Showing papers on "Prim's algorithm published in 2021"

Motion control of multiple humanoids using a hybridized prim’s algorithm-fuzzy controller

Abstract: Prim’s algorithm has demonstrated a very effective and selective method of solving the minimum spanning tree optimization problems. It is a greedy algorithm that starts from an empty spanning tree and reaches its goal by picking the minimum weight edges which alternately optimizes the path in less possible time. In this paper, the capability of prim’s algorithm in designing the behavioural controller of a humanoid robot has been shown. Here, a new hybrid PA–Fuzzy motion planning approach has been proposed that uses the concept of minimizing the distance between the robot and obstacles as well as robot and target. An optimal turning angle is generated by the hybrid controller that helps to avoid the obstacles present in the arena to create a collision-free path. The results obtained from hybrid PA–Fuzzy motion planning procedure show the capability of the controller in achieving the optimal paths in different environments with both static and dynamic obstacles. The results observed from simulation and experimental arenas are found to be in satisfactory agreement with each other producing minimal error limits. The developed hybrid technique is compared with some existing methodologies, and significant improvement is found in relation to path length and computational time.

Global Path Optimization of Humanoid NAO in Static Environment Using Prim’s Algorithm

Abstract: This paper focuses on navigation of a humanoid robot cluttered with obstacles, avoiding collisions in static environment using Prim’s algorithm. Prim’s algorithm is a minimum spanning tree (MST) method with greedy approach which uses the concept of sets. It generates the MST by selecting least weights from the weighted graph and randomly forms disjoint sets with picking one least weight edge from the ones remaining for creating node incident to form the tree. Similar approach repeats for selecting all ‘n – 1’ edges to the tree which is the path direction to humanoid NAO. The developed algorithm is implemented in both simulation and experimental platforms to obtain the navigational results. The simulation and experimental navigational results confirm the efficiency of the path planning strategy as the percentage of deviations of navigational parameters is below 6%.

Functional Correctness of C implementations of Dijkstra’s, Kruskal’s, and Prim’s Algorithms

Abstract: We develop machine-checked verifications of the full functional correctness of C implementations of the eponymous graph algorithms of Dijkstra, Kruskal, and Prim. We extend Wang et al.’s CertiGraph platform to reason about labels on edges, undirected graphs, and common spatial representations of edge-labeled graphs such as adjacency matrices and edge lists. We certify binary heaps, including Floyd’s bottom-up heap construction, heapsort, and increase/decrease priority.

A Tool-path Planning Method Used in Computer Controlled Optical Surfacing Based on Improved Prim Algorithm

Abstract: In view of the disadvantages of existing planning methods used in CCOS techniques, such as low efficiency and workpieces contain obvious mid-frequency error after polishing, a new tool-path planning method based on improved Prim algorithm was proposed, of which the core idea was consist by following steps: surface data reading, mesh generation, distribution of resident points determining and polishing path generating. After that, comparison of raster path and the path based on improved Prim algorithm was carried out by simulated experiments from aspects of path length and polishing texture. The results indicated that the path based on improved Prim algorithm could shorten path length as well as increase polishing efficiency, moreover, both the texture and mid-frequency errors can be improved by using the path presented. It was concluded that the presented planning method could improve polishing efficiency and machining quality. Then, comparison between raster path and the path based on improved Prim algorithm was carried out by simulated experiments, from two sides of path length and polishing texture. The results indicated that the path based on improved Prim algorithm could shorten path length as well as increase polishing efficiency, moreover, both the texture and mid-frequency errors would be improved by using the presented path. Finally, the validity of presented planning method was proved in machining experiments.

An Optimal Tree-Structured Repair Scheme of Multiple Failure Nodes for Distributed Storage Systems

