Redundant Nodes Research Articles

An Experimental Comparison of Self-Adaptive Differential Evolution Algorithms to Induce Oblique Decision Trees

This study addresses the challenge of generating accurate and compact oblique decision trees using self-adaptive differential evolution algorithms. Although traditional decision tree induction methods create explainable models, they often fail to achieve optimal classification accuracy. To overcome these limitations, other strategies, such as those based on evolutionary computation, have been proposed in the literature. In particular, we evaluate the use of self-adaptive differential evolution variants to evolve a population of oblique decision trees encoded as real-valued vectors. Our proposal includes (1) an alternative initialization strategy that reduces redundant nodes and (2) a fitness function that penalizes excessive leaf nodes, promoting smaller and more accurate decision trees. We perform a comparative performance analysis of these differential evolution variants, showing that while they exhibit similar statistical behavior, the Single-Objective real-parameter optimization (jSO) method produces the most accurate oblique decision trees and is second best in compactness. The findings highlight the potential of self-adaptive differential evolution algorithms to improve the effectiveness of oblique decision trees in machine learning applications.

Path planning of mobile robot based on improved PRM and APF

In the field of mobile robot path planning, the artificial potential field (APF) method has been widely researched and applied due to its intuitiveness and efficiency. However, the APF algorithm often encounters challenges such as local minima and unreachable goals in complex environments. To address these issues, this paper proposes innovative path planning algorithm that integrates the advantages of the probabilistic roadmaps method (PRM), by introducing Sobol sampling and elliptical constraints to enhance PRM. The improved PRM not only reduces redundant nodes but also enhances the quality of sampling points. Furthermore, this paper uses the path nodes from the improved PRM as virtual target points for the APF algorithm, and effectively solves the inherent flaws of the APF algorithm through the segmented processing of the attractive force function and the introduction of a relative distance factor in the repulsive force function. Simulation results show that the algorithm reduces planning time, node count, and path length, demonstrate significant improvements in efficiency and performance. In addition, experiments with omnidirectional mobile robots further confirm the effectiveness and reliability of the algorithm in practical applications.

Evolutionary stochastic configuration networks for industrial data analytics

Stochastic configuration network (SCN) with compact architecture is expected for data modeling. However, the hidden-node parameters (HNPs) randomly configured may result in a slow learning process due to the redundant nodes embedded in the model. To resolve this problem, an evolutionary SCN based on an improved differential evolution (DE) algorithm is proposed in this paper. Specifically, the improved DE reuses the assignment information of last hidden node to find an appropriate search scope for the current one; employs a space reduction method to seed a promising population in the scope; and develops a performance-aware scheme to adjust the scale factor of mutation operators. The proposed evolutionary SCNs are compared with other methods on six datasets and then applied for two real-world applications. Experimental results demonstrate that the proposed method obtains superior performance in terms of compactness and accuracy, with great potential for real-world data analysis.

Research on Autonomous Underwater Vehicle Path Optimization Using a Field Theory-Guided A* Algorithm

Autonomous Underwater Vehicles (AUVs) have become indispensable tools in the fields of ocean exploration, resource exploitation, and environmental monitoring. Path planning and obstacle avoidance are crucial to improve the operational capabilities of AUVs. However, most algorithms focus only on macro-global or micro-local path planning and rarely address both problems simultaneously. This study extends the classical A* algorithm by integrating field theory principles. The resulting Field Theory Augmented A* (FT-A*) algorithm combines the constraints in the AUV’s dynamics and the threats posed by obstacles to ensure a safe navigation distance. The paths planned by the FT-A* algorithm were subsequently re-optimised in conjunction with Dubins curves, taking into account path smoothness and redundant node problems. Simulation experiments confirm that the improved algorithm can effectively help AUVs navigate safely around obstacles, which greatly improves navigation safety and increases the arithmetic power and navigation efficiency. The proposed FT-A* algorithm provides a robust solution for underwater path planning and demonstrates great practical value for AUV operation in complex marine environments.

