial atom-mapping and full atom-mapping reaction templates is constructed utilizing well-studied organic reactions from literature. Second, numerous virtual reactions are generated from reaction templates with respect to the target molecule, and reaction thermodynamic models based on the GC method are developed to validate the effectiveness of those virtual reactions in a timely fashion. Finally, Breadth-First Search (BFS) algorithm is employed to search candidate retrosynthesis pathways which are thermodynamically feasible. In this procedure, five evaluation criteria are used to identify the top-ranked retrosynthesis pathways through evaluating and optimizing the candidate retrosynthesis pathways, including Fathead Minnow 96-hr LC50 (LC50FM), flash point (Fp), Natural Product-likeness Score (NPScore), Synthesis Accessibility Score (SAScore), and Synthesis Complexity Score (SCScore). A retrosynthetic analysis tool called “RetroSynX” is developed using the proposed framework. With the help of the developed framework and tool, synthesis routes considering thermodynamic feasibility can be obtained. Three case studies involving Aspirin, Ibuprofen and ZatoSetron are presented to highlight the feasibility and reliability of the proposed framework.