Abstract: To reduce recovery cost of repairing multiple failed nodes, many repair schemes have been proposed for erasure codes based distributed storage systems. However, most of the existing researches ignore the network topology of storage devices. Motivated by such considerations, we combine delay repair schemes with network topology and propose a tree-structured model based on fountain codes with large value of $\left ({n,k,r }\right)$ to improve the repair efficiency. More precisely, with the consideration of network topology, a new target named data recovery cost is defined to measure the efficiency of coded fragment download and source file reconstruction, and then the optimal recovery threshold is derived to minimize the average data recovery cost of general tree-structured model. Moreover, we analyze and compare the average data recovery cost of general tree-structure with different systematic parameters. To further improve the data transmission efficiency, an optimal tree-structured scheme based on improved tabu search algorithm (ITSA-ORT) is proposed. Compared with other algorithms, the ITSA-ORT scheme uses Prim algorithm to generate the initial solution and then uses special method to obtain the corresponding neighborhood structure. The experimental results show that the proposed scheme can find a globally optimal solution and obtain lower cost of data recovery. In addition, the ITSA-ORT scheme has lower computational complexity than the optimal tree-structured scheme based on particle swarm optimization algorithm (PSO-ORT) and the optimal tree-structured scheme based on firefly algorithm (FA-ORT).

Anonymizing Global Edge Weighted Social Network Graphs.

Abstract: Privacy protection of individual users in social networks is becoming more and more important, thus it requires effective anonymization techniques. In this paper, we use Kruskal and Prim algorithms to model the linear programming of the minimum spanning tree. Finally, we execute the experiments on the number of anonymity solutions and time with different edge weights to analyze the Kruskal algorithm and Prim algorithm to verify their anonymity feasibility.

Simulation of sports action recognition based on maximum spanning tree algorithm

Abstract: Aiming at the situation that the motion recognition of sports athletes is interfered by a variety of factors and the recognition results are not ideal, this paper uses the maximum spanning tree algorithm as the model basis to use machine learning ideas to construct a sports player motion recognition model based on the maximum spanning tree algorithm. Moreover, this article combines a region growing algorithm based on simple surface fitting and morphological reconstruction to initially segment sports actions. After that, this paper improves the prim algorithm, and combines an optimized watershed segmentation framework to construct a new energy function using the T-prim minimum spanning tree algorithm proposed in this paper. The constructed T-prim tree is combined with this optimized watershed segmentation framework to complete the segmentation of sports images.Finally, this paper designs experiments to verify the actual effect of the method proposed in this paper. It can be seen from the research results that the model constructed in this paper basically achieves the expected goal.

A Combination of K-means Algorithm and Optimal Path Selection Method for Lifetime Extension in Wireless Sensor Networks

Abstract: Wireless Sensor Networks (WSN) are special types of wireless networks where hundreds or thousands of sensor nodes are working together. Since the lifetime of each sensor is equivalent to a battery, the energy issue is considered a major challenge. Clustering has been proposed as a strategy to extend the lifetime of wireless sensor networks. Many clustering algorithms consider the residual energy and distance between the nodes in the selection of cluster heads and others rotate the selection of cluster heads periodically. We propose in this article a CH selection followed by making clusters using the K-means algorithm and we present the PRIM algorithm to transmit the packets in multi-hop transmission between CHs and BS and choose the optimal path. The clustering scheme allows to decrease intra-cluster communications and to gain energy efficiency for sensor nodes. Computer simulation results show that our method aims to extend the lifetime of the wireless sensor network efficiently compared to other existing methods.

An Efficient Fault-Tolerant Mechanism to Ameliorate Performance in Wireless Sensor Networks

Abstract: The fault tolerance in wireless sensor networks (WSN) has become the foremost task as the sensor networks are involved in every aspect of human life. The Fault Tolerance supports energy efficiency, and energy efficiency is directly related to network lifetime, which is a prominent parameter in sensor networks. In this paper, an attempt has been made to present an improved handoff (Imp-Handoff) algorithm as an efficient fault-tolerant technique over the traditional handoff algorithm. At the first stage of the proposed work, a minimum spanning tree (MST) had been generated using the traditional PRIMS algorithm and various Swarm Intelligence approaches, viz. the Ant Colony Algorithm (ACO), Particle Swarm Algorithm (PSO), Firefly Algorithm (FF), and Imperialistic Competitive Algorithm (ICA). Then, data transmission had been performed on these spanning trees using the handoff and the proposed Imp-handoff algorithm. A comparative analysis of the said algorithms is presented in the result analysis section on some critical parameters, i.e., throughput, end-to-end delay, and energy dissipation.