A Dynamic Path Planning Method for UAVs Based on Improved Informed-RRT* Fused Dynamic Windows

In recent years, navigating rotor drones in complex and dynamic environments has been a significant challenge. This paper proposes an improved path planning method by integrating the enhanced Informed-RRT* algorithm with the Dynamic Window Approach (DWA) algorithm. Firstly, the probability weight function for sampling direction is dynamically adjusted based on the Euclidean distance between the start and goal points to obtain higher-quality sampling points. Secondly, the Artificial Potential Field (APF) concept is introduced during the generation of new nodes, applying the improved APF method to the elliptical sampling area to guide the direction of new node generation. This significantly reduces the number of redundant nodes, enhances the convergence speed, and improves the obstacle avoidance efficiency. Then, a polynomial fitting method is employed to optimize the path, yielding a smoother trajectory. Finally, the improved DWA algorithm is integrated. Initially, the Informed-RRT* algorithm generates an optimal path from the starting point to the goal point in the global space, which is then provided as an initial reference path to the DWA algorithm. During actual operation, the DWA algorithm dynamically adjusts control inputs based on the current position, speed, and reference path of the drone, ensuring that the drone can avoid dynamic obstacles and unknown static obstacles in real time while staying as close to the reference path as possible.

In-Depth Analysis of Molecular Network Based on Liquid Chromatography Coupled with Tandem Mass Spectrometry in Natural Products: Importance of Redundant Nodes Discovery.

The identification of molecules within complex mixtures is a major bottleneck in natural products (NPs) research. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has emerged as the main tool for the high-throughput characterization of NPs. The large amount of data sets by LC-MS/MS presents a challenge for data processing and interpretation, and the LC-MS/MS molecular network (MN) is one of the most prominent tools for analyzing large MS/MS data sets, widely used for rapid classification, identification, and structural speculation of unknown compounds. However, the existence of a large number of redundant nodes leads to false-positive results. To solve this problem, we proposed the in-depth analysis of MN. In this study, in-depth analysis of MN of five NPs representing the common structures of saponin, steroid, flavonoid, alkaloid, and phenolic acid revealed the presence of redundant nodes (including other adducts, isotope, and in-source fragmentation) in addition to the normal nodes, which can lead to false-positive identification results. Additionally, the reasons for different redundant nodes are discussed and experimentally verified, and it was found that the impact of redundant nodes can be mitigated by optimizing the experimental conditions and employing Feature-Based Molecular Networking. Furthermore, Ion Identity Molecular Networking can rapidly discover and screen redundant nodes, simplifying the in-depth analysis of MN and improving the network connectivity of structurally related molecules. Finally, a combination formulation of 7 NPs is used as an example to provide a guide for in-depth analysis of MN for comprehensive characterization of complex systems. This study highlights the importance of an in-depth analysis of MN for better understanding and utilization of MS/MS data in complex systems to reduce the false-positive rate of identification by screening and filtering redundant nodes.

Robot Path Planning Based on Improved A* Algorithm and Artificial Potential Field Method

In response to the problems of long planning paths, large turning angles, and inability to avoid dynamic obstacles in traditional A* algorithm robot path planning, this paper proposes a path planning algorithm that combines improved A* algorithm with artificial potential field method. Firstly, the improved A* algorithm searches the neighborhood and heuristic function. Compared with some algorithms, the improved A* algorithm reduces the optimal path distance, search nodes, simulation time, and turning angles by 22.78%, 80.65%, 69.84%, and 50% respectively. The improved A* algorithm is further optimized by removing redundant nodes and smoothing the path, reducing the optimal path, simulation time, and turning angles by 2.08%, 9.1%, and 36.36% respectively compared to the first optimization. For local path planning using artificial potential field, the artificial potential field function and adaptive step size are improved. Simulation results show that the improved algorithm can overcome the problems of local minima and unreachable targets. Finally, the integrated algorithm simulation shows that it can solve the problem of A* algorithm s inability to avoid dynamic obstacles and guide the robot to move along the optimal path.