An Approach of Community Search with Minimum Spanning Tree Based on Node Embedding

Abstract: Community search is a query-oriented variant of community detection problem, and the goal is to retrieve a single community from a given set of nodes. Most of the existing community search methods adopt handcrafted features, so there are some limitations in applications. Our idea is motivated by the recent advances of node embedding. Node embedding uses deep learning method to obtain feature representation of nodes directly from graph structure automatically and offers a new method to measure the distance between two nodes. In this paper, we propose a two-stage community search algorithm with a minimum spanning tree strategy based on node embedding. At the first stage, we propose a node embedding model NEBRW and map nodes to the points in a low-dimensional vector space. At the second stage, we propose a new definition of community from the distance viewpoint, transform the problem of community search to a variant of minimum spanning tree problem, and uncover the target community with an improved Prim algorithm. We test our algorithm on both synthetic and real-world network datasets. The experimental results show that our algorithm is more effective for community search than baselines.

Power impedance topological graph generation method and system for power distribution area

Abstract: The invention discloses a power impedance topological graph generation method and system for a power distribution area, and belongs to the technical field of power distribution areas. Current line impedance identification is large in calculated amount and long in required time. The invention relates to a power impedance topological graph generation method and system for a power distribution area,and the method comprises the steps: firstly obtaining the A/B/C three phases of a power distribution transformer side and the time sequence voltage data of each user load node, determining the phase relation of each load node through a K-means clustering method, and constructing a feature coefficient matrix for the divided A/B/C three phases; generating a single-phase topological adjacency matrixfor the characteristic coefficient matrix by adopting a prim algorithm, and finally fusing the obtained three-phase topological adjacency matrix of the transformer area with line impedance informationand geographic information coordinates to form an electric power information map of the whole power distribution transformer area; and the problems that in a traditional topology generation and parameter identification method, the hardware cost is too high, the node load phase cannot be determined, and the calculation time is long can be solved.

Active Distribution Network Reconfiguration Based on Prim Algorithm and Chaos Particle Swarm Optimization

Abstract: According to the character of active distribution network that it could actively control and manage distributed energy resources, this paper puts forward a distribution network reconfiguration method for active distribution network. Because of the random power of distributed generation driven by renewable energy, this paper regards power of controllable load and electrical energy storage as controllable value and takes the effect of V2G(Vehicle-to-grid)into consideration, thus establishes a multi-objective optimization model. Based on knowledge of Graph Theory, this paper proposes a reconfiguration strategy containing island partition, and uses a hybrid algorithm of Prim algorithm and chaos particle swarm optimization to solve the model and optimize reconfiguration project. The validity of the proposed method of active distribution network reconfiguration is verified by simulation results of IEEE 33-node distribution system.

Optimization Model of Pipeline Laying Based on Immune Genetics and Improved Second-order Prim Algorithm

Abstract: The optimization of the water supply pipeline route is of great significance to the design of rural water supply projects and the reform of the rural water supply management system. In this paper, a Minimum Spanning Tree Model based on classic graph theory is established, and the Improved second-order Prim Algorithm and Immune Genetic Algorithm are used to solve the model. Aiming at the minimum spanning tree problem with special constraints, the distance relationship between water supply stations is converted into a weight matrix, and a second-order Prim algorithm is designed to solve the laying plan with the smallest total pipeline mileage. Taking the Euclidean distance between nodes as the cost of the edge, by constraining the generation order of the minimum spanning tree, the total mileage is minimized under the premise of optimizing the mileage of the I-level pipeline. It is calculated that the laying mileage of type II pipeline is 403.40km, and the minimum laying sum of type I pipeline and type II pipeline is 524.34km. At the same time, considering the pipeline laying plan under the power limit of the water supply station, this article puts forward the concept of isolated points under the premise of analyzing the power constraints, and deeply explores the impact of isolated points on rural water supply. Based on the previous analysis, it is concluded that To achieve comprehensive water supply, all isolated points that cannot be connected to other water supply stations must be eliminated. The group coded the level II water supply station, designed an immune genetic algorithm, and achieved comprehensive water supply by optimizing the minimum number of level II water supply stations that need to be upgraded. It is determined that at least one secondary water supply station needs to be upgraded, and the total mileage of pipeline laying in this configuration is at least 415.09km. Finally, the sensitivity analysis of the solution results is carried out. The advantages of this paper are: 1. Based on the classical graph theory algorithm Prim algorithm, the solution obtained can be proved to be the global optimal solution. 2. The optimization calculation based on the immune genetic algorithm makes the algorithm very robust, and takes into account both the local search ability and the global search ability. The algorithm complexity is much lower than the traversal solution.