Assessing relinquishment of positivity as a central symptom bridging anxiety and depression

Anxiety and depression are often comorbid and chronic disorders. Previous research indicates that positivity relinquishment is a moderator of anxiety and depression, such that only anxious individuals who endorsed relinquishing positivity were also depressed. We sought to extend those findings by conducting three network analyses with self-report measures of anxiety, depression, activity avoidance, and perceived positivity of avoided activities (N = 104). We pre-registered our hypothesis for the first two networks that relinquishment of positivity would emerge as a central bridge symptom between anxiety and depressive symptoms. After combining redundant nodes, we estimated three networks and investigated the bridge symptoms in each network. Relinquishment of positivity bridged the symptom clusters in the first network, and avoidance of positivity was found to bridge the two symptom clusters of anxiety and depression in networks two and three. Additionally, an anhedonia circuit was uncovered in all three networks in which loss of interest/worthlessness, loss of energy, and loss of pleasure/pessimism connected to anxiety through relinquishment or avoidance. Our findings suggest that both relinquishment of positivity as well as avoidance of positivity could be potential pathways explaining the development and maintenance of anxiety and depression and should be properly targeted in treatment.

Self-adaptive selection graph pooling based fault diagnosis method under few samples and noisy environment

Neural network (NN)-based methods are extensively used for intelligent fault diagnosis in industrial systems. Nevertheless, due to the limited availability of faulty samples and the presence of noise interference, most existing NN-based methods perform limited diagnosis performance. In response to these challenges, a self-adaptive selection graph pooling method is proposed. Firstly, graph encoders with sharing parameters are designed to extract local structure-feature information (SFI) of multiple sensor-wise sub-graphs. Then, the temporal continuity of the SFI is maintained through time-by-time concatenation, resulting in a global sensor graph and reducing the dependency on data volume from the perspective of adding prior knowledge. Subsequently, leveraging a self-adaptive node selection mechanism, the noise interference of redundant and noisy sensor-wise nodes in the graph is alleviated, allowing the networks to concentrate on the fault-attention nodes. Finally, the local max pooling and global mean pooling of the node-selection graph are incorporated in the readout module to get the multi-scale graph features, which serve as input to a multi-layer perceptron for fault diagnosis. Two experimental studies involving different mechanical and electrical systems demonstrate that the proposed method not only achieves superior diagnosis performance with limited data, but also maintains strong anti-interference ability in noisy environments. Additionally, it exhibits good interpretability through the proposed self-adaptive node selection mechanism and visualization methods.

Global–local spatio-temporal graph convolutional networks for video summarization

Video summarization aims to create concise and accurate summary to enable users to quickly grasp the key content of the original video for facilitating efficient video browsing. Most existing video summarization methods mainly employ recurrent neural networks to capture long-term dependencies in videos, yielding remarkable results. Nevertheless, these methods overlook the potential spatial features inside the video when modeling the video. To tackle this issue, we introduce a global–local spatio-temporal graph convolutional networks for video summarization (GL-STGCN). Initially inspired by the concept of 3-D convolution, we segment the video into non-overlapping segments to capture localized spatial features of sequential frames. Subsequently, a spatial graph is constructed for each segment, with a fixed time interval between neighboring spatial graphs. Then we use the pooling method to randomly delete the redundant nodes in the graph. We then leverage a temporal gating convolutional network to extract the global temporal relationships within the video. Employing the spatial features, a spatial graph convolutional network is utilized to capture the spatial connections among frames. As the graph node information evolves, the node features furnish a more precise depiction of the video content. Consequently, we employ the temporal gating convolutional network once more to refine the global temporal relations within the video. Extensive experiments on two public datasets are conducted in this paper, showing that our proposed method outperforms most state-of-the-art video summarization methods in terms of performance. Experimental results demonstrate the effectiveness of integrating global temporal and local spatial relationships.

An improved Bi-RRT*-based path planning algorithm with adaptive search strategy assignment mechanism for ultra-low-altitude penetration of fixed-wing aircraft

The ultra-low-altitude penetration with complex three-dimensional terrain environment usually requires the path planning algorithm to be computationally efficient to generate feasible optimized flight paths in a limited period of time. This paper investigates an improved bidirectional rapidly-exploring random tree* (RRT*) path planning algorithm with adaptive search strategy assignment mechanism to accelerate the exploration speed. The greedy optimization method and the three-dimensional Dubins method are utilized to reduce redundant nodes and optimize the flight path to satisfy the kinematic constraints of the fixed-wing aircraft. The model predictive control method is then employed to design the path-following controller, in which multiple types of constraints can be addressed. To demonstrate the superior planning efficiency of the proposed adaptive-alternating-exploration RRT* method, the existing widely-used path planning algorithms are used as benchmarks in the simulation studies. Simulation results show that the path planning time can be significantly reduced by up to 90% with the proposed adaptive-alternating-exploration RRT* method, simultaneously with optimized path length. Moreover, the designed model predictive path-following controller succeeds to track the planned collision-free path with maximum tracking errors of less than 2 meters. The effectiveness of the path planning algorithm and the designed controller is thoroughly demonstrated.

A Multiple Environment Available Path Planning Based on an Improved A* Algorithm

The objective of the path planning for a mobile robot is to generate a collision-free path from a starting position to a target position, aiming to realize a higher quality of path planning, an improved A* algorithm and a hybrid approach incorporating the dynamic window algorithm have been proposed for robot path planning in various environments in this paper. In global path planning, first, a bidirectional search strategy was introduced into to improve the searching efficiency, and an adaptive heuristic function was designed to reduce redundant search nodes. In the meantime, a filtering function for key path nodes and an enhanced jump point optimization method help to remove redundant nodes in the path, reduce turning angles, greatly shorten the path length, and smooth the path using cubic B-spline curves. Furthermore, in local path planning, the combination of key path nodes and the dynamic window approach (DWA) algorithm is utilized to achieve obstacle avoidance in dynamic environments and adjust the heading angle of the section enables seamless locomotion of the robot. Finally, the simulation experiments and physical experiments on the robot were conducted to validate that the proposed improved algorithm significantly improves the speed of path planning while also reducing the length of the planned path and improve the reliability of the algorithm when compared with other algorithms.

Simulation of Dynamic Path Planning of Symmetrical Trajectory of Mobile Robots Based on Improved A* and Artificial Potential Field Fusion for Natural Resource Exploration

With the rapid development of new-generation artificial intelligence and Internet of Things technology, mobile robot technology has been widely used in various fields. Among them, the autonomous path-planning technology of mobile robots is one of the cores for realizing their autonomous driving and obstacle avoidance. This study conducts an in-depth discussion on the real-time and dynamic obstacle avoidance capabilities of mobile robot path planning. First, we proposed a preprocessing method for obstacles in the grid map, focusing on the closed processing of the internal space of concave obstacles to ensure the feasibility of the path while effectively reducing the number of grid nodes searched by the A* algorithm, thereby improving path search efficiency. Secondly, in order to achieve static global path planning, this study adopts the A algorithm. However, in practice, algorithm A has problems such as a large number of node traversals, low search efficiency, redundant path nodes, and uneven turning angles. To solve these problems, we optimized the A* algorithm, focusing on optimizing the heuristic function and weight coefficient to reduce the number of node traversals and improve search efficiency. In addition, we use the Bezier curve method to smooth the path and remove redundant nodes, thereby reducing the turning angle. Then, in order to achieve dynamic local path planning, this study adopts the artificial potential field method. However, the artificial potential field method has the problems of unreachable target points and local minima. In order to solve these problems, we optimized the repulsion field so that the target point is at the lowest point of the global energy of the gravitational field and the repulsive field and eliminated the local optimal point. Finally, for the path-planning problem of mobile robots in dynamic environments, this study proposes a hybrid path-planning method based on a combination of the improved A* algorithm and the artificial potential field method. In this study, we not only focus on the efficiency of mobile robot path planning and real-time dynamic obstacle avoidance capabilities but also pay special attention to the symmetry of the final path. By introducing symmetry, we can more intuitively judge whether the path is close to the optimal state. Symmetry is an important criterion for us to evaluate the performance of the final path.

Trajectory planning for AGV based on the improved artificial potential field- A* algorithm

Abstract There are many redundant nodes and inflection points in the path planned by the traditional A* algorithm, leading to the inefficient trajectory planning of the Automatic Guided Vehicle(AGV) in the multi-static obstacles environment. The artificial potential field algorithm suffers from the problem of unreachable objectives and falling into optimal local value. This article studies the trajectory optimization of AGVs to improve the trajectory planning algorithm's iteration efficiency and shorten the trajectory's total length. This article establishes the forward kinematic and Unified Robot Description Format(URDF) model of the AGV and proposes the Artificial Potential Field-A*(APF-A*) algorithm for trajectory planning. The search cost and the number of turns are effectively optimized. The article simulates the APF-A* algorithm, the results are compared with the trajectory before optimization, and the optimized time is 60% less than that before optimization. The experimental platform of AGV trajectory planning is built, and the algorithm verification experiment of AGV trajectory planning is carried out. The experimental results show that the algorithm studied in this article achieves path smoothing and trajectory length optimization.

Research on Autonomous Vehicle Path Planning Algorithm Based on Improved RRT* Algorithm and Artificial Potential Field Method.

For the RRT* algorithm, there are problems such as greater randomness, longer time consumption, more redundant nodes, and inability to perform local obstacle avoidance when encountering unknown obstacles in the path planning process of autonomous vehicles. And the artificial potential field method (APF) applied to autonomous vehicles is prone to problems such as local optimality, unreachable targets, and inapplicability to global scenarios. A fusion algorithm combining the improved RRT* algorithm and the improved artificial potential field method is proposed. First of all, for the RRT* algorithm, the concept of the artificial potential field and probability sampling optimization strategy are introduced, and the adaptive step size is designed according to the road curvature. The path post-processing of the planned global path is carried out to reduce the redundant nodes of the generated path, enhance the purpose of sampling, solve the problem where oscillation may occur when expanding near the target point, reduce the randomness of RRT* node sampling, and improve the efficiency of path generation. Secondly, for the artificial potential field method, by designing obstacle avoidance constraints, adding a road boundary repulsion potential field, and optimizing the repulsion function and safety ellipse, the problem of unreachable targets can be solved, unnecessary steering in the path can be reduced, and the safety of the planned path can be improved. In the face of U-shaped obstacles, virtual gravity points are generated to solve the local minimum problem and improve the passing performance of the obstacles. Finally, the fusion algorithm, which combines the improved RRT* algorithm and the improved artificial potential field method, is designed. The former first plans the global path, extracts the path node as the temporary target point of the latter, guides the vehicle to drive, and avoids local obstacles through the improved artificial potential field method when encountered with unknown obstacles, and then smooths the path planned by the fusion algorithm, making the path satisfy the vehicle kinematic constraints. The simulation results in the different road scenes show that the method proposed in this paper can quickly plan a smooth path that is more stable, more accurate, and suitable for vehicle driving.

An Improved Hybrid Path Planning Algorithm in Indoor Environment

Abstract During the path planning of robots in the indoor unstructured complex environment, there are often problems such as unreachable target points, deflection in the planning process, and failure to avoid dynamic obstacles in time. To solve these problems, an improved hybrid indoor path planning algorithm was proposed, wherein the improved global path planning algorithm was effectually integrated with improved local path planning algorithm. Firstly, the heuristic factor of traditional A-Star algorithm was optimized, search range and nodes were reduced, and then the path generated by traditional A-Star algorithm for path planning was smoothed using the angle bisector tangent point method. Secondly, combining path and environment information, local path planning was undertaken by utilizing the improved artificial potential field algorithm, and the unreachable target points problem was addressed by adjusting the repulsive field parameters. Additionally, dynamic potential field function was constructed to make it have the ability to resolve dynamic obstacles. Finally, in the part of actual environment verification, a comparison was made in this paper to assess the performance of the traditional hybrid algorithm against the improved algorithm in terms of path planning. The consequences showed that, by the hybrid algorithm proposed in this paper, the path planning length was reduced by 10.3%, the running time was decreased by 12.5%, and 34 redundant nodes were eliminated. The consequences indicated that the hybrid algorithm can effectively address the indoor unstructured and complex path planning problems.

Wireless sensor network coverage optimization based on novel multi‐strategy gold rush optimizer

AbstractIn Wireless Sensor Networks (WSN), there are three conflicting important issues, including extensive coverage, low node redundancy, and energy consumption for secondary deployment. To balance network coverage and longevity, a WSN coverage model with three objectives is developed. A novel multi‐strategy gold rush optimizer is proposed for the coverage optimization problem in WSN. First, the fusion search method becomes extensive and flexible by combining the migration strategy with the Levy flight mechanism and joining a follower group. Then, the dynamic opposite learning strategy is used to increase population diversity in the panning stage. In addition, a multi‐player collaborative strategy is utilized to improve search ability of the algorithm in the collaboration stage. Finally, the random differential mutation method is used to try to assist the algorithm in escaping local optima. The simulation results show that this algorithm compared with other algorithms performs better on all three objectives considered.

Intelligent Vehicle Path Planning Based on Optimized A* Algorithm.

With the rapid development of the intelligent driving technology, achieving accurate path planning for unmanned vehicles has become increasingly crucial. However, path planning algorithms face challenges when dealing with complex and ever-changing road conditions. In this paper, aiming at improving the accuracy and robustness of the generated path, a global programming algorithm based on optimization is proposed, while maintaining the efficiency of the traditional A* algorithm. Firstly, turning penalty function and obstacle raster coefficient are integrated into the search cost function to increase the adaptability and directionality of the search path to the map. Secondly, an efficient search strategy is proposed to solve the problem that trajectories will pass through sparse obstacles while reducing spatial complexity. Thirdly, a redundant node elimination strategy based on discrete smoothing optimization effectively reduces the total length of control points and paths, and greatly reduces the difficulty of subsequent trajectory optimization. Finally, the simulation results, based on real map rasterization, highlight the advanced performance of the path planning and the comparison among the baselines and the proposed strategy showcases that the optimized A* algorithm significantly enhances the security and rationality of the planned path. Notably, it reduces the number of traversed nodes by 84%, the total turning angle by 39%, and shortens the overall path length to a certain extent.

A novel stochastic configuration network with enhanced feature extraction for industrial process modeling

Stochastic configuration networks (SCNs), possessing sound generalization performance and low computational burden, have been extensively investigated in data analysis field. But in practice, it performs poor. This is mainly caused by two aspects: (1) the increment of random hidden nodes with incompact constraints may bring the emergence of redundant nodes, thereby resulting in poor modeling performance, and (2) SCNs have limited feature extraction capabilities. To address these issues, a novel SCN inserting feature layer, termed as FSCN, is presented in this paper. First, the embedded feature layer establishes a connection between the input and hidden layers, thus mapping the input data into a suitable feature space. Second, an inequality constraint based on Greville iterative method is established. It can not only facilitate the construction of high-quality hidden nodes but also guarantee convergence of the built model. Then, the Greville iterative method is also employed for output weight updating. Finally, comparative experiments on a real-valued nonlinear function, four real-world data sets and the modeling of two real industrial processes are conducted and the comparison results with other modeling methods show that FSCN has superior performance in terms of both model accuracy and compactness.

A Distributed Deployment Method for Wireless Sensor Networks Based on Multi Agent Systems

This paper proposes a distributed deployment method for wireless sensor networks based on multi-agent systems, which addresses the issues of poor self adaptive deployment capability and high deployment costs caused by numerous and diverse types of nodes in the deployment process of wireless sensor networks, leading to blind spots in coverage and redundant nodes. Due to the characteristics of intelligent perception, intelligent communication, and clustering of nodes in wireless sensor network, sensor nodes can be seen as sensing agents, and sensor networks can be seen as distributed multi-agent systems composed of numerous sensing agents. Through the sensing agents' perception of the surrounding environment and communication interaction, a unified deployment model and distributed deployment algorithm with multiple nodes (cluster head nodes) as leaders are established, achieving effective integration of autonomous and leadership mechanisms, distributed management, and centralized management. Taking the problem of sensor network coverage as an example, simulation experiments were conducted, including 12 sensor agents and 1 20 * 20 monitoring areas, to verify how the sensor agents perceive the environment and optimize the migration and deployment process under the coordination of cluster head nodes. The experimental results confirmed the effectiveness of the above models and algorithms. The research results provide a theoretical model and practical approach for the distributed deployment and scheduling of sensor nodes in wireless sensor networks, and can be extended to intelligent Internet of Things systems, such as urban intelligent agent systems, logistics intelligent agent systems, etc., to improve the intelligence of wireless sensor networks deployment and scheduling.